Machine room environment management and control method, system and equipment based on digital twinning technology

Abstract

The invention is suitable for the technical field of machine rooms, and provides a machine room environment management and control method based on a digital twinning technology, which comprises the following steps: constructing a digital twinning machine room model based on a three-dimensional structure of a machine room and a machine room equipment layout; real-time load data of each IT device, real-time power data of each cooling device and environmental parameter data of each type of sensor are obtained, thermal load distribution of the machine room is predicted, a preset environmental control strategy is combined, corresponding working conditions are simulated and generated for the machine room in a digital twin machine room model, PUE values of the machine room under the working conditions are obtained through calculation, and the PUE values of the machine room under the working conditions are obtained. Dynamically adjusting an environment control strategy; running state parameters of the IT equipment and video image data in the machine room are obtained in real time, the running state parameters and the video image data are analyzed, and potential abnormal equipment and abnormal areas are determined in the digital twin machine room model. According to the invention, comprehensive optimization of machine room energy efficiency, operation and maintenance efficiency and environment resource configuration is realized, and the intelligent level of machine room management and control is improved.

Description

Technical Field

[0001] The present application belongs to the field of computer room technology, and in particular, relates to a computer room environment control method, system and equipment based on digital twin technology. Background Art

[0002] In the computer room environment monitoring and control scenario, in order to achieve real-time monitoring and early warning of the internal environment of the computer room, digital twin technology is usually used to build a digital twin model of the computer room. Specifically, by deploying temperature and humidity sensors inside the computer room, environmental parameter data at different locations are collected and transmitted to the digital twin platform. The digital twin platform reconstructs a digital twin computer room that reflects the real computer room environment based on the above environmental parameter data.

[0003] However, due to the wide variety of equipment in the computer room and the complex layout, the heat generation and heat dissipation effects of different equipment vary greatly, which leads to the accumulation of heat in local areas. The traditional finite element analysis method is difficult to accurately predict the heat generated in the above local areas. At the same time, it is difficult to accurately predict the changing trend of the internal environment of the computer room in the future period of time only by relying on the data collected by temperature and humidity sensors.

[0004] Therefore, how to monitor and control the real-time environment of the computer room in the digital twin model is an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present application provide a computer room environment control method, system and device based on digital twin technology, which can monitor and control the environment of the computer room in real time.

[0006] In a first aspect, an embodiment of the present application provides a method for controlling a computer room environment based on digital twin technology, including:

[0007] A digital twin computer room model is constructed based on the three-dimensional structure of the computer room and the layout of the equipment in the computer room, wherein the equipment in the computer room includes multiple IT equipment, multiple cooling equipment, and multiple types of sensors;

[0008] Acquire the real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predict the heat load distribution of the computer room based on the real-time load data, real-time power data, and environmental parameter data;

[0009] Based on the heat load distribution and in combination with a preset environmental control strategy, a corresponding operating condition is generated for the computer room simulation in the digital twin computer room model, a PUE value of the computer room under the operating condition is calculated, and based on the PUE value, the environmental control strategy is dynamically adjusted;

[0010] The operating status parameters of the IT equipment and the video image data in the computer room are acquired in real time, the operating status parameters and the video image data are analyzed, and potential abnormal equipment and abnormal areas are determined in the digital twin computer room model.

[0011] Furthermore, the acquiring of the real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predicting the heat load distribution of the computer room based on the real-time load data, the real-time power data, and the environmental parameter data, includes:

[0012] A support vector machine regression algorithm is used to establish a first relationship model, where the first relationship model is a corresponding relationship between the load data of the IT device and the heating capacity of the IT device;

[0013] A least squares regression algorithm is used to establish a second relationship model, where the second relationship model is a corresponding relationship between the device power of the cooling device and the cooling capacity of the cooling device;

[0014] According to the environmental parameter data, an inverse distance weighted interpolation algorithm is used to obtain a real-time environmental parameter distribution map of the computer room;

[0015] Based on the first relationship model, obtaining the heating amount of the IT equipment according to the real-time load data;

[0016] Based on the second relationship model, and according to the real-time power data, obtaining the cooling capacity of the cooling device;

[0017] Based on the heating amount, the cooling capacity and the real-time environmental parameter distribution, a computational fluid dynamics method is used to predict the heat load distribution of the computer room.

[0018] Further, the calculation to obtain the PUE value of the computer room under the working conditions includes:

[0019] Performing operation simulation on the working condition in the digital twin computer room model, and recording the computer room airflow distribution, temperature field distribution, total energy consumption of IT equipment, and total energy consumption of cooling equipment under the working condition;

[0020] Based on the total energy consumption of the IT equipment and the total energy consumption of the cooling equipment, the total energy consumption and PUE value of the computer room under the working conditions are obtained.

[0021] Furthermore, the dynamic environmental control strategy based on the PUE value includes:

[0022] Using a machine learning algorithm, a first correlation model between the PUE value and the environmental control strategy is established, wherein the first correlation model is used to predict a change trend of the PUE value under different environmental control strategies;

[0023] By using big data analysis technology, the correlation law between the environmental parameter data and the operating status of the IT equipment is obtained, and a second correlation model is established;

[0024] Comparing the PUE value under the operating condition with a preset threshold;

[0025] If the PUE value is lower than the preset threshold, outputting the current environmental control strategy as the optimal solution;

[0026] If the PUE value is higher than the preset threshold, an optimization process is triggered, and the optimization process is to optimize the environmental control strategy of the computer room based on the first correlation model and the second correlation model.

[0027] Furthermore, the optimizing the environment control strategy of the computer room based on the first association model and the second association model includes:

[0028] Setting a plurality of environmental control strategies for the working conditions, and comparing the changing trends of PUE values ​​under different environmental control strategies through the first correlation model;

[0029] Selecting an environmental control strategy that reduces the PUE value to the preset threshold within a preset time as a candidate solution, and analyzing the impact of different candidate solutions on the energy consumption of the computer room;

[0030] For the plurality of candidate solutions, based on the second association model and according to the environmental parameter data in the candidate solutions, evaluating the operating status of each of the IT devices;

[0031] Based on the degree of influence of different candidate solutions on the energy consumption of the computer room and the operating status of each IT equipment, a decision matrix is ​​established, and a comprehensive score is given to each candidate solution based on the decision matrix, and the candidate solution with the highest comprehensive score is selected as the optimal solution;

[0032] A two-way feedback channel is established for the environmental control strategy and the environmental parameter data, and the environmental control strategy is optimized in real time through the two-way feedback channel.

