CN113837324A - Electric quantity data monitoring method and system, storage medium and electronic equipment - Google Patents
Electric quantity data monitoring method and system, storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a system for monitoring electric quantity data, a storage medium and electronic equipment, and belongs to the technical field of electric quantity monitoring. The electric quantity data monitoring method fully considers the difficulty of abnormal data detection, firstly detects the obviously abnormal data, defines the abnormal deviation rate to represent the abnormal possibility of the data, replaces the obviously abnormal data with 0, then accurately detects the replaced data by using an ensemble empirical mode decomposition algorithm, fits the concave-convex change based on the time sequence of the electric quantity data, fills the abnormal value according to the fitting result to obtain more accurate electric quantity data, and feeds back an alarm according to the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value, thereby achieving the accurate correction of the abnormal data, ensuring that the alarm feedback is more accurate and reducing the false alarm.
Description
Technical Field
The present disclosure relates to the field of power monitoring technologies, and in particular, to a power data monitoring method, a power data monitoring system, a storage medium, and an electronic device.
Background
The electric quantity data is an important basis for electric power market trading settlement. At present, electric quantity data are generally counted monthly, the electric quantity data relate to a plurality of departments and a plurality of places for management, the data are difficult to be synchronously managed, equipment management is mainly taken as a center in the aspect of data acquisition, the error check and the running state detection are emphasized, and the analysis and the processing of the electric quantity data are not related. In addition, the operation problem of the data acquisition device is difficult to find in time due to the regular inspection period.
The conventional correction processing method is rough and cannot accurately correct the abnormal electric quantity data.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides a method and a system for monitoring quantity data, a storage medium and electronic equipment, which can accurately detect and correct the abnormity of the quantity data.
In a first aspect, an embodiment of the present disclosure provides an electric quantity data monitoring method, where the method includes:
acquiring original electric quantity data;
detecting abnormal data existing in the original electric quantity data;
correcting abnormal data, and taking the corrected data as basic electric quantity data;
and feeding back and alarming the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value.
With reference to the embodiments of the first aspect, in some embodiments, the detecting abnormal data existing in the raw power data includes:
processing the original electric quantity data according to the time sequence to obtain the time sequence electric quantity data to form a first sequence
，i=1, 2, 3, …, t, t representing time points of a time series;
according to a first sequence
Determining the median med of the electric quantity data
And determining each electric quantity data and median med in the first sequence
Ratio of
；
Setting the ratio threshold to
，
If, if
Or is or
If the electric quantity data is an abnormal value, replacing the abnormal value with 0, processing the replaced electric quantity data according to the time sequence to obtain time sequence electric quantity data, and forming a second sequence
；
According to the second numerical sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a third number sequence
；
The third array is subjected to the ensemble empirical mode decomposition algorithm
Decomposing to obtain n components, arranging according to frequency from high to low, discarding high-frequency components, and correspondingly summing m low-frequency components to obtain a fourth sequence
；
Based on fourth sequence of numbers
And the third array
Defining the degree of deviation of
，
Hypothesis abnormal deviation rate threshold
If the abnormal deviation rate is larger than the abnormal deviation rate threshold value, the abnormal value is obtained, and the second sequence is used
Is replaced by 0 to obtain a fifth sequence
。
In combination with an embodiment of the first aspect, in some embodiments, n: m =5: 4.
With reference to the embodiments of the first aspect, in some embodiments, the correcting the abnormal data and taking the corrected data as the basic electric quantity data includes:
according to the fifth sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a sixth number sequence
；
The sixth array is subjected to a set empirical mode decomposition algorithm
Decomposing to obtain
The components are arranged from high to low according to frequency, the high frequency component is discarded, and
corresponding summation of low-frequency components to obtain the seventh sequence
Seventh series of numbers
As a trend term;
according to the fifth sequence
Whether or not the unevenness is changed is corrected by an abnormal value: if the fifth sequence
If the concavity and convexity of (2) is not changed, the curve is fitted directly, if the fifth array is
When the concave-convex property is changed, the piecewise curve is fitted, and the fitting results of the piecewise curve are spliced according to the time sequence;
and correcting the abnormal value according to the curve fitting result.
In combination with embodiments of the first aspect, in some embodiments,
：
=3:2。
with reference to embodiments of the first aspect, in some embodiments, if the fifth array
The concave-convex property of the surface is not changed, and then the direct curve fitting is carried out, specifically:
screening out according to the positive number of the fifth sequence to obtain the number of samples
，
Indicating the position where a positive number occurs, in number of samples
And (6) performing curve fitting.
