Method and device for dynamically adjusting energy consumption of computer room environment

Abstract

The present application relates to a kind of method and device for dynamically adjusting computer room environment energy consumption, belong to adjusting energy consumption technical field, the method includes: collecting computer room environment energy consumption data, set this monitoring energy consumption initial threshold;According to energy consumption data, early warning and generating visual report;If the actual energy consumption in visual report this time is higher than historical average baseline and prediction rise and fall probability trend value is consistent, energy use efficiency value, energy use efficiency prediction value and difference are obtained, according to the size of difference, optimize this monitoring energy consumption initial threshold;When computer room environment energy consumption data is abnormal, locate current submodule, generate data set and mark by correlation relationship mining;The similarity of data set is calculated, set next monitoring energy consumption initial threshold;Determine optimal threshold;Optimal configuration threshold is generated under the computer room environment energy consumption data of optimal configuration threshold is stored, compare the computer room environment energy consumption data generated under optimal threshold and the computer room environment energy consumption data generated under monitoring energy consumption initial threshold.

Description

Technical Field

[0001] This invention belongs to the field of energy consumption adjustment technology for computer room environment, and particularly relates to a method and apparatus for dynamically adjusting energy consumption in computer room environment. Background Technology

[0002] Monitoring the environmental energy consumption of a data center is typically achieved through a data center environmental monitoring system. This system can monitor the energy consumption of various devices within the data center in real time, including factors such as voltage, current, and power, and provide energy optimization suggestions through data analysis. Furthermore, the data center environmental monitoring system can also monitor environmental parameters such as temperature and humidity to ensure that these environmental factors do not affect the operating efficiency and lifespan of equipment, thereby indirectly impacting energy consumption. These issues not only increase the data center's energy consumption but may also affect the normal operation and lifespan of the data center equipment.

[0003] The main factors affecting energy consumption in monitoring computer rooms include energy conservation in air conditioning, heat dissipation, IT equipment, and power distribution systems. Existing solutions for energy conservation primarily include installing environmental monitoring systems, power management, software system management, optimizing data management and backup strategies, skills training and development, team collaboration and workflow optimization, staffing optimization, and cost monitoring and analysis. However, these solutions suffer from drawbacks such as outdated information, high labor costs, difficulty in controlling environmental anomalies, and computer room security issues.

[0004] Patent 1 "A Method for Dynamic Analysis and Optimization of AI Energy Consumption Thresholds in Data Centers"

[0005] Patents CN115630709A, CN110069017A ("A method for controlling equipment energy consumption, storage medium and its control terminal"), and CN112288159B ("A method and device for estimating the energy-saving potential of an electricity-using site") all fully consider the impact of energy consumption thresholds on energy saving. Essentially, they are all based on threshold adjustment technology that adjusts thresholds according to the correlation of control thresholds. By analyzing data and optimizing energy consumption thresholds, they aim to reduce energy consumption and improve operational efficiency. However, these patents have the following problems: the AI ​​accuracy is insufficient, easily leading to inaccurate threshold adjustment and settings. The AI ​​algorithm itself also has significant uncertainties in its calculation results, and the optimal solution obtained is a local threshold optimal solution rather than a globally optimal solution. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of the invention is to provide a method and device for dynamically adjusting the energy consumption of the computer room environment, which can accurately predict the energy efficiency value. By combining the predicted energy efficiency value with threshold analysis, it helps to improve the work efficiency of maintenance personnel and save work costs.

[0007] In a first aspect, the present invention provides a method for dynamically adjusting the energy consumption of a computer room environment, comprising seven steps S1 to S7:

[0008] S1: Collect energy consumption data of the computer room environment and set the initial energy consumption threshold for this monitoring.

[0009] S2: Provide early warnings and generate visual reports based on data center energy consumption data;

[0010] S3: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, calculate the energy use efficiency value, the predicted energy use efficiency value, and the difference between the energy use efficiency value and the predicted energy use efficiency value. Optimize the initial threshold of the monitored energy consumption based on the difference between the energy use efficiency value and the predicted energy use efficiency value.

[0011] S4: When the energy consumption data of the data center environment is abnormal, locate the current sub-module, mine the relationship between the current sub-module, generate a dataset for merging and labeling;

[0012] S5: Calculate the similarity of the labeled data sets and set the initial threshold for energy consumption for the next monitoring.

[0013] S6: Repeat steps S3 to S5 to determine the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold.

