CN109840312B - Abnormal value detection method and device for boiler load rate-energy efficiency curve - Google Patents
Abnormal value detection method and device for boiler load rate-energy efficiency curve Download PDFInfo
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Abstract
The invention discloses a method for detecting an abnormal value of a boiler load factor-energy efficiency curve, which comprises the following steps: s1: acquiring a load rate-energy efficiency data set in the operation of a boiler; s2: calculating an error value array in the load rate-energy efficiency data set by a cross validation method; s3: and determining an abnormal value from the error value array according to a preset abnormal evaluation criterion. Meanwhile, the invention also discloses a device for detecting the abnormal value of the boiler load factor-energy efficiency curve, which comprises the following components: the device comprises a data acquisition module, an error calculation module and an abnormality determination module. According to the invention, the problem of deviation of learning result distribution caused by selecting one plane from the training set can be reduced by adopting cross validation, so that the influence of abnormal values on the regressor is reduced, and the abnormal points in the load efficiency-energy efficiency curve can be more accurately and effectively detected.
Description
Technical Field
The invention relates to the technical field of digital energy, in particular to a method and a device for detecting an abnormal value of a boiler load factor-energy efficiency curve.
Background
Gas boilers consume natural gas and produce hot water or steam. Its load factor (average over time) means (actual load/maximum load) × 100%, and the energy efficiency of the boiler (average over time) means the mass of hot water or steam (unit: ton/cubic meter) produced by consuming a unit volume of natural gas. When the load factor is X and the energy efficiency is Y, a load factor-energy efficiency curve of the gas boiler can be constructed (similarly, when the energy efficiency is X and the load factor is Y, an energy efficiency-load factor curve of the gas boiler can be constructed). The load factor-energy efficiency curve of the boiler has important guiding significance for high-efficiency use of the boiler and optimal scheduling when a plurality of boilers operate, and is an important basis for boiler research.
However, the load factor-energy efficiency curve is often extracted from data of actual operation of the boiler (for example, by using a fitting, interpolation or regression algorithm), and since the operation of the boiler is suddenly affected by many external factors or errors occur in the data acquisition process, the load factor-energy efficiency data obtained actually has many abnormal points, and the abnormal points cause a large error to be introduced when the data is fitted.
At present, algorithms on the problem of abnormal value detection of multi-dimensional data are numerous, but the basic idea is that an unsupervised clustering method is used for a lot, the clustering algorithm has certain universality, and the problem of univariate regression of load rate-energy efficiency is not targeted, and because of the implicit quantity relationship of variables in the data, the clustering algorithm cannot be found out necessarily. Therefore, a new solution is urgently sought.
Disclosure of Invention
The embodiment of the invention provides a method and a device for detecting an abnormal value of a boiler load rate-energy efficiency curve, which can effectively detect an abnormal point in the load rate-energy efficiency curve.
In a first aspect, an embodiment of the present invention provides a method for detecting an abnormal value of a boiler load factor-energy efficiency curve, where the method includes:
s1: acquiring a load rate-energy efficiency data set in the operation of a boiler;
s2: calculating an error value array in the load rate-energy efficiency data set by a cross validation method;
s3: and determining an abnormal value from the error value array according to a preset abnormal evaluation criterion.
Preferably, the first and second liquid crystal display panels are,
the specific process of step S2 includes:
s21: dividing the load rate-energy efficiency data set into a preset number of data packets;
s22: selecting one of the data packets as a test set, and using the other remaining data packets as a training set;
s23: training the regression algorithm through a training set to obtain a regression model;
s24: calculating a prediction error value of each data in the test set by using a regression model;
s25: judging whether the preset number of data packets are subjected to the test set, if so, executing the step S26; otherwise, one of the data packets which have not been subjected to the test set is used as the test set, and the other rest data packets are used as the training sets, and then the step S23 is executed;
s26: and forming an error value array by using all the calculated prediction error values.
Preferably, the first and second electrodes are formed of a metal,
the specific process of step S3 includes:
n31: determining the selection proportion of the abnormal value;
n32: calculating the number of abnormal data points according to the determined selection proportion;
n33: and selecting a corresponding number of prediction error values from all the prediction error values of the error value array from large to small according to the number of the abnormal data points, and determining the data points corresponding to the selected prediction error values as abnormal values.
