CN115789871A - Energy consumption reason analysis method and device - Google Patents
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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
Technical Field
The invention relates to the field of control of equipment, in particular to an energy consumption reason analysis method and device.
Background
With the improvement of living standard and the increasing shortage of energy, the requirement for the energy consumption of the equipment is gradually increased, and the determination of the energy consumption reason of the equipment has important significance for the improvement of the performance of the equipment, the control of the equipment and the like.
In recent years, a prior art for determining the cause of device energy consumption by a model has appeared.
For example, the prior art discloses a method for analyzing the cause of energy consumption, which inputs the current energy consumption data and the historical energy consumption data of the same period into a model for comparison, so as to obtain the cause of energy consumption change.
It should be noted that the above description of the background art is provided for the sake of clarity and complete description of the technical solutions of the present invention, and for the understanding of those skilled in the art. Such solutions are not considered to be known to the person skilled in the art merely because they have been set forth in the background section of the invention.
Disclosure of Invention
However, the inventor found that, in the prior art, various operation data of the equipment are directly input into the model, the energy consumption reason is directly analyzed according to various operation parameters, the operation amount is large, the accuracy of the obtained energy consumption reason is poor, and in addition, when the energy consumption reason is multiple, the data amount of the operation data needing to be input is further increased, the operation amount is further increased, and the analysis efficiency is low.
In order to solve at least one of the above problems, embodiments of the present invention provide an energy consumption reason analysis method and apparatus. 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.
According to a first aspect of the embodiments of the present invention, there is provided an energy consumption reason analysis method, including: acquiring a plurality of operating parameters of equipment; and inputting the plurality of operating parameters or a plurality of parameter combinations determined according to the plurality of operating parameters into an energy consumption reason analysis model to obtain energy consumption reasons respectively corresponding to the plurality of parameter combinations.
According to a second aspect of the embodiments of the present invention, there is provided an energy consumption cause analysis apparatus, the apparatus including: a memory for storing a computer program; a processor, configured to implement the method for energy consumption reason analysis according to the first aspect of the embodiment of the present invention when the computer program is executed.
According to a third aspect of the embodiments of the present invention, there is provided a computer-readable storage medium including a stored computer program, wherein the computer program executes the energy consumption cause analysis method according to the first aspect of the embodiments of the present invention.
One of the beneficial effects of the embodiment of the invention is as follows: 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.
Specific embodiments of the present invention are disclosed in detail with reference to the following description and drawings, indicating the manner in which the principles of the invention may be employed. It should be understood that the embodiments of the invention are not so limited in scope. The embodiments of the invention include many variations, modifications and equivalents within the spirit and scope of the appended claims.
The feature information described and illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, in combination with or instead of the feature information in the other embodiments.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps or components.
Drawings
Many aspects of the invention can be better understood with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. For convenience in illustrating and describing some parts of the present invention, corresponding parts may be enlarged or reduced in the drawings. Elements and feature information described in one drawing or one embodiment of the invention may be combined with elements and feature information shown in one or more other drawings or embodiments. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views, and may be used to designate corresponding parts for use in more than one embodiment.
In the drawings:
FIG. 1 is a flowchart of an energy consumption cause analyzing method according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of an application scenario of the energy consumption reason analysis method according to embodiment 1 of the present invention;
fig. 3 is another schematic diagram of an application scenario of the energy consumption reason analysis method according to embodiment 1 of the present invention;
fig. 4 is a schematic block diagram of a system configuration of an energy consumption cause analysis apparatus according to embodiment 2 of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described below with reference to the accompanying drawings.
Example 1
The embodiment 1 of the invention provides an energy consumption reason analysis method. Fig. 1 is a flowchart of an energy consumption reason analysis method according to embodiment 1 of the present invention. As shown in fig. 1, the method includes:
step 101: acquiring a plurality of operating parameters of equipment; and
step 102: and inputting the plurality of operating parameters or a plurality of parameter combinations determined according to the plurality of operating parameters into the energy consumption reason analysis model to obtain energy consumption reasons respectively corresponding to the plurality of parameter combinations.
