Machine room energy consumption management method and device

Abstract

The invention discloses a machine room energy consumption management method and device. The method comprises the following steps: acquiring first equipment energy consumption data of a plurality of pieces of equipment in a machine room in a first time period; converting the first equipment energy consumption data into a question set in a preset data format matched with the input of the energy consumption prediction model; analyzing the question set by using an energy consumption prediction model obtained by training based on historical equipment energy consumption data to obtain second equipment energy consumption data in a second time period, the second time period being a time period after the first time period; verifying the second equipment energy consumption data by using a preset energy consumption abnormity threshold condition, and if a verification result indicates that energy consumption abnormity exists, generating abnormity alarm information; and recording the first equipment energy consumption data, the second equipment energy consumption data and the verification result to an energy consumption data wide table corresponding to the machine room. The technical problem that energy consumption data of a large amount of machine room equipment in a machine room operation and maintenance scene is difficult to efficiently and accurately manage and control is solved.

Description

Technical Field

[0001] The present application relates to the technical field of computer room operation and maintenance, and in particular to a method and device for managing energy consumption in a computer room. Background Art

[0002] The monitoring of the energy consumption of the computer room is usually achieved through the computer room dynamic environment monitoring system, which can monitor the energy consumption of various equipment in the computer room in real time, including voltage, current, power and other factors, and provide energy consumption optimization suggestions through data analysis. In addition, the computer room dynamic environment monitoring system can also monitor the environmental parameters of the computer room, such as temperature and humidity, to ensure that these environmental factors will not affect the operating efficiency and life of the equipment, thereby indirectly affecting energy consumption. These problems not only increase the energy consumption of the computer room, but may also affect the normal operation and service life of the equipment in the computer room. Therefore, effective management and technical measures need to be taken to solve them. In the current field of computer room dynamic environment monitoring and energy consumption management, it relies on fixed threshold warning and preliminary energy consumption data analysis, but faces challenges such as insufficient energy consumption prediction accuracy, low data processing efficiency, superficial data value assessment, limitations of artificial intelligence algorithms, and weak data security and privacy protection. These technical bottlenecks limit the accurate identification and management optimization of energy consumption anomalies, especially in the complex and changeable operating environment of the data center, it is difficult to maximize the efficiency, thus affecting the energy utilization efficiency and the realization of energy conservation and emission reduction goals.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and device for managing energy consumption in a computer room, so as to at least solve the technical problem that it is difficult to efficiently and accurately manage the energy consumption data of a large number of computer room equipment in a computer room operation and maintenance scenario.

[0005] According to one aspect of an embodiment of the present application, a method for managing energy consumption in a computer room is provided, including: obtaining energy consumption data of a first device of multiple devices in a computer room within a first time period; converting the energy consumption data of the first device into a question set in a preset data format that matches the input of an energy consumption prediction model; analyzing the question set using the energy consumption prediction model to obtain energy consumption data of a second device within a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical device energy consumption data, and the second time period is a time period after the first time period; verifying the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generating abnormal alarm information when the verification result indicates that energy consumption abnormality exists; recording the energy consumption data of the first device, the energy consumption data of the second device and the verification result into a wide energy consumption data table corresponding to the computer room.

[0006] Optionally, the types of equipment in the computer room include: air-conditioning equipment, wind heat transfer equipment, information technology equipment and power distribution equipment, among which the types of wind heat transfer equipment include: chillers, humidifiers, lighting auxiliary equipment and switch devices, and the types of power distribution equipment include: uninterruptible power supplies, power distribution units; the equipment energy consumption data of the air-conditioning equipment includes at least: power consumption; the equipment energy consumption data of the wind heat transfer equipment includes at least: temperature and humidity data, power consumption; the equipment energy consumption data of the information technology equipment includes at least: central processing unit temperature, process-level server load; the equipment energy consumption data of the power distribution equipment includes at least: power consumption.

[0007] Optionally, converting the energy consumption data of the first device into a question set in a preset data format that matches the input of the energy consumption prediction model includes: performing data preprocessing on the energy consumption data of the first device, wherein the data preprocessing method includes at least one of the following: outlier deletion, null value interpolation, and data standardization processing; determining the first total energy consumption of each type of equipment based on the energy consumption data of the first device, and determining the sum of all first total energy consumptions as the second total energy consumption of the computer room; determining the ratio of the first total energy consumption of each type of equipment to the second total energy consumption as the first energy consumption proportion of the equipment, and determining the ratio of the second total energy consumption to the first total energy consumption corresponding to the information technology equipment as the first energy utilization efficiency of the computer room; generating question data of each type of equipment based on the first total energy consumption and the first energy consumption proportion of the equipment, and generating a question set based on all question data and the first energy utilization efficiency.

