CN111401796A - Method and device for establishing equipment energy efficiency model - Google Patents
Method and device for establishing equipment energy efficiency model Download PDFInfo
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Abstract
The invention relates to the technical field of energy, and provides a method and a device for establishing an equipment energy efficiency model, wherein the method comprises the following steps: acquiring data information of target equipment, wherein the data information at least comprises input quantity data, output quantity data and rated capacity data; carrying out data cleaning and data integration processing on the data information to obtain an energy efficiency mean value and a load rate mean value corresponding to the data information; carrying out interval division and clustering processing on the energy efficiency mean value and the load rate mean value to obtain a micro cluster corresponding to each interval; processing the micro-clusters corresponding to each interval to obtain a standard clustering center corresponding to each interval; and acquiring an energy efficiency curve model of the target equipment according to the standard clustering center corresponding to each interval. The feasibility of modeling data is guaranteed by cleaning and integrating the data; clustering the preprocessed data to obtain an energy efficiency model of the actual running state of the equipment; the method improves the efficiency and accuracy of energy efficiency modeling and reduces the cost of energy efficiency modeling.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a method and a device for establishing an equipment energy efficiency model.
Background
In digital operation and maintenance and intelligent operation and maintenance, the energy efficiency curve of the equipment plays an important role in the scenes of optimal scheduling, equipment maintenance, effect evaluation and the like of the energy station. The traditional energy efficiency modeling mode is mainly used for establishing an equipment model according to an expert experience system, so that the requirement on the professional performance of technicians is high, and the modeling time is long.
At present, although a manufacturer of equipment can provide an equipment energy efficiency curve according to a specific model when the equipment is manufactured, after the equipment runs for a period of time, due to differences of running time, running state, running environment and the like, an actual energy efficiency model of each equipment can be changed to a certain extent, and how to quickly and accurately obtain the actual running energy efficiency model of the equipment is important. Generally, a device technician can give an energy efficiency model according to the actual running condition of the device and the input and output quantities of the device in different states, but the method has low efficiency and high production cost.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for establishing an energy efficiency model of a device, a terminal device, and a computer-readable storage medium, so as to solve the technical problem in the prior art that an energy efficiency model in an actual operating state of a device cannot be obtained quickly and accurately.
In a first aspect of the embodiments of the present invention, a method for establishing an equipment energy efficiency model is provided, including:
acquiring data information of target equipment, wherein the data information at least comprises input quantity data, output quantity data and rated capacity data;
carrying out data cleaning and data integration processing on the data information to obtain an energy efficiency mean value and a load rate mean value corresponding to the data information;
carrying out interval division and clustering processing on the energy efficiency mean value and the load factor mean value to obtain a micro cluster corresponding to each interval;
processing the micro-cluster corresponding to each interval to obtain a standard cluster center corresponding to each interval;
and acquiring an energy efficiency curve model of the target equipment according to the standard clustering center corresponding to each interval.
In a second aspect of the embodiments of the present invention, an apparatus for establishing an equipment energy efficiency model is provided, including:
the system comprises an information acquisition module, a data processing module and a data processing module, wherein the information acquisition module is used for acquiring data information of target equipment, and the data information at least comprises input quantity data, output quantity data and rated capacity data;
the mean value acquisition module is used for carrying out data cleaning and data integration processing on the data information to acquire an energy efficiency mean value and a load factor mean value corresponding to the data information;
the micro-cluster obtaining module is used for carrying out interval division and clustering processing on the energy efficiency mean value and the load rate mean value to obtain micro-clusters corresponding to each interval;
the standard clustering center acquisition module is used for processing the micro-clusters corresponding to each interval to acquire a standard clustering center corresponding to each interval;
and the model acquisition module is used for acquiring an energy efficiency curve model of the target equipment according to the standard clustering center corresponding to each interval.
In a third aspect of the embodiments of the present invention, a terminal device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for establishing a device energy efficiency model when executing the computer program.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where a computer program is stored, and the computer program, when being executed by a processor, implements the steps of the method for establishing a device energy efficiency model.