In this way, since the analysis of the energy consumption cause is performed based on the plurality of parameter combinations rather than directly based on the plurality of operating parameters, the amount of calculation is small and the analysis result is accurate, and in addition, the plurality of energy consumption causes corresponding to the plurality of parameter combinations, respectively, can be obtained.
In the embodiment of the present invention, as an object of analyzing the cause of energy consumption by applying the method, the apparatus may be various apparatuses that generate energy consumption, for example, the apparatus is an air processing apparatus.
For example, the air treatment device includes at least one of an air conditioning device, a fresh air device, and an air purification device.
In the embodiment of the invention, the air conditioning equipment can be air conditioning equipment with various models, various types, various forms and various capacities.
For example, the air conditioner may be a one-to-one (one outdoor unit and one indoor unit) or one-to-many (one outdoor unit and multiple indoor units) air conditioner or a multi-to-many (multiple outdoor units and multiple indoor units) air conditioner, or may be a central air conditioning system.
For example, the indoor unit of the air conditioning equipment can be in the form of peripheral air outlet, two-side air outlet, a ducted air conditioner, ground air outlet, skirting line air outlet and the like; the outdoor unit of the air conditioning equipment can be in the form of single-fan upper air outlet, double-fan upper air outlet, single-fan front air outlet, double-fan front air outlet and the like.
In the embodiment of the present invention, an air conditioning apparatus is taken as an example for description, but the embodiment of the present invention does not limit the specific kind of the apparatus.
In step 101, the plurality of operating parameters of the device may be a plurality of operating parameters of a predetermined kind of current operation of the device.
For example, the operating mode of the air conditioning apparatus, the ambient temperature, the high pressure saturation temperature, the indoor air pipe temperature, the indoor liquid pipe temperature, and the like.
In the embodiment of the present invention, the plurality of operating parameters refers to at least two operating parameters.
In step 101, the plurality of operating parameters are obtained in real time without limitation, for example, by actual measurement data of the plant and detection at the site of use of the plant.
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 reason analysis model, and energy consumption reasons respectively corresponding to the plurality of parameter combinations are obtained.
That is, in the embodiment of the present invention, a plurality of operating parameters may be preprocessed to obtain a plurality of parameter combinations, or a plurality of operating parameters may be input into the energy consumption analysis model, and the plurality of operating parameters are combined into a plurality of parameter combinations inside the energy consumption analysis model, and when the energy consumption reason analysis is performed, the energy consumption analysis model is based on the plurality of parameter combinations, so that the calculation amount can be reduced and the accuracy of the analysis can be improved.
For example, as shown in fig. 1, the method may further include:
step 103: based on the plurality of operating parameters, a plurality of parameter combinations are determined.
In this case, in step 102, the plurality of parameter combinations determined in step 103 are input into the energy consumption cause analysis model.
In step 103, for example, a plurality of operating parameters are input into the parameter combination model to obtain a plurality of parameter combinations. In this way, the preprocessing of a plurality of operating parameters can be performed quickly and accurately.
For example, the parameter combination model is obtained by establishing a relationship between the operation parameters and the parameter combination.
In the embodiment of the present invention, the parameter combination model may be various models based on machine learning or artificial intelligence algorithm.
In the embodiment of the present invention, the parameter combination may be a combination composed of at least two operation 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, 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 an exclusion condition for determining the reason for energy consumption, and the index parameter is used for directly determining the reason for energy consumption.
In this way, by combining the two types of specific parameter combinations and inputting the combined parameters into the energy consumption cause analysis model, the accuracy of the energy consumption cause analysis can be further improved.
In an embodiment of the invention, the condition parameter may include at least one of an ambient temperature and an operation mode of the equipment, and the index parameter may include an operation parameter directly related to the determination of the cause of the energy consumption.
For example, the condition parameters include outdoor temperature and operation mode, and the index parameters include high pressure saturation temperature, indoor gas 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: the plurality of operating parameters are classified into condition parameters or index parameters, respectively.
Thus, in step 102, a plurality of parameter combinations can be conveniently determined.
