Data center air conditioner terminal energy consumption efficiency online diagnosis and optimization method

Abstract

The invention relates to the technical field of data processing and optimization, in particular to a data center air conditioner terminal energy consumption efficiency online diagnosis and optimization method. The method comprises the following steps that a data center tail end air conditioning system is obtained; carrying out real-time monitoring on the electricity consumption of machine room IT equipment and the electricity consumption of tail end air conditioning equipment on a tail end air conditioning system of the data center to obtain equipment electricity consumption data; performing real-time analysis processing on the power utilization data of the equipment, and calculating a utilization efficiency evaluation index; and performing machine room energy utilization efficiency evaluation on the utilization efficiency evaluation index to obtain a machine room energy utilization efficiency evaluation result. Through the data processing technology, the mode recognition technology and the intelligent optimization technology, the energy consumption efficiency of the machine room tail end is judged in real time, the energy efficiency short board of the machine room air conditioner tail end equipment is rapidly recognized, meanwhile, tail end air conditioner operation optimization is conducted from the air conditioner tail end operation mechanism, and therefore the operation energy consumption of an air conditioner tail end system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and optimization, and in particular to an online diagnosis and optimization method for energy efficiency of air conditioning terminals in a data center. Background Art

[0002] Data centers are known for their high energy consumption, of which the energy consumption of air conditioning systems accounts for about 40% of the total energy consumption of data centers. The key to improving the energy efficiency of data centers is to reduce the carbon emissions of data centers; the air conditioning system is mainly composed of air conditioning water systems and air conditioning terminal systems. Due to the complex types and large number of equipment involved, the air conditioning terminal system is one of the most energy-consuming components in data centers. Technologies to improve the energy efficiency of air conditioning terminal systems include natural cooling, cold plate cooling, heat pipe cooling, immersion cooling and spray cooling; however, the application of these new technologies is highly dependent on geographical conditions, so their actual application is limited. At present, most data centers still adopt traditional energy-saving transformation to save energy on air conditioning. When traditional air conditioning terminal systems are energy-saving, they only focus on the optimization of the air supply temperature and water valve opening parameters of the terminal equipment, without considering the impact of the joint operation of the terminal equipment on the energy efficiency of the air conditioning terminal system. Summary of the invention

[0003] Based on this, it is necessary to provide an online diagnosis and optimization method for the energy efficiency of data center air conditioning terminals to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for online diagnosis and optimization of energy efficiency of air conditioning terminals in a data center is provided, the method comprising the following steps:

[0005] Step S1: Acquire the terminal air conditioning system of the data center; perform real-time monitoring of the power consumption of the IT equipment in the computer room and the terminal air conditioning equipment in the terminal air conditioning system of the data center to obtain the power consumption data of the equipment;

[0006] Step S2: Analyze and process the equipment power consumption data in real time to calculate the utilization efficiency evaluation index; evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain the power efficiency evaluation result of the computer room;

[0007] Step S3: Locate the computer room with low energy efficiency according to the evaluation result of the power efficiency of the computer room, and obtain a list of computer rooms to be optimized; optimize the terminal air conditioning operation parameters of the list of computer rooms to be optimized, and obtain the air conditioning operation optimization parameters;

[0008] Step S4: verify the energy consumption reduction effect of the air-conditioning operation optimization parameters to obtain the energy consumption optimization verification results; perform energy utilization efficiency feedback adjustment based on the energy consumption optimization verification results to obtain the terminal air-conditioning operation optimization report to perform online diagnosis and optimization of the air-conditioning terminal energy efficiency.

[0009] The present invention obtains the terminal air conditioning system of the data center and monitors the power consumption of the IT equipment and the terminal air conditioning equipment in the computer room in real time, can accurately collect the equipment power consumption data, and provide an accurate energy consumption data basis for the data center. By analyzing and processing these data in real time, the utilization efficiency evaluation index is calculated, and then the energy efficiency of the computer room is evaluated, the energy efficiency performance of the data center is quantified, and the low energy efficiency link is identified. According to the evaluation results, the computer room with low energy efficiency is located, the list of computer rooms to be optimized is obtained, and the terminal air conditioning operation parameters are optimized in a targeted manner to improve the energy efficiency performance of the inefficient computer room. By verifying the energy consumption reduction effect of the air conditioning operation optimization parameters, and based on these verification results, feedback adjustment of energy utilization efficiency is performed, and finally a terminal air conditioning operation optimization report is formed to ensure the effectiveness of the optimization measures, achieve energy consumption reduction, improve energy utilization efficiency, and provide a scientific basis for the online diagnosis and optimization of air conditioning terminal energy efficiency. Therefore, the present invention uses data processing technology, pattern recognition technology and intelligent optimization technology to judge the energy efficiency of the computer room terminal in real time, quickly identify the energy efficiency shortcomings of the computer room air conditioning terminal equipment, and at the same time, optimize the terminal air conditioning operation based on the air conditioning terminal operation mechanism, thereby reducing the operating energy consumption of the air conditioning terminal system.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire the terminal air conditioning system of the data center;

[0012] Step S12: measuring the power consumption of IT equipment in the computer room on the terminal air conditioning system of the data center to obtain the power consumption data of IT equipment in the computer room;

[0013] Step S13: measuring the power consumption of the terminal air-conditioning equipment of the terminal air-conditioning system of the data center to obtain the power consumption data of the terminal air-conditioning equipment;

[0014] Step S14: Integrate the power consumption data of the IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain the equipment power consumption data.

[0015] The present invention obtains the terminal air-conditioning system of the data center, and this process provides the necessary basis for the subsequent energy consumption data collection. By measuring the power consumption of the IT equipment in the computer room, the power consumption data of the IT equipment in the computer room can be accurately obtained, providing key input parameters for the energy consumption analysis of the data center. Next, the power consumption of the terminal air-conditioning equipment is measured to obtain the power consumption data of the terminal air-conditioning equipment, which further enriches the dimension of the energy consumption data of the data center. Finally, the power consumption data of the IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment are integrated to obtain comprehensive equipment power consumption data, which provides detailed data support for the energy consumption management of the data center, so that energy consumption analysis and optimization work can be carried out based on complete and accurate data, thereby improving the accuracy and effectiveness of the energy efficiency management of the data center.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: pre-processing the power consumption data of IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain equipment pre-processing data;

[0018] Step S142: performing feature synchronization on the device preprocessing data to obtain device feature synchronization data; performing feature alignment on the device feature synchronization data to generate device feature alignment data;

[0019] Step S143: Perform structured inspection on the device feature alignment data to obtain structured inspection data; perform data fusion on the structured inspection data to obtain device power consumption data.

[0020] The present invention pre-processes the power consumption data of the IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain the equipment pre-processing data. This step ensures the cleanliness and consistency of the data and provides a high-quality data basis for subsequent analysis. Next, the equipment pre-processing data is feature synchronized to obtain the equipment feature synchronization data, and the features of these data are aligned to generate the equipment feature alignment data. This process optimizes the structure of the data and makes it more suitable for analysis and modeling. Then, the equipment feature alignment data is structured and tested to obtain structured test data. This test step ensures the accuracy and reliability of the data and provides a solid foundation for further processing of the data. Finally, the structured test data is fused to obtain the equipment power consumption data. This fusion process integrates multi-source data to form a comprehensive data set, which provides detailed and structured power consumption information for data center energy consumption analysis and optimization. Through this series of data processing steps, the data center can obtain more accurate and structured power consumption data, thereby improving the efficiency and accuracy of energy consumption analysis.