The method for establishing the equipment energy efficiency model provided by the embodiment of the invention has the beneficial effects that at least: the invention provides an energy efficiency modeling method for common equipment (such as a boiler, a refrigerator, an internal combustion engine and the like) of an energy station, which ensures the feasibility of modeling data by preprocessing (such as data cleaning and integration) the data; clustering the preprocessed data, and further obtaining an energy efficiency model of the actual running state of the equipment; the method improves the efficiency and accuracy of energy efficiency modeling and reduces the cost of energy efficiency modeling.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an implementation of a method for establishing an equipment energy efficiency model according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an implementation process of acquiring data information of the target device in the method for establishing a device energy efficiency model according to the embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating an implementation process of obtaining an energy efficiency average value and a load factor average value corresponding to the data information in the method for establishing an equipment energy efficiency model according to the embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating an implementation process of acquiring a micro cluster corresponding to each interval in the method for establishing an equipment energy efficiency model according to the embodiment of the present invention;
fig. 5 is a schematic flow chart illustrating an implementation process of obtaining a standard clustering center corresponding to each interval in the method for establishing an equipment energy efficiency model according to the embodiment of the present invention;
FIG. 6 is a schematic diagram of an apparatus for building an equipment energy efficiency model according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an information obtaining module in an apparatus for building an equipment energy efficiency model according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a mean value obtaining module in the apparatus for building an equipment energy efficiency model according to the embodiment of the present invention;
fig. 9 is a schematic diagram of a micro-cluster obtaining module in the apparatus for establishing an equipment energy efficiency model according to the embodiment of the present invention;
fig. 10 is a schematic diagram of a standard clustering center obtaining module in the apparatus for establishing an equipment energy efficiency model according to the embodiment of the present invention;
fig. 11 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
In the digital operation and maintenance era, the operation and maintenance data of the energy station is accessed to the Internet of things and has already been provided with the basis of digital analysis. After minute-scale (or second-scale) internet of things data is acquired, it is possible to acquire the instantaneous energy efficiency of the computing device.
Referring to fig. 1, a schematic flow chart of an implementation of a method for establishing an equipment energy efficiency model according to an embodiment of the present invention is shown, where the method may include:
step S10: and acquiring data information of the target equipment, wherein the data information at least comprises input quantity data, output quantity data and rated capacity data.
In order to obtain data information of a target device, the data information of the target device needs to be collected, and the data information at least comprises input quantity data, output quantity data and rated capacity data. Fig. 2 is a schematic view of an implementation flow of acquiring data information of the target device in the method for establishing a device energy efficiency model according to the embodiment of the present invention, where in this embodiment, a manner of acquiring data information of the target device may include the following steps:
step S101: and acquiring historical data and real-time operation data of the target equipment according to a preset data acquisition frequency.
The data collection frequency may be one minute or one fifth minute depending on the actual situation, and it should be understood that the collection frequency may be preset, and is not limited herein.
Step S102: and acquiring data information of the target equipment according to the historical data and the real-time operation data, wherein the data information at least comprises input quantity data, output quantity data and rated capacity data.
And modeling the energy efficiency of the equipment, wherein the used data comprises input and output data of the equipment and a rated capacity value (on an equipment nameplate) of the equipment, and of course, the used data can also comprise other equipment data information parameters and the like, and the method is not limited here.
Referring to fig. 1, further, after acquiring the data information of the target device, the following steps may be performed:
step S20: and carrying out data cleaning and data integration processing on the data information to obtain an energy efficiency mean value and a load rate mean value corresponding to the data information.
Further, in order to obtain an energy efficiency average value and a load factor average value corresponding to the data information, data cleaning processing needs to be performed on the data information. Fig. 3 is a schematic flow chart illustrating an implementation process of obtaining an energy efficiency average value and a load rate average value corresponding to the data information in the method for establishing an energy efficiency model of a device according to the embodiment of the present invention, where in this embodiment, a manner of obtaining the energy efficiency average value and the load rate average value corresponding to the data information may include the following steps:
step S201: and carrying out data cleaning processing on the data information according to a preset data cleaning rule to obtain first intermediate data, wherein the data cleaning rule at least comprises the step of removing abnormal data which do not accord with the running state of the target equipment.
Due to the problems that the field condition of the energy station is relatively severe, the data remote transmission distance is too long and the like, and the data is easy to have abnormal points, the energy efficiency modeling needs to be preprocessed and cleaned when the real-time operation data is used for modeling, so that the feasibility of the modeling data is ensured.