In step 103, the plurality of parameter combinations are input to the energy consumption reason analysis model, and energy consumption reasons corresponding to the plurality of parameter combinations are obtained.
For example, the operation parameters obtained in step 101 include parameter A, B, C, D, E, F, G, where parameter A, B, C belongs to the condition parameter, parameter E, F, G belongs to the index parameter, and parameter D is the other parameter.
In step 104, classifying the parameter A, B, C into a condition parameter, and classifying the parameter E, F, G into an index parameter; in step 102, the following parameter combinations are determined: parameter combination 1 (parameter A, B, E), parameter combination 2 (parameter A, C, F), and parameter combination 3 (parameter B, C, G); in step 103, the parameter combination 1 (parameter A, B, E), the parameter combination 2 (parameter A, C, F) and the parameter combination 3 (parameter B, C, G) are input to the energy consumption cause analysis model, and the energy consumption cause 1 corresponding to the parameter combination 1, the energy consumption cause 2 corresponding to the parameter combination 2 and the energy consumption cause 3 corresponding to the parameter combination 3 are output.
In the embodiment of the present invention, the energy consumption reasons corresponding to each parameter combination may be the same.
For example, among the energy consumption causes corresponding to the plurality of parameter combinations, the energy consumption causes corresponding to at least two parameter combinations are the same. For example, the above-described energy consumption reason 1 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.
Therefore, the same energy consumption reason is confirmed through the combination of a plurality of parameters, and the accuracy of the analysis result can be further ensured.
In the embodiment of the present invention, the energy consumption reasons corresponding to the respective parameter combinations may also be different. For example, the causes of energy consumption respectively corresponding to the plurality of parameter combinations are different from each other. For example, the energy consumption cause 1, the energy consumption cause 2, and the energy consumption cause 3 are different from each other.
In the embodiment of the present invention, the energy consumption reasons may be various reasons causing abnormal energy consumption, for example, for an air conditioner, the energy consumption reasons may include blockage of an indoor unit heat exchanger, blockage of an outdoor unit heat exchanger, blockage of an indoor unit filter screen, and the like.
In the embodiment of the application, when the energy consumption reason is determined through the energy consumption reason analysis model, the occurrence probability of each parameter combination can be further considered.
For example, in step 102, the plurality of parameter combinations and the occurrence ratios of the parameter combinations are input into the energy consumption reason analysis model, and energy consumption reasons corresponding to the plurality of parameter combinations are obtained.
In an embodiment of the present invention, the ratio of the occurrence of the parameter combination indicates the probability of the occurrence of the parameter combination, which may include the ratio of the occurrence time of the operating parameter in the parameter combination to the total operating time of the device within a preset time period.
The occurrence time of the operation parameters in the parameter combination in the preset time period refers to the cumulative time during which all the operation parameters in the parameter combination simultaneously occur or meet the preset condition in the preset time period.
For example, the parameter combination 1 includes a parameter A, B, E, and the time at which the parameters A, B, E occur simultaneously is taken as the occurrence time of the operating parameter in the parameter combination within a preset time period.
For example, if the parameter a is the operation mode, the parameter B is the outdoor temperature, and the parameter E is the high-pressure saturation temperature, the time when the operation mode is the cooling operation, the outdoor temperature is greater than 30 degrees, and the high-pressure saturation temperature is 50 degrees or higher is set as the occurrence time of the operation parameter in the combination of the parameters.
The ratio of the occurrence time of the operating parameter in the parameter combination to the total operating time of the device in the preset time period refers to the occurrence time of the operating parameter in the parameter combination in the preset time period and the total operating time of the device in the preset time period.
For example, in the preset time period T, if the time when the operation mode is the 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 operation time of the air conditioning equipment is T2, the ratio of the parameter combination is T1/T2.
In the embodiment of the present invention, the energy consumption reason analysis model may be various models based on machine learning or artificial intelligence algorithm.