[0033] Furthermore, the real-time acquisition of the operating status parameters of the IT equipment and the video image data in the computer room, analysis of the operating status parameters and the video image data, and determination of potential abnormal equipment and abnormal areas in the digital twin computer room model include:

[0034] Acquire historical operation data of the IT equipment, and build an equipment failure prediction model based on the historical operation data, wherein the historical operation data includes operation time, maintenance records, and failure records;

[0035] Inputting the operating status parameters of each IT device into the device failure prediction model, determining whether each IT device is a potential abnormal device, and generating potential failure information and a predictive maintenance plan for the abnormal device;

[0036] Mapping and associating the operating status parameters, the potential fault information, and the video image data with the digital twin computer room model;

[0037] The video image data is used to analyze the equipment operation status of each IT device in the computer room. Based on the equipment operation status, the abnormal area is determined in the digital twin computer room model. The equipment operation status includes changes in indicator light status, abnormal noise sources and equipment vibration amplitude. The abnormal area is used to provide maintenance personnel with spatial location guidance for faulty equipment.

[0038] Furthermore, analyzing the equipment operation status of each IT device in the computer room through the video image data, and determining abnormal areas in the digital twin computer room model based on the equipment operation status, includes:

[0039] Preprocessing the video image data by image preprocessing technology to obtain image sequence data, and performing sound separation processing on the video image data to obtain the audio signal in the computer room;

[0040] Using a target detection algorithm to detect and identify IT devices in the image sequence data, obtaining device information of each IT device in the computer room and image information of each IT device in the image sequence data, wherein the device information includes location information, attribute information, operating status parameters, and potential abnormality information;

[0041] Extracting and classifying the audio signal by an audio analysis algorithm to determine whether there is abnormal noise in the computer room, and determining the source of the abnormal noise by sound source localization technology;

[0042] For each of the IT devices, image features of the indicator light area of ​​the IT device are obtained from the image information, and a state change of the indicator light is determined by an image classification algorithm to determine an abnormal change of the indicator light of the IT device;

[0043] For each of the IT devices, based on the operating status parameters, a vibration signal is obtained, the vibration signal is analyzed in the time and frequency domains by using a signal processing technique, a vibration feature of the vibration signal is extracted, and based on the vibration feature, an abnormality detection algorithm is used to determine whether the IT device has abnormal vibration;

[0044] The source location of the abnormal noise, abnormal changes in indicator lights, and abnormally vibrating IT equipment are mapped and associated with the digital twin computer room model, and the abnormal area of ​​the computer room is determined in the digital twin computer room model.

[0045] Furthermore, the method further comprises:

[0046] In the digital twin computer room model, the computer room is divided into multiple sub-areas according to the type of equipment in the computer room, environmental parameter data of each sub-area is obtained, and an environmental digital model is constructed, wherein the environmental digital model is used to reflect the environmental conditions in the computer room;

[0047] Based on the digital environment model, an independent environment control strategy is set for each sub-area.

[0048] In a second aspect, the embodiment of the present application provides a computer room environment control system based on digital twin technology, including:

[0049] The first processing module is used to build a digital twin computer room model based on the three-dimensional structure of the computer room and the layout of the equipment in the computer room, where the equipment in the computer room includes multiple IT equipment, multiple cooling equipment, and multiple types of sensors;

[0050] The second processing module is used to obtain the real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predict the heat load distribution of the computer room based on the real-time load data, real-time power data and environmental parameter data;

[0051] A third processing module is used to simulate and generate corresponding working conditions for the computer room in the digital twin computer room model based on the heat load distribution and in combination with a preset environmental control strategy, calculate and obtain the PUE value of the computer room under the working conditions, and dynamically adjust the environmental control strategy based on the PUE value;

[0052] The fourth processing module is used to obtain the operating status parameters of the IT equipment and the video image data in the computer room in real time, analyze the operating status parameters and the video image data, and determine potential abnormal equipment and abnormal areas in the digital twin computer room model.

[0053] In the third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned computer room environment control method based on digital twin technology is implemented.

[0054] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0055] The present application discloses a computer room environment control method based on digital twin technology, which can comprehensively and intuitively display the actual situation of the computer room by accurately modeling the three-dimensional space of the computer room and the layout of its internal equipment. In addition, by obtaining the real-time load data of IT equipment, the real-time power data of cooling equipment and the environmental parameter data of various sensors, the operation status of the computer room can be fully grasped, and the heat load distribution of the computer room can be predicted, providing a key basis for the energy efficiency management and thermal management of the computer room. In addition, in the digital twin computer room model, the working conditions corresponding to the computer room are simulated and generated, and the environmental control strategy is dynamically adjusted through the PUE value under the working conditions to maximize the energy efficiency of the computer room. By analyzing the operating status parameters and video image data, potential abnormal equipment and abnormal areas can be quickly identified in the digital twin computer room model, providing timely and accurate early warning information for the operation and maintenance management of the computer room, realizing the comprehensive optimization of the energy efficiency, operation and maintenance efficiency and environmental resource allocation of the computer room, and improving the intelligent level of computer room control. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 It is a flow chart of a method for controlling a computer room environment based on digital twin technology provided by an embodiment of the present invention;

[0058] Figure 2 It is a structural schematic diagram of a computer room environment control system based on digital twin technology provided by an embodiment of the present invention;

[0059] Figure 3 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0061] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0062] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0063] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0064] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0065] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0066] See also Figure 1 As shown, the present invention is a computer room environment control method based on digital twin technology, comprising the following steps:

[0067] S100, constructing a digital twin computer room model based on the three-dimensional structure of the computer room and the layout of the equipment in the computer room, wherein the equipment in the computer room includes multiple IT equipment, multiple cooling equipment, and multiple types of sensors;

[0068] In this embodiment, three-dimensional structural data of the computer room is obtained, wherein the three-dimensional structural data includes computer room size information and building structure information, the computer room size information includes the length, width and height of the computer room, and the building structure information includes the wall, floor and ceiling, and the above three-dimensional structural data is converted into a digital three-dimensional model through three-dimensional modeling software to form a virtual three-dimensional structure of the computer room; the layout information of each computer room equipment in the computer room is obtained, including the model, size and placement position of each computer room equipment, and the corresponding equipment model is deployed in the three-dimensional model according to the above layout information, and the equipment information of each computer room equipment is associated with the above equipment model to form a complete digital representation of the equipment layout, wherein the equipment information includes location information, attribute information, operation status parameters and potential abnormality information.

