CN118550392A - Method and system for adjusting energy consumption of machine room based on AI - Google Patents
Method and system for adjusting energy consumption of machine room based on AI Download PDFInfo
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Abstract
The invention relates to the field of machine room energy consumption regulation, in particular to a method and a system for regulating machine room energy consumption based on AI, wherein the method comprises the steps of acquiring energy consumption data and environment data of the machine room in real time, wherein the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment; inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data; the predicted energy consumption data is identified and analyzed through an AI algorithm to obtain a load energy consumption reason, and system equipment adjusting parameters and/or environment equipment adjusting parameters are generated according to the load energy consumption reason; the system device and/or the environmental device are adjusted based on the system device adjustment parameter and/or the environmental device adjustment parameter. Corresponding adjusting parameters are generated according to different load energy consumption reasons, and equipment of a machine room is automatically adjusted according to the adjusting parameters, so that resource consumption and pollutant emission caused by load are reduced.
Description
Technical Field
The application relates to the field of machine room energy consumption adjustment, in particular to a method and a system for adjusting machine room energy consumption based on AI.
Background
Machine rooms are commonly referred to as telecommunications, network access, mobility, two-wire, electricity, government or business, etc., where servers are stored to provide IT services to users and employees. At present, with the continuous expansion of the scale of a data center and the increase of energy consumption, how to effectively reduce the energy consumption of a machine room becomes an important problem.
Patent literature (CN 116185757 a) discloses an intelligent monitoring system for machine room energy consumption, which is characterized by comprising a data acquisition end, a monitoring center and a display terminal; the monitoring center comprises a time period energy consumption analysis unit, an early warning time period analysis unit, a machine room parameter analysis unit, a threshold value unit and a database; the data acquisition end is used for acquiring the energy consumption parameters of the machine room equipment in real time and transmitting the energy consumption parameters acquired in real time into the monitoring center; the time period energy consumption analysis unit is used for receiving energy consumption parameters of different devices, analyzing time period energy consumption according to early warning energy consumption and corresponding early warning duration of different devices in different time periods, obtaining time period energy consumption parameters, comparing the time period energy consumption parameters with a preset threshold value of the threshold value unit, generating different comparison signals according to comparison results, and transmitting the comparison signals into the display terminal; the early warning period analysis unit is used for receiving early warning periods belonging to different devices in the same machine room, determining and analyzing the over-warning periods of the appointed machine room according to the received multiple groups of different early warning periods, and transmitting the over-warning periods obtained by analysis processing into the display terminal; and the machine room parameter analysis unit is used for acquiring the overall energy consumption parameters of the appointed machine room, carrying out average value processing on the overall energy consumption parameters of a plurality of groups of different machine rooms, determining the difference energy consumption of the different machine rooms according to the average value obtained by processing, marking the machine room with the largest difference energy consumption as an early warning machine room according to the determination result, and transmitting the machine room to the display terminal. By means of the above method, depending on manual operation and fixation strategies, it is difficult to adjust the equipment of the machine room to different environmental and load variations, resulting in increased resource consumption and polluting emissions. Accordingly, the prior art has drawbacks and needs improvement.
Disclosure of Invention
In order to solve one or more problems in the prior art, the main purpose of the application is to provide a method and a system for adjusting the energy consumption of a machine room based on AI.
In order to achieve the above object, the present application provides a method for adjusting energy consumption of a machine room based on AI, the method comprising:
acquiring energy consumption data and environment data of a machine room in real time, wherein the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment;
inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data;
Based on the predicted energy consumption data, carrying out identification analysis on the predicted energy consumption data through an AI algorithm to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason;
And adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
Further, the identifying and analyzing the predicted energy consumption data through the AI algorithm to obtain a load energy consumption cause includes:
acquiring historical energy consumption data, wherein the historical energy consumption data comprises daily historical energy consumption data, weekly historical energy consumption data and monthly historical energy consumption data;
Extracting standard energy consumption data from the daily historical energy consumption data, the weekly historical energy consumption data and the monthly historical energy consumption data;
Performing difference comparison on the predicted energy consumption data and the standard energy consumption data through an AI algorithm to obtain the same quantity and variable of the predicted energy consumption data and the standard energy consumption data;
extracting time period data of a variable as a load parameter based on the same quantity and variable of the predicted energy consumption data and the standard energy consumption data;
and determining a load energy consumption reason according to the load parameter and the environment data.