Preferably, the first and second electrodes are formed of a metal,
the specific process of step S3 includes:
m31: determining a threshold value for an error value;
m32: and determining data points corresponding to the prediction error values of which the prediction error values are larger than the threshold value in the error value array as abnormal values.
In a second aspect, an embodiment of the present invention provides an abnormal value detection apparatus for a boiler load factor-energy efficiency curve, the apparatus including: a data acquisition module, an error calculation module, and an anomaly determination module, wherein,
the data acquisition module is used for acquiring a load rate-energy efficiency data set in the operation of the boiler;
the error calculation module is used for calculating an error value array in the load rate-energy efficiency data set through a cross verification method;
and the abnormity determining module is used for determining an abnormal value from the error value array according to a preset abnormity evaluation criterion.
Preferably, the first and second liquid crystal display panels are,
the error calculation module includes: a data grouping unit, a data classification unit, a model training unit, an error calculation unit, a cycle judgment unit and an error grouping unit, wherein,
the data grouping unit is used for dividing the load rate-energy efficiency data set into a preset number of data packets;
the data classification unit is used for selecting one of the data packets as a test set and the other remaining data packets as a training set;
the model training unit is used for training a regression algorithm through a training set to obtain a regression model;
the error calculation unit is used for calculating the prediction error value of each data in the test set by using a regression model;
the cycle judgment unit is used for judging whether all the data packets with preset number of copies are subjected to the test set, and if so, triggering the error grouping unit; otherwise, one of the data packets which have not been subjected to the test set is used as the test set, and the other rest data packets are used as the training sets to trigger the model training unit;
and the error grouping unit is used for grouping all the calculated prediction error values into an error value array.
Preferably, the first and second electrodes are formed of a metal,
the anomaly determination module includes: a proportion determining unit, a quantity determining unit and an abnormality selecting unit, wherein,
the proportion determining unit is used for determining the selection proportion of the abnormal value;
the quantity determining unit is used for calculating the quantity of the abnormal data points according to the determined selection proportion;
and the abnormal selection unit is used for selecting a corresponding number of prediction error values from all the prediction error values of the error value array from large to small according to the number of the abnormal data points, and determining the data points corresponding to the selected prediction error values as abnormal values.
Preferably, the first and second electrodes are formed of a metal,
the anomaly determination module includes: a threshold determination unit and an abnormality determination unit, wherein,
the threshold value determining unit is used for determining a threshold value of an error value;
and the abnormity determining unit is used for determining data points corresponding to the prediction error values of which the prediction error values are larger than the threshold value in the error value array as abnormal values.
Third invention, an embodiment of the present invention provides a readable medium, which includes execution instructions, and when a processor of an electronic device executes the execution instructions, the electronic device executes the abnormal value detection method for the boiler load factor-energy efficiency curve according to any one of the first aspect.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus; the memory is used for storing execution instructions, the processor is connected with the memory through the bus, and when the electronic device runs, the processor executes the execution instructions stored in the memory, so that the processor executes the abnormal value detection method of the boiler load factor-energy efficiency curve according to any one of the first aspect.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the invention, the problem of deviation of learning result distribution caused by selecting one plane from the training set can be reduced by adopting cross validation, so that the influence of abnormal values on the regressor is reduced, and the abnormal points in the load efficiency-energy efficiency curve can be more accurately and effectively detected.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for detecting an abnormal value of a boiler load factor-energy efficiency curve according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of obtaining an error value array by a calculation according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of determining outliers according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of another method for determining outliers provided by an embodiment of the present invention;
FIG. 5 is a block diagram illustrating an abnormal value detection apparatus for a boiler load factor-energy efficiency curve according to an embodiment of the present invention;
FIG. 6 is a block diagram of an error calculation module according to an embodiment of the present invention;
FIG. 7 is a block diagram of an exception determination module according to an embodiment of the present invention;
FIG. 8 is a block diagram of another exception determination module provided by an embodiment of the present invention;
fig. 9 is an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for detecting an abnormal value of a boiler load factor-energy efficiency curve, which may include the following steps:
s1: acquiring a load rate-energy efficiency data set in the operation of a boiler;
s2: calculating an error value array in the load rate-energy efficiency data set by a cross validation method;
s3: and determining an abnormal value from the error value array according to a preset abnormal evaluation criterion.