[0069] In this embodiment, data related to the three-dimensional model and equipment model of the computer room are imported into the digital twin platform, a digital twin computer room model is constructed through the digital twin platform, and a panoramic display and interactive operation of the computer room are achieved through the visual interface of the digital twin platform.

[0070] In addition, by using technologies such as physics engines and game engines, physical simulation and behavioral simulation are performed on the digital twin model of the computer room based on the physical characteristics of the computer room and the operating rules of the computer room equipment, so that the virtual environment can truly reflect the operating status and changes of the computer room.

[0071] S200, obtaining real-time load data of each IT device, real-time power data of each cooling device, and environmental parameter data of each type of sensor, and predicting the heat load distribution of the computer room based on the real-time load data, real-time power data, and environmental parameter data;

[0072] In some embodiments, the step S200 includes:

[0073] A support vector machine regression algorithm is used to establish a first relationship model, where the first relationship model is a corresponding relationship between the load data of the IT device and the heating capacity of the IT device;

[0074] A least squares regression algorithm is used to establish a second relationship model, where the second relationship model is a corresponding relationship between the device power of the cooling device and the cooling capacity of the cooling device;

[0075] According to the environmental parameter data, an inverse distance weighted interpolation algorithm is used to obtain a real-time environmental parameter distribution map of the computer room;

[0076] Based on the first relationship model, obtaining the heating amount of the IT equipment according to the real-time load data;

[0077] Based on the second relationship model, and according to the real-time power data, obtaining the cooling capacity of the cooling device;

[0078] Based on the heating amount, the cooling capacity and the real-time environmental parameter distribution, a computational fluid dynamics method is used to predict the heat load distribution in the computer room.

[0079] In this embodiment, for the real-time heating amount, the historical load data of the IT equipment and the heating amount data at the corresponding moment are obtained, and a support vector machine regression algorithm is used to train and establish a first relationship model that associates the IT equipment load with the heating amount. Then, the real-time load data of the IT equipment is obtained and input into the first relationship model to obtain the real-time heating amount of the IT equipment. Similarly, for the real-time cooling capacity, the historical power data of the cooling equipment and the cooling capacity data at the corresponding moment are obtained, and a least squares regression algorithm is used to train and establish a second relationship model that associates the cooling equipment power with the cooling capacity. The real-time power data of the cooling equipment is input into the second relationship model to obtain the real-time cooling capacity of the cooling equipment.

[0080] In this embodiment, for the real-time environmental parameter distribution map in the computer room, environmental parameter data in the computer room are collected by various types of sensors, and the inverse distance weighted interpolation algorithm is used according to the sensor position coordinates to calculate the real-time environmental parameter distribution of each position in the computer room. Specifically, the basic formula of the inverse distance weighted interpolation algorithm is: Among them, IDW is the real-time environmental parameter of each location in the computer room obtained by the above inverse distance weighted difference algorithm, w i represents the weight of the distance from the i-th known point to the interpolation point, the known point is the sensor location coordinate, and the interpolation point is the location coordinate of the real-time environmental parameter to be calculated in the computer room. It can be understood that the weight is usually inversely proportional to the distance between the interpolation point and the known point, and its weight value is usually the reciprocal of the distance or the square of the reciprocal. Preferably, in this embodiment, the weight value is the reciprocal of the distance. In addition, v i It represents the value of the i-th known point, that is, the environmental parameter data of the i-th type of sensor. Depending on the type of sensor, the value of the known point is different, usually including temperature and humidity. Based on this, the environmental parameter data of the coordinates of each position in the computer room is calculated to generate a real-time environmental parameter distribution map of the computer room. The real-time environmental parameter distribution map can intuitively display the environmental conditions of each position in the computer room, so that the operation and maintenance personnel can understand the overall environmental conditions of the computer room at a glance.

[0081] In some embodiments, for the real-time environmental parameter distribution map, the computer room can be divided into multiple grid areas according to a preset division method, such as divided in a 1m×1m manner to obtain multiple grid areas. For each grid area, the center position of the grid area is used as the position coordinate, and the above-mentioned inverse distance weighted interpolation algorithm is used to calculate the environmental conditions of each grid area, and finally generate a real-time environmental parameter distribution map of the computer room.

[0082] In this embodiment, based on the numerical simulation method of computational fluid dynamics, the heat distribution and airflow distribution in the computer room are simulated and calculated to obtain the predicted results of the heat load distribution in each area of ​​the computer room. Specifically, the digital twin computer room model is meshed, wherein the quality and density of the meshing directly affect the accuracy and efficiency of the calculation results, and a denser grid is used for key areas, such as near the server cabinet and the outlet of the cooling equipment. Then, according to the actual situation of the computer room, boundary conditions are set, such as inlet wind speed, temperature, outlet pressure, thermal conductivity coefficient of the wall and the ground, etc., to ensure the consistency of the simulation results with the actual physical phenomena. At the same time, a suitable solver is selected, such as ANSYS Fluent, CFX, etc., and solution parameters, such as iteration steps, convergence criteria, turbulence model, are set, and the solver is run to numerically solve the parameters such as airflow velocity and temperature in the computer room to obtain the airflow velocity and temperature data of each point inside the computer room. According to the basic principles and formulas of convective heat transfer, such as Newton's law of cooling, combined with the air flow velocity and temperature data inside the computer room, the convective heat transfer coefficient of each area of ​​the computer room surface is calculated based on the convective heat transfer coefficient and the temperature difference between each area inside the computer room. The heat flux on the surface of each area is calculated. The heat flux represents the amount of heat passing through a unit area per unit time. The heat flux data on the surface of each area is summarized and sorted to obtain the heat load distribution of each area inside the computer room. By analyzing the heat load distribution, the hot spots and cold spots inside the computer room are found. The hot spots are areas with higher heat loads and may require additional cooling measures; the cold spots are areas with lower heat loads and may require optimized cooling strategies to avoid energy waste.