[0014] S7: Summarize the data center energy consumption data generated under the optimal storage configuration threshold, and compare the data center energy consumption data generated under the optimal threshold with the data center energy consumption data generated under the initial monitoring energy consumption threshold.

[0015] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the collection of computer room environment energy consumption data includes:

[0016] Data on energy consumption in the computer room environment is collected through energy-saving management modules for air conditioning, heat transfer, IT equipment, and power distribution systems deployed in the computer room.

[0017] The data collection method is program-based inspection data collection.

[0018] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the initial threshold for monitoring energy consumption is optimized based on the difference between the energy usage efficiency value and the predicted energy usage efficiency value, including:

[0019] If the difference is less than or equal to the preset threshold, the initial threshold for monitoring energy consumption will not be optimized.

[0020] If the difference is greater than the preset threshold, optimize the initial threshold for monitoring energy consumption.

[0021] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the process of mining the relationships between the current sub-modules to generate a dataset merging label includes:

[0022] Discover data that is related to the current submodule;

[0023] Hardware devices that can mine data that is related to the current submodule;

[0024] The data set is obtained by merging the data that is related to the current submodule and the hardware devices that are related to the data in the current submodule;

[0025] Label the data set;

[0026] The dataset includes: a subset of alarm types, a subset of alarm quantities, a subset of false alarm quantities, and a subset of engineer evaluations.

[0027] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of a computer room environment, calculating the similarity of the labeled data sets includes:

[0028] The similarity of the labeled data sets is obtained by inputting the subsets of labeled alarm types, alarm quantities, false alarm quantities, and engineer evaluations into the data diversity identification model.

[0029] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, after determining the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold, it also includes:

[0030] Calculate the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption;

[0031] Determine whether the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption is within a preset range;

[0032] If the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption is within a preset range, the optimized initial threshold for monitoring energy consumption is updated to the optimal threshold.

[0033] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the formula for optimizing the initial threshold of monitoring energy consumption is:

[0034] Monitoring energy consumption initial threshold + [Energy efficiency value - predicted energy efficiency value - ((energy efficiency value / predicted energy efficiency value) maximized × 10%)].

[0035] A second aspect of the present invention also provides a device for dynamically adjusting the energy consumption of a computer room environment, comprising:

[0036] Data Acquisition Module: Used to collect energy consumption data of the computer room environment and set the initial energy consumption threshold for this monitoring.

[0037] Generation module: Used to generate early warnings and visual reports based on data center energy consumption data;

[0038] Optimization module: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, it calculates the energy efficiency value, the predicted energy efficiency value, and the difference between the energy efficiency value and the predicted energy efficiency value, and optimizes the initial threshold of the monitored energy consumption based on the difference between the energy efficiency value and the predicted energy efficiency value.

[0039] Mining module: When abnormal energy consumption data occurs in the computer room environment, it locates the current sub-module, mines the relationship between the current sub-module, and generates a dataset for merging and labeling.

[0040] Calculation module: Used to calculate the similarity of the labeled data sets and set the initial threshold for energy consumption in the next monitoring.

[0041] Determining Module: Used to repeatedly execute the optimization module, mining module, and calculation module to determine the initial monitoring energy consumption threshold corresponding to the maximum similarity of the labeled data set as the optimal threshold;

[0042] The comparison module is used to summarize the data center energy consumption data generated under the optimal storage configuration threshold and compare the data center energy consumption data generated under the optimal threshold with the data center energy consumption data generated under the initial monitoring energy consumption threshold.

[0043] A third aspect of the present invention also provides an electronic device comprising: a processor and a memory;

[0044] The processor executes a method for dynamically adjusting the energy consumption of the computer room environment, such as one of the above methods, by calling programs or instructions stored in memory.

[0045] In a fourth aspect, the present invention also provides a computer-readable storage medium storing a program or instructions that cause a computer to perform a method for dynamically adjusting the energy consumption of a computer room environment as described in any of the preceding claims.