[0008] Optionally, the training process of the energy consumption prediction model includes: constructing an initial prediction model; obtaining multiple groups of historical equipment energy consumption data of multiple devices in the computer room in multiple continuous historical time periods, wherein one historical time period corresponds to a group of historical equipment energy consumption data; for each historical time period, determining the third total energy consumption and the second energy consumption ratio of each type of equipment, and the second energy utilization efficiency of the computer room based on the historical equipment energy consumption data corresponding to the historical time period, and forming the third total energy consumption, the second energy consumption ratio and the second energy utilization efficiency into a training sample, and using the third total energy consumption, the second energy consumption ratio and the second energy utilization efficiency corresponding to the next historical time period of the historical time period as sample labels of the training sample; iteratively training the initial prediction model based on multiple training samples and sample labels to obtain an energy consumption prediction model.

[0009] Optionally, the energy consumption data of the second device includes: the fourth total energy consumption and the proportion of the third energy consumption of various types of equipment in the computer room in the second time period in the future, and the third energy utilization efficiency of the computer room in the second time period in the future, and the energy consumption data of the second device is verified using a preset energy consumption abnormality threshold condition, including: comparing the third energy utilization efficiency with the preset efficiency threshold; when the third energy utilization efficiency is greater than the preset efficiency threshold, determining that there is an energy consumption abnormality in the computer room, and determining the energy consumption growth range of each type of equipment based on the fourth total energy consumption and the first total energy consumption, or the proportion of the third energy consumption and the first total energy consumption of each type of equipment; determining that equipment with an energy consumption growth rate greater than the preset range threshold has energy consumption abnormality.

[0010] Optionally, after generating the abnormal alarm information, the method also includes: obtaining inspection results for the energy consumption abnormal equipment; when the inspection results indicate that the energy consumption abnormal equipment is abnormal, using the energy consumption data of the first equipment as a positive sample to update the energy consumption prediction model; when the inspection results indicate that the energy consumption abnormal equipment is not abnormal, adjusting the preset amplitude threshold, and using the energy consumption data of the first equipment as a negative sample to update the energy consumption prediction model.

[0011] Optionally, the wide table of energy consumption data is used to record the energy consumption information of each device in the computer room, wherein each device corresponds to at least the following fields: device identification, device type, device installation location, time period identification, energy consumption data of the first device in the corresponding time period, question set, energy consumption data of the second device, energy consumption abnormal threshold conditions, verification results, and abnormal inspection results.

[0012] Optionally, in response to a data query instruction of the target object, a target field matching a keyword in the data query instruction is retrieved from the energy consumption data wide table, and a target device corresponding to the target field is determined; and energy consumption information of the target device is fed back to the target object.

[0013] Optionally, the method also includes: scoring the energy consumption data of the first device from multiple dimensions to obtain a sub-score for each dimension, wherein the multiple dimensions include at least: data integrity dimension, data standardization dimension, data accuracy dimension, data timeliness dimension, data richness dimension, and data coverage dimension; determining a weight coefficient for each dimension, and weighted summing the sub-scores of each dimension based on the weight coefficient to obtain a comprehensive score for the energy consumption data of the first device; and recording the comprehensive score in a wide table of energy consumption data.

[0014] According to another aspect of the embodiment of the present application, a computer room energy consumption management device is also provided, including: an acquisition module, used to acquire energy consumption data of a first device of multiple devices in the computer room within a current first time period; a conversion module, used to convert the energy consumption data of the first device into a question set in a preset data format that matches the input of an energy consumption prediction model; a prediction module, used to analyze the question set using the energy consumption prediction model to obtain energy consumption data of a second device within a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical device energy consumption data, and the second time period is a time period after the first time period; an analysis module, used to verify the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generate abnormal alarm information when the verification result indicates that there is an energy consumption abnormality; a statistical module, used to record the energy consumption data of the first device, the energy consumption data of the second device and the verification result into the energy consumption data wide table corresponding to the computer room.

[0015] According to another aspect of an embodiment of the present application, a computer program product is further provided, the computer program product comprising: a computer program, wherein when the computer program is executed by a processor, the above-mentioned method for managing energy consumption in a computer room is implemented.

[0016] According to another aspect of an embodiment of the present application, an electronic device is further provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned computer room energy consumption management method through the computer program.