The data cleaning rule can be used for cleaning unreasonable abnormal data, and the rule method is mainly adopted for screening abnormal points: screening some simple data which do not conform to the running state according to the characteristics of the equipment, wherein the simple data mainly comprise that when the equipment stops, the output data of the equipment is equal to 0; the rated capacity of the device is the maximum value of the energy produced by the device, above which it is evident that the data is anomalous; it should be understood that the data cleansing rule may include not only cleansing for unreasonably abnormal data, but also cleansing or processing data such as repeated data, and is not limited herein.
Step S202: and acquiring the average value of the input quantity and the average value of the output quantity of the target equipment according to the first intermediate data based on the data acquisition frequency.
Due to the large amount of data and the weakening of the influence of individual data on the overall modeling. Firstly, data integration is carried out on the collected data, namely the average value of data of the collection point of the input quantity and the output quantity of the computing equipment every 5min is calculated.
The input quantity mean value obtaining mode is as follows:
wherein the content of the first and second substances,
is the mean value of the input quantity, i is the number of times of the data acquisition frequency, xiAnd M is the data collection total number in the preset time.
The output quantity average value obtaining mode is as follows:
wherein the content of the first and second substances,
is the mean value of the output, yiThe output quantity of the ith time.
It should be understood that the above 5min is only a specific example of the present embodiment, and any other preset time can be also used, which is not limited herein.
Step S203: and acquiring an energy efficiency average value according to the input quantity average value and the output quantity average value.
The energy efficiency mean value is obtained in the following mode:
wherein the content of the first and second substances,
and the energy efficiency mean value.
M represents the total number of data acquisitions within 5min,
respectively, mean values of 5min input and output. It should be understood that the above 5min is only a specific example of the present embodiment, and any other preset time can be also used, which is not limited herein.
Step S204: and acquiring a load rate mean value according to the output quantity mean value and the rated capacity data.
The obtaining mode of the load rate mean value is as follows:
wherein the content of the first and second substances,
the load factor mean, ptrade is the rated capacity.
Referring to fig. 1, further, after obtaining the energy efficiency average value and the load factor average value corresponding to the data information, the following steps may be performed:
step S30: and carrying out interval division and clustering processing on the energy efficiency mean value and the load rate mean value to obtain a micro cluster corresponding to each interval.
Further, in order to obtain the micro-cluster corresponding to each interval, interval division and clustering processing need to be performed on the energy efficiency average value and the load factor average value. Fig. 4 is a schematic flow chart illustrating an implementation process of acquiring a micro cluster corresponding to each interval in the method for establishing an equipment energy efficiency model according to the embodiment of the present invention, where in this embodiment, one manner of acquiring a micro cluster corresponding to each interval may include the following steps:
step S301: and acquiring load efficiency energy efficiency data according to the energy efficiency mean value and the load efficiency mean value.
For the modeling of the energy efficiency of the equipment, the K-means clustering algorithm is mainly adopted to carry out data cleaning on the data one step at first and then carry out modeling according to the cleaned data. It should be understood that the clustering algorithm may be a K-means clustering algorithm, or may be other clustering algorithms, and is not limited herein.
Step S302: and carrying out interval division on the load efficiency energy efficiency data according to a preset step length so as to divide the load efficiency energy efficiency data into corresponding intervals.
And (3) sorting the data after the data preprocessing step, taking 1% of the load rate-energy efficiency data as fragments according to the load rate interval, and forming a plurality of micro-clusters by respectively adopting a K-means clustering algorithm for each fragment.
In practical applications, the load factor may be theoretically divided into segments smaller than 100% according to actual situations, but in order to make full use of data and accurately fit the equipment energy efficiency model, 1% is generally used as a segment, which is not limited herein.
Step S303: and processing each interval by adopting a clustering analysis algorithm to obtain the micro-cluster corresponding to each interval.
Referring to fig. 1, further, after acquiring the micro-cluster corresponding to each interval, the following steps may be performed:
step S40: and processing the micro-cluster corresponding to each interval to obtain a standard cluster center corresponding to each interval.
