WO2023030522A1 - Data center air conditioning system diagnosis method and apparatus - Google Patents

Abstract

The present application discloses a data center air conditioning system diagnosis method and apparatus, comprising: establishing energy consumption models of an air conditioning system; using the energy consumption models, acquiring first optimized values of operating parameters when energy consumption is in a minimum state; according to the energy consumption models and the first optimized values of the operating parameters, obtaining energy consumption data curves of the air conditioning system; according to the energy consumption data curves, obtaining energy consumption data when a total energy consumption of the air conditioning system is in a minimum state, and determining second optimized values of the operating parameters; reading a current load rate and an outdoor meteorological parameter at a specified time interval, and cyclically executing the following steps: using the energy consumption models, the load rate, the outdoor meteorological parameters, and the second optimized values of the operating parameters to perform step-wise adjustment of different operating parameters, calculating energy consumption data in optimal working conditions, and determining third optimized values of the corresponding operating parameters; and storing the third optimized values of the operating parameters and sending same to a controller, so as to implement regulation of the air conditioning system.

Description

Diagnosis method and device for data center air conditioning system

Cross References to Related Applications

This application is based on the Chinese patent application with the application number 202111038443.6, the application date is September 06, 2021, and the application title is "A Diagnosis Method and Device for Data Center Air Conditioning System", and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated into this application by way of reference.

technical field

The present application relates to the field of air-conditioning energy saving, and in particular to a diagnostic method and device for an air-conditioning system in a data center.

Background technique

The energy consumption management system of the data center is mainly the cold source group control system and the power environment monitoring system, and the following problems arise during the operation process: the workload of design, deployment and maintenance of control equipment is large; the operation control strategy of the air conditioning system adopts the control of the central air conditioning Strategy; air-conditioning operation control strategy lacks energy-saving, safe and intelligent energy-saving optimization algorithms.

In the prior art, when performing energy-saving processing on data center air conditioners, the real-time operation data is generally collected based on the knowledge base of historical data to diagnose the normal operation or abnormal operation of the air-conditioning system, but the energy-saving diagnosis of the air-conditioning system cannot be performed; Energy efficiency analysis is performed by simulating and calculating the energy efficiency ratio of the air conditioning system, but this processing method is low in energy efficiency diagnosis, and considering that the data center air conditioner will face different operating conditions, this processing method cannot give the data center under different operating strategies. How to deal with energy saving in the air conditioning system.

Contents of the invention

In view of the above problems, the present application is proposed to provide a data center air conditioning system diagnosis method and device that overcome the above problems or at least partially solve the above problems.

In the first aspect, an embodiment of the present application provides a method for diagnosing a data center air-conditioning system, which includes:

Obtain the working condition parameters of the air conditioning system, and establish each energy consumption model of the air conditioning system according to the historical operation data of the air conditioning system; and use each energy consumption model to obtain the first optimal value of the operating parameters of each energy consumption in the minimum state; Each energy consumption model includes chiller energy consumption model, water pump and fan energy consumption model; operating parameters include chilled water flow, cooling water flow, chilled water temperature, cooling water temperature, fan flow and head;

According to each energy consumption model and the first optimized value of the operating parameters, each energy consumption data curve of the air conditioning system is obtained; and according to each energy consumption data curve, each energy consumption data of the air conditioning system under the state of minimum total energy consumption is obtained, and the corresponding a second optimal value of the operating parameter;

Every specified time interval, read the current load rate and outdoor meteorological parameters, and perform the following steps in a loop to complete the optimization of the air conditioning system:

Use the energy consumption model, load rate, outdoor meteorological parameters, and the second optimized value of the operating parameters to adjust the step size for different operating parameters, calculate the energy consumption data under the optimal working condition, and determine the third value of the corresponding operating parameters Optimized value: store and send the third optimized value of the operating parameter to the controller, so as to realize the regulation and control of the air conditioning system.

In the second aspect, the embodiment of the present application provides a data center air-conditioning system diagnostic device, which includes:

The first optimization module is adapted to obtain the working condition parameters of the air conditioning system, and establish each energy consumption model of the air conditioning system according to the historical operation data of the air conditioning system; and use each energy consumption model to obtain the operation of each energy consumption in the minimum state The first optimal value of the parameter; each energy consumption model includes the energy consumption model of the chiller, the water pump and the fan energy consumption model; the operating parameters include the chilled water flow rate, the cooling water flow rate, the chilled water temperature, the cooling water temperature, the fan flow rate and the head;

The second optimization module is adapted to obtain each energy consumption data curve of the air conditioning system according to each energy consumption model and the first optimized value of the operating parameter; and according to each energy consumption data curve, obtain each Energy consumption data, determining the second optimal value of the corresponding operating parameter;

The third optimization module is adapted to read the current load rate and outdoor meteorological parameters at a specified time interval, and perform the following operations in a loop: using the energy consumption model, the load rate and the second optimal value of the outdoor meteorological parameters and operating parameters, respectively Adjust the step size for different operating parameters, calculate the energy consumption data under the optimal working condition, and determine the third optimal value of the corresponding operating parameters; store the third optimal value of the operating parameters and send it to the controller to realize the Regulation of the air conditioning system.

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory configured to store a computer program that can run on the processor, wherein, when the processor is configured to run the computer program, The steps of the method of the first aspect are performed.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method in the first aspect are implemented.

The data center air-conditioning system diagnosis method and device of the embodiment of the present application establish multiple energy consumption models of the air-conditioning system, so as to optimize the energy consumption model by using a large amount of real-time operation data, obtain the first optimal value of different operation parameters, and realize the air-conditioning system. Basic optimization of the system. On this basis, according to different operation strategies, the second optimal value of the operating parameters of the air-conditioning system under the state of minimum total energy consumption is obtained, and the automatic optimization of the air-conditioning system is completed. Combined with the real environment of the air conditioning system, the simulation optimization is carried out, and the third optimal value of the operating parameters is determined through cyclic calculation at a specified time interval, and the controller is issued to realize the diagnosis of the air conditioning system of the data center, and also achieve continuous control of the air conditioning system of the data center The purpose of adjustment and continuous optimization.