[0021] Preferably, step S2 comprises the following steps:

[0022] Step S21: performing real-time analysis on the power consumption data of the equipment to obtain a power consumption data stream; extracting key features from the power consumption data stream to obtain key power consumption parameters;

[0023] Step S22: performing power consumption pattern recognition on key power consumption parameters to obtain a power consumption behavior pattern; performing power consumption trend monitoring on the power consumption behavior pattern to obtain power consumption trend data;

[0024] Step S23: quantifying the electricity utilization efficiency of the electricity consumption trend data and generating an efficiency evaluation index;

[0025] Step S24: Evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain an evaluation result of the power efficiency of the computer room.

[0026] The present invention performs real-time analysis on the power consumption data of the equipment to obtain the power consumption data stream. This process ensures the timeliness and dynamic monitoring capability of the data and provides a continuous data stream for capturing power consumption behavior. By extracting key features from the power consumption data stream, key power consumption parameters are obtained. This step can accurately identify the key factors affecting energy consumption and lay the foundation for in-depth analysis of power consumption behavior. Next, the power consumption pattern is identified for the key power consumption parameters to obtain the power consumption behavior pattern, and the power consumption trend is monitored for the power consumption behavior pattern to obtain the power consumption trend data, which helps to reveal the regularity and change trend of power consumption behavior and provide a basis for prediction and optimization. Then, the power consumption trend data is quantified for power utilization efficiency and a utilization efficiency evaluation index is generated. This step converts the power consumption trend into a quantifiable efficiency index and provides a specific metric for evaluating energy efficiency. Finally, the utilization efficiency evaluation index is used to evaluate the energy efficiency of the computer room to obtain the power consumption energy efficiency evaluation result of the computer room. This evaluation result directly reflects the energy efficiency level of the computer room and provides clear guidance and basis for energy efficiency management and optimization. Through this series of coherent data processing and analysis steps, the data center can achieve an in-depth understanding of electricity usage behavior and accurate assessment of energy efficiency, thereby effectively guiding the implementation of energy-saving and consumption-reduction measures.

[0027] Preferably, step S24 includes the following steps:

[0028] Step S241: normalizing the utilization efficiency evaluation index to obtain a standardized evaluation index; and weighting the standardized evaluation index to obtain a weighted evaluation index;

[0029] Step S242: Comprehensively calculate the weighted evaluation index to obtain a comprehensive score of the power efficiency of the computer room; classify the comprehensive score of the power efficiency of the computer room to obtain a level evaluation of the power efficiency of the computer room;

[0030] Step S243: presenting the result of the level evaluation of the power efficiency of the computer room, and obtaining the evaluation result of the power efficiency of the computer room.

[0031] The present invention normalizes the utilization efficiency evaluation index to obtain a standardized evaluation index. This step ensures the comparability between different indicators and provides a unified data standard for subsequent analysis. Next, the standardized evaluation index is weighted to obtain a weighted evaluation index. This process reasonably allocates weights according to the importance of each indicator in the energy efficiency evaluation, so that the evaluation result is more scientific and objective. Then, the weighted evaluation index is comprehensively calculated to obtain a comprehensive score of the power efficiency of the computer room. This step integrates multiple evaluation indicators into a quantitative score, which intuitively reflects the overall level of the power efficiency of the computer room. The comprehensive score of the power efficiency of the computer room is graded to obtain a grade evaluation of the power efficiency of the computer room. This division provides a clear grade mark for the energy efficiency of the computer room, which is convenient for comparing and identifying the level of energy efficiency. Finally, the grade evaluation of the power efficiency of the computer room is presented to obtain the evaluation result of the power efficiency of the computer room. This presentation makes the energy efficiency evaluation result more intuitive and easy to understand, which is convenient for decision makers to quickly grasp the energy efficiency status of the computer room and take corresponding optimization measures. Through this series of data processing and evaluation steps, the data center can achieve accurate assessment and effective management of the power efficiency of the computer room, thereby guiding energy efficiency optimization work and improving energy utilization efficiency.

[0032] Preferably, step S3 comprises the following steps:

[0033] Step S31: setting a threshold for the evaluation results of the power efficiency of the computer room to obtain preliminary screening of inefficient computer room data;

[0034] Step S32: removing abnormal values ​​from the preliminarily screened inefficient computer room data to obtain inefficient parameter removal data;

[0035] Step S33: Prioritize the preliminarily screened inefficient computer room data based on the inefficient parameter elimination data to obtain an inefficient computer room priority list; extract computer rooms with low energy efficiency from the inefficient computer room priority list to obtain a list of computer rooms to be optimized;

[0036] Step S34: Optimize the terminal air-conditioning operation parameters of the list of computer rooms to be optimized to obtain the air-conditioning operation optimization parameters.

[0037] The present invention sets a threshold for the evaluation results of the power efficiency of the computer room to obtain the data of the preliminary screening of inefficient computer rooms. This step identifies the computer rooms with poor energy efficiency performance by setting the threshold, and provides a target object for the subsequent optimization work. Next, the data of the preliminary screening of inefficient computer rooms is subjected to abnormal value elimination to obtain the inefficient parameter elimination data. This process ensures the accuracy of the data, excludes the abnormal data that affects the analysis results, and improves the reliability of the identification of inefficient computer rooms. Then, the data of the preliminary screening of inefficient computer rooms is prioritized based on the inefficient parameter elimination data to obtain the priority list of inefficient computer rooms. This step sorts them according to the energy efficiency performance of the computer rooms, and provides a basis for optimizing the allocation of resources. The energy-efficient computer rooms are extracted from the priority list of inefficient computer rooms to obtain the list of computer rooms to be optimized. This extraction process further clarifies the computer rooms that need to be optimized for energy efficiency optimization, and ensures the pertinence of the optimization work. Finally, the terminal air-conditioning operation parameters are optimized for the list of computer rooms to be optimized to obtain the air-conditioning operation optimization parameters. This optimization process directly targets the specific conditions of the inefficient computer rooms and formulates improvement measures to improve the energy efficiency performance of these computer rooms. Through this series of precise data screening, outlier processing, priority sorting and parameter optimization steps, the data center can efficiently identify and optimize inefficient computer rooms, thereby improving overall energy efficiency.

[0038] Preferably, step S34 includes the following steps:

[0039] Step S341: collecting initial operating parameters of the terminal air conditioners in the list of computer rooms to be optimized to obtain basic operating data of the air conditioners;

[0040] Step S342: Perform performance analysis on the basic data of air conditioner operation to obtain operation performance indicators;

[0041] Step S343: Optimizing the key performance indicators to obtain performance indicator optimization data;

[0042] Step S344: adjusting the operating parameters of the list of computer rooms to be optimized based on the performance indicator optimization data to obtain air conditioning operation optimization parameters.

[0043] The present invention collects the initial operating parameters of the terminal air conditioners for the list of computer rooms to be optimized, and obtains the basic operating data of the air conditioners. This step provides the original and necessary data support for the air conditioner performance analysis. The basic operating data of the air conditioners is subjected to performance analysis to obtain the operating performance indicators. This analysis process can reveal the current performance level and existing problems of the air conditioning system. The key performance indicators are optimized to obtain the performance indicator optimization data. This optimization processing step formulates specific improvement measures based on the performance analysis results, aiming to improve the performance of the air conditioning system. Based on the performance indicator optimization data, the operating parameters of the list of computer rooms to be optimized are adjusted to obtain the air conditioning operation optimization parameters. This parameter adjustment step concretizes the optimization measures and provides optimized parameter settings for the actual operation of the air conditioning system. Through this series of coherent steps from data collection to performance analysis, and then to optimization processing and parameter adjustment, the data center can accurately optimize the performance of the terminal air conditioning system to ensure that the air conditioning system operates in the best state, thereby improving the energy efficiency and stability of the entire computer room.