In the embodiment of the present application, when the plurality of operating parameters are preprocessed in step 103 to determine the plurality of parameter combinations, the energy consumption reason analysis model may be obtained by establishing a corresponding relationship between the parameter combinations and the energy consumption reasons.
In the embodiment of the application, the energy consumption reason analysis model can be obtained by establishing a corresponding relation between the parameter combination and the occurrence proportion thereof and the energy consumption reason.
In the embodiment of the present application, when the plurality of operating parameters are determined by not preprocessing the plurality of operating parameters in step 103 but by combining the plurality of operating parameters in advance through the energy consumption cause analysis model itself, the energy consumption cause analysis model may be obtained by establishing the relationship between the operating parameters and the parameter combination and the corresponding relationship between the parameter combination and the energy consumption cause.
For example, the energy consumption reason analysis model includes a first sub-model and a second sub-model, the first sub-model is obtained by establishing a relationship between an operation parameter and a parameter combination, the second sub-model is obtained by establishing a corresponding relationship between the parameter combination and the energy consumption reason, the first sub-model combines a plurality of operation parameters into a plurality of parameter combinations, and the second sub-model analyzes the energy consumption reason according to the plurality of parameter combinations output by the first sub-model.
Alternatively, the energy consumption reason analysis model may also implement the functions of the first sub-model and the second sub-model by training one model.
For example, in a training stage of an energy consumption reason analysis model, measured data and field detection data of equipment are obtained to obtain a plurality of operation parameters, each operation parameter is labeled and combined to obtain a plurality of parameter combinations, and the plurality of parameter combinations are used as training data; and inputting the training data into a model based on an artificial intelligence algorithm, and training to obtain the energy consumption reason 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: and when the energy consumption reasons respectively corresponding to the parameter combinations comprise a plurality of energy consumption reasons, determining the main energy consumption reason according to a preset rule.
In the embodiment of the present invention, the main energy consumption reason may be determined according to the probabilities of the energy consumption reasons output by the energy consumption reason analysis model, for example, the probability of the energy consumption reason 1 is 0.2, the probability of the energy consumption reason 2 is 0.7, and the probability of the energy consumption reason 3 is 0.1, and then the energy consumption reason 2 is taken as the main energy consumption reason.
In the embodiment of the present invention, the preset rule may also be another rule.
For example, the preset rule may be that priority is set in advance for each energy consumption reason, and the energy consumption reason with high priority is taken as the main energy consumption reason. For example, among the energy consumption reasons 1 corresponding to the parameter combination 1, the energy consumption reasons 2 corresponding to the parameter combination 2, and the energy consumption reasons 3 corresponding to the parameter combination 3 output by the energy consumption reason analysis model, the priority ranking of the energy consumption reasons 1, the energy consumption reasons 2, and the energy consumption reasons 3 is as follows: energy consumption reason 2, energy consumption reason 1, energy consumption reason 3. Then, the power consumption cause 2 is taken as the main power consumption cause.
For example, the main energy consumption reason is determined according to the number of parameter combinations corresponding to the determined energy consumption reasons. For example, the energy consumption cause corresponding to the most parameter combinations is taken as the main energy consumption cause. The embodiment of the invention does not limit the content of the preset rule. For example, the energy consumption reason corresponding to the 2 parameter combinations is energy consumption reason 2,1 is energy consumption reason 1, and then the energy consumption reason 2 is taken as the main energy consumption reason.
In an embodiment of the present invention, for example, as shown in fig. 1, the method may further include:
step 106: and formulating a solution corresponding to the energy consumption reason according to the energy consumption reasons respectively corresponding to the plurality of parameter combinations.
For example, for each energy consumption reason, a solution corresponding thereto is preset.
For example, for energy consumption reason 1, the solution is scheme 1, for energy consumption reason 2, the solution is scheme 2, and for energy consumption reason 3, the solution is scheme 3.
Therefore, the situation of abnormal energy consumption can be specifically and comprehensively eliminated by formulating the solutions corresponding to the energy consumption reasons.
In the embodiment of the present invention, the solution method corresponding to the energy consumption reason is, for example: notification or reminder, remote energy saving setting or optimization, etc.