Description of drawings

FIG. 1 is a schematic flowchart of a method for diagnosing a data center air-conditioning system provided in an embodiment of the present application;

Fig. 2 is a schematic diagram of the composition of a data center air-conditioning system diagnosis optimization operation strategy module and the information transmission relationship provided in the embodiment of the present application;

FIG. 3 is a conceptual schematic diagram of energy-saving optimization on the chilled water side provided in the embodiment of the present application;

4a-4c are schematic diagrams of the calculation process of the third optimal value of the operating parameters in the embodiment of the present application;

FIG. 5 is a functional block diagram of a data center air-conditioning system diagnostic device provided in an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.

Detailed ways

In order to understand the characteristics and technical contents of the embodiments of the present application in more detail, the implementation of the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. The attached drawings are only for reference and description, and are not intended to limit the embodiments of the present application.

FIG. 1 is a schematic flowchart of a method for diagnosing a data center air-conditioning system provided in an embodiment of the present application. As shown in FIG. 1 , the method for diagnosing the data center air-conditioning system specifically includes the following steps:

Step S101, obtain the operating condition parameters of the air conditioning system, and establish each energy consumption model of the air conditioning system according to the historical operation data of the air conditioning system, and use each energy consumption model to obtain the first operating parameter of each energy consumption in the minimum state. optimized value.

In this embodiment, according to the characteristics of the air-conditioning system in the data center, the air-conditioning operating conditions are divided into summer operating conditions, transition season operating conditions and winter operating conditions. In this case, the natural cold source can be fully utilized to achieve the purpose of energy saving during operation throughout the year. For the above various operating conditions, the operating condition parameters of the air conditioning system can be obtained, and a large amount of historical operating data can be obtained at the same time. Consumption, energy-saving diagnosis and optimization of the air-conditioning system. The energy consumption model is the energy consumption model of the hourly air-conditioning system that can correspond to 8760 hours of the year. By changing the cooling water temperature and cooling water flow of the air conditioning system, the number and speed of the fans of the cooling tower, the chilled water temperature, the chilled water flow and other operating parameters, on the premise of meeting the cooling demand, make full use of the outdoor weather conditions to generate different The energy consumption data curve of the air-conditioning system under the operation strategy, and the first optimal value of the operating parameters of the air-conditioning system is output through the energy-saving algorithm to realize the energy-saving operation of the air-conditioning system. Here, the optimization of the energy-saving algorithm can be obtained through methods such as artificial neural network, deep learning, mathematical modeling, etc., to obtain the operating parameter configuration optimization scheme and energy-saving operation strategy of the air-conditioning system in different regions, different seasons, and different load rate scenarios. Here, the algorithm may adopt an algorithm such as traversal optimization, or a genetic algorithm, etc., and the energy-saving algorithm is not specifically limited.

Each energy consumption model includes chiller energy consumption model, water pump and fan energy consumption model. The operating parameters involve chilled water flow, cooling water flow, chilled water temperature, cooling water temperature, fan flow and head. These operating parameters involve the temperature of the chilled water inlet and outlet, the cooling pump water volume, the cooling water inlet and outlet temperature of the tower, the cooling pump water volume, and the cooling tower air volume as shown in Figure 2.

The energy consumption model of the chiller is as follows:

Q＝G _e C _p (T _ei -T _eo ) (2)

COP＝(a ₁ PLR ² +b ₁ PLR+c ₁ )(a ₂ PFR _e ² +b ₂ PFR _e ² +c ₂ )(a ₄ r _Te ² +b ₄ r _Te +c ₄ )(3)

Among them, E _chiller is the energy consumption power of the chiller equipment; Q is the cooling capacity; COP is the energy efficiency coefficient of the chiller; G _e is the chilled water flow rate; C _p is the water specific heat; _{T ei is} the chilled water inlet temperature; Chilled water outlet temperature; a ₁ , a ₂ , a ₄ , b ₁ , b ₂ , b ₄ , c ₁ , c ₂ , and c ₄ are undetermined coefficients of the model obtained by fitting the performance parameters provided by the manufacturer of the refrigeration equipment; PLR is the chiller load rate; PFR is the chilled water flow ratio; Q _r is the cooling capacity rating; G is the water flow, including chilled water flow and cooling water flow; G _r is the water flow rating, including the chilled water flow rating and the rated value of cooling water flow; r _Te is the dimensionless chilled water temperature; T _eo,r is the rated value of chilled water outlet temperature; T _ei,r is the rated value of chilled water inlet temperature.

Various data related to the rated value (such as Q _r , G _r , T _eo,r , T _ei,r, etc.) can be directly obtained according to the relevant equipment parameters of the air conditioning system, which are related to the equipment model and manufacturer of the refrigerator in the air conditioning system, etc. relevant. C _p can be the specific heat of water at constant pressure according to the implementation situation, and take the specified data, such as 4.18, which is not limited here. a ₁ , a ₂ , a ₄ , b ₁ , b ₂ , b ₄ , c ₁ , c ₂ , c ₄ etc. are fitted according to the performance parameters provided by the manufacturer of the refrigerator equipment to get the undetermined coefficients of the model. Model, manufacturer, etc. are determined, and the undetermined coefficients of different chiller equipment models are different. When using the chiller energy consumption model, it can be adjusted according to historical operating data. The above rated values, undetermined coefficients of the model and other data, as well as the chilled water flow G _e , chilled water inlet temperature T _ei , chilled water outlet temperature T _eo , water flow (including chilled water flow and cooling water Flow rate) G and other data are input into the chiller energy consumption model to obtain the energy consumption power E _chiller of the chiller equipment. Determine the energy consumption power E _chiller of the chiller equipment in the minimum energy consumption state from the obtained energy consumption power E _chiller of multiple chiller equipment, and the input chilled water will be obtained when the energy consumption power E _chiller of the chiller equipment in the minimum state is obtained Flow G _e , chilled water inlet temperature T _ei , chilled water outlet temperature T _eo , water flow (including chilled water flow and cooling water flow) G and other data are used as the first optimal value of the operating parameters.

Further, in order to simplify the energy consumption model, when determining the chilled water flow and chilled water temperature (chilled water inlet temperature, chilled water outlet temperature) related to the chilled side, the chilled water flow rate and chilled water temperature related to the chilled side can be calculated by the rated Value data to calculate. After determining the first optimal value of chilled water flow and chilled water temperature, calculate the first optimal value of cooling water flow and cooling water temperature related to the cooling side.