[0044] Preferably, step S344 includes the following steps:

[0045] Step S3441: Identify the factors affecting air conditioning energy efficiency based on the performance index optimization data to obtain an energy efficiency optimization target;

[0046] Step S3442: performing parameter association mapping on the energy efficiency optimization target to obtain parameter adjustment mapping data;

[0047] Step S3443: dynamically adjust the list of computer rooms to be optimized according to the parameter adjustment mapping data to obtain computer room operation adjustment data;

[0048] Step S3444: Perform feedback loop optimization on the computer room operation adjustment data to obtain air conditioning operation optimization parameters.

[0049] The present invention identifies the factors affecting the energy efficiency of air conditioners on the performance index optimization data to obtain the energy efficiency optimization target. This step can clarify the key factors affecting the energy efficiency of air conditioners and provide a clear improvement direction for subsequent energy efficiency optimization. Parameter association mapping is performed on the energy efficiency optimization target to obtain parameter adjustment mapping data. This process provides a scientific basis for precise adjustment by identifying the relationship between energy efficiency influencing factors and adjustable parameters. The list of computer rooms to be optimized is dynamically adjusted according to the parameter adjustment mapping data to obtain computer room operation adjustment data. This dynamic adjustment step enables the operating parameters of the computer room to be optimized according to real-time data to adapt to the ever-changing energy efficiency requirements. Feedback loop optimization is performed on the computer room operation adjustment data to obtain air conditioning operation optimization parameters. This feedback loop optimization step ensures that the optimization measures can be continuously iterated and adjusted according to the actual operating results to achieve the best energy efficiency performance. Through this series of coherent steps, the data center can achieve refined management of the air conditioning system, optimize energy efficiency, reduce energy consumption, and improve the overall energy efficiency level.

[0050] Preferably, step S4 comprises the following steps:

[0051] Step S41: Compare the energy consumption data before and after the implementation of the air conditioner operation optimization parameters to obtain energy consumption comparison data;

[0052] Step S42: Statistically analyzing the energy consumption comparison data to obtain energy consumption statistical results; and identifying trends in the energy consumption statistical results to obtain energy consumption trend data;

[0053] Step S43: Evaluate the effect of the energy consumption trend analysis to obtain energy consumption optimization verification results;

[0054] Step S44: Based on the energy consumption optimization verification result, energy utilization efficiency feedback adjustment is performed to obtain a terminal air-conditioning operation optimization report to perform online diagnosis and optimization of air-conditioning terminal energy efficiency.

[0055] The present invention compares the energy consumption data before and after the implementation of the air conditioning operation optimization parameters to obtain energy consumption comparison data. This step can directly show the effect of the optimization measures and provide a quantitative basis for evaluating the optimization effect. The energy consumption comparison data is statistically analyzed to obtain energy consumption statistical results, and the energy consumption statistical results are trend identified to obtain energy consumption trend data, which helps to understand the regularity and long-term trend of energy consumption changes and provide data support for further energy consumption prediction and optimization. The energy consumption trend is analyzed and the effect is evaluated to obtain the energy consumption optimization verification result. This evaluation result can verify the actual effect of the optimization measures and ensure the correctness of the optimization direction. Based on the energy consumption optimization verification result, feedback adjustment of energy utilization efficiency is performed to obtain a terminal air conditioning operation optimization report. This report provides detailed guidance and basis for performing online diagnosis and optimization of air conditioning terminal energy efficiency, ensuring the continuous improvement of optimization measures and maximization of implementation effects. Through this series of steps, the data center can realize continuous monitoring and optimization of the energy efficiency of the air conditioning system, improve energy utilization efficiency, and reduce operating costs.

[0056] Preferably, step S44 includes the following steps:

[0057] Step S441: performing energy consumption reduction analysis on the energy consumption optimization verification result to obtain energy consumption analysis data;

[0058] Step S442: Efficiency impact assessment is performed on the energy consumption analysis data to obtain efficiency impact factors; weight adjustment is performed on the efficiency impact factors to obtain adjusted weight data;

[0059] Step S443: Apply the adjusted weights in a feedback loop to obtain real-time optimization parameters; monitor the effects of the real-time optimization parameters to obtain a terminal air conditioning operation optimization report;

[0060] Step S444: Continuously optimize the terminal air conditioning system of the data center based on the terminal air conditioning operation optimization report to perform online diagnosis and optimization of the air conditioning terminal energy efficiency.

[0061] The present invention performs energy consumption reduction analysis on the energy consumption optimization verification results to obtain energy consumption analysis data. This step can analyze the specific values ​​and composition of energy consumption reduction in detail, and provide accurate data support for further energy efficiency improvement. The energy consumption analysis data is evaluated for efficiency impact, efficiency influencing factors are obtained, and these efficiency influencing factors are weighted to obtain adjusted weight data. This process ensures that the importance of each influencing factor in energy efficiency optimization is reasonably reflected, and optimizes resource allocation and optimization strategies. The adjusted weights are applied in a feedback loop to obtain real-time optimization parameters, and the effects of these parameters are monitored to obtain a terminal air conditioning operation optimization report. This step realizes dynamic adjustment and real-time monitoring of optimization measures, ensuring the effectiveness and timeliness of optimization measures. Based on the terminal air conditioning operation optimization report, the terminal air conditioning system of the data center is continuously optimized to perform online diagnosis and optimization of air conditioning terminal energy efficiency. This continuous optimization process ensures that the energy efficiency of the terminal air conditioning system of the data center is continuously improved, and the maximization of energy utilization and the optimization of costs are achieved. Through this series of coherent steps, the data center can achieve refined management of the air conditioning system, optimize energy efficiency, reduce energy consumption, improve the overall energy efficiency level, and ensure the continuous effectiveness of optimization measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of the steps of an online diagnosis and optimization method for the energy efficiency of air conditioning terminals in a data center;

[0063] Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0064] Figure 3 for Figure 2 Detailed implementation steps of step S44 are shown in the flowchart;

[0065] Figure 4 It is a control logic diagram of an online diagnosis and optimization method for the energy efficiency of air conditioning terminals in a data center;

[0066] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities are implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0069] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiment, the first unit is referred to as the second unit, and similarly the second unit is referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0070] To achieve this, please refer to Figures 1 to 4 , a method for online diagnosis and optimization of energy efficiency of air conditioning terminals in a data center, the method comprising the following steps:

[0071] Step S1: Acquire the terminal air conditioning system of the data center; perform real-time monitoring of the power consumption of the IT equipment in the computer room and the terminal air conditioning equipment in the terminal air conditioning system of the data center to obtain the power consumption data of the equipment;

[0072] Step S2: Analyze and process the equipment power consumption data in real time to calculate the utilization efficiency evaluation index; evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain the power efficiency evaluation result of the computer room;

[0073] Step S3: Locate the computer room with low energy efficiency according to the evaluation result of the power efficiency of the computer room, and obtain a list of computer rooms to be optimized; optimize the terminal air conditioning operation parameters of the list of computer rooms to be optimized, and obtain the air conditioning operation optimization parameters;

[0074] Step S4: verify the energy consumption reduction effect of the air-conditioning operation optimization parameters to obtain the energy consumption optimization verification results; perform energy utilization efficiency feedback adjustment based on the energy consumption optimization verification results to obtain the terminal air-conditioning operation optimization report to perform online diagnosis and optimization of the air-conditioning terminal energy efficiency.