[0046] The beneficial effects of this invention are as follows: This invention collects energy consumption data of the computer room environment and sets an initial threshold for energy consumption monitoring; it issues early warnings and generates visual reports based on the energy consumption data; if the actual energy consumption in the visual report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, it calculates the energy efficiency value, the predicted energy efficiency value, and the difference, and optimizes the initial threshold for energy consumption monitoring based on the difference; when abnormalities occur in the computer room environment energy consumption data, it locates the current sub-module, performs correlation mining to generate a dataset merging and marking; it calculates the similarity of the data set and sets the initial threshold for energy consumption monitoring for the next time; it determines the optimal threshold; it summarizes and stores the computer room environment energy consumption data generated under the optimal configuration threshold, and compares the computer room environment energy consumption data generated under the optimal threshold with the computer room environment energy consumption data generated under the initial threshold for monitoring. This invention can accurately predict energy efficiency values. By combining the predicted energy efficiency values ​​with threshold analysis, it helps improve the work efficiency of maintenance personnel, saves work costs, and the optimal threshold is the globally optimal solution. Attached Figure Description

[0047] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0048] Figure 1 This invention provides a method for dynamically adjusting the energy consumption of a computer room environment. Figure 1 ;

[0049] Figure 2 This is a diagram illustrating a method for mining relationships within the current submodule to generate a dataset with merged labels, provided by an embodiment of the present invention.

[0050] Figure 3 This invention provides a method for dynamically adjusting the energy consumption of a computer room environment. Figure 2 ;

[0051] Figure 4 A diagram of a device for dynamically adjusting the energy consumption of a computer room environment provided in an embodiment of the present invention;

[0052] Figure 5 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0053] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0054] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0055] In the description of this invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0057] This invention proposes a method, device, electronic equipment, and storage medium for dynamically adjusting the energy consumption of a computer room environment. It can accurately predict energy usage efficiency values. By combining the predicted energy usage efficiency values ​​with threshold analysis, it helps to improve the work efficiency of maintenance personnel and save operating costs.

[0058] Before introducing the embodiments of the present invention, we will first introduce the technical terms involved in the embodiments of the present invention.

[0059] Energy consumption threshold: Primarily based on historical data and benchmark base station data analysis, a primary energy consumption model for base stations in typical scenarios is established through a combination of univariate linear regression fitting and mechanistic analysis. This model is then used to set the warning threshold for base stations, enabling abnormal energy consumption alarms. Setting base station energy consumption thresholds not only improves warning accuracy and optimizes energy-saving management but also prevents channel conflicts and improves work efficiency, making it crucial for achieving energy conservation and consumption reduction at base stations.

[0060] Energy efficiency: This is an indicator for evaluating the energy efficiency of a data center. It is the ratio of all energy consumed by the data center to the energy consumed by the IT load. Total data center energy consumption / IT equipment energy consumption, where total data center energy consumption includes the energy consumption of IT equipment and systems such as cooling and power distribution. A value greater than 1 indicates that the non-IT equipment consumes less energy, meaning a better energy efficiency level.

[0061] Diversity identification model: This is a method and framework for measuring and optimizing content diversity in recommendation systems. It aims to measure the diversity of content in recommendation lists using different metrics and algorithms to ensure that recommendations not only meet users' current needs but also promote the development of users' long-term interests and a balanced display of system content.

[0062] Method Implementation Examples

[0063] Figure 1 This invention provides a method for dynamically adjusting the energy consumption of a computer room environment. Figure 1 .

[0064] In a first aspect, the present invention provides a method for dynamically adjusting the energy consumption of a computer room environment, comprising seven steps S1 to S7:

[0065] S1: Collect energy consumption data of the computer room environment and set the initial threshold for energy consumption in this monitoring.

[0066] Specifically, in this embodiment of the invention, a data center service management platform is deployed in the data center to manage the hosted equipment. The data center service management platform consists of a data center environmental data collection module and a network management and analysis module. The data center environmental data collection module is used to collect data on the energy consumption of the data center environment. The data center environmental data collection module includes four sub-modules: an air conditioning energy-saving management module, an air-movement heat energy-saving management module, an IT equipment energy-saving management module, and a power distribution system energy-saving management module. The data center environmental energy consumption data is collected through the four sub-modules, and the initial threshold for the energy consumption of this monitoring is set.

[0067] S2: Provide early warnings and generate visual reports based on data from the data center's energy consumption environment.

[0068] Specifically, in this embodiment of the invention, the energy consumption prediction model provides early warnings and generates a visual report based on the data center environmental energy consumption data collected by the four sub-modules. The visual report includes: the names of the four sub-modules: air conditioning energy-saving management module, air-moving heat energy-saving management module, IT equipment energy-saving management module, and power distribution system energy-saving management module; the names of the subsystems corresponding to the air conditioning energy-saving management module, air-moving heat energy-saving management module, IT equipment energy-saving management module, and power distribution system energy-saving management module; and the actual energy consumption value for the current period, the average energy consumption of the last 10 times, and the AI ​​energy consumption prediction value for the next inspection for each subsystem.