Further, the analyzing and predicting the energy data and the environmental data through the energy consumption model to obtain predicted energy consumption data includes:
Preprocessing the energy consumption data and the environment data, and extracting characteristics of the preprocessed energy consumption data and the preprocessed environment data to obtain indoor temperature, humidity, equipment quantity, total energy consumption and equipment configuration data;
The indoor temperature, the indoor humidity, the equipment number, the total energy consumption and the equipment configuration data are respectively input into an energy consumption model to conduct energy consumption prediction, and predicted energy consumption data are obtained;
verifying the predicted energy consumption data, and generating an energy consumption change graph from the predicted energy consumption data;
acquiring a historical actual energy consumption change graph;
Carrying out change trend analysis on the energy consumption change graph and the historical actual energy consumption change graph, and judging whether the change trend is in a preset fluctuation range or not;
and if the change trend of the energy consumption change graph is in a preset fluctuation range, outputting predicted energy consumption data.
Further, the generating system device adjustment parameters and/or environment device adjustment parameters according to the load energy consumption cause includes:
when the load energy consumption is caused by the fact that the system equipment is in the unused time period and the environment equipment is continuously refrigerated, acquiring unused idle time of the system equipment;
generating a closing and/or dormancy instruction based on the idle time, and determining the system equipment adjusting parameter according to the closing and/or dormancy instruction;
Generating a stop cooling and/or a decrease cooling command based on the idle time, determining the environmental device adjustment parameter based on the stop cooling and/or decrease cooling command.
Further, the generating system device adjustment parameters and/or environment device adjustment parameters according to the load energy consumption reasons further includes:
when the energy consumption of the load is too high due to the fact that the environmental temperature is too high, generating a refrigerating instruction for increasing the environmental equipment and a refrigerating duration increasing instruction;
determining the environmental equipment adjusting parameters according to the instruction for increasing the refrigeration time length and the instruction for increasing the refrigeration time length;
When the load energy consumption is that the target position is unmanned and the illumination brightness of the environment equipment is too high, generating an illumination brightness reducing or illumination closing instruction of the environment equipment;
and determining the environment equipment adjusting parameters according to the illumination lowering brightness or the illumination turning-off instruction.
Further, the generating system device adjustment parameters and/or environment device adjustment parameters according to the load energy consumption reasons further includes:
When the load causes that the energy consumption of the system equipment is too high due to the too high ambient humidity, generating an instruction for increasing the refrigeration and an instruction for increasing the exhaust speed of the ambient equipment;
And determining the environmental equipment adjusting parameter according to the cooling increasing and exhausting increasing speed command.
When the load causes are that the wind speed of the environmental equipment is too high, and the energy consumption of the system equipment is too high, generating a wind speed reducing instruction of the environmental equipment;
And determining the environmental equipment adjusting parameters according to the wind speed reducing instruction.
Further, the remote module is used for sending control instructions and remote monitoring, and the method comprises the following steps:
receiving the control instruction, analyzing the control instruction, and obtaining control parameters, wherein the control parameters comprise a remote closing parameter, a remote opening parameter, a system equipment adjusting parameter and an environmental equipment adjusting parameter;
Adjusting the system device and/or the environmental device based on the control parameter.
The embodiment of the application also provides a system for adjusting the energy consumption of the machine room based on the AI, which comprises:
The system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring energy consumption data and environment data of a machine room in real time, and the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment;
the prediction unit is used for inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data;
The analysis unit is used for carrying out identification analysis on the predicted energy consumption data through an AI algorithm based on the predicted energy consumption data to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason;
And the adjusting unit is used for adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
The application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
The application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the preceding claims.
According to the method and the system for adjusting the energy consumption of the machine room based on the AI, the terminal inputs the energy consumption data and the environment data into the preset energy consumption model by acquiring the energy consumption data and the environment data of the machine room, predicts the energy consumption of the future time through the energy consumption model, and can conveniently give out the reason of corresponding load energy consumption according to the predicted result through the prediction of the future energy consumption, so that a solving strategy is given, and the energy consumption of each device is adjusted according to the strategy, so that the reasonable consumption of the energy is realized. The predicted energy consumption data is input into an AI algorithm trained in advance, the cause of load energy consumption is analyzed on the preset energy consumption data through the AI algorithm, the cause of load is possibly too high due to environmental factors or improper operation of each device, the AI algorithm can judge the cause of load energy consumption according to the predicted energy consumption data, and corresponding adjusting parameters are generated according to the cause of load energy consumption. The terminal adjusts the parameters to adjust the environment equipment and the system equipment. From the analysis, the embodiment of the application can generate corresponding adjusting parameters according to different reasons of load energy consumption, and automatically adjust equipment of a machine room according to the adjusting parameters, thereby reducing resource consumption caused by load and pollutant emission.