[0075] The present invention obtains the terminal air conditioning system of the data center and monitors the power consumption of the IT equipment and the terminal air conditioning equipment in the computer room in real time, can accurately collect the equipment power consumption data, and provide an accurate energy consumption data basis for the data center. By analyzing and processing these data in real time, the utilization efficiency evaluation index is calculated, and then the energy efficiency of the computer room is evaluated, the energy efficiency performance of the data center is quantified, and the low energy efficiency link is identified. According to the evaluation results, the computer room with low energy efficiency is located, the list of computer rooms to be optimized is obtained, and the terminal air conditioning operation parameters are optimized in a targeted manner to improve the energy efficiency performance of the inefficient computer room. By verifying the energy consumption reduction effect of the air conditioning operation optimization parameters, and based on these verification results, feedback adjustment of energy utilization efficiency is performed, and finally a terminal air conditioning operation optimization report is formed to ensure the effectiveness of the optimization measures, achieve energy consumption reduction, improve energy utilization efficiency, and provide a scientific basis for the online diagnosis and optimization of air conditioning terminal energy efficiency. Therefore, the present invention uses data processing technology, pattern recognition technology and intelligent optimization technology to judge the energy efficiency of the computer room terminal in real time, quickly identify the energy efficiency shortcomings of the computer room air conditioning terminal equipment, and at the same time, optimize the terminal air conditioning operation based on the air conditioning terminal operation mechanism, thereby reducing the operating energy consumption of the air conditioning terminal system.

[0076] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic flow chart of the steps of an online diagnosis and optimization method for the energy efficiency of a data center air conditioner terminal of the present invention. In this example, the online diagnosis and optimization method for the energy efficiency of a data center air conditioner terminal includes the following steps:

[0077] Step S1: Acquire the terminal air conditioning system of the data center; perform real-time monitoring of the power consumption of the IT equipment in the computer room and the terminal air conditioning equipment in the terminal air conditioning system of the data center to obtain the power consumption data of the equipment;

[0078] In the embodiment of the present invention, firstly, by deploying intelligent sensors and industrial intelligent gateways and other equipment, real-time data collection of power consumption of IT equipment and terminal air-conditioning equipment in the computer room is realized. These sensors can monitor electrical indicators including but not limited to current, voltage, temperature, power, etc., and upload the data to the cloud platform through Internet of Things communication technologies such as DLT / 645, Modbus, etc. The cloud platform integrates the Internet of Things, cloud computing, and big data technologies, compares, calculates, analyzes and records the collected data, and realizes remote control, real-time alarm, statistical calculation, energy-saving management and big data analysis of power safety and energy management. Then, the data center computer room environment monitoring system monitors power supply and distribution information, precision air-conditioning terminal information, temperature and humidity, water leakage and other environmental parameters in real time, and uploads them to the unified network management monitoring room (center) through the local area network to realize unified supervision. When the system detects that a certain parameter exceeds the safety threshold, it can provide multiple alarm methods such as interface alarm, multimedia voice alarm, SMS alarm, telephone alarm, etc., and record events for calling and analysis. Finally, the intelligent control system of the refrigeration system collects relevant operating parameters of systems such as the refrigeration station, terminal air conditioners and IT loads, and uses automated management tools to reduce the dimension, reduce noise, and clean the parameters. After that, special tools are used to perform correlation analysis on the tables after management to find out the key parameters related to the electricity utilization ratio. Using artificial intelligence (AI) technology, the energy efficiency of the refrigeration system is optimized by automatically adjusting the parameters of the refrigeration system.

[0079] Step S2: Analyze and process the equipment power consumption data in real time to calculate the utilization efficiency evaluation index; evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain the power efficiency evaluation result of the computer room;

[0080] In the embodiment of the present invention, first, the terminal air conditioning system of the data center collects real-time power consumption data of IT equipment and air conditioning equipment through intelligent sensors and gateway devices, including electrical indicators such as current, voltage, and power. These data are uploaded to the cloud platform for storage and analysis through the Internet of Things communication technology. The cloud platform uses big data analysis technology to compare, calculate, analyze and record the collected data, and realize remote control, real-time alarm, statistical calculation, energy-saving management and big data analysis of power safety and energy management. Specifically, the data center collects relevant operating parameters of systems such as refrigeration stations, terminal air conditioners and IT loads, uses automated governance tools to reduce dimensions, reduce noise, clean parameters, and perform correlation analysis to find out key parameters related to the power utilization ratio. Using artificial intelligence (AI) technology, the energy efficiency of the refrigeration system is optimized by automatically adjusting the parameters of the refrigeration system. The technical principle includes a real-time operation data collection cycle of 5 minutes / time, an accuracy of the power utilization ratio model of up to 99.50%, and a power utilization ratio reduction of 8% to 15%. Finally, the data center intelligent operation and management platform manages the data center's power system, environmental system, security system, power distribution system, HVAC system, fire protection system, server and other infrastructure through a unified platform, and maximizes the data center's operational efficiency and reliability through data analysis and aggregation.

[0081] Step S3: Locate the computer room with low energy efficiency according to the evaluation result of the power efficiency of the computer room, and obtain a list of computer rooms to be optimized; optimize the terminal air conditioning operation parameters of the list of computer rooms to be optimized, and obtain the air conditioning operation optimization parameters;

[0082] In the embodiment of the present invention, firstly, the energy efficiency of the computer room is monitored and evaluated in real time through the data center intelligent operation management platform (WiseEMP-DCIM) combined with energy efficiency evaluation indicators such as PUE (Power Usage Effectiveness). The platform uses big data analysis technology, intelligent prediction and aggregation data to identify computer rooms with low energy efficiency, and optimize the terminal air-conditioning operation parameters for the list of computer rooms to be optimized. Using AI technology, such as Huawei's iCooling@AI solution, through the reinforcement learning MPC algorithm, various scenarios are covered, including the number of air conditioners, internal and external machine models, layout differences, etc., to achieve micro-module support for cloud-based reasoning and optimization. On this basis, the genetic algorithm (GA) is used to optimize the refrigeration system. The algorithm simulates the natural selection and genetics mechanism of biological evolution, starting from a group of solutions (populations), and improving the solution through iterative selection, crossover and mutation until the optimization stop condition is met.

[0083] Step S4: verify the energy consumption reduction effect of the air-conditioning operation optimization parameters to obtain the energy consumption optimization verification results; perform energy utilization efficiency feedback adjustment based on the energy consumption optimization verification results to obtain the terminal air-conditioning operation optimization report to perform online diagnosis and optimization of the air-conditioning terminal energy efficiency.

[0084] In an embodiment of the present invention, first, a neural network optimization algorithm is used to automatically adjust the air-conditioning operating parameters to achieve energy consumption reduction. The algorithm simulates human brain neurons to establish a neural network model including an input layer, a hidden layer, and an output layer. By collecting historical energy consumption data as training samples, the neural network model is trained to predict energy consumption and adjust operating parameters. Next, the card edge control algorithm is applied to monitor the operating status of the air-conditioning system in real time, and the operating parameters are dynamically adjusted to ensure the comfort of the indoor environment while reducing energy consumption. The algorithm monitors indoor and outdoor temperatures, air-conditioning operating time, APF and other data in real time, and judges the comfort of the indoor environment based on the monitoring data, and then adjusts the air-conditioning operating parameters to perform online diagnosis and optimization of air-conditioning terminal energy efficiency.