[0083] S300, based on the heat load distribution and in combination with a preset environmental control strategy, simulating the energy consumption of the computer room under different working conditions in the digital twin computer room model, calculating the PUE value of the computer room under different working conditions, and dynamically adjusting the environmental control strategy based on the PUE value;

[0084] In this embodiment, based on the prediction results of the heat load distribution, it is determined whether there are local overheating areas in the computer room. For the local overheating areas, the environmental control strategy is dynamically adjusted according to the location of the local overheating areas, such as adjusting the output power of the cooling equipment or the air outlet direction, or adjusting the air conditioning temperature setting of the local area inside the computer room, so as to achieve a balanced distribution of the heat load in the computer room and avoid the impact of local overheating on the operation of IT equipment.

[0085] In some of the embodiments, based on the heat load distribution and in combination with a preset environmental control strategy, a corresponding operating condition is simulated and generated for the computer room in the digital twin computer room model, a PUE value of the computer room under the operating condition is calculated, and based on the PUE value, the environmental control strategy is dynamically adjusted;

[0086] In this embodiment, the environmental control strategy includes a cooling strategy and an airflow organization method. The cooling strategy includes the opening and closing status of each cooling device, and the airflow organization method is the installation method of the air outlet of each cooling device; the cooling equipment includes refrigeration equipment and air supply equipment, such as refrigeration equipment such as air-conditioning units and chillers, and air supply equipment such as fans and air supply ducts.

[0087] In this embodiment, according to the predicted heat load distribution, a suitable environmental control strategy is selected, and the corresponding operating conditions of the computer room are simulated and generated in the digital twin computer room model. For example, according to the environmental control strategy, corresponding cooling equipment parameters are set for the cooling equipment, such as the cooling capacity of the air-conditioning unit, the speed of the fan, etc. At the same time, according to the layout of the computer room and the environmental control strategy, a suitable airflow organization method is selected for the air supply equipment, such as setting the size, position and air supply direction of the vents.

[0088] In one embodiment, according to the prediction of the heat load distribution in the computer room, there are usually the following four operating conditions. In the first operating condition, the overall heat load in the computer room is relatively low and is mainly concentrated in certain specific areas, such as near some server cabinets. The preset environmental control strategy is usually to shut down some redundant cooling equipment, such as air-conditioning units or fans, to reduce energy consumption, while keeping the cooling equipment in key areas turned on to ensure the temperature of these areas is stable. For the corresponding airflow organization method, an underfloor air supply method is adopted to directly deliver cold air to the bottom of the server cabinet to improve the cooling efficiency. At the same time, the size and position of the air supply port are adjusted to optimize the airflow distribution and avoid local overheating. Under the second working condition, the overall heat load in the computer room is moderate and the distribution is relatively uniform, but the heat load near some high-power consumption equipment is high. The preset environmental control strategy is to turn on all necessary cooling equipment to ensure the temperature in the computer room is stable. At the same time, according to the heat load distribution, the operating power of the cooling equipment is adjusted to balance energy consumption and cooling effect. For air flow organization, the duct top air supply method is adopted to evenly deliver cold air to various areas of the computer room. For high-power consumption equipment, additional air supply outlets are set near it or the air supply direction is adjusted to improve the cooling effect of the area where the high-power consumption equipment is located. Under the third working condition, the overall heat load in the computer room is high and unevenly distributed, with obvious hot spots in some areas. The preset environmental control strategy is to turn on all cooling equipment and adjust them to maximum power operation to reduce the temperature in the computer room as quickly as possible. At the same time, according to the heat load distribution, local enhanced cooling measures are taken, such as using mobile cooling equipment or adding temporary air supply vents. For air flow organization, the hood-top air supply method is adopted to send cold air directly to the top of the computer room, and through air flow organization optimization, the cold air can be evenly distributed throughout the computer room. In addition, additional air supply vents are set in hot spots or high-efficiency air supply equipment is used to improve the cooling efficiency of these areas. The fourth operating condition is usually the operating condition under special meteorological conditions. Under this condition, the external environment of the computer room changes significantly, such as extremely high temperature or low temperature weather, which causes changes in the heat load distribution in the computer room. The corresponding environmental control strategy is to adjust the operating mode and power of the cooling equipment according to the external ambient temperature. For example, in extremely high temperature weather, turn on all cooling equipment and strengthen local cooling measures; or in extremely low temperature weather, appropriately reduce the operating power of the cooling equipment or take insulation measures. For the airflow organization method, adjust the installation method and position of the air supply outlet according to changes in the external environment. For example, in extremely high temperature weather, increase the number of air supply outlets or adjust the air supply direction to improve cooling efficiency; or in extremely low temperature weather, close some air supply outlets or adjust the air supply angle to reduce the impact of cold air on the temperature in the computer room.

[0089] In some embodiments, the calculating and obtaining the PUE value of the computer room under the working conditions includes:

[0090] Performing operation simulation on the working condition in the digital twin computer room model, and recording the computer room airflow distribution, temperature field distribution, total energy consumption of IT equipment, and total energy consumption of cooling equipment under the working condition;

[0091] Based on the total energy consumption of the IT equipment and the total energy consumption of the cooling equipment, the total energy consumption and PUE value of the computer room under the working conditions are obtained.

[0092] It is understandable that the environmental control strategy selected based on the prediction of the heat load distribution in the computer room is the environmental control strategy preset by the digital twin computer room model based on the historical heat load distribution. It is universal, but it cannot accurately reflect the heat load distribution characteristics under current or future specific working conditions. Therefore, for each working condition, the digital twin computer room model is used to simulate the operation and energy consumption data of IT equipment, cooling equipment, etc. in the computer room, and the PUE value of the computer room is calculated. The PUE value is used to evaluate the energy efficiency performance of the computer room under the current environmental control strategy, optimize the cooling strategy and airflow organization method, and generate an energy-saving optimization plan.

[0093] Specifically, a simulation is run in the digital twin model to observe and record the airflow distribution and temperature field distribution in the computer room under the current working conditions. For example, the temperature, humidity, airflow velocity and other parameters at each point in the computer room during the simulation can be recorded through the above-mentioned real-time environmental parameter distribution diagram.