Drawings
FIG. 1 is a flow chart of a method for adjusting energy consumption of a machine room based on AI according to an embodiment of the application;
FIG. 2 is a flowchart of a method for adjusting energy consumption of a machine room based on AI according to an embodiment of the application;
FIG. 3 is a schematic block diagram of a system for adjusting energy consumption of a machine room based on AI according to an embodiment of the application;
Fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Referring to fig. 1, in an embodiment of the present application, a method for adjusting energy consumption of a machine room based on AI is provided, where the method includes:
S1, acquiring energy consumption data and environment data of a machine room in real time, wherein the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment;
S2, inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data;
S3, based on the predicted energy consumption data, carrying out identification analysis on the predicted energy consumption data through an AI algorithm to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason;
S4, adjusting the system equipment and/or the environmental equipment based on the system equipment adjusting parameters and/or the environmental equipment adjusting parameters.
As described in step S1, the terminal obtains the energy consumption data and the environmental data of the machine room, where the machine room includes equipment and environmental equipment of the server system, and the environmental equipment may include air conditioning equipment, humidification equipment or lighting equipment, and the energy consumption data is the total energy consumption of the machine room. The environmental parameters may include temperature, humidity, etc. of the machine room environment.
As described in step S2, the energy consumption data and the environmental data are input into a preset energy consumption model, and the energy consumption of the future time is predicted by the energy consumption model. The energy consumption model can adopt a regression model to predict the energy consumption of the machine room, and the data is cleaned and preprocessed by collecting historical machine room energy consumption data comprising various characteristic variables (such as time, temperature, humidity and the like) and corresponding energy consumption values, so that the integrity and consistency of the data are ensured. This may include processing missing values, outliers, etc., selecting and processing feature variables. According to the actual situation, the characteristic variable related to the energy consumption is selected, and the proper data conversion and standardization are carried out. Time-dependent features such as hours, dates, seasons, etc. may be considered for the introduction to capture the effects of time trends. The dataset is partitioned into a training set and a validation set. Typically, the earlier data is used as the training set and the newer data is used as the validation set. And determining the proportion of the training set and the verification set according to the actual situation. And predicting the characteristic data in the verification set by using the trained regression model to obtain an energy consumption prediction result in a future period of time. By predicting the future energy consumption, the reason of the corresponding load energy consumption can be conveniently given according to the predicted result, so that a solution strategy is given, and the energy consumption of each device is regulated according to the strategy, so that the reasonable energy consumption is realized.
As described in the above steps S3-S4, the predicted energy consumption data is input into an AI algorithm (AI is artificial intelligence) trained in advance, and the cause of load energy consumption is analyzed on the preset energy consumption data by the AI algorithm, where the cause of load may be too high due to environmental factors or simultaneous high-power operation of multiple devices. In actual operation, since the load condition of the equipment may vary, the actual power consumption may vary according to the load condition, and the power consumption of the equipment in the machine room is generally defined using watt (W) as a unit. Rated power consumption refers to the power consumed by a device under standard conditions (typically a full load state). For example: there is a server rated for 300 watts. This means that the power consumption of the server is 300 watts in the full load state. However, in actual operation, the load of the server may vary. For example, in an idle or low load state, the power consumption of the server may be only 100 watts. While in high load conditions, the power consumption may approach or reach 300 watts of rated power. Therefore, the reason of the load is judged through an AI algorithm, and a corresponding strategy is given according to the reason of the load energy consumption, wherein the strategy can comprise the adjustment parameters of the system equipment and the adjustment parameters of the environmental equipment. Also for example: the first system device operates at a temperature of 40 c with a power consumption of 100 watts, and at a temperature of 60 c with a power consumption of 170 watts, the AI algorithm may generate a parameter of increased cooling capacity of the environmental device based on the cause of this excessive temperature. The environmental device is conditioned by increasing a parameter of the cooling capacity. The first system equipment is cooled by increasing the refrigerating capacity of the environmental equipment, so that the power consumption of the first system equipment can be reduced when the temperature of the first system equipment is reduced, and the lower PUE value is reached. The PUE (PowerUsage Effectiveness) value is an index for measuring the energy utilization efficiency of the data center. IT represents the ratio of the total energy consumption of the data center to the calculated energy consumption, i.e. the ratio of the total energy consumption to the IT equipment energy consumption. The lower the PUE value, the higher the energy utilization efficiency of the data center. Data centers with PUE values between 1.0 and 1.5 are generally considered to fall into the category of higher efficiency, while PUE values exceeding 2.0 mean that the energy waste of the data center is quite serious.