In combination with an embodiment of the first aspect, in some embodiments, further comprising
Carrying out secondary detection and secondary correction on the basic electric quantity data to obtain final electric quantity data, which specifically comprises the following steps:
extracting a characteristic curve on the basis of basic electric quantity data based on a nonparametric kernel density estimation method;
obtaining historical electric quantity data, and determining the maximum value and the minimum value of the electric quantity data at the same moment;
determining an upper limit value and a lower limit value of a historical data domain by comparing the characteristic curve with the maximum value of the historical electric quantity data;
determining a threshold coefficient allowing change according to historical data and determining an upper limit value and a lower limit value of a feasible region of electric quantity data;
detecting basic electric quantity data, wherein the basic electric quantity data is a normal value when the data to be detected is positioned between an upper limit value and a lower limit value of a feasible region, and the basic electric quantity data is an abnormal value if the data to be detected is not positioned between the upper limit value and the lower limit value of the feasible region;
and determining a scaling ratio according to the total power consumption of the section corresponding to the abnormal value of the characteristic curve, and correcting the abnormal value according to the scaling ratio.
In a second aspect, an embodiment of the present disclosure provides an electric quantity data monitoring system, which includes
The acquisition module is used for acquiring original electric quantity data; the anomaly detection module is used for detecting abnormal data existing in the original electric quantity data; the abnormal value correction module is used for correcting abnormal data and taking the corrected data as basic electric quantity data; and the alarm module is used for carrying out feedback alarm on the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value.
With reference to the second aspect, in some embodiments, the electric quantity data monitoring system further includes:
and the rechecking and correcting module is used for carrying out secondary detection and secondary correction on the basic electric quantity data to obtain final electric quantity data.
With reference to the embodiments of the second aspect, in some embodiments, the acquisition module is configured to transmit the electric quantity data to the data monitoring platform through the LoRA gateway according to a preset time and a preset frequency.
With reference to the embodiment of the second aspect, in some embodiments, the acquisition module is configured to transmit the electric quantity data to the data monitoring platform through the LoRA gateway at 6 times per day and 1 time per day.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the electric quantity data monitoring method according to the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the steps of the electrical quantity data monitoring method as described above in the first aspect.
The electric quantity data monitoring method provided by the embodiment of the disclosure fully considers the difficulty of abnormal data detection, firstly detects obviously abnormal data, defines the abnormal deviation rate to represent the possibility of data abnormality, replaces the obviously abnormal data with 0, then accurately detects the replaced data again by using a set empirical mode decomposition algorithm, fits the concave-convex change based on the time sequence of the electric quantity data, fills up the abnormal value according to the fitting result, thereby obtains more accurate electric quantity data, and feeds back an alarm according to the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value, thereby achieving the accurate correction of the abnormal data, the alarm feedback is more accurate, and the false alarm is reduced.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a flow chart diagram of one embodiment of a charge data monitoring method according to the present disclosure;
fig. 2 is a schematic structural diagram of a power data monitoring system of the present disclosure;
fig. 3 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Referring to fig. 1, a flow of an embodiment of a method for monitoring power data according to the present disclosure is shown. The electric quantity data monitoring method can be applied to electric quantity data detection and correction. As shown in fig. 1, the method for monitoring the electric quantity data comprises the following steps:
Here, the acquisition module is used for transmitting the electric quantity data to the data monitoring platform through the LoRA gateway according to preset time and preset frequency. The LoRA gateway can transmit the distributed discrete equipment data of a plurality of points to the gateway through a wireless Lora node, and the Lora gateway transmits the data to the server through the Ethernet or the 4G network after processing the data.
The acquisition module is used for transmitting the electric quantity data to the data monitoring platform through the LoRA gateway according to preset time and preset frequency.
With reference to the embodiment of the second aspect, in some embodiments, the acquisition module is configured to transmit the electric quantity data to the data monitoring platform through the LoRA gateway at 6 times per day and 1 time per day. Of course, other time or frequency can be set for collecting and transmitting the electric quantity data.
And 102, detecting abnormal data existing in the original electric quantity data.
Here, the original electric quantity data is processed according to the time sequence to obtain the time sequence electric quantity data, and a first sequence is formed
，i=1, 2, 3, …, t, t denotes time points (e.g. days) of a time series.
According to a first sequence
Determining the median med of the electric quantity data
And determining each electric quantity data and median med in the first sequence
Ratio of
。
Setting the ratio threshold to
，
If, if
Or is or
If the electric quantity data is an abnormal value, replacing the abnormal value with 0, processing the replaced electric quantity data according to the time sequence to obtain time sequence electric quantity data, and forming a second sequence
。
According to the second numerical sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a third number sequence
。
The third array is subjected to the ensemble empirical mode decomposition algorithm
Decomposing to obtain n components, arranging according to frequency from high to low, discarding high-frequency components, and correspondingly summing m low-frequency components to obtain a fourth sequence
. Here, the value of m is closely related to the detection of an abnormal value, when the value of m is unreasonable, false detection or missed detection is easily caused, and according to practical experience, n: when m =5:4, the detection effect is better.
Based on fourth sequence of numbers
And the third array
Defining the degree of deviation of
。
Hypothesis abnormal deviation rate threshold
If the abnormal deviation rate is larger than the abnormal deviation rate threshold value, the abnormal value is obtained, and the second sequence is used
Is replaced by 0 to obtain a fifth sequence
。
And 103, correcting the abnormal data, and taking the corrected data as basic electric quantity data.