[0069] S3: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, calculate the energy use efficiency value, the predicted energy use efficiency value, and the difference between the energy use efficiency value and the predicted energy use efficiency value. Optimize the initial threshold for the monitored energy consumption based on the difference between the energy use efficiency value and the predicted energy use efficiency value.

[0070] Specifically, in this embodiment of the invention, if the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, the energy usage efficiency value is calculated based on the actual energy consumption value of the subsystem in this period, and the predicted energy usage efficiency value is calculated based on the predicted energy usage efficiency value of the subsystem in this period. For example, the energy usage efficiency value (IT equipment or infrastructure) is 100% / 55% = 1.82, and the predicted energy usage efficiency value is 100% / 61% = 1.63. The difference between the energy usage efficiency value and the predicted energy usage efficiency value determines whether to optimize the initial threshold of the monitored energy consumption. The specific determination method and optimization method are described in detail below.

[0071] S4: When the energy consumption data of the data center environment is abnormal, locate the current sub-module, mine the relationship between the current sub-module, generate a dataset for merging and labeling.

[0072] Specifically, in this embodiment of the invention, when the data acquisition module of the computer room environmental monitoring system is abnormal, the current abnormal sub-module is located. The source of the fault of the abnormal sub-module may be other sub-modules. Therefore, it is necessary to mine the correlation. This application uses log service to obtain the index data of the classification and matching correlation between the data collected by the sub-modules in the massive network information data to mine the correlation. The mining index includes the index of correlation with other data of the current sub-module and the index of correlation between sub-modules. The method of mining, generating datasets and merging labels is described in detail below.

[0073] S5: Calculate the similarity of the labeled data sets and set the initial threshold for energy consumption for the next monitoring.

[0074] Specifically, in this embodiment of the invention, the average similarity of the new data set with labels is calculated using a data diversity discrimination model, and the initial threshold for the next energy consumption collection alarm is set.

[0075] S6: Repeat steps S3 to S5 to determine the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold.

[0076] Specifically, in this embodiment of the invention, the higher the similarity, the lower the diversity in the dataset, and the more accurate the initial threshold for evaluation. The initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data set is the optimal threshold.

[0077] S7: Summarize the data center energy consumption data generated under the optimal storage configuration threshold, and compare the data center energy consumption data generated under the optimal threshold with the data center energy consumption data generated under the initial monitoring energy consumption threshold.

[0078] Specifically, in this embodiment of the invention, the energy consumption data of the computer room environment generated under the optimal threshold and the energy consumption data of the computer room environment generated under the initial threshold of monitoring energy consumption are compared to obtain the effect achieved by the energy saving and emission reduction data of the optimal threshold.

[0079] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the collection of computer room environment energy consumption data includes:

[0080] Data on energy consumption in the computer room environment is collected through energy-saving management modules for air conditioning, heat transfer, IT equipment, and power distribution systems deployed in the computer room.

[0081] The data collection method is program-based inspection data collection.

[0082] Specifically, in this embodiment of the invention, the subsystem name in the air conditioning energy-saving management module is air conditioning. It obtains the current period's consumption index through program inspection and calculates the previous period's air conditioning energy consumption index by analyzing historical energy consumption data. The subsystem names in the wind-moving heat energy-saving management module are chiller, humidifier, lighting and auxiliary equipment, and switch / generator. It obtains the temperature (°C) and humidity (RH) units of the sensors for each hardware device in the current period through program inspection and calculates the previous period's energy consumption index by analyzing historical energy consumption data. The subsystem names in the IT equipment energy-saving management module are CPU temperature data and process-level server load status. It obtains the current period's IT equipment CPU temperature (°C) data and process-level server load energy consumption data percentage (%) through program inspection and calculates the previous period's energy consumption index by analyzing historical IT equipment energy consumption data. The subsystem names in the power distribution system energy-saving management module are UPS and PDU. It obtains the current period's power distribution system energy consumption data in kilovolt-amperes (kVA) through program inspection and then calculates the previous period's energy consumption index by analyzing historical power distribution system energy consumption data.