According to the method, the terminal inputs the energy consumption data and the environment data into a preset energy consumption model by acquiring the energy consumption data and the environment data of the machine room, predicts the energy consumption of the future time through the energy consumption model, and can conveniently give out the reason of corresponding load energy consumption according to the predicted result through the prediction of the future energy consumption, so that a solution strategy is given, and the energy consumption of each device is regulated according to the strategy to realize reasonable energy consumption. The predicted energy consumption data is input into an AI algorithm trained in advance, the cause of load energy consumption is analyzed on the preset energy consumption data through the AI algorithm, the cause of load is possibly too high due to environmental factors or improper operation of each device, the AI algorithm can judge the cause of load energy consumption according to the predicted energy consumption data, and corresponding adjusting parameters are generated according to the cause of load energy consumption. The terminal adjusts the parameters to adjust the environment equipment and the system equipment. From the analysis, the embodiment of the application can generate corresponding adjusting parameters according to different reasons of load energy consumption, and automatically adjust equipment of a machine room according to the adjusting parameters, thereby reducing resource consumption caused by load and pollutant emission.
In a possible embodiment, there are typically multiple devices in the machine room, including servers, network devices, storage devices, etc., which may have different workloads at different times. By acquiring the energy consumption data and the environment data in real time, the energy consumption of each device in the machine room can be monitored and analyzed in real time, and the power and the running state of each device can be dynamically adjusted according to the current working load condition, so that the energy consumption of the machine room is reduced to the maximum extent. And sensors such as temperature, humidity and air quality are used in the machine room, environmental parameter data are collected, and are analyzed and processed by an AI algorithm to predict and control the energy consumption of the machine room. When some devices in the machine room are in idle or low-load state, the energy consumption can be reduced by dynamically adjusting the power or switching the operation mode of the devices. In the high-load state, the power or the running state of the equipment can be dynamically adjusted to meet the service requirement and simultaneously reduce the energy consumption to the maximum extent.
In one embodiment, the identifying and analyzing the predicted energy consumption data by using an AI algorithm to obtain a load energy consumption cause includes:
acquiring historical energy consumption data, wherein the historical energy consumption data comprises daily historical energy consumption data, weekly historical energy consumption data and monthly historical energy consumption data;
Extracting standard energy consumption data from the daily historical energy consumption data, the weekly historical energy consumption data and the monthly historical energy consumption data;
performing difference comparison on the predicted energy consumption data and the standard energy consumption data through an AI algorithm, and extracting time period data with difference as a load parameter;
and determining a load energy consumption reason according to the load parameter and the environment data.
As described in the above steps, by acquiring the historical energy consumption data, various data related to the energy demand and supply can be collected through the historical energy consumption data, including energy consumption data, supply data, characteristic variables (such as time, weather conditions, etc.), and other factors that may affect the energy consumption, by combining daily historical energy consumption data, weekly historical energy consumption data, and monthly historical energy consumption data for analysis, standard energy consumption data corresponding to the time period of the predicted energy consumption data can be analyzed, so that AI can be facilitated to recognize the difference between the two. And comparing the predicted energy consumption data with the historical energy consumption data in a difference mode through an AI algorithm. And extracting the time period data with the difference as a load parameter according to the difference comparison mode between the same quantity and variable of the current prediction data and the standard energy consumption data. The working state of the target equipment can be determined according to each target equipment corresponding to the load parameters, and the reasons of the load can be determined according to the working state of the target equipment and the environment parameters. If there is a server whose rated power is 300 watts, this means that in the full load state, the power consumption of the server is 300 watts, and the power consumption of the same period of time as the standard energy consumption data is 100 watts, and the power consumption of a certain period of time is 150 watts, and the current ambient temperature is 50 ℃, then in combination with the period of time and the ambient data for predicting the exceeded power consumption, it can be determined that the load cause of the server is that the power consumption increases due to the overhigh temperature of the certain period of time.
