CN115789871A - Energy consumption reason analysis method and device - Google Patents

Abstract

The embodiment of the invention provides an energy consumption reason analysis method and device. The energy consumption reason corresponding to each parameter combination can be obtained by inputting a plurality of parameter combinations determined according to a plurality of operating parameters into the energy consumption reason analysis model for analysis, or determining a plurality of parameter combinations according to a plurality of operating parameters in the process of using the energy consumption reason analysis model and then analyzing based on the plurality of parameter combinations.

Description

Energy consumption cause analysis method and device

technical field

The invention relates to the field of equipment control, in particular to a method and device for analyzing the cause of energy consumption.

Background technique

With the improvement of people's living standards and the increasing shortage of energy, the requirements for equipment energy consumption are gradually increasing, and determining the cause of equipment energy consumption is of great significance for the improvement of equipment performance and equipment control.

In recent years, existing technologies have emerged that use models to determine the cause of equipment energy consumption.

For example, the prior art discloses a method for analyzing the causes of energy consumption, which compares current energy consumption data with historical energy consumption data in the same period into a model to obtain the reasons for energy consumption changes.

It should be noted that the above introduction of the technical background is only for the convenience of a clear and complete description of the technical solution of the present invention, and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are known to those skilled in the art just because these solutions are described in the background of the present invention.

Contents of the invention

However, the inventors found that in the prior art, various operating data of the equipment are directly input into the model, and the cause of energy consumption is directly analyzed according to various operational parameters, the amount of calculation is relatively large, and the accuracy of the obtained energy consumption cause In addition, when there are multiple reasons for energy consumption, the amount of operating data that needs to be input is further increased, resulting in a further increase in the amount of calculation and low analysis efficiency.

In order to solve at least one of the above problems, embodiments of the present invention provide a method and device for analyzing energy consumption causes. Input multiple parameter combinations determined according to multiple operating parameters into the energy consumption cause analysis model for analysis, or, in the process of using the energy consumption cause analysis model, first determine multiple parameter combinations based on multiple operating parameters and then based on multiple Combination of parameters can be analyzed to obtain the energy consumption reasons corresponding to each parameter combination, and because the analysis of energy consumption reasons is based on multiple parameter combinations instead of directly based on multiple operating parameters, the calculation amount is small and the analysis results are accurate , in addition, multiple causes of energy consumption corresponding to multiple parameter combinations can be obtained.

According to the first aspect of an embodiment of the present invention, there is provided a method for analyzing causes of energy consumption, the method comprising: acquiring multiple operating parameters of equipment; and determining the multiple operating parameters or according to the multiple operating parameters The multiple parameter combinations of the model are input into the energy consumption cause analysis model, and the energy consumption causes corresponding to the multiple parameter combinations are obtained.

According to the second aspect of the embodiments of the present invention, there is provided a device for analyzing causes of energy consumption, the device comprising: a memory for storing a computer program; a processor for implementing the present invention when executing the computer program The energy consumption cause analysis method described in the first aspect of the embodiment of the invention.

According to a third aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, the storage medium includes a stored computer program, wherein the computer program executes the function described in the first aspect of the embodiments of the present invention consumption reason analysis method.

One of the beneficial effects of the embodiments of the present invention is: input multiple parameter combinations determined according to multiple operating parameters into the analysis model of energy consumption causes for analysis, or, in the process of using the analysis model of energy consumption The operation parameters determine multiple parameter combinations and then analyze based on multiple parameter combinations, the energy consumption causes corresponding to each parameter combination can be obtained, and, because the analysis of energy consumption reasons is based on multiple parameter combinations rather than directly based on multiple operating The parameters are analyzed, the calculation amount is small and the analysis results are accurate. In addition, multiple energy consumption causes corresponding to multiple parameter combinations can be obtained.

With reference to the following description and accompanying drawings, there are disclosed in detail specific embodiments of the invention, indicating the manner in which the principles of the invention may be employed. It should be understood that embodiments of the invention are not limited thereby in scope. Embodiments of the invention encompass many changes, modifications and equivalents within the spirit and scope of the appended claims.

Feature information described and shown for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with feature information in other embodiments, or replace features in other embodiments information.

It should be emphasized that the term "comprising/comprising" when used herein refers to the presence of characteristic information, integers, steps or components, but does not exclude the presence or addition of one or more other characteristic information, integers, steps or components.