Optionally, this embodiment may also include a cooling tower model:

Lc _p dt = Gdh = K'a(h'-h _a )dV (7)

Among them, a is the heat transfer area per unit volume of the heat transfer part of the cooling tower; c _p is the water-to-heat ratio; G is the air flow rate of the cooling tower; _{h 1 is} the air enthalpy value of the cooling tower inlet; ' is the saturated air enthalpy at the same temperature as the cooling water; h _a is the air enthalpy for heat exchange with the cooling water; K' is the comprehensive heat transfer coefficient; L is the cooling water flow rate at the cooling tower inlet; t ₁ is the cooling tower outlet cooling Water temperature; t ₂ is the cooling water temperature at the inlet of the cooling tower; V is the volume of the heat transfer part of the cooling tower. The cooling tower model is a purely physical model, which divides the heat exchange volume in the cooling tower into tiny units, and establishes the heat transfer balance equation between air and water, as shown in the above formula (7). Integrate formula (7) to obtain the overall heat transfer balance equation of the cooling tower, as shown in formula (8) and formula (9). Among them, the integral result of formula (7) is called the number of heat transfer units NTU (number of transfer units) of the cooling tower, which represents the change in water temperature caused by the heat corresponding to the change in air enthalpy value, and is a measure of the difficulty of heat exchange between air and water. The physical quantity of ease is determined by the inherent physical characteristics of the cooling tower. After the cooling tower model and specification are determined, its NTU number is determined. The performance of the cooling tower can be verified by calculating whether the NTU number of the cooling tower in actual operation is consistent with the rated NTU. The calculation model of the cooling tower can be obtained through the above formula, and the first optimal value of the cooling water temperature and cooling water flow in the formula can also be deduced according to the minimum energy consumption of the model.

The energy consumption models of pumps and fans are as follows:

Among them, E _t is the total energy consumption power of the water pump and fan; V is the flow rate of the fan; ΔP is the head; _{η t} is _the total efficiency including the efficiency of the fan, motor and transmission; The undetermined coefficient of the model obtained by fitting the performance parameters of the inverter; C _f is the dimensionless flow; INV is the output frequency of the inverter; INV _r is the rated value of the output frequency of the inverter; N _r is the rated value of the speed; is the impeller diameter.

The various data related to the rated value (such as INV _r , N _r , etc.) and the speed N, impeller diameter D, frequency converter output frequency INV, etc. can be directly obtained according to the relevant equipment parameters of the air conditioning system, which are related to the equipment of water pumps and fans in the air conditioning system model, manufacturer, etc. e ₀ _e ₄ , etc. The undetermined coefficients of the model obtained by fitting the performance parameters of the pump, fan, motor, and frequency converter are determined by the model and manufacturer of the pump, fan, motor, and frequency converter. The undetermined coefficients of the model are different for different equipment. Adjustments can be made based on historical operating data when using pump and fan energy consumption models. The above rated values, undetermined coefficients of the model and other data, as well as the fan flow V and head ΔP in the collected historical operation data are input into the pump and fan energy consumption model to obtain the total energy consumption power E _t of the water pump and fan. Determine the total energy consumption power _Et of water pumps and fans in the minimum energy consumption state from the obtained total energy consumption power Et of multiple water pumps and fans, and the input fan flow rate when the total energy consumption power _Et of _water pumps and fans in the minimum state is obtained V, head ΔP and other data are used as the first optimal value of the operating parameters.

Further, in this embodiment, on the basis of the health diagnosis of the air-conditioning system, the operating status of the air-conditioning is identified to confirm that the air-conditioning system is in a normal and healthy operating status. The adjustment is based on the chilled water temperature, cooling water temperature, and the designed temperature difference between supply and return water. When the cooling load of the air conditioner is a partial load or changes, according to the adjustment of the electric two-way valve of the terminal air conditioner and the water supply and return water pressure of the manifold Adjust the frequency of the chilled water pump to adjust the flow rate; the chiller unit adjusts the cooling capacity by adjusting the loading and unloading of the chiller unit according to the change of the water supply temperature. The cooling tower adjusts the cooling capacity by adjusting the speed of the fan according to the change of the outlet temperature of the cooling water. Through the above control logic, the energy regulation of the air-conditioning system at partial load is realized, so as to achieve the purpose of energy saving.

Step S102, according to each energy consumption model and the first optimized value of the operating parameter, obtain each energy consumption data curve of the air conditioning system; and according to each energy consumption data curve, obtain each energy consumption data of the air conditioning system under the state of minimum total energy consumption, A second optimal value of the corresponding operating parameter is determined.

Chilled water supply temperature and flow optimization, and cooling water supply temperature and flow optimization are a typical optimization problem determined by the energy consumption characteristics of chillers, cooling towers, air-conditioning chilled water pumps, air-conditioning cooling water pumps, and air-conditioning terminals. The optimization goal is to minimize the total energy consumption of chillers, cooling towers, water pumps, and air-conditioning terminals. The optimization parameters are chilled water temperature, chilled water flow rate, cooling water temperature, cooling water flow rate, cooling tower air volume, and air-conditioning terminal air volume. The freezing side and the cooling side need to be optimized as a whole. In order to reduce the amount of calculation in actual control, the iterative optimization calculation method of first optimizing the freezing side, then controlling the cooling side, and then optimizing the freezing measurement can be used to finally obtain the optimization of the entire cooling system. Operating parameters. The concept of optimization is shown in Figure 3. According to each energy consumption model and the first optimal value of the operating parameters, each energy consumption data curve of the air conditioning system can be calculated separately. Each energy consumption includes the energy consumption power of the chiller equipment and the total energy consumption power of the water pump and fan, and correspondingly obtains the energy consumption of the chiller (energy consumption of the chiller equipment), water pump energy consumption, and AHU fan energy consumption. The values in each energy consumption data curve are correspondingly accumulated to obtain the total energy consumption curve of the air conditioning system. Select the minimum value of energy consumption in the total energy consumption curve of the air conditioning system (that is, the optimal point of the total energy consumption curve in Figure 3) corresponding to each target value of each energy consumption data curve (that is, the dotted line in Figure 3 and each energy consumption data curve) intersection point), the second optimal value of the operating parameter can be determined according to each target value. Specifically, the target value corresponds to each energy consumption power, and according to each energy consumption power, the data of chilled water flow, chilled water temperature, cooling water flow, cooling water temperature, fan flow, and lift in the operating parameters can be determined, that is, the second optimal value.