Referring to fig. 2, in an embodiment, the analyzing and predicting the energy data and the environmental data by the energy consumption model to obtain predicted energy consumption data includes:
s21, preprocessing the energy consumption data and the environment data, and extracting characteristics of the preprocessed energy consumption data and the preprocessed environment data to obtain indoor temperature, humidity, equipment quantity, total energy consumption and equipment configuration data;
S22, respectively inputting the indoor temperature, the indoor humidity, the equipment number, the total energy consumption and the equipment configuration data into an energy consumption model to predict the energy consumption, and obtaining predicted energy consumption data;
s23, verifying the predicted energy consumption data, and generating an energy consumption change graph from the predicted energy consumption data;
s24, acquiring a historical actual energy consumption change chart;
s25, carrying out change trend analysis on the energy consumption change graph and the historical actual energy consumption change graph, and judging whether the change trend is in a preset fluctuation range or not;
S26, outputting predicted energy consumption data if the change trend of the energy consumption change graph is in a preset fluctuation range.
As described above, the energy consumption data and the environmental data are preprocessed to implement smoothing of the data, so that formats of the energy consumption data and the environmental data can be identified by the energy consumption model, the indoor temperature, the humidity, the number of devices, the total energy consumption and the device configuration data are obtained by extracting features of the preprocessed energy consumption data and the preprocessed environmental data, and the predicted energy consumption data can be obtained by respectively inputting the indoor temperature, the humidity, the number of devices, the total energy consumption and the device configuration data into the energy consumption model to perform energy consumption prediction. Indoor temperature and humidity changes can also affect the energy consumption of the machine room. The temperature and humidity data are used as input features of the model, so that the energy demand can be predicted better. Information such as the number of devices in the machine room, power consumption, etc. are also important parameters. Combining this information with the energy consumption data, the energy demand and supply can be predicted more accurately. It is worth mentioning that if renewable energy sources (such as solar energy, wind energy, etc.) are used in the machine room, these energy supply data are taken into account to evaluate the utilization of the renewable energy sources. And after the prediction is completed, verifying the predicted energy consumption data, and generating an energy consumption change graph, such as a bar graph or a line graph, by using the predicted energy consumption data. The fluctuation is judged by comparing the current energy consumption change diagram with the historical actual energy consumption change diagram, and whether the fluctuation trend and the range are in a normal range or not is judged. If the predicted energy consumption data is within the normal range, the predicted result is a normal predicted value, and the predicted energy consumption data is output. In one embodiment, the method of verification may further include the calculation of a Relative Error (RE): and calculating the relative error percentage between the predicted value and the actual value, and measuring the predicted deviation degree.
The formula is: re= (|predicted value-actual value|/actual value) ×100%.
In an embodiment, the generating system device adjustment parameters and/or environment device adjustment parameters according to the load energy consumption cause includes:
when the load energy consumption is caused by the fact that the system equipment is in the unused time period and the environment equipment is continuously refrigerated, acquiring unused idle time of the system equipment;
generating a closing and/or dormancy instruction based on the idle time, and determining the system equipment adjusting parameter according to the closing and/or dormancy instruction;
Generating a stop cooling and/or a decrease cooling command based on the idle time, determining the environmental device adjustment parameter based on the stop cooling and/or decrease cooling command.
As described above, when the corresponding adjustment parameters are generated according to the load energy consumption reasons, the system equipment is used too low to cause the refrigerating time of the environmental equipment to be too high. For example: the actual working time of the server equipment in the machine room is 3 hours, and the server equipment is not used for working at other times, and the environment equipment is continuously refrigerated for the server equipment in the state that the server is actually working, so that the environment equipment is caused to continuously refrigerate the load energy consumption caused. The idle time of the system equipment is obtained, the system equipment is controlled to reduce energy consumption through the state of being turned off or dormant by generating an instruction of turning off and/or dormant when the system equipment is in idle time, the environment equipment is controlled according to the instruction of stopping cooling and/or reducing cooling by generating an instruction of stopping cooling and/or reducing cooling by generating the instruction of stopping cooling of the environment equipment, and the cooling power consumption of the environment equipment can be reduced. The energy consumption of the environment equipment and the terminal equipment is reduced under the condition that the normal operation of the machine room is not affected.