[0085] Preferably, step S1 comprises the following steps:

[0086] Step S11: Acquire the terminal air conditioning system of the data center;

[0087] Step S12: measuring the power consumption of IT equipment in the computer room on the terminal air conditioning system of the data center to obtain the power consumption data of IT equipment in the computer room;

[0088] Step S13: measuring the power consumption of the terminal air-conditioning equipment of the terminal air-conditioning system of the data center to obtain the power consumption data of the terminal air-conditioning equipment;

[0089] Step S14: Integrate the power consumption data of the IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain the equipment power consumption data.

[0090] In the embodiment of the present invention, first, the terminal air conditioning system of the data center is monitored in real time by deploying smart sensors and smart meters. The smart sensors are responsible for collecting environmental parameters in the computer room, such as temperature and humidity, while the smart meters are installed at the input and output ends of the UPS, and the power consumption of the power supply system is measured by the difference between the two. The power consumption of the refrigeration system includes the power consumption of the compressor, chilled water pump, cooling water system, and terminal chilled water system in the indoor air conditioning terminal and the water-cooled refrigeration system. Next, for the measurement of the power consumption of the IT equipment in the computer room, a high-precision power quality analyzer is used to monitor the power consumption of the IT equipment in real time. The device can provide detailed power parameters, including current, voltage, power factor, power consumption, etc. The power consumption measurement of the terminal air conditioning equipment is achieved by installing meters at key nodes of the air conditioning system, including but not limited to cooling towers, chillers, cooling pumps, and freezing pumps. Then, the power consumption data of the IT equipment in the computer room and the power consumption data of the terminal air conditioning equipment are integrated. This step involves data fusion technology. By constructing a multimodal data integration model, the collected power data is transmitted to the model for weighting, feature fusion, and data dimension reduction processing. Finally, the data center energy consumption analysis system is used to comprehensively analyze the collected power consumption data of IT equipment and air-conditioning equipment to obtain the PUE (Power Usage Effectiveness) and CLF (Cooling Load Factor) values ​​of each data center. These values ​​are key indicators for measuring the energy efficiency of data centers.

[0091] Preferably, step S14 includes the following steps:

[0092] Step S141: pre-processing the power consumption data of IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain equipment pre-processing data;

[0093] Step S142: performing feature synchronization on the device preprocessing data to obtain device feature synchronization data; performing feature alignment on the device feature synchronization data to generate device feature alignment data;

[0094] Step S143: Perform structured inspection on the device feature alignment data to obtain structured inspection data; perform data fusion on the structured inspection data to obtain device power consumption data.

[0095] In the embodiment of the present invention, first, in the data preprocessing stage, the original power consumption data is processed by using data cleaning technology, including steps such as removing outliers, filling missing values, and data standardization. For example, the missing current readings are processed by the mean filling method, and the voltage data is standardized by the Z-score standardization method to eliminate the influence of different dimensions. Then, in the feature synchronization stage, the multimodal data integration model is used to time-align the data from different sensors to ensure the consistency of different data sources in the time series. For example, the temperature sensor data is synchronized with the power data recorded by the power quality analyzer through timestamp matching for subsequent analysis. In the feature alignment stage, the features of different modes are mapped to the same feature space through cross-modal alignment technology for comparison and analysis. For example, the power consumption data of IT equipment and the power consumption data of air-conditioning equipment are reduced in dimension using principal component analysis (PCA) to extract key features. Subsequently, in the structured inspection stage, the device feature alignment data is structured to ensure the consistency and integrity of the data. For example, by building a data model, check whether the data conforms to the expected format and type, and exclude data records that do not meet the structural requirements. Finally, in the data fusion stage, data fusion technology is used to integrate the structured test data to form a comprehensive view of equipment power consumption data. For example, using a weighted fusion algorithm, different weights are assigned to data from different sources based on the reliability and accuracy of the data, and the final equipment power consumption data is obtained.

[0096] Preferably, step S2 comprises the following steps:

[0097] Step S21: performing real-time analysis on the power consumption data of the equipment to obtain a power consumption data stream; extracting key features from the power consumption data stream to obtain key power consumption parameters;

[0098] Step S22: performing power consumption pattern recognition on key power consumption parameters to obtain a power consumption behavior pattern; performing power consumption trend monitoring on the power consumption behavior pattern to obtain power consumption trend data;

[0099] Step S23: quantifying the electricity utilization efficiency of the electricity consumption trend data and generating an efficiency evaluation index;

[0100] Step S24: Evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain an evaluation result of the power efficiency of the computer room.

[0101] In an embodiment of the present invention, first, the power consumption data of the equipment is analyzed in real time to obtain a power consumption data stream. In this step, power quality analyzers such as E6500 and VICTOR5000 are used to monitor and record parameters such as current, voltage, and power in real time to generate power consumption data streams. These data streams contain detailed power consumption information of IT equipment and air conditioning equipment at different time points. Next, key features are extracted from the power consumption data stream to obtain key power consumption parameters. Data preprocessing techniques such as missing data processing and data merging are used to ensure data integrity and consistency. Then, feature extraction techniques such as principal component analysis (PCA) are used to extract key power consumption parameters such as peak current and average power from a large amount of raw data. Subsequently, power consumption patterns are identified for key power consumption parameters to obtain power consumption behavior patterns. In this step, machine learning algorithms such as K-Means clustering are applied to perform pattern recognition on power consumption parameters to distinguish different power consumption behaviors, such as high load periods and low load periods. Further, power consumption trends are monitored for power consumption behavior patterns to obtain power consumption trend data. Through time series analysis techniques, such as structural equation models, the trend of electricity consumption behavior over time is monitored to obtain electricity consumption trend data. Then, the electricity consumption efficiency of the electricity consumption trend data is quantified to generate efficiency evaluation indicators. Using data fusion technology, electricity consumption data from different sources are combined to quantify electricity efficiency and generate evaluation indicators such as energy efficiency ratio (EER) and coefficient of performance (COP). Finally, the energy efficiency of the computer room is evaluated based on the efficiency evaluation indicators to obtain the evaluation results of the electricity efficiency of the computer room. Through the weighted fusion algorithm, multiple evaluation indicators are comprehensively considered to evaluate the electricity efficiency of the computer room and obtain the final evaluation results, such as PUE value, to evaluate the energy efficiency level of the computer room.

[0102] Preferably, step S24 includes the following steps:

[0103] Step S241: normalizing the utilization efficiency evaluation index to obtain a standardized evaluation index; and weighting the standardized evaluation index to obtain a weighted evaluation index;

[0104] Step S242: Comprehensively calculate the weighted evaluation index to obtain a comprehensive score of the power efficiency of the computer room; classify the comprehensive score of the power efficiency of the computer room to obtain a level evaluation of the power efficiency of the computer room;

[0105] Step S243: presenting the result of the level evaluation of the power efficiency of the computer room, and obtaining the evaluation result of the power efficiency of the computer room.