[0017] In the embodiment of the present application, the energy consumption data of the equipment is obtained in real time, including key indicators such as temperature, humidity, and power, and the energy consumption data of the first equipment is converted into a question set format that can be understood by the energy consumption prediction model. This step ensures the standardization and structuring of the model input data, which is conducive to improving the prediction accuracy of the model; the energy consumption prediction model trained based on the historical equipment energy consumption data can predict the energy consumption data of the second equipment in the second time period in the future. This prediction function provides forward-looking decision support for computer room management, allowing managers to plan energy use in advance and optimize equipment operation strategies, thereby achieving the purpose of energy conservation and emission reduction; the predicted energy consumption data of the second equipment is verified using the preset energy consumption abnormality threshold condition, It can identify energy consumption anomalies in a timely manner. Once energy consumption anomalies are detected, the system will automatically generate abnormal alarm information and notify relevant personnel to handle it; the energy consumption data of the first device, the energy consumption data of the second device and the verification results of the anomaly detection are recorded in the wide table of energy consumption data corresponding to the computer room. This not only facilitates long-term storage and historical comparison of data, but also provides a basis for subsequent in-depth data analysis. Through the wide table, energy consumption data can be analyzed in multiple dimensions, including but not limited to equipment health status analysis, energy consumption trend prediction, fault mode identification, etc., to further optimize the energy management and equipment maintenance of the computer room, thereby solving the technical problem of difficult to efficiently and accurately manage the energy consumption data of a large number of computer room equipment in the computer room operation and maintenance scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a flow chart of an optional method for managing energy consumption in a computer room according to an embodiment of the present application;

[0020] Figure 2 is a structural schematic diagram of an optional computer room energy consumption management device according to an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] In order to better understand the embodiments of the present application, some nouns or terms that appear in the description of the embodiments of the present application are first translated and explained as follows:

[0025] Query Set: It is the energy consumption data of the first device after preprocessing and conversion. It is formatted into a structure that matches the input of the energy consumption prediction model. Specifically, the query set is a series of feature vectors or data points constructed to enable the model to understand and process. Each query data point usually contains information in multiple dimensions, which reflects the energy consumption of various types of equipment in the computer room and its related attributes within a specific time period.

[0026] The information collected in the embodiments of the present application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0027] Example 1

[0028] According to an embodiment of the present application, a method for managing energy consumption in a computer room is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 is a flow chart of a method for managing energy consumption in a computer room provided according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0030] Step S102, obtaining energy consumption data of a first device of a plurality of devices in a computer room within a first time period;

[0031] Step S104, converting the energy consumption data of the first device into a question set in a preset data format that matches the input of the energy consumption prediction model;

[0032] Step S106, analyzing the question set using the energy consumption prediction model to obtain energy consumption data of a second device in a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical device energy consumption data, and the second time period is a time period after the first time period;

[0033] Step S108, verifying the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generating abnormal alarm information when the verification result indicates that energy consumption is abnormal;

[0034] Step S110, recording the energy consumption data of the first device, the energy consumption data of the second device and the verification result into the energy consumption data wide table corresponding to the computer room.

[0035] The following describes the various steps of the computer room energy consumption management method in conjunction with the specific implementation process.

[0036] Obtain energy consumption data of a first device of a plurality of devices in a computer room within a first time period;

[0037] As an optional implementation, the types of equipment in the computer room include: air-conditioning equipment, wind heat transfer equipment, information technology equipment and power distribution equipment, among which the types of wind heat transfer equipment include: chillers, humidifiers, lighting auxiliary equipment and switch devices, and the types of power distribution equipment include: uninterruptible power supplies, power distribution units; the equipment energy consumption data of air-conditioning equipment includes at least: power consumption; the equipment energy consumption data of wind heat transfer equipment includes at least: temperature and humidity data, power consumption; the equipment energy consumption data of information technology equipment includes at least: central processing unit temperature, process-level server load; the equipment energy consumption data of power distribution equipment includes at least: power consumption. The data collection of each device is to understand its operating status and energy usage, and to provide basic information for subsequent energy consumption prediction and optimization.

[0038] For the collection of energy consumption data of the first device, corresponding sensors can be deployed on these devices, such as power sensors to monitor power consumption, temperature and humidity sensors to monitor environmental conditions, CPU temperature sensors and server load monitoring tools to evaluate the operating status of the equipment. Within the set time period (first time period), data is automatically collected regularly through the deployed sensors. For air-conditioning equipment, the main information on its power consumption can be collected. For wind-transfer heat equipment, its temperature and humidity data and power consumption can be collected. For information technology equipment, the temperature of the CPU and the load of the process-level server can be collected. For power distribution equipment, its power consumption can be collected.

[0039] After obtaining the energy consumption data of the first device, the energy consumption data of the first device is converted into a question set in a preset data format that matches the input of the energy consumption prediction model. The process may take the following steps:

[0040] Performing data preprocessing on the energy consumption data of the first device, wherein the data preprocessing method includes at least one of the following: outlier deletion (such as statistical outliers), null value interpolation (such as using a specific algorithm or value to fill missing data), and data standardization (such as scaling the data to the same range to facilitate model processing);

[0041] The first total energy consumption of each type of equipment is determined according to the first equipment energy consumption data, and the sum of all the first total energy consumptions is determined as the second total energy consumption of the computer room. It should be noted that each type of equipment mentioned here is not necessarily limited to a large category, but can be further subdivided according to actual needs and analysis purposes. For example, wind heat transfer equipment can be regarded as a large category, but its internal chillers, humidifiers, etc. can be regarded as finer subcategories;