In an embodiment, the generating system device adjustment parameters and/or environment device adjustment parameters according to the load energy consumption cause further includes:
when the energy consumption of the load is too high due to the fact that the environmental temperature is too high, generating a refrigerating instruction for increasing the environmental equipment and a refrigerating duration increasing instruction;
determining the environmental equipment adjusting parameters according to the instruction for increasing the refrigeration time length and the instruction for increasing the refrigeration time length;
When the load energy consumption is that the target position is unmanned and the illumination brightness of the environment equipment is too high, generating an illumination brightness reducing or illumination closing instruction of the environment equipment;
and determining the environment equipment adjusting parameters according to the illumination lowering brightness or the illumination turning-off instruction.
As described above, when the energy consumption of the system device is too high due to the excessively high ambient temperature, it means that the cooling air volume and the duration need to be increased to reduce the ambient temperature, so that the energy consumption of the system device is reduced while the system device is cooled. Because the power consumption of the system equipment is higher than that of the environment equipment, the power consumption of the system equipment needs to be reduced by priority. The environmental device is controlled by generating an instruction to increase the cooling time period and an instruction to increase the cooling time period. When the load energy consumption is caused by unmanned control of the target position, the illumination brightness of the environment equipment is too high. It means that when no staff is detected at a certain position in the machine room, the ambient light is still at the illumination brightness when the staff is normally present, and thus the power consumption will increase, and the environmental device is controlled by generating the illumination brightness reduction or illumination turning-off command.
In an embodiment, the generating system device adjustment parameters and/or environment device adjustment parameters according to the load energy consumption cause further includes:
When the load causes that the energy consumption of the system equipment is too high due to the too high ambient humidity, generating an instruction for increasing the refrigeration and an instruction for increasing the exhaust speed of the ambient equipment;
And determining the environmental equipment adjusting parameter according to the cooling increasing and exhausting increasing speed command.
When the load causes are that the wind speed of the environmental equipment is too high, and the energy consumption of the system equipment is too high, generating a wind speed reducing instruction of the environmental equipment;
And determining the environmental equipment adjusting parameters according to the wind speed reducing instruction.
As described above, when a load occurs due to an excessively high ambient humidity, the system equipment is excessively consumed. Since the humidity of the environment is too high or too low, which affects the power consumption of the system device, it is necessary to keep the humidity of the environment at a normal value to stabilize the low power consumption of the system device. When the ambient humidity is too high, the ambient equipment is controlled by generating a speed instruction for increasing the exhaust gas, and the ambient equipment circulates the indoor air rapidly by the speed instruction for increasing the exhaust gas, so that the humidity is recovered to a normal humidity value. In addition, the environment equipment can be controlled by adding the refrigerating instruction, so that the environment humidity is reduced. When the load causes that the wind speed of the environmental equipment is too high and the energy consumption of the system equipment is too high, the wind speed of the environmental equipment needs to be reduced, and the power consumption of the system equipment is influenced by the too high environmental temperature and the too low temperature. The wind speed may be suitably reduced to reduce the power consumption of the system equipment when the ambient temperature is at normal temperature.
In an embodiment, the system further comprises a remote module for sending control instructions and remote monitoring, the method comprising:
receiving the control instruction, analyzing the control instruction, and obtaining control parameters, wherein the control parameters comprise a remote closing parameter, a remote opening parameter, a system equipment adjusting parameter and an environmental equipment adjusting parameter;
Adjusting the system device and/or the environmental device based on the control parameter.
As described above, the remote module can be used for remotely monitoring the working state of the machine room, such as whether the system equipment is in normal operation or not and the working state of the environmental equipment. The user can send an instruction through the remote module, the terminal controls the equipment room equipment by receiving the instruction issued by the user, and the control instruction comprises a remote closing parameter, a remote opening parameter, a system equipment adjusting parameter and an environment equipment adjusting parameter.
According to the method for adjusting the energy consumption of the machine room based on the AI, the terminal inputs the energy consumption data and the environment data into the preset energy consumption model by acquiring the energy consumption data and the environment data of the machine room, predicts the energy consumption in the future time through the energy consumption model, and can conveniently give out the reason of corresponding load energy consumption according to the predicted result through the prediction of the future energy consumption, so that a solving strategy is given, and the energy consumption of each device is adjusted according to the strategy, so that the reasonable consumption of energy is realized. The predicted energy consumption data is input into an AI algorithm trained in advance, the cause of load energy consumption is analyzed on the preset energy consumption data through the AI algorithm, the cause of load is possibly too high due to environmental factors or improper operation of each device, the AI algorithm can judge the cause of load energy consumption according to the predicted energy consumption data, and corresponding adjusting parameters are generated according to the cause of load energy consumption. The terminal adjusts the parameters to adjust the environment equipment and the system equipment. From the analysis, the embodiment of the application can generate corresponding adjusting parameters according to different reasons of load energy consumption, and automatically adjust equipment of a machine room according to the adjusting parameters, thereby reducing resource consumption caused by load and pollutant emission.