Description of drawings

Many aspects of the invention can be better understood with reference to the following figures. The components in the figures are not drawn to scale, merely illustrating the principles of the invention. In order to facilitate illustration and description of some parts of the present invention, corresponding parts in the drawings may be exaggerated or reduced. Element and characteristic information described in one drawing or one embodiment of the present invention may be combined with element and characteristic information shown in one or more other drawings or embodiments. Furthermore, in the drawings, like numerals indicate corresponding parts in the several figures and may be used to indicate corresponding parts used in more than one embodiment.

In the attached picture:

Fig. 1 is a flowchart of the method for analyzing the cause of energy consumption in Embodiment 1 of the present invention;

FIG. 2 is a schematic diagram of an application scenario of the energy consumption cause analysis method in Embodiment 1 of the present invention;

FIG. 3 is another schematic diagram of an application scenario of the energy consumption cause analysis method in Embodiment 1 of the present invention;

Fig. 4 is a schematic block diagram of the system configuration of the energy consumption cause analysis device according to Embodiment 2 of the present invention.

Detailed ways

Preferred embodiments of the present invention will be described below with reference to the drawings.

Example 1

Embodiment 1 of the present invention provides a method for analyzing causes of energy consumption. FIG. 1 is a flow chart of a method for analyzing energy consumption causes in Embodiment 1 of the present invention. As shown in Figure 1, the method includes:

Step 101: Obtain multiple operating parameters of the device; and

Step 102: Input multiple operating parameters or multiple parameter combinations determined according to the multiple operating parameters into the energy consumption cause analysis model to obtain energy consumption causes respectively corresponding to the multiple parameter combinations.

In this way, since the analysis of the causes of energy consumption is based on multiple parameter combinations rather than directly based on multiple operating parameters, the calculation amount is small and the analysis results are accurate. In addition, multiple energy consumption corresponding to multiple parameter combinations can be obtained reason.

In the embodiment of the present invention, as the object of applying the method to analyze the cause of energy consumption, the equipment may be various equipment that generates energy consumption, for example, the equipment is air processing equipment.

For example, the air processing equipment includes at least one of air conditioning equipment, fresh air equipment and air cleaning equipment.

In the embodiment of the present invention, the air conditioner may be air conditioner of various models, types, forms, and capacities.

For example, the air conditioner can be one-to-one (one outdoor unit and one indoor unit) or one-to-multiple (one outdoor unit and multiple indoor units) or multi-to-multiple (multiple outdoor units and multiple indoor units) The air conditioner can also be a central air conditioning system.

For example, the form of the indoor unit of the air conditioner can be in the form of air outlet from all sides, air outlet from both sides, duct machine, air outlet from the ground, and air outlet from skirting; Dual fan top air outlet, single fan front air outlet, dual fan front air outlet, etc.

In the embodiment of the present invention, an air conditioner is taken as an example for description, however, the embodiment of the present invention does not limit the specific type of the device.

In step 101, obtaining multiple operating parameters of the device may be obtaining various operating parameters of the current predetermined type of the device.

For example, multiple parameters in the operating mode of the air conditioner, ambient temperature, high pressure saturation temperature, indoor gas pipe temperature, and indoor liquid pipe temperature.

In the embodiment of the present invention, multiple operating parameters refer to at least two operating parameters.

In step 101, there is no limitation on the manner of acquiring the multiple operating parameters, for example, they can be acquired in real time through actual measurement data of the equipment and detection at the equipment usage site.

In step 102, a plurality of operating parameters or a plurality of parameter combinations determined according to the plurality of operating parameters are input into the energy consumption cause analysis model to obtain energy consumption causes corresponding to the plurality of parameter combinations respectively.

That is to say, in the embodiment of the present invention, multiple operating parameters can be preprocessed first to obtain multiple parameter combinations, or multiple operating parameters can also be input into the energy consumption analysis model, and in the energy consumption analysis model The internal combination of multiple operating parameters into multiple parameter combinations, and when analyzing the cause of energy consumption, the energy consumption analysis model is based on multiple parameter combinations, so it can reduce the amount of calculation and improve the accuracy of analysis.

For example, as shown in Figure 1, the method may also include:

Step 103: Determine multiple parameter combinations according to the multiple operating parameters.

In this case, in step 102 , what is input into the energy consumption cause analysis model is the combination of multiple parameters determined in step 103 .