Further, in order to obtain the standard cluster center corresponding to each interval, the micro-cluster corresponding to each interval needs to be processed. Fig. 5 is a schematic flow chart illustrating an implementation process of obtaining a standard clustering center corresponding to each interval in the method for establishing an equipment energy efficiency model according to the embodiment of the present invention, where in this embodiment, one way of obtaining a standard clustering center corresponding to each interval may include the following steps:
step S401: and acquiring the distance between any two micro-clusters in each interval.
The data structure of a micro-cluster is defined as C ═ NnumLs，Ss，Cs，Bs，p0，p1]In which N isnumIndicates the number of data points in the micro-cluster, LsRepresenting the linear sum of data, i.e. the sum of data, Ss、Cs、BsRespectively representing the sum of squares, the sum of cubes, and the sum of the fourth power, p0、p1Respectively representing the initial position of the micro-cluster generation and the updated position.
The distance between two micro-clusters is obtained by the following method:
wherein D is the distance between the two micro-clusters, N is the number of data points in the micro-clusters, N is1Is the number of data points in the first micro-cluster, N2Is the number of data points in the second micro-cluster, Ss1Is the sum of squares, S, of the data in the first micro-clusters2Sum of squares of data in the second micro-cluster, Ls1L being the sum of the data in the first micro-clusters2Is the sum of the data in the second micro-cluster;
step S402: and when the distance between the two micro-clusters in the interval is smaller than a preset threshold value, merging the two micro-clusters.
The preset threshold value obtaining mode is as follows:
A＝μ+3σ
wherein A is the threshold, mu is the mean of the historical data, and sigma is the standard deviation of the historical data.
Step S403: and carrying out mean processing on the micro-clusters which are larger than the preset threshold value in the interval to obtain a standard clustering center corresponding to the interval.
Referring to fig. 1, further, after obtaining the standard cluster center corresponding to the interval, the following steps may be performed:
step S50: and acquiring an energy efficiency curve model of the target equipment according to the standard clustering center corresponding to each interval.
And connecting the obtained points of the standard clustering center corresponding to each region by using a curve, thereby representing the energy efficiency curve model of the equipment. The model is a dynamic model which changes with time, is continuously adjusted with the increase of data, and quickly shows the energy efficiency of the equipment in real time.
It should be understood that the above-mentioned letters and/or symbols are only used for clearly explaining the meaning of specific parameters of the device or steps, and other letters or symbols can be used for representing the device or steps, which is not limited herein.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The method for establishing the equipment energy efficiency model provided by the embodiment of the invention has the beneficial effects that at least: the invention provides an energy efficiency modeling method for common equipment (such as a boiler, a refrigerator, an internal combustion engine and the like) of an energy station, which ensures the feasibility of modeling data by preprocessing (such as data cleaning and integration) the data; clustering the preprocessed data, and further obtaining an energy efficiency model of the actual running state of the equipment; the method improves the efficiency and accuracy of energy efficiency modeling and reduces the cost of energy efficiency modeling.
Fig. 6 is a schematic diagram of the apparatus for establishing an equipment energy efficiency model according to the embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present application are shown.
Referring to fig. 6, an apparatus for building an equipment energy efficiency model includes an information obtaining module 61, a mean obtaining module 62, a micro-cluster obtaining module 63, a standard clustering center obtaining module 64, and a model obtaining module 65. The information acquisition module 61 is configured to acquire data information of a target device, where the data information at least includes input quantity data, output quantity data, and rated capacity data; the mean value obtaining module 62 is configured to perform data cleaning and data integration processing on the data information, and obtain an energy efficiency mean value and a load factor mean value corresponding to the data information; the micro-cluster obtaining module 63 is configured to perform interval division and clustering processing on the energy efficiency average value and the load factor average value to obtain a micro-cluster corresponding to each interval; the standard clustering center obtaining module 64 is configured to process the micro-clusters corresponding to each interval to obtain a standard clustering center corresponding to each interval; the model obtaining module 65 is configured to obtain an energy efficiency curve model of the target device according to the standard clustering center corresponding to each interval.