Referring to fig. 3, the embodiment of the application further provides a system for adjusting energy consumption of a machine room based on AI, which includes:
the system comprises an acquisition unit 1, a control unit and a control unit, wherein the acquisition unit is used for acquiring energy consumption data and environment data of a machine room in real time, and the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment;
the prediction unit 2 is used for inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data;
The analysis unit 3 is used for carrying out identification analysis on the predicted energy consumption data through an AI algorithm based on the predicted energy consumption data to obtain a load energy consumption reason, and generating a system equipment adjusting parameter and/or an environment equipment adjusting parameter according to the load energy consumption reason;
And the adjusting unit 4 is used for adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
As described above, it may be understood that each component of the system for adjusting energy consumption of a machine room based on AI provided in the present application may implement a function of any one of the methods for adjusting energy consumption of a machine room based on AI as described above, and a specific structure is not described herein.
Referring to fig. 4, in an embodiment of the present application, there is further provided a computer device, which may be a server, and the internal structure of the computer device may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as monitoring data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for adjusting machine room energy consumption based on AI.
The method for adjusting the energy consumption of the machine room based on the AI by the processor comprises the following steps: acquiring energy consumption data and environment data of a machine room in real time, wherein the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment; inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data; based on the predicted energy consumption data, carrying out identification analysis on the predicted energy consumption data through an AI algorithm to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason; and adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
According to the method for adjusting the energy consumption of the machine room based on the AI, the terminal inputs the energy consumption data and the environment data into the preset energy consumption model by acquiring the energy consumption data and the environment data of the machine room, predicts the energy consumption in the future time through the energy consumption model, and can conveniently give out the reason of the corresponding load energy consumption according to the predicted result through the prediction of the future energy consumption, so that a solution strategy is given, and the energy consumption of each device is adjusted according to the strategy, so that the reasonable consumption of energy is realized. The predicted energy consumption data is input into an AI algorithm trained in advance, the cause of load energy consumption is analyzed on the preset energy consumption data through the AI algorithm, the cause of load is possibly too high due to environmental factors or improper operation of each device, the AI algorithm can judge the cause of load energy consumption according to the predicted energy consumption data, and corresponding adjusting parameters are generated according to the cause of load energy consumption. The terminal adjusts the parameters to adjust the environment equipment and the system equipment. From the analysis, the embodiment of the application can generate corresponding adjusting parameters according to different reasons of load energy consumption, and automatically adjust equipment of a machine room according to the adjusting parameters, thereby reducing resource consumption caused by load and pollutant emission.
An embodiment of the present application further provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method for adjusting energy consumption of a machine room based on AI, including the steps of: acquiring energy consumption data and environment data of a machine room in real time, wherein the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment; inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data; based on the predicted energy consumption data, carrying out identification analysis on the predicted energy consumption data through an AI algorithm to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason; and adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present application and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the application.
Claims (10)
1. The method for adjusting the energy consumption of the machine room based on the AI is characterized by comprising the following steps:
acquiring energy consumption data and environment data of a machine room in real time, wherein the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment;
inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data;
Based on the predicted energy consumption data, carrying out identification analysis on the predicted energy consumption data through an AI algorithm to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason;
And adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
2. The method for adjusting the energy consumption of the machine room based on the AI according to claim 1, wherein the identifying and analyzing the predicted energy consumption data by the AI algorithm to obtain the load energy consumption cause comprises:
acquiring historical energy consumption data, wherein the historical energy consumption data comprises daily historical energy consumption data, weekly historical energy consumption data and monthly historical energy consumption data;
Extracting standard energy consumption data from the daily historical energy consumption data, the weekly historical energy consumption data and the monthly historical energy consumption data;
Performing difference comparison on the predicted energy consumption data and the standard energy consumption data through an AI algorithm to obtain the same quantity and variable of the predicted energy consumption data and the standard energy consumption data;
extracting time period data of a variable as a load parameter based on the same quantity and variable of the predicted energy consumption data and the standard energy consumption data;
and determining a load energy consumption reason according to the load parameter and the environment data.