[0042] Determine the ratio of the first total energy consumption to the second total energy consumption of each type of equipment as the first energy consumption ratio of the equipment, and determine the ratio of the second total energy consumption to the first total energy consumption corresponding to the information technology equipment as the first energy usage efficiency (i.e., PUE (Power Usage Effectiveness)) of the computer room;

[0043] The question data of each type of equipment is generated based on the first total energy consumption and the first energy consumption ratio of the equipment, and a question set is generated based on all the question data and the first energy use efficiency. The question set is an input format specially designed for the energy consumption prediction model. It can be understood that the question data is constructed for each type of equipment. These data may include information such as equipment type, time period, total energy consumption and energy consumption ratio. The question data will be summarized into a question set. The structure and format of the question set need to match the input of the energy consumption prediction model to ensure that the model can correctly parse these question data.

[0044] After obtaining the question set, the energy consumption prediction model is used to analyze the question set to obtain the energy consumption data of the second device in the second time period output by the energy consumption prediction model. The second time period is the time period after the first time period. The energy consumption prediction model is trained based on historical equipment energy consumption data. It can predict the energy consumption of various types of equipment in a certain time period in the future based on the question set, that is, predict the total energy consumption of each device in the future, the energy consumption proportion and the overall PUE of the computer room.

[0045] As an optional implementation, the training process of the energy consumption prediction model may include the following steps:

[0046] S1, build the initial prediction model;

[0047] The initial prediction model can be a model suitable for processing time series data, such as LSTM (Long Short-Term Memory) and GRU (Gate Recurrent Unit). It can effectively capture the long-term dependencies in the time series and is very suitable for predicting the energy consumption of equipment in the computer room.

[0048] S2, obtaining multiple groups of historical equipment energy consumption data of multiple equipment in the computer room in multiple continuous historical time periods, wherein one historical time period corresponds to one group of historical equipment energy consumption data;

[0049] S3, for each historical time period, determine the third total energy consumption and the second energy consumption ratio of each type of equipment and the second energy use efficiency of the computer room according to the historical equipment energy consumption data corresponding to the historical time period, and form a training sample with the third total energy consumption, the second energy consumption ratio and the second energy use efficiency, and use the third total energy consumption, the second energy consumption ratio and the second energy use efficiency corresponding to the next historical time period of the historical time period as the sample label of the training sample;

[0050] S4, iteratively training the initial prediction model based on multiple training samples and sample labels to obtain an energy consumption prediction model.

[0051] After the energy consumption data of the second device is obtained, the energy consumption data of the second device is verified using a preset energy consumption abnormality threshold condition, and when the verification result indicates that energy consumption is abnormal, abnormal alarm information is generated.

[0052] Among them, as an optional implementation method, the predicted result, i.e., the second equipment energy consumption data, includes: the fourth total energy consumption and third energy consumption proportion of various equipment in the computer room in the future second time period, and the third energy utilization efficiency of the computer room in the future second time period.

[0053] Specifically, as an optional implementation, the energy consumption data of the second device is verified using a preset energy consumption abnormality threshold condition, including: comparing the third energy use efficiency with the preset efficiency threshold, where the preset efficiency threshold can be determined based on the average value of the historical data of the average energy consumption level of each type of equipment under normal operating conditions, or can be determined by other means. When the third energy use efficiency is greater than the preset efficiency threshold, this may indicate that the energy use efficiency of the computer room is lower than expected, and it is determined that there is an energy consumption abnormality in the computer room. And based on the fourth total energy consumption and the first total energy consumption of each type of equipment, or the third energy consumption ratio and the first total energy consumption of each type of equipment, the energy consumption growth rate of the type of equipment is determined, and it is determined that the equipment with an energy consumption growth rate greater than the preset amplitude threshold has an energy consumption abnormality. That is to say, if the predicted energy consumption of a certain type of equipment has increased significantly compared to its historical average energy consumption, and the growth rate exceeds the preset amplitude threshold, this may also indicate that the equipment is abnormal.

[0054] If an abnormality is detected, an abnormal alarm message will be generated. After receiving the alarm, the maintenance personnel will inspect the corresponding equipment to check whether there is an actual fault, or perform other inspections.

[0055] As an optional implementation, the inspection results can be further used to update the energy consumption prediction model. The specific process is as follows: obtain the inspection results for the energy consumption abnormal equipment; when the inspection results indicate that the energy consumption abnormal equipment is abnormal, use the energy consumption data of the first device as a positive sample to update the energy consumption prediction model to improve the diagnostic accuracy of the model in similar situations; when the inspection results indicate that the energy consumption abnormal equipment is not abnormal, adjust the preset amplitude threshold, and use the energy consumption data of the first device as a negative sample to update the energy consumption prediction model. This method helps to adjust the prediction threshold of the model and avoid false alarms in the future.

[0056] The energy consumption data of the first device, the energy consumption data of the second device and the verification result obtained above are recorded in the energy consumption data wide table corresponding to the computer room.