[0083] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the initial threshold for monitoring energy consumption is optimized based on the difference between the energy usage efficiency value and the predicted energy usage efficiency value, including:

[0084] If the difference is less than or equal to the preset threshold, the initial threshold for monitoring energy consumption will not be optimized.

[0085] If the difference is greater than the preset threshold, optimize the initial threshold for monitoring energy consumption.

[0086] Specifically, in this embodiment of the invention, if the energy efficiency value (IT equipment or infrastructure) is 100% / 55% = 1.82, the predicted energy efficiency value is 100% / 61% = 1.63, the preset threshold is 10%, and 1.82-1.63>10%, the initial threshold for energy consumption monitoring is optimized.

[0087] Figure 2 This diagram illustrates a method for mining relationships within the current submodule to generate a dataset with merged labels, as provided in an embodiment of the present invention.

[0088] Furthermore, in the aforementioned method for dynamically adjusting the energy consumption of the computer room environment, the relationship mining of the current sub-module generates a dataset for merging and labeling, combined with... Figure 2 ,include:

[0089] S21: Extract data that is related to the current submodule;

[0090] S22: Hardware devices that mine data that is related to the current submodule;

[0091] S23: Merge the data that is associated with the current submodule and the hardware devices that are associated with the current submodule to obtain a data set;

[0092] S24: Labeled data set;

[0093] The dataset includes: a subset of alarm types, a subset of alarm quantities, a subset of false alarm quantities, and a subset of engineer evaluations.

[0094] Specifically, in this embodiment of the invention, the mining indicators corresponding to the data associated with the current sub-module include: indicators that are associated with other data in the current sub-module and indicators that are associated with each other in the sub-module; the hardware devices for mining the data associated with the current sub-module include: the CPU number of the device board associated with the current sub-module, the memory region ID, the disk region, the thread ID, and the virtual machine IP of the device associated with the current sub-module; the data associated with the current sub-module and the hardware devices associated with the data associated with the current sub-module are merged to obtain a dataset for merging and labeling.

[0095] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of a computer room environment, calculating the similarity of the labeled data sets includes:

[0096] The similarity of the labeled data sets is obtained by inputting the subsets of labeled alarm types, alarm quantities, false alarm quantities, and engineer evaluations into the data diversity identification model.

[0097] Specifically, in this embodiment of the invention, the similarity value of the labeled data set ranges from 0 to 1. The higher the average value of the subset, which measures IntraSimilarity, the higher the IntraSimilarity(L) value. u The lower the individual copy diversity, the more accurate the initial threshold for evaluation.

[0098]

[0099] Where similarity(i,j) represents the similarity metric, L u The similarity of a subset within the recommended dataset can be measured by the average of the average similarities of all related subsets in other recommended datasets. IntraSimilarity (Lu) represents the average similarity measure of a subset; i and j represent the indices of two distinct elements in the list, i.e., i and j are both position markers in list Lu, and i is not equal to j; IntraSimilarity represents the average internal similarity of the entire set of related subsets, which is the final result we want to calculate; n represents the number of related subsets.

[0100] Figure 3 This invention provides a method for dynamically adjusting the energy consumption of a computer room environment. Figure 2 .

[0101] Furthermore, in the aforementioned method for dynamically adjusting the energy consumption of the computer room environment, after determining the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold, and combining it with... Figure 3 It also includes three steps, S31 to S33:

[0102] S31: Calculate the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption;

[0103] S32: Determine whether the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption is within the preset range;

[0104] S33: If the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption is within a preset range, update the optimized initial threshold for monitoring energy consumption to the optimal threshold.

[0105] Specifically, in this embodiment of the invention, the difference between the optimal threshold obtained in step S6 and the optimized threshold in step S3 is calculated, and it is compared whether the difference between the optimal threshold obtained in step S6 and the optimized threshold in step S3 is within a preset range. The preset range is flexibly set according to the actual situation. If the difference is within the preset range, the optimized monitoring energy consumption initial threshold is updated to the optimal threshold; otherwise, the optimal threshold is used.

[0106] Furthermore, in the above-mentioned method for dynamically adjusting the energy consumption of the computer room environment, the formula for optimizing the initial threshold of monitoring energy consumption is:

[0107] Monitoring energy consumption initial threshold + [Energy efficiency value - predicted energy efficiency value - ((energy efficiency value / predicted energy efficiency value) maximized × 10%)].