In this embodiment, the load factor-energy efficiency dataset is a dataset composed of X load factors and Y energy factors corresponding to the load factors at the same time, and can be expressed as D = { (X, Y) | X ∈ X, Y ∈ Y }. And the condition to be satisfied is that the proportion of the abnormal data in the data set D in the data set is very small, such as not more than 5-10%. The cross verification method can be a K-fold cross verification method, so that the problem that learning result distribution is deviated due to the fact that the training set selects one plane can be solved, the influence of abnormal values on the regressor is reduced, and abnormal points in the load efficiency-energy efficiency curve can be detected more accurately and effectively.
As shown in fig. 2, in the embodiment of the present invention, the specific process of step S2 includes:
s21: dividing the load rate-energy efficiency data set into a preset number of data packets;
s22: selecting one of the data packets as a test set, and using the other remaining data packets as a training set;
s23: training the regression algorithm through a training set to obtain a regression model;
s24: calculating a prediction error value of each data in the test set by using a regression model;
s25: judging whether the preset number of data packets are subjected to the test set, if so, executing the step S26; otherwise, executing step S23 after taking one of the data packets which have not been subjected to the test set as the test set and the other remaining data packets as the training set;
s26: and forming an error value array by using all the calculated prediction error values.
In this embodiment, the data set is uniformly divided into a predetermined number of data packets, which is represented by K, so that K data packets are D [1], D [2], …, and dk; and then selecting a univariate regression algorithm to train by using the training set to obtain a regressor (regression model) for each i epsilon {1,2, …, K }, taking the data packet D [ i ] as a test set and the rest of data packets as a training set, calculating a prediction error value of each data in the test set, and obtaining all prediction error values after K times, wherein all the prediction error values form an error value array.
It should be noted that many univariate regression algorithms can be selected, and common machine learning regression algorithms generally can be, for example, neural network regression, and many models can be tried in actual use to select an optimal algorithm model. Because few abnormal value data have little influence on training of a regressor and the principle of K-fold cross validation and univariate regression algorithm is used, the method can effectively utilize most normal data and discover the implicit relationship between load efficiency and energy efficiency from the normal data. Therefore, the information contained in the data can be better utilized, and beneficial guidance is provided for finding abnormal values.
As shown in fig. 3, in the embodiment of the present invention, the specific process of step S3 includes:
n31: determining the selection proportion of the abnormal value;
n32: calculating the number of abnormal data points according to the determined selection proportion;
n33: and selecting a corresponding number of prediction error values from all the prediction error values of the error value array from large to small according to the number of the abnormal data points, and determining the data points corresponding to the selected prediction error values as abnormal values.
In this embodiment, given the selection ratio of the abnormal values, the number of the selected prediction error values may satisfy the given ratio, for example, 5 prediction error values are selected to satisfy the given selection ratio, and the data points corresponding to the prediction error values ranked in the top 5 from large to small are the abnormal values. Where the absolute values of the prediction error values are compared.
As shown in fig. 4, in an embodiment of the present invention, the specific process of step S3 includes:
m31: determining a threshold value for an error value;
m32: and determining data points corresponding to the prediction error values of which the prediction error values are larger than the threshold value in the error value array as abnormal values.
In this embodiment, given a determination threshold of an abnormal value, a data point corresponding to a prediction error value greater than the given threshold among all prediction error values is determined as the abnormal value. For example, given a threshold of 0.20 and existing prediction error values of 0.11, 0.19, 0.21, and 0.34, respectively, 0.21 and 0.34 exceed the threshold, so the data points corresponding to 0.21 and 0.34 are outliers
As shown in fig. 5, an embodiment of the present invention provides an abnormal value detection apparatus for a boiler load factor-energy efficiency curve, the apparatus including: a data acquisition module, an error calculation module, and an anomaly determination module, wherein,
the data acquisition module is used for acquiring a load factor-energy efficiency data set in the operation of the boiler;
the error calculation module is used for calculating an error value array in the load rate-energy efficiency data set by a K-fold cross verification method;
and the abnormity determining module is used for determining an abnormal value from the error value array according to a preset abnormity evaluation criterion.