3. The method for adjusting energy consumption of a machine room based on AI of claim 1, wherein the analyzing and predicting the energy data and the environmental data by the energy consumption model to obtain predicted energy consumption data includes:
Preprocessing the energy consumption data and the environment data, and extracting characteristics of the preprocessed energy consumption data and the preprocessed environment data to obtain indoor temperature, humidity, equipment quantity, total energy consumption and equipment configuration data;
The indoor temperature, the indoor humidity, the equipment number, the total energy consumption and the equipment configuration data are respectively input into an energy consumption model to conduct energy consumption prediction, and predicted energy consumption data are obtained;
verifying the predicted energy consumption data, and generating an energy consumption change graph from the predicted energy consumption data;
acquiring a historical actual energy consumption change graph;
Carrying out change trend analysis on the energy consumption change graph and the historical actual energy consumption change graph, and judging whether the change trend is in a preset fluctuation range or not;
and if the change trend of the energy consumption change graph is in a preset fluctuation range, outputting predicted energy consumption data.
4. The AI-based machine room energy consumption method of claim 1, wherein the generating system equipment adjustment parameters and/or environmental equipment adjustment parameters according to the load energy consumption cause comprises:
when the load energy consumption is caused by the fact that the system equipment is in the unused time period and the environment equipment is continuously refrigerated, acquiring unused idle time of the system equipment;
generating a closing and/or dormancy instruction based on the idle time, and determining the system equipment adjusting parameter according to the closing and/or dormancy instruction;
Generating a stop cooling and/or a decrease cooling command based on the idle time, determining the environmental device adjustment parameter based on the stop cooling and/or decrease cooling command.
5. The AI-based machine room energy consumption adjustment method of claim 1, wherein the generating system equipment adjustment parameters and/or environmental equipment adjustment parameters according to the load energy consumption cause further comprises:
when the energy consumption of the load is too high due to the fact that the environmental temperature is too high, generating a refrigerating instruction for increasing the environmental equipment and a refrigerating duration increasing instruction;
determining the environmental equipment adjusting parameters according to the instruction for increasing the refrigeration time length and the instruction for increasing the refrigeration time length;
When the load energy consumption is that the target position is unmanned and the illumination brightness of the environment equipment is too high, generating an illumination brightness reducing or illumination closing instruction of the environment equipment;
and determining the environment equipment adjusting parameters according to the illumination lowering brightness or the illumination turning-off instruction.
6. The AI-based machine room energy consumption adjustment method of claim 1, wherein the generating system equipment adjustment parameters and/or environmental equipment adjustment parameters according to the load energy consumption cause further comprises:
When the load causes that the energy consumption of the system equipment is too high due to the too high ambient humidity, generating an instruction for increasing the refrigeration and an instruction for increasing the exhaust speed of the ambient equipment;
determining the environmental equipment adjusting parameters according to the instruction of increasing refrigeration and increasing exhaust speed;
When the load causes are that the wind speed of the environmental equipment is too high, and the energy consumption of the system equipment is too high, generating a wind speed reducing instruction of the environmental equipment;
And determining the environmental equipment adjusting parameters according to the wind speed reducing instruction.
7. The AI-based method of adjusting machine room energy consumption of any of claims 1-6, further comprising a remote module for sending control instructions and remote monitoring, the method comprising:
receiving the control instruction, analyzing the control instruction, and obtaining control parameters, wherein the control parameters comprise a remote closing parameter, a remote opening parameter, a system equipment adjusting parameter and an environmental equipment adjusting parameter;
Adjusting the system device and/or the environmental device based on the control parameter.
8. A system for adjusting energy consumption of a machine room based on AI (advanced technology attachment) is characterized by comprising:
The system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring energy consumption data and environment data of a machine room in real time, and the energy consumption data comprise energy consumption data of system equipment and energy consumption data of the environment equipment;
the prediction unit is used for inputting the energy consumption data and the environment data into a preset energy consumption model, and analyzing and predicting the energy consumption data and the environment data through the energy consumption model to obtain predicted energy consumption data;
The analysis unit is used for carrying out identification analysis on the predicted energy consumption data through an AI algorithm based on the predicted energy consumption data to obtain a load energy consumption reason, and generating system equipment adjusting parameters and/or environment equipment adjusting parameters according to the load energy consumption reason;
And the adjusting unit is used for adjusting the system equipment and/or the environment equipment based on the system equipment adjusting parameter and/or the environment equipment adjusting parameter.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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