[0108] For example: Energy efficiency value (for IT equipment or infrastructure) 100% / 55% = 1.82, predicted energy efficiency value 100% / 61% = 1.63.

[0109] Optimize the initial threshold for monitoring energy consumption = 1.5 + (1.82 - 1.63 - (1.82 × 10%))

[0110] The initial threshold for monitoring energy consumption is 1.5.

[0111] Device Examples

[0112] Figure 4 This is a diagram of a device for dynamically adjusting the energy consumption of a computer room environment, provided in an embodiment of the present invention.

[0113] A second aspect of the present invention also proposes a device for dynamically adjusting the energy consumption of a computer room environment, combined with... Figure 4 ,include:

[0114] Data Acquisition Module 41: Used to collect energy consumption data of the computer room environment and set the initial threshold for energy consumption in this monitoring.

[0115] Specifically, in this embodiment of the invention, a data center service management platform is deployed in the data center to manage the hosted equipment. The data center service management platform consists of a data center environmental data acquisition module and a network management and analysis module. The data center environmental data acquisition module is used to collect data on the energy consumption of the data center environment. The data center environmental data acquisition module includes four sub-modules: an air conditioning energy-saving management module, an air-movement heat energy-saving management module, an IT equipment energy-saving management module, and a power distribution system energy-saving management module. The data center environmental energy consumption data is collected through the acquisition module 41 of the four sub-modules, and the initial threshold for the energy consumption of this monitoring is set.

[0116] Generation module 42: Used to generate early warnings and visual reports based on data center energy consumption data.

[0117] Specifically, in this embodiment of the invention, the energy consumption prediction model generation module 42 generates an early warning and a visual report based on the data center environmental energy consumption data collected by the four sub-modules. The visual report includes: the names of the four sub-modules: air conditioning energy-saving management module, air-moving heat energy-saving management module, IT equipment energy-saving management module, and power distribution system energy-saving management module; the names of the subsystems corresponding to the air conditioning energy-saving management module, air-moving heat energy-saving management module, IT equipment energy-saving management module, and power distribution system energy-saving management module; and the actual energy consumption value for the current period, the average energy consumption of the last 10 times, and the AI ​​energy consumption prediction value for the next inspection for each subsystem.

[0118] Optimization Module 43: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, calculate the energy use efficiency value, the predicted energy use efficiency value, and the difference between the energy use efficiency value and the predicted energy use efficiency value, and optimize the initial threshold of the monitored energy consumption based on the difference between the energy use efficiency value and the predicted energy use efficiency value.

[0119] Specifically, in this embodiment of the invention, if the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, the energy use efficiency value is calculated based on the actual energy consumption value of the subsystem in this period, and the predicted energy use efficiency value is calculated based on the predicted energy use efficiency value of the subsystem in this period. For example, the energy use efficiency value (IT equipment or infrastructure) is 100% / 55% = 1.82, and the predicted energy use efficiency value is 100% / 61% = 1.63. The optimization module 43 determines whether to optimize the initial threshold of the monitored energy consumption based on the difference between the energy use efficiency value and the predicted energy use efficiency value.

[0120] Mining Module 44: When abnormal energy consumption data occurs in the computer room environment, it locates the current sub-module, mines the relationship between the current sub-module, and generates a dataset for merging and labeling.

[0121] Specifically, in this embodiment of the invention, when the data acquisition module of the computer room environmental monitoring system is abnormal, the current abnormal sub-module is located. The source of the fault of the abnormal sub-module may be other sub-modules. Therefore, module 44 needs to perform correlation mining. This application uses log service to obtain the index data of the classification and matching correlation of the data collected between sub-modules in the massive network information data to mine the correlation. The mining index includes the index of correlation with other data of the current sub-module and the index of correlation between sub-modules.

[0122] Calculation module 45: Used to calculate the similarity of the labeled data set and set the initial threshold for energy consumption for the next monitoring.

[0123] Specifically, in this embodiment of the invention, the calculation module 45 calculates the average similarity of the new data set with labels using a data diversity discrimination model, and sets the initial threshold for the next energy consumption collection alarm.

[0124] Determine Module 46: Used to repeatedly execute the optimization module, mining module, and calculation module to determine the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data set as the optimal threshold.

[0125] Specifically, in this embodiment of the invention, the higher the similarity, the lower the diversity in the dataset, and the more accurate the initial threshold for evaluation. The determining module 46 determines the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data set as the optimal threshold.