For specific processing, the following model can be used for the chilled water temperature and chilled water flow rate:

minE＝E _chiller +E _pump +E _AHUfan (14)

Among them, E includes the total energy consumption of chillers, water pumps, and air-conditioning terminals; E _chiller is the energy consumption of the chiller equipment corresponding to the chiller, E _pump is the energy consumption of water pumps, and E _AHUfan is the energy consumption of air-conditioning terminals, including the machine room The power consumption of cabinets, power distribution cabinets, etc., can be displayed by measuring instruments, (here, the energy consumption at the end of the default air conditioner can be directly read from the instrument, or the data can be added); R _ew is the ratio of chilled water flow and rated flow Ratio; T _eo is the chilled water outlet temperature; E _AHUfan is the percentage of air-conditioning fan speed; st is the constraint condition. According to the above constraints and the minimum total energy consumption, the second optimal value of the chilled water flow, the chilled water temperature, and the second optimal value of the fan flow and head can be determined.

For cooling water flow and cooling water temperature, the following model can be used:

minE＝E _chiller +E _pump +E _fan

E _chiller is the energy consumption of the chiller equipment corresponding to the chiller, E _pump is the energy consumption of the water pump, and E _fan is the energy consumption of the cooling tower fan (here it can be read directly according to the measuring instrument). According to the determined second optimal values of the chilled water flow rate and the chilled water temperature, the second optimal values of the cooling water flow rate and the cooling water temperature can be determined.

In this step, on the basis of realizing the basic optimization, and under the condition that the environment of the cooling machine room at the end of the air conditioner is normal, various artificial intelligence energy consumption optimization algorithms are used to issue control commands to the cold source group control system through the host computer. Chilled water supply temperature and flow, cooling water supply temperature and flow, as well as cooling tower air volume, terminal air volume and other configuration parameters are optimized to optimize the total energy consumption of the air conditioning system.

Step S103, using the energy consumption model, load rate, outdoor meteorological parameters, and the second optimal value of the operating parameters to adjust the step size for different operating parameters, calculate the energy consumption data under the optimal working condition, and determine the corresponding operating parameters The third optimal value of the operating parameter is stored and delivered to the controller, so as to realize the control of the air conditioning system of the data center.

This step reads the current load rate and outdoor meteorological parameters for each specified time interval, and executes this step cyclically to complete the optimization of the air-conditioning system. Among them, the specified time can be, for example, 1 hour, and the optimization calculation is initiated to obtain the third optimal value of the operating parameters at the next specified time, and send it to the corresponding chiller controller and water pump controller to achieve the purpose of cycle optimization . Based on the above operations, this embodiment uses, for example, an ergodic optimization algorithm to calculate the third optimization value, and may also use, for example, a genetic algorithm, a particle swarm optimization algorithm, etc., which are not limited here.

Since the energy consumption optimization objective function of the air conditioning system is a multi-dimensional high-cost function, it is difficult to obtain the analytical solution of the optimal value by deriving the extreme value, and it is necessary to use genetic algorithm, particle swarm optimization algorithm, traversal algorithm and other methods to solve it. If the computing power of the server is sufficient, the optimization problem can be solved in real time on site, and the optimal operating parameters can be set in real time. If the computing power of the server is not enough to solve the optimization problem in real time, offline optimization calculation can be used in advance, and empirical rules can be summarized according to the optimization results, and the decision tree method based on empirical rules can be used on site to achieve near-optimal control.

Specifically, the load rate and the outdoor meteorological parameters are obtained first. The load rate can be calculated from the total power consumption (IT cabinet + power distribution cabinet + air conditioner power consumption), and the total power consumption can be obtained by reading or adding data from measuring instruments. The outdoor meteorological parameters include, for example, the outdoor air temperature, which can be obtained directly. After obtaining the above data, taking the load rate of the current operation and the second optimal value of the cooling water temperature and cooling water flow in the operating parameters as the precondition, traverse the cooling water under all combinations of the temperature change of the chilled water and a certain range of the chilled water flow ratio. The total energy consumption of machines and water pumps, the combination of chilled water temperature and chilled water flow when the total energy consumption takes the minimum value is the third optimal value. Specifically, as shown in FIG. 4a, according to the energy consumption model of the chiller, the third optimal value of the chilled water temperature and the chilled water flow rate is determined first. For chilled water temperature and chilled water flow, first determine the first value range corresponding to the chilled water flow data and the second value range corresponding to the chilled water temperature data, and obtain multiple chilled water flow input data according to the first value range , according to the second value range to obtain a plurality of chilled water temperature input data. Among them, the chilled water flow input data is adjusted from the start data of the first value range to the end data of the first value range according to the specified step; the chilled water temperature input data is adjusted from the start of the second value range according to the specified step The data is adjusted to the end data of the second value range. In Figure 4a, the first value range of the chilled water flow is 0.6-1, the second value range of the chilled water temperature is 11 degrees-17 degrees, and the specified step size is 1/100, and multiple chilled water flow input data can be obtained and chilled water temperature input data. For any chilled water flow input data and chilled water temperature input data, the chilled water flow input data and chilled water temperature input data are combined with the load rate and the second optimal value of the outdoor meteorological parameters, cooling water flow rate and cooling water temperature, Input the energy consumption model of the chiller together to obtain multiple corresponding output energy consumption data. Determine the energy consumption data under the optimal working condition, that is, the minimum energy consumption data, from the multiple output energy consumption data obtained by traversing multiple chilled water flow input data and chilled water temperature input data. The water flow input data serves as the third optimal value of the chilled water flow, and the corresponding chilled water temperature input data serves as the third optimal value of the chilled water temperature.