[0094] For the total energy consumption of IT equipment and cooling equipment, the energy consumption of each device is calculated separately to obtain the total energy consumption of IT equipment, the total energy consumption of cooling equipment and the total energy consumption of the computer room. Specifically, the calculation formula for the energy consumption of a single IT device or cooling device is as follows: Equipment energy consumption = equipment rated power × actual load factor × running time, where the equipment rated power is obtained from the equipment specification sheet of each device, and the actual load factor is a value between 0 and 1, which is used to indicate the load condition of the equipment during actual operation; for the total energy consumption of IT equipment, the calculation formula is: Equipment energy consumption, where n is the number of IT devices. Similarly, for the total energy consumption of cooling equipment, the calculation formula is as follows: Cooling equipment energy consumption, where m is the number of cooling equipment. Then, add the total energy consumption of IT equipment and the total energy consumption of cooling equipment to get the total energy consumption of the computer room under the current working conditions. The ratio of the total energy consumption of the computer room to the total energy consumption of IT equipment is used to calculate the PUE value under the current working conditions: PUE = total energy consumption of the computer room / total energy consumption of IT equipment.

[0095] In some embodiments, the dynamic environmental control strategy based on the PUE value includes:

[0096] Using a machine learning algorithm, a first correlation model between the PUE value and the environmental control strategy is established, wherein the first correlation model is used to predict a change trend of the PUE value under different environmental control strategies;

[0097] By using big data analysis technology, the correlation law between the environmental parameter data and the operating status of the IT equipment is obtained, and a second correlation model is established;

[0098] Comparing the PUE value under the operating condition with a preset threshold;

[0099] If the PUE value is lower than the preset threshold, outputting the current environmental control strategy as the optimal solution;

[0100] If the PUE value is higher than the preset threshold, an optimization process is triggered, and the optimization process is to optimize the environmental control strategy of the computer room based on the first correlation model and the second correlation model.

[0101] Specifically, PUE is an indicator for evaluating the energy efficiency in the computer room, reflecting the overall energy utilization efficiency of the computer room. The lower the PUE value, the higher the energy efficiency level of the computer room, the less energy waste, and the more conducive to energy conservation and emission reduction. Therefore, in this embodiment, the setting of the preset threshold is the basis for the energy efficiency management of the computer room, and the PUE value is an important indicator for measuring the energy efficiency of the computer room. According to the comparison result between the PUE value and the preset threshold, the environmental control strategy can be flexibly adjusted to achieve energy-saving optimization of the computer room.

[0102] Specifically, the preset threshold is a reasonable threshold of the PUE value set by the industry standard of energy efficiency in the computer room or the specific requirements of the operator for the computer room environment. If the PUE value is lower than the preset threshold, it means that the environmental control strategy under the current working conditions makes the energy efficiency of the computer room within the preset ideal range. Therefore, the current environmental control strategy is output as the optimal solution and applied to the computer room to achieve energy-saving optimization of the computer room. If the PUE value is higher than the preset threshold, it means that the energy efficiency level of the computer room under the environmental control strategy is low and fails to meet the expected standards. The environmental control strategy needs to be optimized. By adjusting the cooling strategy and airflow organization method, a refined energy-saving strategy can be formulated to achieve energy-saving optimization of the computer room.

[0103] In some embodiments, optimizing the environment control strategy of the computer room based on the first association model and the second association model includes:

[0104] Setting a plurality of environmental control strategies for the working conditions, and comparing the changing trends of PUE values ​​under different environmental control strategies through the first correlation model;

[0105] Selecting an environmental control strategy that reduces the PUE value to the preset threshold within a preset time as a candidate solution, and analyzing the impact of different candidate solutions on the energy consumption of the computer room;

[0106] For the plurality of candidate solutions, based on the second association model and according to the environmental parameter data in the candidate solutions, evaluating the operating status of each of the IT devices;

[0107] Based on the degree of influence of different candidate solutions on the energy consumption of the computer room and the operating status of each IT equipment, a decision matrix is ​​established, and a comprehensive score is given to each candidate solution based on the decision matrix, and the candidate solution with the highest comprehensive score is selected as the optimal solution;

[0108] A two-way feedback channel is established for the environmental control strategy and the environmental parameter data, and the environmental control strategy is optimized in real time through the two-way feedback channel.

[0109] In this embodiment, for the first association model and the second association model, historical data are collected from various types of sensors, IT equipment and cooling equipment in the computer room as training data for the first association model and the second association model. Specifically, environmental parameter data of the computer room, such as temperature, humidity and air quality, are obtained through various types of sensors, and historical operating status parameters of IT equipment, such as CPU usage, memory occupancy, power consumption, and operating data of cooling equipment, such as cooling water flow, fan speed, cooling equipment on / off status, etc. The collected data is cleaned to remove noise, outliers and missing values ​​to ensure data quality and consistency. At the same time, the data is normalized for subsequent model training.

[0110] For the first association model, key features are extracted from the environmental control strategy, such as the open and closed status of the cooling equipment, the installation method of the air outlet, such as side air supply or down air supply, air supply volume, temperature set point, etc. A machine learning model such as a neural network is used to train the first association model with historical environmental control strategies and PUE values ​​corresponding to the environmental control strategies as training data.

[0111] For the second association model, big data analysis technology is used to analyze the association rules between environmental parameter data and IT equipment operating status parameters. Based on the analysis results, a second association model is constructed. The second association model can reflect the potential impact of environmental parameter changes on the operating status of IT equipment.

[0112] In this embodiment, for working conditions where the PUE value is lower than the preset threshold, multiple feasible environmental control strategies are formulated according to the actual needs and constraints of the computer room, such as the heat dissipation requirements of the equipment in the computer room, the energy budget and the operating cost, etc. These strategies may include different cooling equipment opening and closing states, air outlet installation methods, air supply volumes, temperature set points, etc. The formulated environmental control strategy is used as the input of the first association model, and the first association model is used to predict the change trend of the PUE value under different environmental control strategies, such as increase, decrease or stability.

[0113] Among multiple environmental control strategies, select the environmental control strategy whose PUE value changes in a downward trend and can be reduced to a preset threshold within a preset time as a candidate solution. At the same time, analyze the impact of each candidate solution on the energy consumption of the computer room. Specifically, calculate the power consumption of the computer room under different environmental control strategies, including total power consumption and power consumption per unit time. Calculate the energy consumption changes of the cooling system under different strategies, conduct a comprehensive analysis of data such as power consumption and cooling system energy consumption, and evaluate the impact of different environmental control strategies on the overall energy consumption of the computer room, such as calculating the total energy consumption of power consumption and cooling system energy consumption under each environmental control strategy or dividing the total energy consumption by the monitoring time to obtain the average energy consumption per unit time under each environmental control strategy, so as to make a more intuitive comparison.