[0126] Comparison module 47: Used to summarize the data center environment energy consumption data generated under the optimal storage configuration threshold, and compare the data center environment energy consumption data generated under the optimal threshold with the data center environment energy consumption data generated under the initial monitoring energy consumption threshold.

[0127] Specifically, in this embodiment of the invention, the comparison module 47 compares the data center environmental energy consumption data generated under the optimal threshold with the data center environmental energy consumption data generated under the initial threshold of monitoring energy consumption to obtain the effect achieved by the optimal threshold energy saving and emission reduction data.

[0128] A third aspect of the present invention also provides an electronic device comprising: a processor and a memory;

[0129] The processor executes a method for dynamically adjusting the energy consumption of the computer room environment, such as one of the above methods, by calling programs or instructions stored in memory.

[0130] In a fourth aspect, the present invention also provides a computer-readable storage medium storing a program or instructions that cause a computer to perform a method for dynamically adjusting the energy consumption of a computer room environment as described in any of the preceding claims.

[0131] Figure 5 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention.

[0132] like Figure 5 As shown, the electronic device includes at least one processor 501, at least one memory 502, and at least one communication interface 503. The various components in the electronic device are coupled together via a bus system 504. The communication interface 503 is used for information transmission with external devices. It is understood that the bus system 504 is used to implement communication between these components. In addition to a data bus, the bus system 504 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 5 The general designated all buses as Bus System 504.

[0133] It is understood that the memory 502 in this embodiment can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0134] In some implementations, memory 502 stores elements such as executable units or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0135] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. The program implementing any method in the method for dynamically adjusting data center environment energy consumption provided in this embodiment of the invention can be included in the application programs.

[0136] In this embodiment of the invention, the processor 501 executes the steps of various embodiments of the method for dynamically adjusting the energy consumption of the computer room environment provided by the present invention by calling the program or instructions stored in the memory 502, specifically, the program or instructions stored in the application program.

[0137] S1: Collect energy consumption data of the computer room environment and set the initial energy consumption threshold for this monitoring.

[0138] S2: Provide early warnings and generate visual reports based on data center energy consumption data;

[0139] S3: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, calculate the energy use efficiency value, the predicted energy use efficiency value, and the difference between the energy use efficiency value and the predicted energy use efficiency value. Optimize the initial threshold of the monitored energy consumption based on the difference between the energy use efficiency value and the predicted energy use efficiency value.

[0140] S4: When an anomaly occurs in the data collection of the data center environment energy consumption, locate the current sub-module, perform correlation mining on the current sub-module, generate a dataset, and merge and label it;

[0141] S5: Calculate the similarity of the labeled data sets and set the initial threshold for energy consumption for the next monitoring.

[0142] S6: Repeat steps S3 to S5 to determine the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold.

[0143] S7: Summarize the data center energy consumption data generated under the optimal storage configuration threshold, and compare the data center energy consumption data generated under the optimal threshold with the data center energy consumption data generated under the initial monitoring energy consumption threshold.

[0144] Any of the methods in the method for dynamically adjusting the energy consumption of a computer room environment provided in this embodiment of the invention can be applied to, or implemented by, the processor 501. The processor 501 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuits in the hardware of the processor 501 or by instructions in software form. The processor 501 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0145] In any of the steps of the method for dynamically adjusting the energy consumption of a computer room environment provided in this embodiment of the invention, the steps can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software units in the decoding processor. The software units can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 502, and processor 501 reads the information in memory 502 and combines it with its hardware to complete the steps of the method.