Based on the optimization of chilled water temperature and chilled water flow rate on the chilled side, the energy-saving potential analysis of cooling side optimization is added, as shown in Figure 4b, to determine the third value range corresponding to the cooling water flow data and the corresponding According to the fourth value range of , multiple cooling water flow input data are obtained in the third value range, and multiple cooling water temperature input data are obtained in the fourth value range; wherein, the cooling water flow input data is in accordance with the specified step size Adjust from the start data of the third value range to the end data of the third value range; the cooling water temperature input data is adjusted from the start data of the fourth value range to the end data of the fourth value range according to the specified step size . The third value range of cooling water flow is 0.6-1, the fourth value range of cooling water temperature is 20-30 degrees, and the specified step size is 1/100, and multiple cooling water flow input data and cooling water can be obtained temperature input data. For any cooling water flow input data and cooling water temperature input data, the cooling water flow input data and cooling water temperature input data, and the third optimal value of load rate and outdoor meteorological parameters, chilled water flow rate and chilled water temperature, Input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data; determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, that is, the minimum energy consumption data, and use the energy consumption data The corresponding input data of the cooling water flow is used as the third optimal value of the cooling water flow, and the corresponding input data of the cooling water temperature is used as the third optimal value of the cooling water temperature.

Further, in addition to the above-mentioned separate optimization of the freezing side and cooling side, the overall optimization is also carried out, as shown in Figure 4c, first determine the first value range corresponding to the chilled water flow data and the second value corresponding to the chilled water temperature data Range, multiple input data of chilled water flow are obtained according to the first value range, and multiple input data of chilled water temperature are obtained according to the second value range; wherein, the input data of chilled water flow is taken from the first value according to the specified step size The start data of the range is adjusted to the end data of the first value range; the chilled water temperature input data is adjusted from the start data of the second value range to the end data of the second value range according to the specified step size. In Figure 4c, the first value range of the chilled water flow is 0.6-1, the second value range of the chilled water temperature is 11 degrees to 17 degrees, and the specified step size is 1/100, and multiple chilled water flow input data can be obtained and chilled water temperature input data. For any chilled water flow input data and chilled water temperature input data, the chilled water flow input data and chilled water temperature input data, load rate and outdoor meteorological parameters, specified cooling water flow data and specified cooling water temperature data , and input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data. Here, the designated cooling water flow rate data may be 0.71, and the designated cooling water temperature data may be 24.55 degrees. Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the chilled water flow input data corresponding to the energy consumption data as the third optimal value of the chilled water flow, and the corresponding chilled water temperature Enter the data as the third optimum value for chilled water temperature. On the basis of the third optimized value of the chilled water flow and the third optimized value of the chilled water temperature, the cooling side is further optimized. Determine the third value range corresponding to the cooling water flow data and the fourth value range corresponding to the cooling water temperature data, obtain multiple cooling water flow input data according to the third value range, and obtain multiple cooling water flow input data according to the fourth value range cooling water temperature input data; among them, the cooling water flow input data is adjusted from the start data of the third value range to the end data of the third value range according to the specified step size; the cooling water temperature input data is adjusted from the first value range according to the specified step size The start data of the four value ranges are adjusted to the end data of the fourth value range. The third value range of cooling water flow is 0.6-1, the fourth value range of cooling water temperature is 20-30 degrees, and the specified step size is 1/100, and multiple cooling water flow input data and cooling water can be obtained temperature input data. For any cooling water flow input data and cooling water temperature input data, the cooling water flow input data and cooling water temperature input data, and the third optimal value of load rate and outdoor meteorological parameters, chilled water flow rate and chilled water temperature, Input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data. Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the cooling water flow input data corresponding to the energy consumption data as the third optimal value of the cooling water flow, and the corresponding cooling water temperature Enter the data as the third optimum value for the cooling water temperature.

After the third optimal value of each operating parameter is calculated, the third optimal value of the operating parameter is stored and delivered to the controller, so as to realize the regulation and control of the air conditioning system.

The above value ranges and specified data are examples, and are set according to implementation conditions, and are not limited here.

In this step, through the simulation of the hot and humid environment in the computer room, optimize the airflow organization in the computer room and reduce the energy consumption of cold air transportation; through the hydraulic simulation of the water system, optimize the hydraulic loss, reduce the lift of the water pump, and reduce the energy consumption of the pump; , Cooling towers and other equipment efficiency, reduce equipment energy consumption; Through various simulation optimization, verify and optimize the air conditioning and cooling effect and energy saving effect of the data center; Through cycle optimization, machine self-learning and deep learning algorithms, automatic optimization and optimization The artificial intelligence energy-saving algorithm is iterated, so as to achieve the goal of self-saving energy saving through cycle optimization of the air conditioning system, and continuously approaching the optimal energy consumption.

According to the data center air-conditioning system diagnostic method provided in this application, multiple energy consumption models of the air-conditioning system are established, so as to utilize a large amount of real-time operation data to optimize the energy consumption model, obtain the first optimal value of different operating parameters, and realize the air-conditioning system. Basic optimization. On this basis, according to different operation strategies, the second optimal value of the operating parameters of the air-conditioning system under the state of minimum total energy consumption is obtained, and the automatic optimization of the air-conditioning system is completed. Combined with the real environment of the air conditioning system, the simulation optimization is carried out, and the third optimal value of the operating parameters is determined through cyclic calculation at a specified time interval, and the controller is issued to realize the diagnosis of the air conditioning system of the data center, and also achieve continuous adjustment of the air conditioning system of the data center The purpose of adjustment and continuous optimization.

Fig. 5 is a functional block diagram of a data center air-conditioning system diagnosis device provided by an embodiment of the present application. As shown in Figure 5, the data center air-conditioning system diagnosis device 50 includes the following modules:

The first optimization module 510 is configured to obtain the working condition parameters of the air conditioning system, and establish each energy consumption model of the air conditioning system according to the historical operation data of the air conditioning system; and use each energy consumption model to obtain the energy consumption of each energy consumption in the minimum state The first optimized value of the operating parameters; each energy consumption model includes the energy consumption model of the chiller, the water pump and the fan energy consumption model; the operating parameters include the chilled water flow rate, the cooling water flow rate, the chilled water temperature, the cooling water temperature, the fan flow rate and the lift;

The second optimization module 520 is configured to obtain each energy consumption data curve of the air conditioning system according to each energy consumption model and the first optimized value of the operating parameter; For each energy consumption data, determine the second optimal value of the corresponding operating parameter;

The third optimization module 530 is configured to read the current load rate and outdoor meteorological parameters at specified intervals, and perform the following operations in a loop: using the second optimal value of the energy consumption model, load rate, outdoor meteorological parameters, and operating parameters, Adjust the step size for different operating parameters, calculate the energy consumption data under the optimal working condition, and determine the third optimal value of the corresponding operating parameter; store the third optimal value of the operating parameter and send it to the controller to realize Control of the air conditioning system.