[0057] Among them, the wide table of energy consumption data is a table containing multiple fields, which is used to record the energy consumption information of each device in the computer room, wherein each device corresponds to at least the following fields: device identification, device type, equipment installation location, time period identification, energy consumption data of the first device in the corresponding time period, question set, energy consumption data of the second device, energy consumption abnormal threshold conditions, verification results, abnormal inspection results. It should be noted that the data calculated for a type of equipment is shared by this type of equipment.

[0058] In addition to the above steps, further consideration is given to data quality assessment and data query optimization, including: scoring the energy consumption data of the first device from multiple dimensions to obtain sub-scores for each dimension, wherein the multiple dimensions include at least: data integrity dimension. If the data is complete and without missing data, the sub-score for this dimension may be full marks, otherwise points will be deducted based on the degree of missing data. Among them, in the dimension of data standardization, such as whether the temperature data is in degrees Celsius (℃), whether the energy consumption data is in kilowatt-hours (kWh), etc., data that does not meet the standards will be deducted points; in the dimension of data accuracy, such as whether the energy consumption data is reasonable, whether it matches the rated power of the equipment, whether the temperature reading is consistent with the external conditions, etc., inaccurate data will receive a lower score; in the dimension of data timeliness, such as whether it is within the most recent monitoring cycle, outdated data will be given a lower score to reflect its reduced reference value for the current situation; in the dimension of data richness, such as high load, low load, different ambient temperatures, etc., the score will be high if the data coverage is wide, otherwise it will be low; in the dimension of data coverage, check whether the data contains the energy consumption information of all monitored equipment to ensure that nothing is missed, and the score of this dimension will be high if the data coverage is complete; determine the weight coefficient of each dimension, and weighted sum the sub-scores of each dimension according to the weight coefficient to obtain the comprehensive score of the energy consumption data of the first device; record the comprehensive score in the wide table of energy consumption data.

[0059] When the target object needs to query the data, it can also respond to the data query instruction of the target object, retrieve the target field that matches the keyword in the data query instruction from the energy consumption data wide table, and determine the target device corresponding to the target field; and feedback the energy consumption information of the target device to the target object. For example, if the query instruction requires viewing the energy consumption data of all air-conditioning equipment in a specific time period, the system will locate all records containing air conditioners and the specified time period, extract the corresponding specific equipment information (such as equipment ID, type, location, etc.) from the retrieved target field, determine the target device, and return the energy consumption information of the target device to the target object that initiated the query, including the total energy consumption of the equipment during the query time period, abnormal status (if any), and possible PUE values ​​and other key indicators.

[0060] In the embodiment of the present application, the energy consumption data of the equipment is obtained in real time, including key indicators such as temperature, humidity, and power, and the energy consumption data of the first equipment is converted into a question set format that can be understood by the energy consumption prediction model. This step ensures the standardization and structuring of the model input data, which is conducive to improving the prediction accuracy of the model; the energy consumption prediction model trained based on the historical equipment energy consumption data can predict the energy consumption data of the second equipment in the second time period in the future. This prediction function provides forward-looking decision support for computer room management, allowing managers to plan energy use in advance and optimize equipment operation strategies, thereby achieving the purpose of energy conservation and emission reduction; the predicted energy consumption data of the second equipment is verified using the preset energy consumption abnormality threshold condition, It can identify energy consumption anomalies in a timely manner. Once energy consumption anomalies are detected, the system will automatically generate abnormal alarm information and notify relevant personnel to handle it; the energy consumption data of the first device, the energy consumption data of the second device and the verification results of the anomaly detection are recorded in the wide table of energy consumption data corresponding to the computer room. This not only facilitates long-term storage and historical comparison of data, but also provides a basis for subsequent in-depth data analysis. Through the wide table, energy consumption data can be analyzed in multiple dimensions, including but not limited to equipment health status analysis, energy consumption trend prediction, fault mode identification, etc., to further optimize the energy management and equipment maintenance of the computer room, thereby solving the technical problem of difficult to efficiently and accurately manage the energy consumption data of a large number of computer room equipment in the computer room operation and maintenance scenario.

[0061] Example 2

[0062] According to an embodiment of the present application, a computer room energy consumption management device for implementing the computer room energy consumption management method in embodiment 1 is also provided. Figure 2 As shown, the energy consumption management device for a computer room includes at least: an acquisition module 21, a conversion module 22, a prediction module 23, an analysis module 24, and a statistical module 25, wherein:

[0063] An acquisition module 21 is used to acquire energy consumption data of a first device of a plurality of devices in a computer room in a current first time period;

[0064] A conversion module 22, configured to convert the energy consumption data of the first device into a set of questions in a preset data format that matches the input of the energy consumption prediction model;

[0065] A prediction module 23 is used to analyze the question set using an energy consumption prediction model to obtain energy consumption data of a second device within a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical device energy consumption data, and the second time period is a time period after the first time period;

[0066] The analysis module 24 is used to verify the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generate abnormal alarm information when the verification result indicates that there is energy consumption abnormality;

[0067] The statistical module 25 is used to record the energy consumption data of the first device, the energy consumption data of the second device and the verification result into the energy consumption data wide table corresponding to the computer room.