[0114] In this embodiment, for multiple candidate solutions, environmental parameter data is input into the second association model, and the change trends between the operating status parameters of the IT equipment, such as the changing trends of parameters such as the equipment temperature and power consumption of the IT equipment, are output through the second association model. At the same time, the changing trends of each operating status parameter are quantitatively analyzed, such as calculating the change amplitude of each operating status parameter within a preset time, such as calculating the percentage of temperature reduction and the absolute value of power consumption reduction. In addition, by comparing the performance indicators of IT equipment before and after the implementation of the strategy, such as processing speed, response time, etc., the degree of improvement of the environmental control strategy on the performance of the IT equipment is quantified, and quantitative evaluation is performed through indicators such as performance improvement rate and response time reduction ratio. By analyzing indicators such as the failure rate and downtime of IT equipment under the environmental control strategy, the impact of the environmental control strategy on the stability of the equipment is quantified. By using indicators such as the failure rate reduction ratio and the downtime reduction percentage to perform quantitative evaluation, the quantified values ​​of the change trends of the above-mentioned operating status parameters, the quantified values ​​of the IT equipment performance indicators, and the quantified values ​​of the IT equipment stability are calculated separately with the corresponding weight values ​​to obtain a single score, wherein the single score = indicator quantified value × weight, and the weight can be determined by expert scoring, questionnaire survey, data analysis and other methods. In this embodiment, the weight of the corresponding indicator is obtained through expert scoring, and then the single score of each indicator is added to obtain a comprehensive score, and the operating status of the IT equipment in each candidate solution is measured by the comprehensive score.

[0115] In this embodiment, a decision matrix is ​​established based on the degree of influence of each candidate solution on the energy consumption of the computer room and the operating status of each of the IT equipment. Specifically, different environmental control strategies are used as rows of the decision matrix, and the average energy consumption of each environmental control strategy and the comprehensive score measuring the operating status of the IT equipment are used as columns. A five-point scoring method is used to score each environmental control strategy according to its performance under each factor. Five levels are set for the average energy consumption and the comprehensive score, such as "extremely poor", "poor", "average", "good", and "excellent", and are assigned scores of 1-5 points respectively. Then, according to the performance of each environmental control strategy on each factor, a corresponding score is given. For each environmental control strategy, a comprehensive score is calculated to evaluate its overall performance.

[0116] In this embodiment, a two-way feedback channel is also established for the environmental control strategy and the environmental parameter data. Through the two-way feedback channel, real-time interaction between data can be achieved, so that the environmental control strategy can be adjusted in real time. Specifically, various types of sensors can capture changes in environmental parameter data in various areas inside the computer room in real time, and transmit the above environmental parameter data to the control system through the two-way feedback channel. The control system analyzes and processes the environmental parameter data after receiving it, and adjusts the environmental parameter data accordingly according to the preset environmental control strategy. If it is found that the PUE value of the current environmental control strategy is higher than the preset threshold, the control system sends an adjustment request to the environmental control strategy through the feedback channel, thereby realizing continuous optimization of the environmental control strategy. At the same time, the two-way feedback channel also has an anomaly detection function, which can monitor in real time whether the environmental parameter data exceeds the preset safety range. Once abnormal data is found, the early warning mechanism is triggered to notify relevant personnel through text messages, emails or system prompts.

[0117] S400: Acquire the operating status parameters of the IT equipment and the video image data in the computer room in real time, analyze the operating status parameters and the video image data, and determine potential abnormal equipment and abnormal areas in the digital twin computer room model.

[0118] In some embodiments, the step S400 includes:

[0119] Acquire historical operation data of the IT equipment, and build an equipment failure prediction model based on the historical operation data, wherein the historical operation data includes operation time, maintenance records, and failure records;

[0120] Inputting the operating status parameters of each IT device into the device failure prediction model, determining whether each IT device is a potential abnormal device, and generating potential failure information and a predictive maintenance plan for the abnormal device;

[0121] Mapping and associating the operating status parameters, the potential fault information, and the video image data with the digital twin computer room model;

[0122] The video image data is used to analyze the equipment operation status of each IT device in the computer room. Based on the equipment operation status, the abnormal area is determined in the digital twin computer room model. The equipment operation status includes changes in indicator light status, abnormal noise sources and equipment vibration amplitude. The abnormal area is used to provide maintenance personnel with spatial location guidance for faulty equipment.

[0123] In this embodiment, by acquiring the historical operation data of IT equipment and building an equipment failure prediction model, the operation status of IT equipment can be monitored in real time. Specifically, through the failure prediction model, it can output whether the IT equipment is a potential abnormal device. For abnormal equipment, potential failure information and predictive maintenance plans are generated, where the potential failure information includes the failure type, failure location, and possible cause of the failure, etc., to provide guidance for subsequent maintenance. The predictive maintenance plan includes maintenance time, maintenance content, maintenance personnel, etc., to ensure that maintenance work can be completed on time and in quality, so that potential failures can be discovered and handled in a timely manner for each IT equipment in the computer room, reducing equipment failure rate, improving equipment stability and reliability, and ensuring business continuity.

[0124] In this embodiment, the operating status parameters, potential fault information, and video image data in the computer room are synchronously mapped to the digital twin computer room model, and data is updated in the digital twin computer room model to form a maintenance closed loop. In addition, the image video data of the computer room from the video surveillance camera is used to analyze the status changes of the equipment indicator lights in the computer room, the source of abnormal noise, the vibration amplitude of the equipment, and other information, and the abnormal position is accurately located in the digital twin model to guide maintenance personnel to quickly troubleshoot the fault.

[0125] In some embodiments, analyzing the equipment operation status of each IT device in the computer room through the video image data, and determining an abnormal area in the digital twin computer room model based on the equipment operation status, includes:

[0126] Preprocessing the video image data by image preprocessing technology to obtain image sequence data, and performing sound separation processing on the video image data to obtain the audio signal in the computer room;

[0127] Using a target detection algorithm to detect and identify IT devices in the image sequence data, obtaining device information of each IT device in the computer room and image information of each IT device in the image sequence data, wherein the device information includes location information, attribute information, operating status parameters, and potential abnormality information;

[0128] Extracting and classifying the audio signal by an audio analysis algorithm to determine whether there is abnormal noise in the computer room, and determining the source of the abnormal noise by sound source localization technology;

[0129] For each of the IT devices, image features of the indicator light area of ​​the IT device are obtained from the image information, and a state change of the indicator light is determined by an image classification algorithm to determine an abnormal change of the indicator light of the IT device;

[0130] For each of the IT devices, based on the operating status parameters, a vibration signal is obtained, the vibration signal is analyzed in the time and frequency domains by using a signal processing technique, a vibration feature of the vibration signal is extracted, and based on the vibration feature, an abnormality detection algorithm is used to determine whether the IT device has abnormal vibration;

[0131] The source location of the abnormal noise, abnormal changes in indicator lights, and abnormally vibrating IT equipment are mapped and associated with the digital twin computer room model, and the abnormal area of ​​the computer room is determined in the digital twin computer room model.