As shown in fig. 6, in the embodiment of the present invention, the error calculation module includes: a data grouping unit, a data classification unit, a model training unit, an error calculation unit, a cycle judgment unit and an error grouping unit, wherein,
the data grouping unit is used for dividing the load rate-energy efficiency data set into a preset number of data packets;
the data classification unit is used for selecting one of the data packets as a test set and the other remaining data packets as a training set;
the model training unit is used for training a regression algorithm through a training set to obtain a regression model;
the error calculation unit is used for calculating a prediction error value of each data in the test set by using a regression model;
the cycle judgment unit is used for judging whether all the data packets with preset number of copies are subjected to the test set, and if so, triggering the error grouping unit; otherwise, one of the data packets which have not been subjected to the test set is used as the test set, and the other rest data packets are used as the training sets to trigger the model training unit;
and the error grouping unit is used for grouping all the calculated prediction error values into an error value array.
As shown in fig. 7, in the embodiment of the present invention, the abnormality determining module includes: a proportion determining unit, a quantity determining unit and an abnormality selecting unit, wherein,
the proportion determining unit is used for determining the selection proportion of the abnormal value;
the quantity determining unit is used for calculating the quantity of the abnormal data points according to the determined selection proportion;
and the abnormal selection unit is used for selecting a corresponding number of prediction error values from all the prediction error values of the error value array from large to small according to the number of the abnormal data points, and determining the data points corresponding to the selected prediction error values as abnormal values.
As shown in fig. 8, in the embodiment of the present invention, the abnormality determining module includes: a threshold determination unit and an abnormality determination unit, wherein,
the threshold value determining unit is used for determining a threshold value of an error value;
and the abnormity determining unit is used for determining data points corresponding to the prediction error values of which the prediction error values are larger than the threshold value in the error value array as abnormal values.
Because the content of information interaction, execution process, and the like between the modules or units in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
As shown in fig. 9, one embodiment of the present invention provides an electronic device. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor. In a possible implementation manner, the processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program, and the corresponding computer program can also be obtained from other equipment so as to form the abnormal value detection device of the boiler load factor-energy efficiency curve on a logic level. And a processor for executing the program stored in the memory to realize the abnormal value detection apparatus for the boiler load factor-energy efficiency curve provided in any one of the embodiments of the present invention by the executed program.
The method executed by the abnormal value detection apparatus of the boiler load factor-energy efficiency curve according to any one of the embodiments shown in fig. 5-8 of the present invention can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
Embodiments of the present invention also provide a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the abnormal value detection method of a boiler load factor-energy efficiency curve provided in any of the embodiments of the present invention.
The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units or modules by function, respectively. Of course, the functionality of the units or modules may be implemented in the same one or more software and/or hardware when implementing the invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a" does not exclude the presence of other similar elements in a process, method, article, or apparatus that comprises the element.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it is to be noted that: the above description is only a preferred embodiment of the present invention, and is only used to illustrate the technical solutions of the present invention, and not to limit the protection scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (8)
1. A method for detecting an abnormal value of a boiler load rate-energy efficiency curve is characterized by comprising the following steps:
s1: acquiring a load rate-energy efficiency data set in the operation of a boiler;
s2: calculating an error value array in the load rate-energy efficiency data set by a cross validation method;
s3: determining an abnormal value from the error value array according to a preset abnormal judgment criterion;
the specific process of step S2 includes:
s21: dividing the load rate-energy efficiency data set into a preset number of data packets;
s22: selecting one of the data packets as a test set, and using the other remaining data packets as a training set;
s23: training the regression algorithm through a training set to obtain a regression model;
s24: calculating a prediction error value of each data in the test set by using a regression model;
s25: judging whether the preset number of data packets are subjected to the test set, if so, executing the step S26; otherwise, one of the data packets which have not been subjected to the test set is used as the test set, and the other rest data packets are used as the training sets, and then the step S23 is executed;
s26: and forming an error value array by using all the calculated prediction error values.
2. The abnormal value detection method of a boiler load factor-energy efficiency curve according to claim 1,
the specific process of step S3 includes:
n31: determining the selection proportion of the abnormal value;
n32: calculating the number of abnormal data points according to the determined selection proportion;
n33: and selecting a corresponding number of prediction error values from all the prediction error values of the error value array from large to small according to the number of the abnormal data points, and determining the data points corresponding to the selected prediction error values as abnormal values.