[0106] In an embodiment of the present invention, first, the utilization efficiency evaluation index is normalized to obtain a standardized evaluation index. In this step, the Min-Max normalization method is used to scale the original data to the interval [0,1]. Next, the standardized evaluation index is weighted to obtain a weighted evaluation index. The weight allocation is based on the importance of each index to the energy efficiency. For example, the PUE value is given a higher weight because it directly reflects the energy efficiency level of the data center. The weight is determined by an expert scoring method or a hierarchical analysis method (AHP) to ensure that the weight of each index reflects its relative importance in the overall evaluation. Then, the weighted evaluation index is comprehensively calculated to obtain a comprehensive score of the power efficiency of the computer room. In this step, the arithmetic mean method or the weighted harmonic mean method is used to synthesize each weighted index to obtain a comprehensive score that comprehensively reflects the power efficiency of the computer room. Further, the comprehensive score of the power efficiency of the computer room is graded to obtain a grade evaluation of the power efficiency of the computer room. The grading is divided into several levels such as "excellent", "good", "medium", and "poor" according to the numerical range of the score. The specific grading standards are set according to industry standards or internal enterprise standards. Finally, the results of the grade evaluation of the power efficiency of the computer room are presented to obtain the evaluation results of the power efficiency of the computer room. In this step, data visualization technology is used, such as D3.js combined with the React framework, to intuitively display the evaluation results in the form of charts, so that operation and maintenance personnel can quickly grasp the energy efficiency status of the computer room and make corresponding optimization decisions.

[0107] Preferably, step S3 comprises the following steps:

[0108] Step S31: setting a threshold for the evaluation results of the power efficiency of the computer room to obtain preliminary screening of inefficient computer room data;

[0109] Step S32: removing abnormal values ​​from the preliminarily screened inefficient computer room data to obtain inefficient parameter removal data;

[0110] Step S33: Prioritize the preliminarily screened inefficient computer room data based on the inefficient parameter elimination data to obtain an inefficient computer room priority list; extract computer rooms with low energy efficiency from the inefficient computer room priority list to obtain a list of computer rooms to be optimized;

[0111] Step S34: Optimize the terminal air-conditioning operation parameters of the list of computer rooms to be optimized to obtain the air-conditioning operation optimization parameters.

[0112] In an embodiment of the present invention, first, a threshold is set for the evaluation result of the power efficiency of the computer room to obtain preliminary screening of inefficient computer room data. In this step, the threshold is determined by the quantile method in statistics. For example, a computer room with a PUE value greater than a certain percentile value is set as an inefficient computer room. A reasonable threshold, such as the 90th percentile, is determined by the distribution of the PUE value of the computer room, and all computer rooms above the threshold are preliminarily identified as inefficient computer rooms. Next, the preliminarily screened inefficient computer room data is subjected to outlier removal to obtain inefficient parameter removal data. Outlier removal is performed by calculating the mean and standard deviation, and setting a range (such as mean ± 3 times standard deviation) to identify outliers. Values ​​outside this range are considered outliers and are removed from the data set to ensure the accuracy of subsequent analysis. Then, the preliminarily screened inefficient computer room data is prioritized based on the inefficient parameter removal data to obtain an inefficient computer room priority list. In this step, a multi-objective optimization algorithm, such as NSGA-II, is used, which can simultaneously consider multiple optimization objectives and prioritize inefficient computer rooms. Factors considered during sorting include energy consumption, cost, and environmental impact. Furthermore, the energy-inefficient computer rooms are extracted from the priority list of inefficient computer rooms to obtain a list of computer rooms to be optimized. In this step, clustering algorithms such as DBSCAN are used, which can cluster according to the density of the data and identify computer rooms with low energy efficiency. By setting appropriate epsilon and min_samples parameters, the DBSCAN algorithm can identify computer rooms with low density, i.e., computer rooms to be optimized. Finally, the terminal air-conditioning operating parameters of the list of computer rooms to be optimized are optimized to obtain the air-conditioning operating optimization parameters. The optimization process uses a genetic algorithm to continuously iterate and optimize the air-conditioning operating parameters through steps such as initializing the population, fitness evaluation, selection, crossover, and mutation to achieve the purpose of reducing energy consumption. For example, by adjusting parameters such as the temperature setting value and the operating time, the best combination of operating parameters is found to maximize energy efficiency.

[0113] Preferably, step S34 includes the following steps:

[0114] Step S341: collecting initial operating parameters of the terminal air conditioners in the list of computer rooms to be optimized to obtain basic operating data of the air conditioners;

[0115] Step S342: Perform performance analysis on the basic data of air conditioner operation to obtain operation performance indicators;

[0116] Step S343: Optimizing the key performance indicators to obtain performance indicator optimization data;

[0117] Step S344: adjusting the operating parameters of the list of computer rooms to be optimized based on the performance indicator optimization data to obtain air conditioning operation optimization parameters.

[0118] In the embodiment of the present invention, first, the initial parameters of the terminal air conditioner operation are collected for the list of computer rooms to be optimized to obtain the basic data of air conditioner operation. In this step, the building automation system (BAS) or data center infrastructure management (DCIM) software is used to collect the operating parameters of the terminal air conditioner in real time through various sensors installed on the air conditioning system, such as temperature, humidity, pressure sensors, etc., including but not limited to compressor speed, inverter frequency, fan speed, surface cooling valve opening, etc. Next, the basic data of air conditioner operation is analyzed to obtain the operation performance index. In this step, the big data analysis technology is used to perform statistics and analysis on the collected data to calculate key performance indicators, such as energy efficiency ratio (EER), coefficient of performance (COP), energy consumption, etc. Then, the key performance indicators are optimized to obtain performance indicator optimization data. In this step, machine learning algorithms, such as neural networks or support vector machines (SVMs), are applied to perform pattern recognition and prediction on the performance indicators, identify the key factors affecting energy efficiency, and propose optimization directions. Further, the operating parameters of the list of computer rooms to be optimized are adjusted based on the performance indicator optimization data to obtain the air conditioner operation optimization parameters. In this step, genetic algorithms or particle swarm optimization (PSO) algorithms are used to iteratively search for optimal operating parameters, such as set temperature, wind speed, etc., by simulating natural selection or group behavior to maximize energy efficiency.

[0119] Preferably, step S344 includes the following steps:

[0120] Step S3441: Identify factors affecting air conditioning energy efficiency based on the performance index optimization data to obtain an energy efficiency optimization target;

[0121] Step S3442: performing parameter association mapping on the energy efficiency optimization target to obtain parameter adjustment mapping data;

[0122] Step S3443: dynamically adjust the list of computer rooms to be optimized according to the parameter adjustment mapping data to obtain computer room operation adjustment data;

[0123] Step S3444: Perform feedback loop optimization on the computer room operation adjustment data to obtain air conditioning operation optimization parameters.

[0124] In the embodiment of the present invention, first, the air conditioning energy efficiency influencing factors are identified for the performance index optimization data to obtain the energy efficiency optimization target. In this step, the big data analysis technology is used to collect historical energy consumption data, including indoor and outdoor temperature, air conditioning operation time, APF, etc., and the energy consumption prediction model is established using the machine learning algorithm. For example, by analyzing the data, it is found that the number of air supply gears and the set temperature have a significant impact on the air conditioning energy efficiency. Next, the energy efficiency optimization target is parameter-associated mapped to obtain parameter adjustment mapping data. In this step, the particle swarm optimization algorithm (PSO) is used for parameter optimization. Among them, the inertia weight, self-learning factor and group learning factor are key parameters, and the optimal air conditioning operation parameters are found by adjusting these parameters. Then, the list of computer rooms to be optimized is dynamically adjusted according to the parameter adjustment mapping data to obtain the computer room operation adjustment data. In this step, a dynamic adjustment algorithm, such as DQN network parameter adjustment, is applied to achieve performance optimization. The DQN network continuously adjusts the strategy through reinforcement learning to adapt to the changing environment and optimize energy efficiency. Finally, the computer room operation adjustment data is feedback loop optimized to obtain the air conditioning operation optimization parameters. Adopt feedback control mechanisms, such as card edge control algorithms, to monitor the operating status of the air conditioning system in real time and dynamically adjust operating parameters to ensure indoor environmental comfort while reducing energy consumption. For example, real-time monitoring of indoor and outdoor temperature, air conditioning operating time, APF and other data, and adjust air conditioning operating parameters such as temperature setting value and wind speed accordingly.