For example, a specific energy consumption reason is reminded to a user through a terminal APP or a client webpage end, for example, a filter screen is notified of being blocked, and a user is required to replace or clean in time; for example, when the cause of energy consumption is related to improper usage habits of the user, energy saving setting is performed by the server, and remote setting optimization of parameters is performed.
Fig. 2 is a schematic diagram of an application scenario of the energy consumption reason analysis method according to embodiment 1 of the present invention, which corresponds to a case where a plurality of operation parameters are preprocessed through step 103. As shown in fig. 2, a parameter A, C, E, F, G is obtained from measured data of the device 201, a parameter B is obtained from detected data of a sensor in the field of the device, a parameter combination 1, a parameter combination 2, and a reference combination 3 are obtained from a parameter A, B, C, E, F, G, the parameter combination 1, the parameter combination 2, and the reference combination 3 are input to the energy consumption reason analysis model 203, and the energy consumption reason 1, the energy consumption reason 2, and the energy consumption reason 3 are output, and further, probabilities of the energy consumption reason 1, the energy consumption reason 2, and the energy consumption reason 3 can be simultaneously output.
Fig. 3 is another schematic diagram of an application scenario of the energy consumption cause analysis method according to embodiment 1 of the present invention, which corresponds to a case where a plurality of operation parameters are not preprocessed through step 103. As shown in fig. 3, a parameter A, C, E, F, G is obtained from measured data of the device 201, a parameter B is obtained from detected data of a sensor in the field of the device, a parameter A, B, C, E, F, G is input to the energy consumption reason analysis model 203, a parameter combination 1, a parameter combination 2 and a reference combination 3 are obtained from processing of the first submodel, the parameter combination 1, the parameter combination 2 and the reference combination 3 are input to the second submodel, and the energy consumption reason 1, the energy consumption reason 2 and the energy consumption reason 3 are output, and further, probabilities of the energy consumption reason 1, the energy consumption reason 2 and the energy consumption reason 3 can be simultaneously output.
As can be seen from the above embodiments, by inputting a plurality of parameter combinations determined according to a plurality of operating parameters into the energy consumption cause analysis model for analysis, or by determining a plurality of parameter combinations according to a plurality of operating parameters and then performing analysis based on the plurality of parameter combinations in the process of using the energy consumption cause analysis model, the energy consumption cause corresponding to each parameter combination can be obtained, and since the energy consumption cause is analyzed based on the plurality of parameter combinations rather than being directly analyzed based on the plurality of operating parameters, the calculation amount is small and the analysis result is accurate, and in addition, a plurality of energy consumption causes corresponding to the plurality of parameter combinations can be obtained.
Example 2
Embodiment 2 of the present invention provides an energy consumption cause analysis device corresponding to the energy consumption cause analysis method described in embodiment 1, and the specific implementation thereof can refer to the implementation of the method described in embodiment 1, and the same or related contents will not be described again.
In the embodiment of the present invention, the energy consumption reason analyzing apparatus may be disposed locally on the device, or may be disposed remotely, and in addition, the energy consumption reason analyzing apparatus may be a separate device, or may be disposed wholly or partially in the device locally or in a server locally or remotely.
For example, the energy consumption cause analysis device may be provided in a server. The server collects data, trains models, and applies models.
For example, the server may be a local server or a cloud server. The local server is, for example, a server provided in a building in which 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 energy consumption reason analysis apparatus 400 may include a processor 401 and a memory 402; the memory 402 is coupled to the processor 401. The figure is exemplary; other types of structures may also be used in addition to or in place of the structures to implement communication functions or other functions.
In embodiments of the invention, for example, as shown in FIG. 4, processor 401 may include a Central Processing Unit (CPU) 401-1 and a Graphics Processing Unit (GPU) 401-2.
For example, the graphic processor 401-2 is used for training and processing of an energy consumption reason analysis model and a parameter combination model, and the like, and the central processor 401-1 is used for processing of other calculation processes, and the like.
Alternatively, processor 401 may include only a Central Processing Unit (CPU) and no graphics processor.