Referring to fig. 7, the information acquiring module 61 further includes a history data and real-time operation data acquiring unit 611 and a data information acquiring unit 612. The historical data and real-time operation data acquiring unit 611 is configured to acquire historical data and real-time operation data of the target device according to a preset data acquisition frequency; the data information obtaining unit 612 is configured to obtain data information of the target device according to the historical data and the real-time operation data, where the data information at least includes input quantity data, output quantity data, and rated capacity data.
Referring to fig. 8, the average value obtaining module 62 further includes a cleaning unit 621, an input quantity average value and output quantity average value obtaining unit 622, an energy efficiency average value obtaining unit 623, and a load factor average value obtaining unit 624. The cleaning unit 621 is configured to perform data cleaning processing on the data information according to a preset data cleaning rule to obtain first intermediate data, where the data cleaning rule at least includes removing abnormal data that does not conform to the operating state of the target device; the input quantity average value and output quantity average value obtaining unit 622 is configured to obtain the input quantity average value and output quantity average value of the target device according to the first intermediate data based on the data acquisition frequency; the energy efficiency average value obtaining unit 623 is configured to obtain an energy efficiency average value according to the input quantity average value and the output quantity average value; the load rate average obtaining unit 624 is configured to obtain a load rate average according to the output volume average and the rated capacity data.
Referring to fig. 9, the micro-cluster obtaining module 63 further includes a load efficiency and energy efficiency data obtaining unit 631, a section obtaining unit 632, and a micro-cluster obtaining unit 633. The load efficiency energy efficiency data obtaining unit 631 is configured to obtain load efficiency energy efficiency data according to the energy efficiency average value and the load efficiency average value; the interval obtaining unit 632 is configured to perform interval division on the load efficiency energy efficiency data according to a preset step length, so as to divide the load efficiency energy efficiency data into corresponding intervals; the micro-cluster obtaining unit 633 is configured to process each interval by using a cluster analysis algorithm, and obtain a micro-cluster corresponding to each interval.
Referring to fig. 10, the standard cluster center obtaining module 64 further includes a distance obtaining unit 641, a merging processing unit 642 and a standard cluster center obtaining unit 643. The distance obtaining unit 641 is configured to obtain a distance between any two micro clusters in each interval; the merging processing unit 642 is configured to merge two micro-clusters in the interval when a distance between the two micro-clusters is smaller than a preset threshold; the standard clustering center obtaining unit 643 is configured to perform mean processing on the micro clusters that are greater than the preset threshold in the interval, and obtain a standard clustering center corresponding to the interval.
Fig. 11 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 11, the terminal device 7 includes a memory 71, a processor 70, and a computer program 72 stored in the memory 71 and executable on the processor 70, and when the processor 70 executes the computer program 72, the steps of the method for establishing the device energy efficiency model are implemented. Such as steps S10-S50 shown in fig. 1-5.
The terminal device 7 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, the processor 70 and the memory 71. It will be appreciated by those skilled in the art that fig. 11 is merely an example of a terminal device 7 and does not constitute a limitation of the terminal device 7 and may include more or less components than those shown, or some components may be combined, or different components, for example the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or a memory of the terminal device 7. The memory 71 may also be an external storage device of the terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the terminal device 7. The memory 71 is used for storing the computer program and other programs and data required by the terminal device. The memory 71 may also be used to temporarily store data that has been output or is to be output.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Specifically, the present application further provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the memory in the foregoing embodiments; or it may be a separate computer-readable storage medium not incorporated into the terminal device. The computer readable storage medium stores one or more computer programs:
computer-readable storage medium, comprising a computer program stored thereon, which, when being executed by a processor, carries out the steps of the method of establishing a device energy efficiency model.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for establishing an equipment energy efficiency model is characterized by comprising the following steps:
acquiring data information of target equipment, wherein the data information at least comprises input quantity data, output quantity data and rated capacity data;
carrying out data cleaning and data integration processing on the data information to obtain an energy efficiency mean value and a load rate mean value corresponding to the data information;
carrying out interval division and clustering processing on the energy efficiency mean value and the load factor mean value to obtain a micro cluster corresponding to each interval;
processing the micro-cluster corresponding to each interval to obtain a standard cluster center corresponding to each interval;
and acquiring an energy efficiency curve model of the target equipment according to the standard clustering center corresponding to each interval.