[0068] The functions of each module of the computer room energy consumption management device are explained below in conjunction with a specific implementation process.

[0069] The acquisition module acquires energy consumption data of a first device of multiple devices in the computer room within a first time period;

[0070] As an optional implementation, the types of equipment in the computer room include: air-conditioning equipment, wind heat transfer equipment, information technology equipment and power distribution equipment, among which the types of wind heat transfer equipment include: chillers, humidifiers, lighting auxiliary equipment and switch devices, and the types of power distribution equipment include: uninterruptible power supplies, power distribution units; the equipment energy consumption data of the air-conditioning equipment includes at least: power consumption; the equipment energy consumption data of the wind heat transfer equipment includes at least: temperature and humidity data, power consumption; the equipment energy consumption data of the information technology equipment includes at least: central processing unit temperature, process-level server load; the equipment energy consumption data of the power distribution equipment includes at least: power consumption.

[0071] After obtaining the energy consumption data of the first device, the conversion module converts the energy consumption data of the first device into a set of questions in a preset data format that matches the input of the energy consumption prediction model. The process may take the following steps:

[0072] Data preprocessing is performed on the energy consumption data of the first device, wherein the data preprocessing method includes at least one of the following: outlier deletion, null value interpolation, and data standardization processing; the first total energy consumption of each type of equipment is determined according to the energy consumption data of the first device, and the sum of all first total energy consumptions is determined as the second total energy consumption of the computer room; the ratio of the first total energy consumption to the second total energy consumption of each type of equipment is determined as the first energy consumption proportion of the equipment, and the ratio of the second total energy consumption to the first total energy consumption corresponding to the information technology equipment is determined as the first energy utilization efficiency of the computer room; the question data of each type of equipment is generated according to the first total energy consumption and the first energy consumption proportion of the equipment, and a question set is generated according to all the question data and the first energy utilization efficiency.

[0073] After obtaining the question set, the prediction module uses the energy consumption prediction model to analyze the question set to obtain the second device energy consumption data within the second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical device energy consumption data, and the second time period is the time period after the first time period.

[0074] As an optional implementation, the training process of the energy consumption prediction model may include the following steps:

[0075] S1, build the initial prediction model;

[0076] S2, obtaining multiple groups of historical equipment energy consumption data of multiple equipment in the computer room in multiple continuous historical time periods, wherein one historical time period corresponds to one group of historical equipment energy consumption data;

[0077] S3, for each historical time period, determine the third total energy consumption and the second energy consumption ratio of each type of equipment and the second energy use efficiency of the computer room according to the historical equipment energy consumption data corresponding to the historical time period, and form a training sample with the third total energy consumption, the second energy consumption ratio and the second energy use efficiency, and use the third total energy consumption, the second energy consumption ratio and the second energy use efficiency corresponding to the next historical time period of the historical time period as the sample label of the training sample;

[0078] S4, iteratively training the initial prediction model based on multiple training samples and sample labels to obtain an energy consumption prediction model.

[0079] After obtaining the energy consumption data of the second device, the analysis module verifies the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generates abnormal alarm information when the verification result indicates that there is energy consumption abnormality.

[0080] Among them, as an optional implementation method, the energy consumption data of the second equipment includes: the fourth total energy consumption and the proportion of the third energy consumption of various types of equipment in the computer room in the future second time period, and the third energy utilization efficiency of the computer room in the future second time period. The second equipment energy consumption data is verified using a preset energy consumption abnormality threshold condition, including: comparing the third energy utilization efficiency with the preset efficiency threshold; when the third energy utilization efficiency is greater than the preset efficiency threshold, determining that there is an energy consumption abnormality in the computer room, and determining the energy consumption growth range of each type of equipment based on the fourth total energy consumption and the first total energy consumption, or the proportion of the third energy consumption and the first total energy consumption of each type of equipment; determining that equipment with an energy consumption growth rate greater than the preset range threshold has energy consumption abnormality.

[0081] As an optional implementation, after generating the abnormal alarm information, the following process can also be performed: obtaining the inspection results for the energy consumption abnormal equipment; when the inspection results indicate that the energy consumption abnormal equipment is abnormal, using the energy consumption data of the first equipment as a positive sample to update the energy consumption prediction model; when the inspection results indicate that the energy consumption abnormal equipment is not abnormal, adjusting the preset amplitude threshold, and using the energy consumption data of the first equipment as a negative sample to update the energy consumption prediction model.

[0082] The statistical module records the energy consumption data of the first device, the energy consumption data of the second device and the verification result obtained above into the energy consumption data wide table corresponding to the computer room.