[0125] As an example of the present invention, refer to Figure 2 As shown, in this example, step S4 includes:

[0126] Step S41: Compare the energy consumption data before and after the implementation of the air conditioner operation optimization parameters to obtain energy consumption comparison data;

[0127] Step S42: Statistically analyzing the energy consumption comparison data to obtain energy consumption statistical results; and identifying trends in the energy consumption statistical results to obtain energy consumption trend data;

[0128] Step S43: Evaluate the effect of the energy consumption trend analysis to obtain energy consumption optimization verification results;

[0129] Step S44: Based on the energy consumption optimization verification result, energy utilization efficiency feedback adjustment is performed to obtain a terminal air-conditioning operation optimization report to perform online diagnosis and optimization of air-conditioning terminal energy efficiency.

[0130] In an embodiment of the present invention, first, the energy consumption data before and after the implementation of the air-conditioning operation optimization parameters are compared to obtain energy consumption comparison data. In this step, a comprehensive energy management system is used to query the energy consumption comparison of different energy types within the query time period according to regions, departments, circuits, and pipe networks, including year-on-year energy consumption, month-on-month energy consumption, and horizontal comparison. By logging into the energy management system platform, selecting the corresponding hierarchical dimension and energy type, setting the time dimension and range of the query, the system automatically calculates and displays the energy consumption comparison results. Next, the energy consumption comparison data is statistically analyzed to obtain energy consumption statistics. At the same time, the energy consumption statistics are trend identified to obtain energy consumption trend data. In this step, big data analysis technology is used to conduct in-depth analysis of the collected energy consumption data, calculate the monthly energy consumption of each energy source year-on-year, and intuitively display it in the form of charts, such as line charts, bar charts, stacking modes, etc., and support saving pictures to local. Then, the energy consumption trend analysis is evaluated to obtain energy consumption optimization verification results. In this step, energy efficiency evaluation and improvement methods, such as indicators such as energy efficiency ratio (EER), are applied to measure the performance of the electrical energy system, identify and quantify the energy loss in the system, and provide improvement directions. Finally, based on the energy consumption optimization verification results, energy utilization efficiency feedback adjustment is performed to obtain the terminal air conditioning operation optimization report. In this step, the data center air conditioning terminal cooling evaluation index and abnormal diagnosis method are used to judge the balance between the supply and demand of the cooling capacity of the computer room in real time, quickly identify the energy efficiency shortcomings of the terminal equipment of the computer room air conditioning, realize efficient and accurate supply of cooling capacity, and improve the utilization efficiency of cooling capacity.

[0131] As an example of the present invention, refer to Figure 3 As shown, in this example, step S44 includes:

[0132] Step S441: performing energy consumption reduction analysis on the energy consumption optimization verification result to obtain energy consumption analysis data;

[0133] Step S442: Efficiency impact assessment is performed on the energy consumption analysis data to obtain efficiency impact factors; weight adjustment is performed on the efficiency impact factors to obtain adjusted weight data;

[0134] Step S443: Apply the adjusted weights in a feedback loop to obtain real-time optimization parameters; monitor the effects of the real-time optimization parameters to obtain a terminal air conditioning operation optimization report;

[0135] Step S444: Continuously optimize the terminal air conditioning system of the data center based on the terminal air conditioning operation optimization report to perform online diagnosis and optimization of the air conditioning terminal energy efficiency.

[0136] In the embodiment of the present invention, first, the energy consumption optimization verification result is analyzed for energy consumption reduction to obtain energy consumption analysis data. In this step, the data center air conditioning terminal cooling evaluation index and abnormal diagnosis system is used. The system collects the IT power consumption, terminal wind cabinet power consumption and operating parameters of each computer room, calculates the E-ACTE value of each computer room and judges the terminal energy consumption of the computer room air conditioning, thereby obtaining energy consumption analysis data. Next, the energy consumption analysis data is evaluated for efficiency impact to obtain efficiency influencing factors. Then, the efficiency influencing factors are weighted to obtain adjusted weight data. In this step, a multi-objective optimization method based on genetic algorithm is used. This method takes minimizing the annual total energy consumption and annual total operating cost of the system as the objective function, and takes the system's cooling capacity, heating capacity, ice storage capacity, ground source heat pump power, etc. as design variables. The weights of each efficiency influencing factor are adjusted through the optimization algorithm to minimize energy consumption and cost. Then, the adjusted weights are applied in a feedback loop to obtain real-time optimization parameters. At the same time, the effect of the real-time optimization parameters is monitored to obtain a terminal air conditioning operation optimization report. By applying the neural network optimization algorithm and simulating human brain neurons, the air conditioning operation parameters can be automatically adjusted, the air conditioning system operation status can be monitored and optimized in real time, and the indoor environment comfort level can be ensured while reducing energy consumption. Finally, the terminal air conditioning system of the data center is continuously optimized based on the terminal air conditioning operation optimization report to perform online diagnosis and optimization of the air conditioning terminal energy efficiency. In this step, the air conditioning system energy efficiency online detection and diagnosis analyzer and method are used to monitor the room pipeline pressure, temperature, flow and power consumption data of water pumps, cooling towers and units in real time and record them. The water pump efficiency, unit energy efficiency ratio, system energy efficiency ratio, water pump transmission and other data are calculated and analyzed. The analyzed data are compared with the relevant data of similar computer rooms to analyze the energy cost and the status of the equipment in the computer room, analyze the factors affecting energy efficiency, optimize the system operation strategy, improve the system's comprehensive energy efficiency, and reduce operating costs.

[0137] As an example of the present invention, refer to Figure 4 As shown, in this example, a control logic diagram of an online diagnosis and optimization method for energy efficiency of air conditioner terminals in a data center is used to illustrate the specific implementation of the present invention by taking the data center of this application as an example:

[0138] Smart meters are installed on each floor of the data center to obtain the power consumption of the IT equipment in the computer room and the power consumption of the terminal air conditioner. Specifically, each computer room is equipped with a set of smart meters to monitor the total power consumption of the IT equipment in the cabinet and the total power consumption of the terminal air conditioner. Among them, smart meter 1 monitors the total power consumption of all IT equipment in computer room 1 1-1 And the total power consumption of all terminal air conditioners P 1-2 , smart meter 2 monitors the total power consumption P of all IT equipment in computer room 2 2-1 And the total power consumption of all terminal air conditioners P 2-2Similarly, smart meter N monitors the total power consumption P of all IT equipment in room N. N-1 And the total power consumption of all terminal air conditioners P N-2 Install sensors at the air outlet and return air outlet of the terminal air conditioner to monitor the air supply temperature T of the terminal air conditioner. N1 , return air temperature T N2 , used to preliminarily determine whether the terminal cooling supply meets the cooling load demand of the computer room; install a fan inverter and a water valve opening controller on the terminal air conditioner to adjust the fan operating frequency and chilled water flow rate;

[0139] Calculate the C value energy efficiency index of the air conditioner terminal in each computer room:

[0140]

[0141] Among them, C N represents the C value of the Nth computer room; P N-2 represents the IT power consumption of the Nth computer room; P N-1 Indicates the power consumption of the terminal air conditioner in the Nth computer room; for example, Engine Room 2 Computer Room 3 , Room 4 Computer Room N Baseline L base ; Compare the C value data of the computer room cooled by the same type of terminal air conditioner with the energy efficiency baseline.