In the embodiment of the present invention, in step 103, for example, multiple operating parameters are input into the parameter combination model to obtain multiple parameter combinations. In this way, the preprocessing of multiple operating parameters can be performed quickly and accurately.

For example, the parameter combination model is obtained by establishing the relationship between operating parameters and parameter combinations.

In the embodiment of the present invention, the parameter combination model may be various models based on machine learning or artificial intelligence algorithms.

In the embodiment of the present invention, the parameter combination may be a combination composed of at least two operating parameters according to a preset combination rule.

For example, the parameter combination includes at least one condition parameter and at least one index parameter. That is to say, each parameter combination includes at least one condition parameter and at least one index parameter.

In the embodiment of the present invention, the condition parameter is used as a precondition or exclusion condition for determining the cause of energy consumption, and the index parameter is used to directly determine the cause of energy consumption.

In this way, by combining two types of specific parameters into a parameter combination and inputting it into the energy consumption cause analysis model, the accuracy of energy consumption cause analysis can be further improved.

In the embodiment of the present invention, the condition parameter may include at least one of ambient temperature and equipment operation mode, and the index parameter may include an operation parameter directly related to determining the cause of energy consumption.

For example, the condition parameter includes outdoor temperature and operation mode, and the index parameter includes high pressure saturation temperature, indoor air pipe temperature and indoor liquid pipe temperature.

In an embodiment of the present invention, for example, as shown in FIG. 1, the method may further include:

Step 104: classify the plurality of operating parameters into condition parameters or index parameters respectively.

In this way, in step 102, multiple parameter combinations can be conveniently determined.

In step 103, the multiple parameter combinations are input into an energy consumption cause analysis model to obtain energy consumption causes respectively corresponding to the multiple parameter combinations.

For example, the operating parameters acquired in step 101 include parameters A, B, C, D, E, F, and G, wherein, parameters A, B, and C belong to condition parameters, parameters E, F, and G belong to index parameters, and parameters D is another parameter.

In step 104, parameters A, B, C are classified into condition parameters, and parameters E, F, G are classified into index parameters; in step 102, the following parameter combinations are determined: parameter combination 1 (parameters A, B, E), parameter combination 2 (parameter A, C, F) and parameter combination 3 (parameter B, C, G); in step 103, parameter combination 1 (parameter A, B, E), parameter combination 2 (parameters A, C, F) and parameter combination 3 (parameters B, C, G) are input into the energy consumption reason analysis model, and the energy consumption reason 1 corresponding to parameter combination 1 and the energy consumption reason corresponding to parameter combination 2 are output 2 and energy consumption reason 3 corresponding to parameter combination 3.

In the embodiment of the present invention, the causes of energy consumption corresponding to each parameter combination may be the same.

For example, among the energy consumption causes respectively corresponding to the multiple parameter combinations, the energy consumption causes respectively corresponding to at least two parameter combinations are the same. For example, the energy consumption reason 1 mentioned above is different from the energy consumption reasons 2 and 3, and the energy consumption reason 2 is the same as the energy consumption reason 3.

In this way, confirming the same energy consumption cause through multiple parameter combinations can further ensure the accuracy of the analysis results.

In the embodiment of the present invention, the causes of energy consumption corresponding to each parameter combination may also be different. For example, the causes of energy consumption corresponding to the multiple parameter combinations are different. For example, the aforementioned energy consumption reason 1, energy consumption reason 2, and energy consumption reason 3 are different.

In this embodiment of the present invention, the causes of energy consumption may be various reasons that lead to abnormal energy consumption. For example, for air conditioners, the causes of energy consumption may include blockage of heat exchangers of indoor units, blockage of heat exchangers of outdoor units, filtration of indoor units, etc. Network congestion, etc.

In the embodiment of the present application, when the energy consumption cause is determined through the energy consumption cause analysis model, the occurrence probability of each parameter combination may also be considered.

For example, in step 102, the multiple parameter combinations and the occurrence ratio of the parameter combinations are input into the energy consumption cause analysis model to obtain energy consumption causes respectively corresponding to the multiple parameter combinations.

In the embodiment of the present invention, the occurrence ratio of the parameter combination indicates the probability of the parameter combination, which may include the ratio of the occurrence time of the operation parameters in the parameter combination to the total operating time of the device within a preset time period.

The occurrence time of meeting the operation parameters in the parameter combination within the preset time period refers to the cumulative time when all the operation parameters in the parameter combination occur simultaneously or meet the preset conditions within the preset time period.