3. The abnormal value detection method of a boiler load factor-energy efficiency curve according to claim 1,
the specific process of step S3 includes:
m31: determining a threshold value for an error value;
m32: and determining data points corresponding to the prediction error values of which the prediction error values are larger than the threshold value in the error value array as abnormal values.
4. An abnormal value detection apparatus of a boiler load factor-energy efficiency curve, characterized by comprising: a data acquisition module, an error calculation module, and an anomaly determination module, wherein,
the data acquisition module is used for acquiring a load rate-energy efficiency data set in the operation of the boiler;
the error calculation module is used for calculating an error value array in the load rate-energy efficiency data set through a cross verification method;
the abnormity determining module is used for determining an abnormal value from the error value array according to a preset abnormity evaluation criterion;
the error calculation module includes: a data grouping unit, a data classification unit, a model training unit, an error calculation unit, a cycle judgment unit and an error grouping unit, wherein,
the data grouping unit is used for dividing the load rate-energy efficiency data set into a preset number of data packets;
the data classification unit is used for selecting one data packet as a test set and other remaining data packets as training sets;
the model training unit is used for training a regression algorithm through a training set to obtain a regression model;
the error calculation unit is used for calculating a prediction error value of each data in the test set by using a regression model;
the cycle judgment unit is used for judging whether all the data packets with preset number of copies are subjected to the test set, and if so, triggering the error grouping unit; otherwise, one of the data packets which have not been subjected to the test set is used as the test set, and the other rest data packets are used as the training sets to trigger the model training unit;
and the error grouping unit is used for grouping all the calculated prediction error values into an error value array.
5. The apparatus for detecting an abnormal value of a boiler load factor-energy efficiency curve according to claim 4, wherein the abnormality determining module includes: a proportion determining unit, a quantity determining unit and an abnormality selecting unit, wherein,
the proportion determining unit is used for determining the selection proportion of the abnormal value;
the quantity determining unit is used for calculating the quantity of the abnormal data points according to the determined selection proportion;
and the abnormal selection unit is used for selecting a corresponding number of prediction error values from all the prediction error values of the error value array from large to small according to the number of the abnormal data points, and determining the data points corresponding to the selected prediction error values as abnormal values.
6. The apparatus for detecting an abnormal value of a boiler load factor-energy efficiency curve according to claim 4, wherein the abnormality determining module includes: a threshold determination unit and an abnormality determination unit, wherein,
the threshold value determining unit is used for determining a threshold value of an error value;
and the abnormity determining unit is used for determining data points corresponding to the prediction error values of which the prediction error values are larger than the threshold value in the error value array as abnormal values.
7. A readable medium characterized in that the readable medium comprises executable instructions, which when executed by a processor of an electronic device, the electronic device performs the abnormal value detection method of the boiler load factor-energy efficiency curve according to any one of claims 1 to 3.
8. An electronic device, comprising: a processor, a memory, and a bus; the memory is used for storing execution instructions, the processor is connected with the memory through the bus, and when the electronic device runs, the processor executes the execution instructions stored in the memory so as to enable the processor to execute the abnormal value detection method of the boiler load factor-energy efficiency curve according to any one of claims 1 to 3.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910056117.4A CN109840312B (en) | 2019-01-22 | 2019-01-22 | Abnormal value detection method and device for boiler load rate-energy efficiency curve |
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| CN113052938A (en) * | 2019-12-26 | 2021-06-29 | 新奥数能科技有限公司 | Method and device for constructing boiler energy efficiency curve |
| CN113051517A (en) * | 2019-12-27 | 2021-06-29 | 新奥数能科技有限公司 | Method and device for acquiring boiler energy efficiency curve |
| CN113052195A (en) * | 2019-12-27 | 2021-06-29 | 北京国双科技有限公司 | Logging curve abnormal value marking method and device, computer equipment and storage medium |
| CN111122199A (en) * | 2019-12-31 | 2020-05-08 | 新奥数能科技有限公司 | Boiler fault diagnosis method and device |
| CN111625754B (en) * | 2020-05-12 | 2023-05-02 | 新智数字科技有限公司 | Method, device, terminal equipment and storage medium for calculating energy efficiency of boiler |
| CN115600102B (en) * | 2022-04-26 | 2023-11-21 | 南京天洑软件有限公司 | Abnormal point detection method and device based on ship data, electronic equipment and medium |
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