[0142]

[0143] For the equipment room with alarm status, analyze the supply and return air temperature difference of each air cabinet in the equipment room. Take the Nth air cabinet as an example, according to the air cabinet supply air temperature T N1 , return air temperature T N2 Calculate the supply and return air temperature difference ΔT N =T N2 -T N1 If ΔTN>temperature difference limit ΔT limit +deviationT de , indicating that the heat exchange effect of the typhoon cabinet is good. If the supply and return air temperature difference of all terminal air conditioners is greater than the temperature difference limit ΔT limit +deviationT de , indicating that the cooling capacity of the computer room is too small, and the proportional integral valve opening of the terminal air-conditioning fan needs to be increased by 2%; if the supply and return air temperature difference ΔTN of the terminal air-conditioning is ≤ the temperature difference limit, it indicates that the operation of the terminal air-conditioning is abnormal, and further diagnosis of the cause of the abnormal operation of the wind cabinet is required.

[0144] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0145] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for online diagnosis and optimization of energy efficiency of air conditioning terminals in a data center, characterized in that: The following steps are involved: Step S1: Acquire the terminal air conditioning system of the data center; perform real-time monitoring of the power consumption of the IT equipment in the computer room and the terminal air conditioning equipment in the terminal air conditioning system of the data center to obtain the power consumption data of the equipment; Step S2: Analyze and process the equipment power consumption data in real time to calculate the utilization efficiency evaluation index; evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain the power efficiency evaluation result of the computer room; Step S3: Locate the computer room with low energy efficiency according to the evaluation result of the power efficiency of the computer room, and obtain a list of computer rooms to be optimized; optimize the terminal air conditioning operation parameters of the list of computer rooms to be optimized, and obtain the air conditioning operation optimization parameters; Step S4: verifying the energy consumption reduction effect of the air-conditioning operation optimization parameters to obtain energy consumption optimization verification results; Based on the energy consumption optimization verification results, energy utilization efficiency feedback adjustment is performed to obtain the terminal air-conditioning operation optimization report to perform online diagnosis and optimization of air-conditioning terminal energy efficiency.

2. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire the terminal air conditioning system of the data center; Step S12: measuring the power consumption of IT equipment in the computer room on the terminal air conditioning system of the data center to obtain the power consumption data of IT equipment in the computer room; Step S13: measuring the power consumption of the terminal air-conditioning equipment of the terminal air-conditioning system of the data center to obtain the power consumption data of the terminal air-conditioning equipment; Step S14: Integrate the power consumption data of the IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain the equipment power consumption data.

3. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: pre-processing the power consumption data of IT equipment in the computer room and the power consumption data of the terminal air-conditioning equipment to obtain equipment pre-processing data; Step S142: performing feature synchronization on the device preprocessing data to obtain device feature synchronization data; performing feature alignment on the device feature synchronization data to generate device feature alignment data; Step S143: Perform structured inspection on the device feature alignment data to obtain structured inspection data; perform data fusion on the structured inspection data to obtain device power consumption data.

4. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing real-time analysis on the power consumption data of the equipment to obtain a power consumption data stream; extracting key features from the power consumption data stream to obtain key power consumption parameters; Step S22: performing power consumption pattern recognition on key power consumption parameters to obtain a power consumption behavior pattern; performing power consumption trend monitoring on the power consumption behavior pattern to obtain power consumption trend data; Step S23: quantifying the electricity utilization efficiency of the electricity consumption trend data and generating an efficiency evaluation index; Step S24: Evaluate the energy efficiency of the computer room based on the utilization efficiency evaluation index to obtain an evaluation result of the power efficiency of the computer room.

5. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: normalizing the utilization efficiency evaluation index to obtain a standardized evaluation index; and weighting the standardized evaluation index to obtain a weighted evaluation index; Step S242: Comprehensively calculate the weighted evaluation index to obtain a comprehensive score of the power efficiency of the computer room; classify the comprehensive score of the power efficiency of the computer room to obtain a level evaluation of the power efficiency of the computer room; Step S243: presenting the result of the level evaluation of the power efficiency of the computer room, and obtaining the evaluation result of the power efficiency of the computer room.

6. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: setting a threshold for the evaluation results of the power efficiency of the computer room to obtain preliminary screening of inefficient computer room data; Step S32: removing abnormal values ​​from the preliminarily screened inefficient computer room data to obtain inefficient parameter removal data; Step S33: Prioritize the preliminarily screened inefficient computer room data based on the inefficient parameter elimination data to obtain an inefficient computer room priority list; extract computer rooms with low energy efficiency from the inefficient computer room priority list to obtain a list of computer rooms to be optimized; Step S34: Optimize the terminal air-conditioning operation parameters of the list of computer rooms to be optimized to obtain the air-conditioning operation optimization parameters.

7. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 6 is characterized in that: Step S34 includes the following steps: Step S341: collecting initial operating parameters of the terminal air conditioners in the list of computer rooms to be optimized to obtain basic operating data of the air conditioners; Step S342: Perform performance analysis on the basic data of air conditioner operation to obtain operation performance indicators; Step S343: Optimizing the key performance indicators to obtain performance indicator optimization data; Step S344: adjusting the operating parameters of the list of computer rooms to be optimized based on the performance indicator optimization data to obtain air conditioning operation optimization parameters.

8. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 7 is characterized in that: Step S344 includes the following steps: Step S3441: Identify the factors affecting air conditioning energy efficiency based on the performance index optimization data to obtain an energy efficiency optimization target; Step S3442: performing parameter association mapping on the energy efficiency optimization target to obtain parameter adjustment mapping data; Step S3443: dynamically adjusting the list of computer rooms to be optimized according to the parameter adjustment mapping data to obtain computer room operation adjustment data; Step S3444: Perform feedback loop optimization on the computer room operation adjustment data to obtain air conditioning operation optimization parameters.

9. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Compare the energy consumption data before and after the implementation of the air conditioner operation optimization parameters to obtain energy consumption comparison data; Step S42: Statistically analyzing the energy consumption comparison data to obtain energy consumption statistical results; and identifying trends in the energy consumption statistical results to obtain energy consumption trend data; Step S43: Evaluate the effect of the energy consumption trend analysis to obtain energy consumption optimization verification results; Step S44: Based on the energy consumption optimization verification result, energy utilization efficiency feedback adjustment is performed to obtain a terminal air-conditioning operation optimization report to perform online diagnosis and optimization of air-conditioning terminal energy efficiency.

10. The method for online diagnosis and optimization of energy efficiency of air conditioner terminals in a data center according to claim 9, characterized in that: Step S44 includes the following steps: Step S441: performing energy consumption reduction analysis on the energy consumption optimization verification result to obtain energy consumption analysis data; Step S442: Efficiency impact assessment is performed on the energy consumption analysis data to obtain efficiency impact factors; weight adjustment is performed on the efficiency impact factors to obtain adjusted weight data; Step S443: Apply the adjusted weights in a feedback loop to obtain real-time optimization parameters; monitor the effects of the real-time optimization parameters to obtain a terminal air conditioning operation optimization report; Step S444: Continuously optimize the terminal air conditioning system of the data center based on the terminal air conditioning operation optimization report to perform online diagnosis and optimization of the air conditioning terminal energy efficiency.

Images