For example, parameter combination 1 includes parameters A, B, and E, and within a preset time period, the time when parameters A, B, and E occur simultaneously is taken as the occurrence time of the operating parameters in the parameter combination.

For example, parameter A is the operation mode, parameter B is the outdoor temperature, and parameter E is the high-pressure saturation temperature, then the time when the operation mode is cooling operation, the outdoor temperature is greater than 30 degrees, and the high-pressure saturation temperature is greater than or equal to 50 degrees is satisfied as the parameter combination The occurrence time of the running parameters in .

The ratio of the occurrence time of the operating parameters in the parameter combination within the preset time period to the total operating time of the equipment refers to the ratio of the occurrence time of the operating parameters in the parameter combination within the preset time period to the preset time. The total runtime of the device during the time period.

For example, within the preset time period T, the time for which the operation mode is cooling operation, the outdoor temperature is greater than 30 degrees, and the high pressure saturation temperature is greater than or equal to 50 degrees is t1, and the total operating time of the air conditioner is t2, then this parameter combination occurs The ratio of t1/t2.

In the embodiment of the present invention, the energy consumption cause analysis model may be various models based on machine learning or artificial intelligence algorithms.

In the embodiment of the present application, when the multiple operating parameters are preprocessed in step 103 to determine multiple parameter combinations, the energy consumption cause analysis model can be obtained by establishing a correspondence between parameter combinations and energy consumption causes.

In the embodiment of the present application, the energy consumption cause analysis model may also be obtained by establishing a correspondence between parameter combinations and their occurrence ratios and energy consumption causes.

In the embodiment of the present application, when multiple operating parameters are not preprocessed through step 103 to determine multiple parameter combinations, the energy consumption cause analysis model itself first combines multiple operating parameters to determine multiple parameter combinations When , the energy consumption cause analysis model can be obtained by establishing the relationship between operating parameters and parameter combinations and the corresponding relationship between the parameter combination and energy consumption causes.

For example, the energy consumption cause analysis model includes a first sub-model and a second sub-model, the first sub-model is obtained by establishing the relationship between operating parameters and parameter combinations, and the second sub-model is obtained by establishing the correspondence between the parameter combination and the cause of energy consumption Thus, the first sub-model combines multiple operating parameters into multiple parameter combinations, and the second sub-model analyzes the causes of energy consumption according to the multiple parameter combinations output by the first sub-model.

Or, the energy consumption cause analysis model may also realize the functions of the first sub-model and the second sub-model by training a model.

For example, in the training phase of the energy consumption cause analysis model, the actual measurement data and on-site inspection data of the equipment are obtained, and multiple operating parameters are obtained, and each operating parameter is marked and combined to obtain multiple parameter combinations. As training data; input the training data into the model based on the artificial intelligence algorithm, and train to obtain the energy consumption cause analysis model.

In the embodiment of the present application, the artificial intelligence algorithm may be various existing algorithms, for example, the artificial intelligence algorithm is a decision tree algorithm.

In an embodiment of the present invention, for example, as shown in FIG. 1, the method may further include:

Step 105: When the energy consumption causes respectively corresponding to the multiple parameter combinations include multiple energy consumption causes, determine the main energy consumption cause according to a preset rule.

In the embodiment of the present invention, the main energy consumption cause can be determined according to the probability of each energy consumption cause output by the energy consumption cause analysis model. For example, the probability of energy consumption cause 1 is 0.2, and the probability of energy consumption cause 2 is 0.7. The probability of energy consumption cause 3 is 0.1, then, energy consumption cause 2 is taken as the main energy consumption cause.

In this embodiment of the present invention, the preset rule may also be other rules.

For example, the preset rule may be to set priorities for each energy consumption cause in advance, and use the energy consumption cause with a higher priority as the main energy consumption cause. For example, among the energy consumption reason 1 corresponding to parameter combination 1, the energy consumption reason 2 corresponding to parameter combination 2, and the energy consumption reason 3 corresponding to parameter combination 3 output by the energy consumption reason analysis model, energy consumption reason 1, energy consumption Energy consumption reason 2 and energy consumption reason 3 are sorted according to priority: energy consumption reason 2, energy consumption reason 1, and energy consumption reason 3. Then, take energy consumption reason 2 as the main energy consumption reason.