As shown in fig. 4, the energy consumption reason analyzing apparatus 400 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: acquiring a plurality of operating parameters of equipment; and inputting the plurality of operating parameters or a plurality of parameter combinations determined according to the plurality of operating parameters into an energy consumption reason analysis model to obtain energy consumption reasons respectively corresponding to the plurality of parameter combinations.
For example, the processor 401 may be further configured to: based on the plurality of operating parameters, a plurality of parameter combinations are determined.
For example, the determining a plurality of parameter combinations based on the plurality of operating parameters includes: and inputting the plurality of operating parameters into a parameter combination model to obtain a plurality of parameter combinations.
For example, the parameter combination comprises at least one condition parameter and at least one index parameter, the processor 401 may be further configured to: the operating parameters are classified into the condition parameters or the index parameters 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 for directly determining the cause of energy consumption.
For example, the condition parameter includes at least one of an ambient temperature and an operation mode of the equipment, and the index parameter includes an operation parameter directly related to the determination of the cause of the energy consumption.
For example, inputting the plurality of parameter combinations into an energy consumption reason analysis model to obtain energy consumption reasons corresponding to the plurality of parameter combinations respectively includes: and inputting the parameter combinations and the occurrence ratios of the parameter combinations into the energy consumption reason analysis model to obtain energy consumption reasons respectively corresponding to the parameter combinations.
For example, the ratio of the occurrence of the parameter combination includes the ratio of the occurrence time of the operating parameter in the parameter combination to the total operating time of the device within a preset time period.
For example, the energy consumption reason analysis model is obtained by establishing a corresponding relationship between the parameter combination and the energy consumption reason.
For example, the energy consumption reason analysis model is obtained by establishing a relationship between an operation parameter and a parameter combination and a corresponding relationship between the parameter combination and the energy consumption reason.
For example, among the energy consumption causes corresponding to the plurality of parameter combinations, the energy consumption causes corresponding to at least two parameter combinations are the same.
For example, the processor 401 may be further configured to: and when the energy consumption reasons respectively corresponding to the parameter combinations comprise a plurality of energy consumption reasons, determining the main energy consumption reason according to a preset rule.
For example, the processor 401 may be further configured to: and formulating a solution corresponding to the energy consumption reason according to the energy consumption reasons respectively corresponding to the plurality of parameter combinations.
The specific implementation method of the above-described contents can be referred to the description of embodiment 1, and the description thereof will not be repeated.
The energy consumption cause analysis apparatus 400 in the present embodiment does not necessarily include all the components shown in fig. 4.
As shown in fig. 4, processor 401, which is sometimes referred to as a controller or operation control, may include a microprocessor or other processor device and/or logic device, and processor 401 receives inputs and controls the operation of the various components of energy consumption cause analysis apparatus 400.
The memory 402 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. And the processor 401 may execute the program stored in the memory 402 to realize information storage or processing, or the like. The functions of other parts are similar to the prior art and are not described in detail here. The components of the energy consumption reason analysis apparatus 400 may be implemented in dedicated hardware, firmware, software, or combinations thereof, without departing from the scope of the invention.
As can be seen from the above embodiments, a plurality of parameter combinations determined according to a plurality of operating parameters are input to the energy consumption reason analysis model for analysis, or a plurality of parameter combinations are determined according to a plurality of operating parameters in the process of using the energy consumption reason analysis model and then are analyzed based on the plurality of parameter combinations, so that the energy consumption reason corresponding to each parameter combination can be obtained, and since the energy consumption reason is analyzed based on the plurality of parameter combinations rather than being directly analyzed based on the plurality of operating parameters, the calculation amount is small and the analysis result is accurate, and in addition, a plurality of energy consumption reasons corresponding to the plurality of parameter combinations respectively can be obtained.
An embodiment of the present invention further provides a computer-readable program, where when the program is executed in an energy consumption cause analysis apparatus, the program causes a computer to execute the energy consumption cause analysis method described in embodiment 1 in the energy consumption cause analysis apparatus.