[0083] Among them, the wide table of energy consumption data is used to record the energy consumption information of each device in the computer room, wherein each device corresponds to at least the following fields: device identification, device type, device installation location, time period identification, energy consumption data of the first device in the corresponding time period, question set, energy consumption data of the second device, energy consumption abnormal threshold conditions, verification results, and abnormal inspection results.

[0084] Optionally, the method also includes: scoring the energy consumption data of the first device from multiple dimensions to obtain a sub-score for each dimension, wherein the multiple dimensions include at least: data integrity dimension, data standardization dimension, data accuracy dimension, data timeliness dimension, data richness dimension, and data coverage dimension; determining a weight coefficient for each dimension, and weighted summing the sub-scores of each dimension based on the weight coefficient to obtain a comprehensive score for the energy consumption data of the first device; and recording the comprehensive score in a wide table of energy consumption data.

[0085] In response to a data query instruction of a target object, a target field matching a keyword in the data query instruction is retrieved from an energy consumption data wide table, and a target device corresponding to the target field is determined; and energy consumption information of the target device is fed back to the target object.

[0086] It should be noted that each module in the computer room energy consumption management device in the embodiment of the present application corresponds one by one to each implementation step of the computer room energy consumption management method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be repeated here.

[0087] Example 3

[0088] According to an embodiment of the present application, a computer program product is also provided. The computer program product includes a computer program, wherein when the computer program is executed by a processor, the computer room energy consumption management method in Example 1 is implemented.

[0089] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the computer room energy consumption management method in Example 1 by running the computer program.

[0090] According to an embodiment of the present application, a processor is also provided, which is used to run a computer program, wherein the computer room energy consumption management method in Example 1 is executed when the computer program is running.

[0091] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the computer room energy consumption management method in Example 1 through the computer program.

[0092] Specifically, when the computer program is running, the following steps are executed: obtaining energy consumption data of a first device in a first time period of multiple devices in a computer room; converting the energy consumption data of the first device into a question set in a preset data format that matches the input of an energy consumption prediction model; analyzing the question set using the energy consumption prediction model to obtain energy consumption data of a second device in a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical device energy consumption data, and the second time period is a time period after the first time period; verifying the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generating abnormal alarm information when the verification result indicates that there is an energy consumption abnormality; recording the energy consumption data of the first device, the energy consumption data of the second device and the verification result into the energy consumption data wide table corresponding to the computer room.

[0093] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3 The hardware structure block diagram of an electronic device for implementing the energy consumption management method of a computer room is shown. Figure 3 As shown, the electronic device 30 may include one or more (302a, 302b, ..., 302n are used to illustrate) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown.

[0094] It should be noted that the one or more processors 302 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the electronic device 30. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0095] The memory 304 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the computer room energy consumption management method in the embodiment of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, that is, realizing the vulnerability detection method of the above-mentioned application program. The memory 304 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include a memory remotely arranged relative to the processor 302, and these remote memories may be connected to the electronic device 30 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0096] The transmission device 306 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 30. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0097] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 30 .

[0098] The serial numbers of the above embodiments are only for description and do not represent the advantages or disadvantages of the embodiments.

[0099] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0103] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0104] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for managing energy consumption in a computer room, characterized in that: include: Obtain energy consumption data of a first device of a plurality of devices in a computer room within a first time period; Converting the energy consumption data of the first device into a set of questions in a preset data format that matches the input of the energy consumption prediction model; Analyzing the question set using the energy consumption prediction model to obtain second equipment energy consumption data within a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical equipment energy consumption data, and the second time period is a time period after the first time period; Verifying the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generating abnormal alarm information when the verification result indicates that energy consumption is abnormal; The energy consumption data of the first device, the energy consumption data of the second device and the verification result are recorded in the energy consumption data wide table corresponding to the computer room.

2. The method according to claim 1, characterized in that The types of equipment in the computer room include: air conditioning equipment, wind heat transfer equipment, information technology equipment and power distribution equipment, wherein the types of wind heat transfer equipment include: chillers, humidifiers, lighting auxiliary equipment and switch devices, and the types of power distribution equipment include: uninterruptible power supply, power distribution unit; The equipment energy consumption data of the air-conditioning equipment at least includes: power consumption; The equipment energy consumption data of the wind heat transfer equipment at least includes: temperature and humidity data, power consumption; The equipment energy consumption data of the information technology equipment includes at least: CPU temperature, process-level server load; The equipment energy consumption data of the power distribution equipment at least includes: power consumption.