For example, the main energy consumption cause is determined according to the number of parameter combinations corresponding to the determined energy consumption cause. For example, the energy consumption cause corresponding to the most parameter combinations is used as the main energy consumption cause. The embodiment of the present invention does not limit the content of the preset rule. For example, if the energy consumption cause corresponding to two parameter combinations is energy consumption cause 2, and the energy consumption cause corresponding to one parameter combination is energy consumption cause 1, then the energy consumption cause 2 is taken as the main energy consumption cause.

In an embodiment of the present invention, for example, as shown in FIG. 1, the method may further include:

Step 106: According to the energy consumption causes corresponding to the multiple parameter combinations, formulate solutions corresponding to the energy consumption causes.

For example, for each cause of energy consumption, the corresponding solution is set by default.

For example, for energy consumption cause 1, its solution is solution 1, for energy consumption cause 2, its solution is solution 2, and for energy consumption cause 3, its solution is solution 3.

In this way, by formulating solutions corresponding to various causes of energy consumption, abnormal energy consumption can be eliminated in a targeted and comprehensive manner.

In the embodiment of the present invention, the solution corresponding to the energy consumption reason is, for example, notification or reminder, remote energy saving setting or optimization, and the like.

For example, remind the user of specific energy consumption reasons through the terminal APP or client webpage, for example, notify the filter that it is clogged, please replace or clean it in time; Energy-saving setting, remote setting optimization of parameters, etc.

FIG. 2 is a schematic diagram of an application scenario of the method for analyzing the cause of energy consumption according to Embodiment 1 of the present invention, which corresponds to the case where a plurality of operating parameters are preprocessed through step 103 first. As shown in Figure 2, the parameters A, C, E, F, G are obtained according to the measured data of the equipment 201, and the parameter B is obtained according to the detection data of the sensors on the site where the equipment is located. According to the parameters A, B, C, E, F, G, obtain parameter combination 1, parameter combination 2 and reference combination 3, input parameter combination 1, parameter combination 2 and reference combination 3 into the energy consumption reason analysis model 203, and output energy consumption reason 1, energy consumption reason 2 and energy consumption For reason 3, in addition, the respective probabilities of energy consumption reason 1, energy consumption reason 2, and energy consumption reason 3 may also be output at the same time.

FIG. 3 is another schematic diagram of an application scenario of the method for analyzing the cause of energy consumption according to Embodiment 1 of the present invention, which corresponds to the case where step 103 is not used to preprocess multiple operating parameters. As shown in Figure 3, the parameters A, C, E, F, G are obtained according to the measured data of the device 201, and the parameter B is obtained according to the detection data of the sensor on the site where the device is located, and the parameters A, B, C, E, F, G is input into the energy consumption reason analysis model 203, and according to the processing of the first sub-model, parameter combination 1, parameter combination 2 and reference combination 3 are obtained, and parameter combination 1, parameter combination 2 and reference combination 3 are input into the second sub-model Among them, the energy consumption reason 1, the energy consumption reason 2 and the energy consumption reason 3 are output. In addition, the respective probabilities of the energy consumption reason 1, the energy consumption reason 2 and the energy consumption reason 3 can also be output at the same time.

It can be seen from the above-mentioned embodiments that multiple parameter combinations determined according to multiple operating parameters are input into the analysis model of energy consumption causes for analysis, or, in the process of using the energy consumption cause analysis model, firstly determine multiple parameters according to multiple operating parameters The parameter combination is then analyzed based on multiple parameter combinations, and the energy consumption causes corresponding to each parameter combination can be obtained. Moreover, since the analysis of the energy consumption causes is based on multiple parameter combinations rather than directly based on multiple operating parameters, the calculation The amount is small and the analysis result is accurate. In addition, multiple causes of energy consumption corresponding to multiple parameter combinations can be obtained.

Example 2

Embodiment 2 of the present invention provides an energy consumption reason analysis device, which corresponds to the energy consumption cause analysis method described in Embodiment 1, and its specific implementation can refer to the implementation of the method described in Embodiment 1. The contents are the same or related The description will not be repeated.

In the embodiment of the present invention, the device for analyzing the cause of energy consumption can be set locally on the device, or remotely. In addition, the device for analyzing the cause of energy consumption can be a separate device, or it can be set in whole or in part at the local device or set up in a local or remote server.

For example, the device for analyzing the cause of energy consumption can be set in a server. Data collection, model training, and model application are performed through the server.

For example, the server can be a local server or a cloud server. The local server is, for example, a server installed in the building where the device is located.