[0146] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0147] Those skilled in the art will understand that the descriptions of the various embodiments have different focuses, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0148] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention. All such modifications and variations fall within the scope defined by the appended claims. The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0149] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamically adjusting the energy consumption of a computer room environment, characterized in that, It includes seven steps, S1 to S7: S1: Collect energy consumption data of the computer room environment and set the initial energy consumption threshold for this monitoring. S2: Provide early warnings and generate visual reports based on data center energy consumption data; S3: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, calculate the energy use efficiency value, the predicted energy use efficiency value, and the difference between the energy use efficiency value and the predicted energy use efficiency value. Optimize the initial threshold of the monitored energy consumption based on the difference between the energy use efficiency value and the predicted energy use efficiency value. S4: When the energy consumption data of the data center environment is abnormal, locate the current sub-module, mine the relationship between the current sub-module, generate a dataset for merging and labeling; S5: Calculate the similarity of the labeled data sets and set the initial threshold for energy consumption for the next monitoring. S6: Repeat steps S3 to S5 to determine the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold. S7: Summarize the data center environment energy consumption data generated under the optimal storage configuration threshold, and compare the data center environment energy consumption data generated under the optimal threshold with the data center environment energy consumption data generated under the initial monitoring energy consumption threshold. After determining the initial threshold for monitoring energy consumption corresponding to the highest similarity of the labeled data sets as the optimal threshold, the method further includes: Calculate the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption; Determine whether the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption is within a preset range; If the difference between the optimal threshold and the optimized initial threshold for monitoring energy consumption is within a preset range, the optimized initial threshold for monitoring energy consumption is updated to the optimal threshold. The formula for optimizing the initial threshold of energy consumption monitoring is: Monitoring energy consumption initial threshold + [Energy efficiency value - predicted energy efficiency value - ((energy efficiency value / predicted energy efficiency value) maximized × 10%)].

2. The method for dynamically adjusting the energy consumption of a computer room environment according to claim 1, characterized in that, The collected data on energy consumption in the computer room environment includes: Data on energy consumption in the computer room environment is collected through energy-saving management modules for air conditioning, heat transfer, IT equipment, and power distribution systems deployed in the computer room. The data collection method is program-based inspection data collection.

3. The method for dynamically adjusting the energy consumption of a computer room environment according to claim 1, characterized in that, The process of optimizing the initial threshold for energy consumption monitoring based on the difference between the energy efficiency value and the predicted energy efficiency value includes: If the difference is less than or equal to a preset threshold, the initial threshold for monitoring energy consumption will not be optimized. If the difference is greater than a preset threshold, optimize the initial threshold for monitoring energy consumption.

4. The method for dynamically adjusting the energy consumption of a computer room environment according to claim 1, characterized in that, Perform relationship mining on the current submodule to generate dataset merging labels, including: Discover data that is related to the current submodule; Hardware devices that can mine data that is related to the current submodule; The data set is obtained by merging the data that is related to the current submodule and the hardware devices that are related to the data in the current submodule; Label the data set; The dataset includes: a subset of alarm types, a subset of alarm quantities, a subset of false alarm quantities, and a subset of engineer evaluations.

5. The method for dynamically adjusting the energy consumption of a computer room environment according to claim 1, characterized in that, Calculate the similarity of labeled datasets, including: The similarity of the labeled data sets is obtained by inputting the subsets of labeled alarm types, alarm quantities, false alarm quantities, and engineer evaluations into the data diversity identification model.

6. A device for dynamically adjusting the energy consumption of a computer room environment, applied in the method described in any one of claims 1 to 5, characterized in that, include: Data Acquisition Module: Used to collect energy consumption data of the computer room environment and set the initial energy consumption threshold for this monitoring. Generation module: Used to generate early warnings and visual reports based on data center energy consumption data; Optimization module: If the actual energy consumption in the visualization report is higher than the historical average baseline and the predicted rise and fall probability trend is consistent, it calculates the energy efficiency value, the predicted energy efficiency value, and the difference between the energy efficiency value and the predicted energy efficiency value, and optimizes the initial threshold of the monitored energy consumption based on the difference between the energy efficiency value and the predicted energy efficiency value. Mining module: When abnormal energy consumption data occurs in the computer room environment, it locates the current sub-module, mines the relationship between the current sub-module, and generates a dataset for merging and labeling. Calculation module: Used to calculate the similarity of the labeled data sets and set the initial threshold for energy consumption in the next monitoring. Determining Module: Used to repeatedly execute the optimization module, mining module, and calculation module to determine the initial monitoring energy consumption threshold corresponding to the maximum similarity of the labeled data set as the optimal threshold; The comparison module is used to summarize the data center energy consumption data generated under the optimal storage configuration threshold and compare the data center energy consumption data generated under the optimal threshold with the data center energy consumption data generated under the initial monitoring energy consumption threshold.

7. An electronic device, characterized in that, include: Processor and memory; The processor executes a method for dynamically adjusting the energy consumption of a computer room environment as described in any one of claims 1 to 5 by calling programs or instructions stored in the memory.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause the computer to perform a method for dynamically adjusting the energy consumption of a computer room environment as described in any one of claims 1 to 5.

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