2. The method for establishing the equipment energy efficiency model according to claim 1, wherein the collecting data information of the target equipment, the data information at least comprising input quantity data, output quantity data and rated capacity data, comprises:
acquiring historical data and real-time operation data of target equipment according to a preset data acquisition frequency;
and acquiring data information of the target equipment according to the historical data and the real-time operation data, wherein the data information at least comprises input quantity data, output quantity data and rated capacity data.
3. The method for establishing the equipment energy efficiency model according to claim 2, wherein the performing data cleaning and data integration processing on the data information to obtain an energy efficiency mean value and a load rate mean value corresponding to the data information includes:
performing data cleaning processing on the data information according to a preset data cleaning rule to obtain first intermediate data, wherein the data cleaning rule at least comprises removing abnormal data which do not accord with the running state of the target equipment;
acquiring the average value of the input quantity and the average value of the output quantity of the target equipment according to the first intermediate data based on the data acquisition frequency;
acquiring an energy efficiency mean value according to the input quantity mean value and the output quantity mean value;
and acquiring a load rate mean value according to the output quantity mean value and the rated capacity data.
4. The method for establishing the equipment energy efficiency model according to claim 3, wherein the input quantity average value obtaining mode is as follows:
wherein the content of the first and second substances,
is the mean value of the input quantity, i is the number of times of the data acquisition frequency, xiThe number of times of data acquisition is the ith input quantity, and M is the total number of times of data acquisition in the preset time;
the output quantity average value obtaining mode is as follows:
wherein the content of the first and second substances,
is a stand forSaid mean value of output, yiThe output quantity of the ith time;
the energy efficiency mean value is obtained in the following mode:
wherein the content of the first and second substances,
the energy efficiency mean value is obtained;
the obtaining mode of the load rate mean value is as follows:
wherein the content of the first and second substances,
is the mean load factor, PRated valueIs rated capacity.
5. The method according to claim 4, wherein the step of performing interval division and clustering on the energy efficiency mean value and the load factor mean value to obtain the micro-cluster corresponding to each interval comprises:
acquiring load efficiency energy efficiency data according to the energy efficiency mean value and the load efficiency mean value;
dividing the load efficiency energy efficiency data into intervals according to a preset step length so as to divide the load efficiency energy efficiency data into corresponding intervals;
and processing each interval by adopting a clustering analysis algorithm to obtain the micro-cluster corresponding to each interval.
6. The method according to claim 5, wherein the processing the micro-cluster corresponding to each interval to obtain the standard cluster center corresponding to each interval includes:
acquiring the distance between any two micro-clusters in each interval;
when the distance between the two micro-clusters in the interval is smaller than a preset threshold value, merging the two micro-clusters;
and carrying out mean processing on the micro-clusters which are larger than the preset threshold value in the interval to obtain a standard clustering center corresponding to the interval.
7. The method for establishing the equipment energy efficiency model according to claim 6, wherein the distance between two micro-clusters is obtained by:
wherein D is the distance between the two micro-clusters, N1Is the number of data points in the first micro-cluster, N2Is the number of data points in the second micro-cluster, Ss1Is the sum of squares, S, of the data in the first micro-clusters2Sum of squares of data in the second micro-cluster, Ls1L being the sum of the data in the first micro-clusters2Is the sum of the data in the second micro-cluster;
the preset threshold value obtaining mode is as follows:
A＝μ+3σ
wherein A is the threshold, mu is the mean of the historical data, and sigma is the standard deviation of the historical data.
8. An apparatus for building an equipment energy efficiency model, comprising:
the system comprises an information acquisition module, a data processing module and a data processing module, wherein the information acquisition module is used for acquiring data information of target equipment, and the data information at least comprises input quantity data, output quantity data and rated capacity data;
the mean value acquisition module is used for carrying out data cleaning and data integration processing on the data information to acquire an energy efficiency mean value and a load factor mean value corresponding to the data information;
the micro-cluster obtaining module is used for carrying out interval division and clustering processing on the energy efficiency mean value and the load rate mean value to obtain micro-clusters corresponding to each interval;
the standard clustering center acquisition module is used for processing the micro-clusters corresponding to each interval to acquire a standard clustering center corresponding to each interval;
and the model acquisition module is used for acquiring an energy efficiency curve model of the target equipment according to the standard clustering center corresponding to each interval.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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