Fig. 4 is a schematic block diagram of the system configuration of the energy consumption cause analysis device according to Embodiment 2 of the present invention. As shown in FIG. 4 , the apparatus 400 for analyzing the cause of energy consumption may include a processor 401 and a memory 402 ; the memory 402 is coupled to the processor 401 . This diagram is exemplary; other types of structures may also be used in addition to or in place of this structure to implement communication functions or other functions.

In this embodiment of the present invention, for example, as shown in FIG. 4 , the processor 401 may include a central processing unit (CPU) 401-1 and a graphics processing unit (GPU) 401-2.

For example, the graphics processing unit 401-2 is used for training and processing the energy consumption cause analysis model and the parameter combination model, and the central processing unit 401-1 is used for processing other calculation processes.

Alternatively, the processor 401 may also include only a central processing unit (CPU) without a graphics processor.

As shown in FIG. 4 , the apparatus 400 for analyzing the cause of energy consumption may further include: an input unit 403 , a display 404 , and a power supply 405 .

In one embodiment, the processor 401 may be configured to: obtain multiple operating parameters of the device; and input the multiple operating parameters or a combination of multiple parameters determined according to the multiple operating parameters into the energy consumption cause analysis model , to obtain energy consumption causes corresponding to multiple parameter combinations.

For example, the processor 401 may also be configured to: determine multiple parameter combinations according to the multiple operating parameters.

For example, determining multiple parameter combinations according to the multiple operating parameters includes: inputting the multiple operating parameters into a parameter combination model to obtain multiple parameter combinations.

For example, the parameter combination includes at least one condition parameter and at least one index parameter, and the processor 401 may also be configured to: classify the plurality of operating parameters into the condition parameter or the index parameter respectively.

For example, the condition parameter is used as a precondition or an exclusion condition for determining the cause of energy consumption, and the index parameter is used to directly determine the cause of energy consumption.

For example, the condition parameter includes at least one of ambient temperature and equipment operation mode, and the index parameter includes an operation parameter directly related to determining the cause of energy consumption.

For example, inputting the multiple parameter combinations into the energy consumption reason analysis model to obtain the energy consumption causes respectively corresponding to the multiple parameter combinations includes: inputting the multiple parameter combinations and the occurrence ratio of the parameter combinations into the energy consumption analysis model; In the energy consumption reason analysis model, the energy consumption causes corresponding to the multiple parameter combinations are obtained.

For example, the occurrence ratio of the parameter combination includes the ratio of the occurrence time meeting the operating parameters in the parameter combination to the total operating time of the device within a preset time period.

For example, the energy consumption cause analysis model is obtained by establishing a correspondence between the parameter combination and the energy consumption cause.

For example, the energy consumption cause analysis model is obtained by establishing the relationship between operating parameters and parameter combinations and the corresponding relationship between the parameter combination and the energy consumption cause.

For example, among the energy consumption causes respectively corresponding to the multiple parameter combinations, the energy consumption causes respectively corresponding to at least two parameter combinations are the same.

For example, the processor 401 may also be configured to: when the energy consumption causes respectively corresponding to the multiple parameter combinations include multiple energy consumption causes, determine the main energy consumption cause according to a preset rule.

For example, the processor 401 may also be configured to: formulate a solution corresponding to the energy consumption cause according to the energy consumption causes respectively corresponding to the multiple parameter combinations.

For the specific implementation method of the above content, reference may be made to the description in Embodiment 1, and no repeated description is given here.

In this embodiment, the energy consumption cause analysis device 400 does not necessarily include all the components shown in FIG. 4 .

As shown in FIG. 4 , the processor 401 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and/or logic devices. The operation of the component.

The memory 402 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. And the processor 401 can execute the program stored in the memory 402 to implement information storage or processing. The functions of other components are similar to those in the prior art, and will not be repeated here. Each component of the energy consumption cause analysis device 400 may be implemented by dedicated hardware, firmware, software or a combination thereof without departing from the scope of the present invention.

It can be seen from the above-mentioned embodiments that multiple parameter combinations determined according to multiple operating parameters are input into the analysis model of energy consumption causes for analysis, or, in the process of using the energy consumption cause analysis model, firstly determine multiple parameters according to multiple operating parameters The parameter combination is then analyzed based on multiple parameter combinations, and the energy consumption causes corresponding to each parameter combination can be obtained. Moreover, since the analysis of the energy consumption causes is based on multiple parameter combinations rather than directly based on multiple operating parameters, the calculation The amount is small and the analysis result is accurate. In addition, multiple causes of energy consumption corresponding to multiple parameter combinations can be obtained.