3. The method according to claim 1, characterized in that The energy consumption data of the first device is converted into a set of questions in a preset data format that matches the input of the energy consumption prediction model, including: Performing data preprocessing on the energy consumption data of the first device, wherein the data preprocessing method includes at least one of the following: outlier deletion, null value interpolation, and data standardization processing; Determine the first total energy consumption of each type of equipment according to the first equipment energy consumption data, and determine the sum of all the first total energy consumptions as the second total energy consumption of the computer room; Determine respectively the ratio of the first total energy consumption of each type of equipment to the second total energy consumption as the first energy consumption ratio of the type of equipment, and determine the ratio of the second total energy consumption to the first total energy consumption corresponding to the information technology equipment as the first energy efficiency of the computer room; The question data of each type of equipment is generated according to the first total energy consumption and the first energy consumption ratio of the equipment, and the question set is generated according to all the question data and the first energy use efficiency.

4. The method according to claim 3, characterized in that The training process of the energy consumption prediction model includes: Build an initial prediction model; Acquire multiple groups of historical equipment energy consumption data of multiple equipment in the computer room within multiple continuous historical time periods, wherein one historical time period corresponds to one group of historical equipment energy consumption data; For each historical time period, determine the third total energy consumption and the second energy consumption ratio of each type of equipment, and the second energy use efficiency of the computer room according to the historical equipment energy consumption data corresponding to the historical time period, form a training sample with the third total energy consumption, the second energy consumption ratio and the second energy use efficiency, and use the third total energy consumption, the second energy consumption ratio and the second energy use efficiency corresponding to the next historical time period of the historical time period as sample labels of the training sample; The initial prediction model is iteratively trained according to a plurality of the training samples and sample labels to obtain the energy consumption prediction model.

5. The method according to claim 3, characterized in that: The second equipment energy consumption data includes: the fourth total energy consumption and the third energy consumption ratio of various types of equipment in the computer room in the future second time period, and the third energy use efficiency of the computer room in the future second time period. The second equipment energy consumption data is verified using a preset energy consumption abnormality threshold condition, including: comparing the third energy usage efficiency with a preset efficiency threshold; When the third energy efficiency is greater than the preset efficiency threshold, it is determined that the equipment room has abnormal energy consumption, and the energy consumption growth rate of each type of equipment is determined based on the fourth total energy consumption and the first total energy consumption of each type of equipment, or the third energy consumption ratio of each type of equipment and the first total energy consumption; It is determined that the device whose energy consumption growth rate is greater than the preset threshold value has abnormal energy consumption.

6. The method according to claim 5, characterized in that After generating the abnormal warning information, the method further includes: Obtain inspection results for equipment with abnormal energy consumption; When the inspection result indicates that the abnormal energy consumption device is abnormal, the energy consumption data of the first device is used as a positive sample to update the energy consumption prediction model; When the inspection result indicates that the abnormal energy consumption device does not have an abnormality, the preset amplitude threshold is adjusted, and the energy consumption data of the first device is used as a negative sample to update the energy consumption prediction model.

7. The method according to claim 1, characterized in that The wide table of energy consumption data is used to record the energy consumption information of each device in the computer room, wherein each device corresponds to at least the following fields: device identification, device type, device installation location, time period identification, energy consumption data of the first device in the corresponding time period, the question set, energy consumption data of the second device, the energy consumption abnormality threshold condition, the verification result, and abnormal inspection result.

8. The method according to claim 7, characterized in that The method further comprises: In response to a data query instruction of a target object, a target field matching a keyword in the data query instruction is retrieved from the energy consumption data wide table, and a target device corresponding to the target field is determined; Feedback the energy consumption information of the target device to the target object.

9. The method according to claim 7, characterized in that: The method further comprises: Scoring the energy consumption data of the first device from multiple dimensions to obtain sub-scores of each dimension, wherein the multiple dimensions include at least: data integrity dimension, data standardization dimension, data accuracy dimension, data timeliness dimension, data richness dimension, and data coverage dimension; Determine a weight coefficient for each dimension, and perform weighted summation of the sub-scores of each dimension according to the weight coefficient to obtain a comprehensive score for the energy consumption data of the first device; The comprehensive score is recorded in the energy consumption data wide table.

10. A device for managing energy consumption in a computer room, characterized in that: include: An acquisition module, used to acquire energy consumption data of a first device of a plurality of devices in a computer room in a current first time period; A conversion module, used to convert the energy consumption data of the first device into a set of questions in a preset data format that matches the input of the energy consumption prediction model; A prediction module, used to analyze the question set using the energy consumption prediction model to obtain second equipment energy consumption data within a second time period output by the energy consumption prediction model, wherein the energy consumption prediction model is trained based on historical equipment energy consumption data, and the second time period is a time period after the first time period; An analysis module, configured to verify the energy consumption data of the second device using a preset energy consumption abnormality threshold condition, and generate abnormal alarm information when the verification result indicates that energy consumption is abnormal; A statistical module is used to record the energy consumption data of the first device, the energy consumption data of the second device and the verification result into a wide energy consumption data table corresponding to the computer room.

11. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the method for managing energy consumption of a computer room as claimed in any one of claims 1 to 9 is implemented.

12. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer room energy consumption management method according to any one of claims 1 to 9 through the computer program.

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