Optionally, the first optimization module 510 is further configured to:

According to each performance parameter of the chiller equipment, the energy consumption model of the chiller is established; wherein, the energy consumption model of the chiller is used to determine the first optimal value of chilled water flow, cooling water flow, chilled water temperature, and cooling water temperature;

According to the performance parameters of pumps, fans, motors, and frequency converters, the energy consumption models of water pumps and fans are established; among them, the energy consumption models of water pumps and fans are used to determine the first optimal value of fan flow and head.

Optionally, the energy consumption model of the chiller is created according to the undetermined coefficients of the model determined by the related parameters of the refrigeration equipment of the air conditioning system, the rated values of each parameter, and the performance parameters of the refrigeration equipment; the energy consumption model of the chiller is used to calculate the minimum energy consumption state Energy consumption of chiller equipment.

Optionally, the energy consumption model of the water pump and fan is created according to the relevant parameters of the water pump and fan, the rated values of each parameter, and the undetermined coefficients of the model determined by the relevant performance parameters of the pump and fan; the energy consumption model of the water pump and fan is used to calculate and determine the minimum energy consumption The total power consumption of pumps and fans in the state.

Optionally, the first optimization module 510 is further configured to:

According to the energy consumption model of the chiller, input the chilled water flow, chilled water temperature, cooling water flow, and cooling water temperature to obtain the energy consumption power of the chiller equipment; obtain the chilled water flow rate, refrigeration power corresponding to the minimum energy consumption power of the chiller equipment Water temperature, cooling water flow rate, and cooling water temperature are the first optimal values;

According to the energy consumption model of the water pump and fan, input the flow rate and head of the fan to obtain the total power consumption of the pump and fan; obtain the flow rate and head of the fan corresponding to the minimum value of the total energy consumption of the pump and fan as the first optimal value.

Optionally, the second optimization module 520 is further configured to:

According to each energy consumption model and the first optimized value of the operation parameter, each energy consumption data curve of the air conditioning system is calculated respectively; each energy consumption includes the energy consumption power of the refrigerator equipment and the total energy consumption power of the water pump and the fan;

Accumulate the values in each energy consumption data curve correspondingly to obtain the total energy consumption curve of the air conditioning system;

Each target value of each energy consumption data curve corresponding to the minimum value of energy consumption in the total energy consumption curve of the air conditioning system is selected, and the second optimal value of the operating parameter is determined according to each target value.

Optionally, the third optimization module 530 is further configured to:

Determine the first value range corresponding to the chilled water flow data and the second value range corresponding to the chilled water temperature data, obtain multiple chilled water flow input data according to the first value range, and obtain multiple chilled water flow input data according to the second value range chilled water temperature input data; among them, the chilled water flow input data is adjusted from the start data of the first value range to the end data of the first value range according to the specified step size; the chilled water temperature input data is adjusted from the first value range according to the specified step size The start data of the second value range is adjusted to the end data of the second value range;

For any chilled water flow input data and chilled water temperature input data, the chilled water flow input data and chilled water temperature input data are combined with the load rate and the second optimal value of the outdoor meteorological parameters, cooling water flow rate and cooling water temperature, Input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the chilled water flow input data corresponding to the energy consumption data as the third optimal value of the chilled water flow, and the corresponding chilled water temperature Input data as the third optimal value of chilled water temperature;

and / or,

Determine the third value range corresponding to the cooling water flow data and the fourth value range corresponding to the cooling water temperature data, obtain multiple cooling water flow input data according to the third value range, and obtain multiple cooling water flow input data according to the fourth value range cooling water temperature input data; among them, the cooling water flow input data is adjusted from the start data of the third value range to the end data of the third value range according to the specified step size; the cooling water temperature input data is adjusted from the first value range according to the specified step size The starting data of the four value ranges are adjusted to the end data of the fourth value range;

For any cooling water flow input data and cooling water temperature input data, the cooling water flow input data and cooling water temperature input data, and the third optimal value of load rate and outdoor meteorological parameters, chilled water flow rate and chilled water temperature, Input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the cooling water flow input data corresponding to the energy consumption data as the third optimal value of the cooling water flow, and the corresponding cooling water temperature Input data as the third optimal value of cooling water temperature;

and / or,

Determine the first value range corresponding to the chilled water flow data and the second value range corresponding to the chilled water temperature data, obtain multiple chilled water flow input data according to the first value range, and obtain multiple chilled water flow input data according to the second value range chilled water temperature input data; among them, the chilled water flow input data is adjusted from the start data of the first value range to the end data of the first value range according to the specified step size; the chilled water temperature input data is adjusted from the first value range according to the specified step size The start data of the second value range is adjusted to the end data of the second value range;

For any chilled water flow input data and chilled water temperature input data, the chilled water flow input data and chilled water temperature input data, load rate and outdoor meteorological parameters, specified cooling water flow data and specified cooling water temperature data , and input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the chilled water flow input data corresponding to the energy consumption data as the third optimal value of the chilled water flow, and the corresponding chilled water temperature Input data as the third optimal value of chilled water temperature;

Determine the third value range corresponding to the cooling water flow data and the fourth value range corresponding to the cooling water temperature data, obtain multiple cooling water flow input data according to the third value range, and obtain multiple cooling water flow input data according to the fourth value range cooling water temperature input data; among them, the cooling water flow input data is adjusted from the start data of the third value range to the end data of the third value range according to the specified step size; the cooling water temperature input data is adjusted from the first value range according to the specified step size The starting data of the four value ranges are adjusted to the end data of the fourth value range;

For any cooling water flow input data and cooling water temperature input data, the cooling water flow input data and cooling water temperature input data, and the third optimal value of load rate and outdoor meteorological parameters, chilled water flow rate and chilled water temperature, Input the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the cooling water flow input data corresponding to the energy consumption data as the third optimal value of the cooling water flow, and the corresponding cooling water temperature Enter the data as the third optimum value for the cooling water temperature.

For the descriptions of the above modules, refer to the corresponding descriptions in the method embodiments, and details are not repeated here.

The present application also provides a non-volatile computer storage medium, the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the data center air-conditioning system diagnosis method in any method embodiment above.

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The specific embodiment of the present application does not limit the specific implementation of the electronic device.