An embodiment of the present invention also provides a computer-readable program, wherein when the program is executed in the energy consumption cause analysis device, the program causes the computer to execute the energy described in Embodiment 1 in the energy consumption cause analysis device consumption reason analysis method.

An embodiment of the present invention also provides a storage medium storing a computer-readable program, wherein the computer-readable program enables a computer to execute the energy-consumption cause analysis method described in Embodiment 1 in the energy consumption cause analysis device.

The above devices and methods in the embodiments of the present invention may be implemented by hardware, or may be implemented by combining hardware and software. The present invention relates to a computer-readable program that, when executed by logic means, enables the logic means to realize the above means or constituent means, or causes the logic means to realize the above various methods or steps.

Embodiments of the present invention also relate to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, flash memories, and the like.

The present invention has been described above in conjunction with specific embodiments, but those skilled in the art should be clear that these descriptions are all exemplary and not limiting the protection scope of the present invention. Those skilled in the art can make various variations and modifications to the present invention according to the spirit and principle of the present invention, and these variations and modifications are also within the scope of the present invention.

Claims (16)

1. A method for analyzing the cause of energy consumption, characterized in that the method comprises: obtain multiple operating parameters of the device; and The multiple operating parameters or multiple parameter combinations determined according to the multiple operating parameters are input into an energy consumption cause analysis model to obtain energy consumption causes respectively corresponding to the multiple parameter combinations. 2. The method according to claim 1, characterized in that the method further comprises: According to the multiple operating parameters, multiple parameter combinations are determined. 3. The method according to claim 2, wherein said determining a plurality of parameter combinations according to said plurality of operating parameters comprises: The multiple operating parameters are input into the parameter combination model to obtain multiple parameter combinations. 4. The method of claim 2, wherein, The parameter combination includes at least one condition parameter and at least one index parameter, The method also includes: Classify the plurality of operating parameters into the condition parameters or the index parameters respectively. 5. The method of claim 4, wherein, The condition parameter is used as a precondition or an exclusion condition for determining the cause of energy consumption, The index parameters are used to directly determine the cause of energy consumption. 6. The method according to claim 4 or 5, characterized in that, The condition parameters include at least one of ambient temperature and equipment operation mode, The index parameters include operating parameters directly related to determining the cause of energy consumption. 7. The method according to claim 1, wherein a plurality of parameter combinations are input into the energy consumption cause analysis model to obtain energy consumption causes respectively corresponding to the plurality of parameter combinations, including: Input multiple parameter combinations and the occurrence ratio of the parameter combinations into the energy consumption cause analysis model to obtain energy consumption causes respectively corresponding to the multiple parameter combinations. 8. The method of claim 7, wherein, The occurrence ratio of the parameter combination includes the ratio of the occurrence time of the operation parameters in the parameter combination to the total operation time of the device within a preset time period. 9. The method of claim 1, wherein, The energy consumption cause analysis model is obtained by establishing a correspondence between the parameter combination and the energy consumption cause. 10. The method of claim 1, wherein, The energy consumption cause analysis model is obtained by establishing the relationship between operating parameters and parameter combinations and the corresponding relationship between the parameter combinations and the energy consumption causes. 11. The method of claim 1 wherein, Among the energy consumption causes respectively corresponding to the multiple parameter combinations, the energy consumption causes respectively corresponding to at least two parameter combinations are the same. 12. The method of claim 1, further comprising: When the energy consumption causes respectively corresponding to the multiple parameter combinations include multiple energy consumption causes, the main energy consumption cause is determined according to a preset rule. 13. The method of claim 1, further comprising: According to the energy consumption causes respectively corresponding to the multiple parameter combinations, a solution corresponding to the energy consumption causes is formulated. 14. An energy consumption cause analysis device, characterized in that the device comprises: memory for storing computer programs; A processor, configured to implement the energy consumption cause analysis method according to any one of claims 1-13 when executing the computer program. 15. The apparatus of claim 14, wherein: The device is set in the server. 16. A computer-readable storage medium, characterized in that the storage medium includes a stored computer program, wherein the computer program executes the energy consumption cause analysis method according to any one of claims 1-13 .

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