[0132] As a rich and multi-dimensional information carrier, image and video data often contain two core components: image information and audio information. Therefore, when processing and analyzing image and video data, image information and audio information are analyzed and processed independently to obtain detailed data information in their respective fields. Specifically, the location information of IT equipment in the computer room and the image features of the indicator light area can be obtained through image information, and then the existence of abnormal areas in the computer room can be determined. The sound information in the computer room can be obtained through audio information, and then the abnormal vibration of IT equipment in the computer room can be analyzed.

[0133] In this embodiment, the source location of abnormal noise, abnormal changes in indicator lights, and abnormally vibrating IT equipment are mapped and associated with the digital twin computer room model. Through the digital twin computer room model, operation and maintenance personnel can quickly locate the abnormal areas and equipment in the computer room, greatly shortening the troubleshooting time. At the same time, potential abnormal equipment in the digital twin computer room model is also marked with potential fault information and predictive maintenance plans. On this basis, operation and maintenance personnel can discover potential risk points in advance and take preventive measures to avoid the occurrence or expansion of faults. In addition, the digital twin computer room model can also help operation and maintenance personnel better understand the operating status of the computer room and optimize resource allocation, such as adjusting equipment layout, optimizing cooling system, etc., to improve the overall performance and energy efficiency of the computer room.

[0134] In some embodiments, the method further comprises:

[0135] In the digital twin computer room model, the computer room is divided into multiple sub-areas according to the type of equipment in the computer room, environmental parameter data of each sub-area is obtained, and an environmental digital model is constructed, wherein the environmental digital model is used to reflect the environmental conditions in the computer room;

[0136] Based on the digital environment model, an independent environment control strategy is set for each sub-area.

[0137] In this embodiment, the computer room space is divided into multiple sub-areas according to the different types of equipment in the computer room; for each sub-area, the environmental parameter data in the area is obtained, including temperature, humidity, airflow and other parameters; using the obtained environmental parameter data, a digital model reflecting the real environmental conditions of the sub-area is constructed; at the same time, an independent environmental control strategy is set for each sub-area to achieve refined management of the computer room environment, which helps to optimize the overall operating efficiency of the computer room and reduce unnecessary energy consumption.

[0138] See also Figure 2 As shown, the present invention also provides a computer room environment control system based on digital twin technology, the system comprising:

[0139] The first processing module 201 is used to construct a digital twin computer room model based on the three-dimensional structure of the computer room and the layout of the equipment in the computer room, wherein the equipment in the computer room includes multiple IT equipment, multiple cooling equipment, and multiple types of sensors;

[0140] The second processing module 202 is used to obtain the real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predict the heat load distribution of the computer room based on the real-time load data, real-time power data and environmental parameter data;

[0141] The third processing module 203 is used to simulate and generate corresponding working conditions for the computer room in the digital twin computer room model based on the heat load distribution and in combination with a preset environmental control strategy, calculate and obtain the PUE value of the computer room under the working conditions, and dynamically adjust the environmental control strategy based on the PUE value;

[0142] The fourth processing module 204 is used to obtain the operating status parameters of the IT equipment and the video image data in the computer room in real time, analyze the operating status parameters and the video image data, and determine potential abnormal equipment and abnormal areas in the digital twin computer room model.

[0143] It is understandable that if Figure 1The contents of the computer room environment control method embodiment based on digital twin technology shown in the figure are applicable to the computer room environment control system embodiment based on digital twin technology. The functions specifically implemented by the computer room environment control system embodiment based on digital twin technology are similar to those in the embodiment shown in the figure. Figure 1 The embodiment of the computer room environment control method based on digital twin technology shown in the figure is the same as that of the embodiment of the computer room environment control method based on digital twin technology shown in the figure, and the beneficial effects achieved are the same as those of the embodiment of the computer room environment control method based on digital twin technology shown in the figure. Figure 1 The beneficial effects achieved by the embodiment of the computer room environment control method based on digital twin technology shown are also the same.

[0144] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0145] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0146] See also Figure 3 As shown, an embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, a computer room environment control method based on digital twin technology as described in any one of the above methods is implemented.

[0147] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0148] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0149] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0150] An embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer room environment control method based on digital twin technology as described in any of the above methods is implemented.

[0151] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / computer device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0152] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A computer room environment control method based on digital twin technology, characterized in that: include: A digital twin computer room model is constructed based on the three-dimensional structure of the computer room and the layout of the equipment in the computer room, wherein the equipment in the computer room includes multiple IT equipment, multiple cooling equipment, and multiple types of sensors; Acquire the real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predict the heat load distribution of the computer room based on the real-time load data, real-time power data, and environmental parameter data; Based on the heat load distribution and in combination with a preset environmental control strategy, a corresponding operating condition is generated for the computer room simulation in the digital twin computer room model, a PUE value of the computer room under the operating condition is calculated, and based on the PUE value, the environmental control strategy is dynamically adjusted; The operating status parameters of the IT equipment and the video image data in the computer room are acquired in real time, the operating status parameters and the video image data are analyzed, and potential abnormal equipment and abnormal areas are determined in the digital twin computer room model.

2. The method according to claim 1, characterized in that The acquiring real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predicting the heat load distribution of the computer room based on the real-time load data, the real-time power data, and the environmental parameter data, includes: A support vector machine regression algorithm is used to establish a first relationship model, where the first relationship model is a corresponding relationship between the load data of the IT device and the heating capacity of the IT device; A least squares regression algorithm is used to establish a second relationship model, where the second relationship model is a corresponding relationship between the device power of the cooling device and the cooling capacity of the cooling device; According to the environmental parameter data, an inverse distance weighted interpolation algorithm is used to obtain a real-time environmental parameter distribution map of the computer room; Based on the first relationship model, obtaining the heating amount of the IT equipment according to the real-time load data; Based on the second relationship model, and according to the real-time power data, obtaining the cooling capacity of the cooling device; Based on the heating amount, the cooling capacity and the real-time environmental parameter distribution, a computational fluid dynamics method is used to predict the heat load distribution of the computer room.