As shown in FIG. 6 , the electronic device 60 may include: a processor (processor) 602, a communication interface (Communications Interface) 604, a memory (memory) 606, and a communication bus 608.

in:

The processor 602 , the communication interface 604 , and the memory 606 communicate with each other through the communication bus 608 .

The communication interface 604 is used to communicate with network elements of other devices such as clients or other servers.

The processor 602 is configured to execute the program 610, and specifically, may execute relevant steps in the above embodiment of the method for diagnosing the air conditioning system of a data center.

Specifically, the program 610 may include program codes including computer operation instructions.

The processor 602 may be a central processing unit CPU, or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the electronic device may be of the same type, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.

The memory 606 is used for storing the program 610 . The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

The program 610 can be specifically used to make the processor 602 execute the data center air-conditioning system diagnosis method in any of the above method embodiments. For the specific implementation of each step in the program 610, reference may be made to the corresponding steps and corresponding descriptions in the units in the above-mentioned data center air-conditioning system diagnosis embodiment, and details are not repeated here. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the foregoing method embodiments, and details are not repeated here.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other device. Various generic systems can also be used with the teachings based on this. The structure required to construct such a system is apparent from the above description. Furthermore, the present invention is not specific to any particular programming language. It should be understood that various programming languages can be used to implement the content of the present invention described herein, and the above description of specific languages is for disclosing the best mode of the present invention.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, in order to streamline this disclosure and to facilitate an understanding of one or more of the various inventive aspects, various features of the invention are sometimes grouped together in a single embodiment, figure, or its description. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art can understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. Modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will understand that although some embodiments described herein include some features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention. and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components in the data center air-conditioning system diagnostic device according to the embodiment of the present invention. The present invention can also be implemented as an apparatus or an apparatus program (for example, a computer program and a computer program product) for performing a part or all of the methods described herein. Such a program for realizing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

Industrial Applicability

The data center air-conditioning system diagnosis method and device of the embodiment of the present application establish multiple energy consumption models of the air-conditioning system, so as to optimize the energy consumption model by using a large amount of real-time operation data, obtain the first optimal value of different operation parameters, and realize the air-conditioning system. Basic optimization of the system. On this basis, according to different operation strategies, the second optimal value of the operating parameters of the air-conditioning system under the state of minimum total energy consumption is obtained, and the automatic optimization of the air-conditioning system is completed. Combined with the real environment of the air conditioning system, the simulation optimization is carried out, and the third optimal value of the operating parameters is determined through cyclic calculation at a specified time interval, and the controller is issued to realize the diagnosis of the air conditioning system of the data center, and also achieve continuous control of the air conditioning system of the data center The purpose of adjustment and continuous optimization.

Claims (10)

1. A method for diagnosing a data center air-conditioning system, wherein the method includes:

   Obtain the working condition parameters of the air conditioning system, and establish various energy consumption models of the air conditioning system according to the historical operation data of the air conditioning system; and use the various energy consumption models to obtain the first optimization of the operating parameters of each energy consumption in the minimum state value; the various energy consumption models include chiller energy consumption models, water pumps and fan energy consumption models; the operating parameters include chilled water flow, cooling water flow, chilled water temperature, cooling water temperature, fan flow and head;

   According to each energy consumption model and the first optimized value of the operation parameter, each energy consumption data curve of the air conditioning system is obtained; consumption data, and determine the second optimal value of the corresponding operating parameter;

   Every specified time interval, read the current load rate and outdoor meteorological parameters, and perform the following steps in a loop to complete the optimization of the air conditioning system:

   Using the energy consumption model, the load rate, the outdoor meteorological parameters, and the second optimized value of the operating parameters, step adjustments are made for different operating parameters, and the energy consumption data under the optimal working conditions are calculated to determine the corresponding The third optimal value of the operating parameter; storing the third optimal value of the operating parameter and sending it to the controller, so as to realize the regulation and control of the air conditioning system.
2. The method according to claim 1, wherein said acquiring the operating condition parameters of the air conditioning system, and establishing each energy consumption model of the air conditioning system according to the historical operation data of the air conditioning system; and using said each energy consumption model to obtain each The first optimal value of the operating parameters in the minimum state of energy consumption further includes:

   According to each performance parameter of the refrigerator equipment, an energy consumption model of the chiller is established; wherein, the energy consumption model of the chiller is used to determine the first optimal value of chilled water flow, cooling water flow, chilled water temperature, and cooling water temperature;

   According to the performance parameters of the pump, the fan, the motor, and the frequency converter, an energy consumption model of the water pump and the fan is established; wherein, the energy consumption model of the water pump and the fan is used to determine the first optimal value of the flow rate and head of the fan.
3. The method according to claim 2, wherein the energy consumption model of the water chiller is created according to the undetermined coefficients of the model determined by the relevant parameters of the chiller equipment of the air-conditioning system, the rated values of each parameter, and the performance parameters of the chiller equipment; The energy consumption model is used to calculate the energy consumption power of the refrigerator equipment in the state of minimum energy consumption.
4. The method according to claim 2, wherein the energy consumption model of the water pump and the fan is established according to the relevant parameters of the water pump and the fan, the rated values of each parameter, and the model undetermined coefficients determined by the relevant performance parameters of the water pump and the fan; The fan energy consumption model is used to calculate the total energy consumption power of water pumps and fans in the minimum energy consumption state.
5. The method according to claim 3 or 4, wherein said obtaining the first optimal value of the operating parameter of each energy consumption in the minimum state by using said each energy consumption model further comprises:

   According to the energy consumption model of the chiller, input the chilled water flow, chilled water temperature, cooling water flow, and cooling water temperature to obtain the energy consumption power of the chiller equipment; obtain the chilled water flow rate corresponding to the minimum energy consumption power of the chiller equipment , chilled water temperature, cooling water flow rate, and cooling water temperature are the first optimal values;

   According to the energy consumption model of the water pump and fan, input the flow rate and head of the fan to obtain the total energy consumption power of the water pump and fan; obtain the flow rate and head of the fan corresponding to the minimum value of the total energy consumption of the pump and fan as the first optimal value.
6. The method according to claim 1, wherein, according to the various energy consumption models and the first optimized values of the operating parameters, various energy consumption data curves of the air conditioning system are obtained; and according to the various energy consumption data curves , to obtain each energy consumption data in the state of minimum total energy consumption of the air conditioning system, and determining the second optimal value of the corresponding operating parameters further includes:

   According to the various energy consumption models and the first optimized value of the operating parameters, the energy consumption data curves of the air conditioning system are calculated respectively; the various energy consumptions include the energy consumption power of the refrigerator equipment and the total energy consumption power;

   The numerical values in each energy consumption data curve are correspondingly accumulated to obtain the total energy consumption curve of the air conditioning system;

   Each target value of each energy consumption data curve corresponding to the minimum value of energy consumption in the total energy consumption curve of the air conditioning system is selected, and the second optimal value of the operating parameter is determined according to each target value.
7. The method according to claim 1, wherein, using the energy consumption model, the load rate, the outdoor meteorological parameters, and the second optimized value of the operating parameters, step adjustments are performed for different operating parameters, and the calculation Obtaining the energy consumption data under the optimal working condition, and determining the third optimal value of the corresponding operating parameter further includes:

   Determine the first value range corresponding to the chilled water flow data and the second value range corresponding to the chilled water temperature data, obtain a plurality of chilled water flow input data according to the first value range, and obtain a plurality of chilled water flow input data according to the second value range A plurality of chilled water temperature input data are obtained in the range; wherein, the chilled water flow input data is adjusted from the initial data of the first value range to the end data of the first value range according to the specified step size; the chilled water temperature The input data is adjusted from the start data of the second value range to the end data of the second value range according to the specified step size;

   For any chilled water flow input data and chilled water temperature input data, the chilled water flow input data and chilled water temperature input data, and the load rate and outdoor meteorological parameters, the cooling water flow rate and the cooling water temperature The second optimized value is input into the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

   Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the chilled water flow input data corresponding to the energy consumption data as the third optimal value of the chilled water flow, and the corresponding chilled water temperature Input data as the third optimal value of chilled water temperature;

   and / or,

   Determine the third value range corresponding to the cooling water flow data and the fourth value range corresponding to the cooling water temperature data, obtain a plurality of cooling water flow input data according to the third value range, and obtain a plurality of cooling water flow input data according to the fourth value range A plurality of cooling water temperature input data are obtained in the range; wherein, the cooling water flow input data is adjusted from the start data of the third value range to the end data of the third value range according to the specified step size; the cooling water temperature The input data is adjusted from the start data of the fourth value range to the end data of the fourth value range according to the specified step size;

   For any cooling water flow input data and cooling water temperature input data, the cooling water flow input data and cooling water temperature input data are related to the load rate and outdoor meteorological parameters, the chilled water flow rate and the chilled water temperature The third optimized value of is input into the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

   Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the cooling water flow input data corresponding to the energy consumption data as the third optimal value of the cooling water flow, and the corresponding cooling water temperature Input data as the third optimal value of cooling water temperature;

   and / or,

   Determine the first value range corresponding to the chilled water flow data and the second value range corresponding to the chilled water temperature data, obtain a plurality of chilled water flow input data according to the first value range, and obtain a plurality of chilled water flow input data according to the second value range A plurality of chilled water temperature input data are obtained in the range; wherein, the chilled water flow input data is adjusted from the initial data of the first value range to the end data of the first value range according to the specified step size; the chilled water temperature The input data is adjusted from the start data of the second value range to the end data of the second value range according to the specified step size;

   For any chilled water flow input data and chilled water temperature input data, the chilled water flow input data and chilled water temperature input data, and the load rate and outdoor meteorological parameters, the specified cooling water flow data and the specified cooling water The temperature data is input into the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

   Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the chilled water flow input data corresponding to the energy consumption data as the third optimal value of the chilled water flow, and the corresponding chilled water temperature Input data as the third optimal value of chilled water temperature;

   Determine the third value range corresponding to the cooling water flow data and the fourth value range corresponding to the cooling water temperature data, obtain a plurality of cooling water flow input data according to the third value range, and obtain a plurality of cooling water flow input data according to the fourth value A plurality of cooling water temperature input data are obtained in the range; wherein, the cooling water flow input data is adjusted from the start data of the third value range to the end data of the third value range according to the specified step size; the cooling water temperature The input data is adjusted from the start data of the fourth value range to the end data of the fourth value range according to the specified step size;

   For any cooling water flow input data and cooling water temperature input data, the cooling water flow input data and cooling water temperature input data are related to the load rate and outdoor meteorological parameters, the chilled water flow rate and the chilled water temperature The third optimized value of is input into the energy consumption model of the chiller together to obtain the corresponding output energy consumption data;

   Determine the energy consumption data under the optimal working condition from the obtained multiple output energy consumption data, and use the cooling water flow input data corresponding to the energy consumption data as the third optimal value of the cooling water flow, and the corresponding cooling water temperature Enter the data as the third optimum value for the cooling water temperature.
8. A data center air-conditioning system diagnostic device, wherein the device includes:

   The first optimization module is configured to obtain the working condition parameters of the air conditioning system, and establish each energy consumption model of the air conditioning system according to the historical operation data of the air conditioning system; and use the various energy consumption models to obtain the minimum state of each energy consumption The first optimized value of the operating parameters; the various energy consumption models include chiller energy consumption models, pumps and fan energy consumption models; the operating parameters include chilled water flow, cooling water flow, chilled water temperature, cooling water temperature, Fan flow and head;

   The second optimization module is configured to obtain various energy consumption data curves of the air conditioning system according to the various energy consumption models and the first optimized value of the operating parameters; and obtain the total energy consumption of the air conditioning system according to the various energy consumption data curves Each energy consumption data in the state of minimum consumption is used to determine the second optimal value of the corresponding operating parameter;

   The third optimization module is configured to read the current load rate and outdoor meteorological parameters at specified intervals, and perform the following operations in a loop: using the energy consumption model, the load rate and outdoor meteorological parameters, and the operating parameters The second optimal value is to adjust the step size for different operating parameters, calculate the energy consumption data under the optimal working condition, and determine the third optimal value of the corresponding operating parameter; store the third optimal value of the operating parameter and store it. Send the controller to realize the regulation and control of the air conditioning system.
9. An electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface complete mutual communication through the communication bus;

   The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the method for diagnosing the data center air-conditioning system according to any one of claims 1-7.
10. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform the operation corresponding to the data center air-conditioning system diagnosis method according to any one of claims 1-7 .

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