The embodiment of the present invention further provides a storage medium storing a computer-readable program, where the computer-readable program enables a computer to execute the energy consumption reason analysis method described in embodiment 1 in the energy consumption reason analysis apparatus.
The above apparatuses and methods according to the embodiments of the present invention may be implemented by hardware, or may be implemented by hardware in combination with software. The present invention relates to a computer-readable program which, when executed by a logic section, enables the logic section to realize the above apparatus or constituent section, or to realize the above various methods or steps.
The embodiment of the invention also relates to a storage medium for storing the program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory and the like.
While the invention has been described with reference to specific embodiments, it will be apparent to those skilled in the art that these descriptions are illustrative and not intended to limit the scope of the invention. Various modifications and alterations of this invention will become apparent to those skilled in the art based upon the spirit and principles of this invention, and such modifications and alterations are also within the scope of this invention.
Claims (16)
1. An energy consumption reason analysis method, characterized in that the method comprises:
acquiring a plurality of operating parameters of equipment; and
and inputting the plurality of operating parameters or a plurality of parameter combinations determined according to the plurality of operating parameters into an energy consumption reason analysis model to obtain energy consumption reasons respectively corresponding to the plurality of parameter combinations.
2. The method of claim 1, further comprising:
determining a plurality of parameter combinations according to the plurality of operating parameters.
3. The method of claim 2, wherein determining a plurality of parameter combinations based on the plurality of operating parameters comprises:
and inputting the plurality of operating parameters into a parameter combination model to obtain a plurality of parameter combinations.
4. The method of claim 2,
the parameter combination comprises at least one condition parameter and at least one index parameter,
the method further comprises the following steps:
and classifying the plurality of operating parameters into the condition parameters or the index parameters respectively.
5. The method of claim 4,
the condition parameter is used as a precondition or exclusion condition for determining the reason for energy consumption,
the index parameter is used for directly determining the reason of energy consumption.
6. The method according to claim 4 or 5,
the condition parameters include at least one of ambient temperature and equipment operating mode,
the indicator parameters include operating parameters directly related to determining the cause of energy consumption.
7. The method of claim 1, wherein inputting a plurality of parameter combinations into an energy consumption reason analysis model to obtain energy consumption reasons corresponding to the plurality of parameter combinations respectively comprises:
and inputting a plurality of parameter combinations and the occurrence ratios of the parameter combinations into the energy consumption reason analysis model to obtain energy consumption reasons respectively corresponding to the parameter combinations.
8. The method of claim 7,
the proportion of the occurrence of the parameter combination comprises the proportion of the occurrence time of the operation parameters in the parameter combination to the total operation time of the equipment within a preset time period.
9. The method of claim 1,
the energy consumption reason analysis model is obtained by establishing the corresponding relation between the parameter combination and the energy consumption reason.
10. The method of claim 1,
the energy consumption reason analysis model is obtained by establishing a relation between an operation parameter and a parameter combination and a corresponding relation between the parameter combination and the energy consumption reason.
11. The method of claim 1,
among the energy consumption causes corresponding to the plurality of parameter combinations, the energy consumption causes corresponding to at least two parameter combinations are the same.
12. The method of claim 1, further comprising:
and when the energy consumption reasons respectively corresponding to the parameter combinations comprise a plurality of energy consumption reasons, determining a main energy consumption reason according to a preset rule.
13. The method of claim 1, further comprising:
and formulating a solution corresponding to the energy consumption reason according to the energy consumption reasons respectively corresponding to the parameter combinations.
14. An energy consumption cause analysis apparatus, characterized in that the apparatus comprises:
a memory for storing a computer program;
a processor for implementing the energy consumption cause analysis method of any one of claims 1-13 when executing the computer program.
15. The apparatus of claim 14,
the apparatus is provided in a server.
16. A computer-readable storage medium, characterized in that the storage medium comprises a stored computer program, wherein the computer program performs the energy consumption cause analysis method of any one of claims 1-13.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202111062423.2A CN115789871A (en) | 2021-09-10 | 2021-09-10 | Energy consumption reason analysis method and device |
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