3. The method according to claim 1, characterized in that The calculating and obtaining the PUE value of the computer room under the working condition includes: Performing operation simulation on the working condition in the digital twin computer room model, and recording the computer room airflow distribution, temperature field distribution, total energy consumption of IT equipment, and total energy consumption of cooling equipment under the working condition; Based on the total energy consumption of the IT equipment and the total energy consumption of the cooling equipment, the total energy consumption and PUE value of the computer room under the working conditions are obtained.

4. The method according to claim 3, characterized in that The dynamic environmental control strategy based on the PUE value includes: Using a machine learning algorithm, a first correlation model between the PUE value and the environmental control strategy is established, wherein the first correlation model is used to predict a change trend of the PUE value under different environmental control strategies; By using big data analysis technology, the correlation law between the environmental parameter data and the operating status of the IT equipment is obtained, and a second correlation model is established; Comparing the PUE value under the operating condition with a preset threshold; If the PUE value is lower than the preset threshold, outputting the current environmental control strategy as the optimal solution; If the PUE value is higher than the preset threshold, an optimization process is triggered, and the optimization process is to optimize the environmental control strategy of the computer room based on the first correlation model and the second correlation model.

5. The method according to claim 4, characterized in that The optimizing the environment control strategy of the computer room based on the first association model and the second association model includes: Setting a plurality of environmental control strategies for the working conditions, and comparing the changing trends of PUE values ​​under different environmental control strategies through the first correlation model; Selecting an environmental control strategy that reduces the PUE value to the preset threshold within a preset time as a candidate solution, and analyzing the impact of different candidate solutions on the energy consumption of the computer room; For the plurality of candidate solutions, based on the second association model and according to the environmental parameter data in the candidate solutions, evaluating the operating status of each of the IT devices; Based on the degree of influence of different candidate solutions on the energy consumption of the computer room and the operating status of each IT equipment, a decision matrix is ​​established, and a comprehensive score is given to each candidate solution based on the decision matrix, and the candidate solution with the highest comprehensive score is selected as the optimal solution; A two-way feedback channel is established for the environmental control strategy and the environmental parameter data, and the environmental control strategy is optimized in real time through the two-way feedback channel.

6. The method according to claim 1, characterized in that The real-time acquisition of the operating status parameters of the IT equipment and the video image data in the computer room, analyzing the operating status parameters and the video image data, and determining potential abnormal equipment and abnormal areas in the digital twin computer room model include: Acquire historical operation data of the IT equipment, and build an equipment failure prediction model based on the historical operation data, wherein the historical operation data includes operation time, maintenance records, and failure records; Inputting the operating status parameters of each IT device into the device failure prediction model, determining whether each IT device is a potential abnormal device, and generating potential failure information and a predictive maintenance plan for the abnormal device; Mapping and associating the operating status parameters, the potential fault information, and the video image data with the digital twin computer room model; The video image data is used to analyze the equipment operation status of each IT device in the computer room. Based on the equipment operation status, the abnormal area is determined in the digital twin computer room model. The equipment operation status includes changes in indicator light status, abnormal noise sources and equipment vibration amplitude. The abnormal area is used to provide maintenance personnel with spatial location guidance for faulty equipment.

7. The method according to claim 6, characterized in that The analyzing the equipment operation status of each IT equipment in the computer room through the video image data, and determining the abnormal area in the digital twin computer room model based on the equipment operation status, includes: Preprocessing the video image data by image preprocessing technology to obtain image sequence data, and performing sound separation processing on the video image data to obtain the audio signal in the computer room; Using a target detection algorithm to detect and identify IT devices in the image sequence data, obtaining device information of each IT device in the computer room and image information of each IT device in the image sequence data, wherein the device information includes location information, attribute information, operating status parameters, and potential abnormality information; Extracting and classifying the audio signal by an audio analysis algorithm to determine whether there is abnormal noise in the computer room, and determining the source of the abnormal noise by sound source localization technology; For each of the IT devices, image features of the indicator light area of ​​the IT device are obtained from the image information, and a state change of the indicator light is determined by an image classification algorithm to determine an abnormal change of the indicator light of the IT device; For each of the IT devices, based on the operating status parameters, a vibration signal is obtained, the vibration signal is analyzed in the time and frequency domains by using a signal processing technique, a vibration feature of the vibration signal is extracted, and based on the vibration feature, an abnormality detection algorithm is used to determine whether the IT device has abnormal vibration; The source location of the abnormal noise, abnormal changes in indicator lights, and abnormally vibrating IT equipment are mapped and associated with the digital twin computer room model, and the abnormal area of ​​the computer room is determined in the digital twin computer room model.

8. The method according to claim 1, characterized in that The method further comprises: In the digital twin computer room model, the computer room is divided into multiple sub-areas according to the type of equipment in the computer room, environmental parameter data of each sub-area is obtained, and an environmental digital model is constructed, wherein the environmental digital model is used to reflect the environmental conditions in the computer room; Based on the digital environment model, an independent environment control strategy is set for each sub-area.

9. A computer room environment control system based on digital twin technology, characterized in that: include: The first processing module is used to build a digital twin computer room model based on the three-dimensional structure of the computer room and the layout of the equipment in the computer room, where the equipment in the computer room includes multiple IT equipment, multiple cooling equipment, and multiple types of sensors; The second processing module is used to obtain the real-time load data of each IT device, the real-time power data of each cooling device, and the environmental parameter data of each type of sensor, and predict the heat load distribution of the computer room based on the real-time load data, real-time power data and environmental parameter data; A third processing module is used to simulate and generate corresponding working conditions for the computer room in the digital twin computer room model based on the heat load distribution and in combination with a preset environmental control strategy, calculate and obtain the PUE value of the computer room under the working conditions, and dynamically adjust the environmental control strategy based on the PUE value; The fourth processing module is used to obtain the operating status parameters of the IT equipment and the video image data in the computer room in real time, analyze the operating status parameters and the video image data, and determine potential abnormal equipment and abnormal areas in the digital twin computer room model.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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