Here, according to the fifth sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a sixth number sequence
；
The sixth array is subjected to a set empirical mode decomposition algorithm
Decomposing to obtain
The components are arranged from high to low according to frequency, the high frequency component is discarded, and
corresponding summation of low-frequency components to obtain the seventh sequence
Seventh series of numbers
As a trend ofThe potential term is, among other things,
：
trend term when =3:2
Can well represent the sixth array
The trend of change of (c).
According to the fifth sequence
Whether or not the unevenness is changed is corrected by an abnormal value: if the fifth sequence
The concave-convex property of the surface is not changed, and the curve fitting is directly carried out. The positive number according to the fifth sequence is screened out to obtain the number of samples
，
Indicating the position where a positive number occurs, in number of samples
And (6) performing curve fitting. If the fifth sequence
And (4) fitting the segmented curve when the concave-convex property is changed, and splicing the fitting results of the segmented curve according to the time sequence.
And correcting the abnormal value according to the curve fitting result.
And 104, carrying out secondary detection and secondary correction on the basic electric quantity data to obtain final electric quantity data.
Here, the characteristic curve is extracted on the basis of the basic electric quantity data based on the non-parametric kernel density estimation method.
And obtaining historical electric quantity data, and determining the maximum value and the minimum value of the electric quantity data at the same time.
And determining an upper limit value and a lower limit value of the historical data domain by comparing the characteristic curve with the most value of the historical electric quantity data.
And determining a threshold coefficient allowing change according to historical data and determining an upper limit value and a lower limit value of a feasible range of the electric quantity data.
And detecting the basic electric quantity data, wherein the basic electric quantity data is a normal value when the data to be detected is positioned between the upper limit value and the lower limit value of the feasible region, and the basic electric quantity data is an abnormal value if the data to be detected is not positioned between the upper limit value and the lower limit value of the feasible region.
And determining a scaling ratio according to the total power consumption of the section corresponding to the abnormal value of the characteristic curve, and correcting the abnormal value according to the scaling ratio.
And 105, performing feedback alarm on the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value.
It can be seen that, in this embodiment, the difficulty of abnormal data detection is fully considered, firstly, obviously abnormal data is detected, an abnormal deviation rate is defined to represent the possibility of data abnormality, obviously abnormal data is replaced by 0, then, the replaced data is accurately detected again by using an ensemble empirical mode decomposition algorithm, fitting is performed based on the concave-convex change of the time series of the electric quantity data, an abnormal value is filled according to the fitting result, so that more accurate electric quantity data is obtained, feedback alarm is performed according to the data, of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value, so that accurate correction of the abnormal data is achieved, the alarm feedback is more accurate, and false alarm is reduced.
The inventor has found through long-term practice that m and m are in the process of decomposition by using a set empirical mode decomposition algorithm
The value of (A) depends on experience, and possibly causes missed detection and false detection. And the missed detection and the false detection are difficult to find, so that the correction result of the abnormal data cannot be verified.
Therefore, the inventor provides the electricity quantity detection and correction method based on the non-parametric kernel density estimation method, which can fully utilize the prior historical data and determine the upper limit value and the lower limit value of the historical data domain by extracting the information of the characteristic curve and the prior historical electricity quantity data. And determining a threshold coefficient allowing change according to historical data and determining an upper limit value and a lower limit value of a feasible range of the electric quantity data. And detecting the basic electric quantity data, wherein the basic electric quantity data is a normal value when the data to be detected is positioned between the upper limit value and the lower limit value of the feasible region, and the basic electric quantity data is an abnormal value if the data to be detected is not positioned between the upper limit value and the lower limit value of the feasible region. And determining a scaling ratio according to the total power consumption of the section corresponding to the abnormal value of the characteristic curve, and correcting the abnormal value according to the scaling ratio. The method can detect the basic electric quantity data, is used for correcting the last data correction, can form advantage complementation, and can correct the missed detection and the error detection again, thereby further improving the accuracy of the electric quantity data.
With further reference to fig. 2, as an implementation of the methods shown in the above diagrams, the present disclosure provides an electric quantity data monitoring system, an embodiment of the system corresponds to the embodiment of the method shown in fig. 1, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 2, the system of the present embodiment includes: an acquisition module 201, configured to acquire original electric quantity data; an anomaly detection module 202, configured to detect anomalous data existing in the original electric quantity data; an abnormal value correction module 203, which is used for correcting abnormal data and taking the corrected data as basic electric quantity data; a rechecking and correcting module 204, configured to perform secondary detection and secondary correction on the basic electric quantity data to obtain final electric quantity data; and an alarm module 205, configured to perform feedback alarm on data in which a difference between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold.
In some optional embodiments, the acquisition module 201 is configured to transmit the electric quantity data to the data monitoring platform through the LoRA gateway according to a preset time and a preset frequency. The LoRA gateway can transmit the distributed discrete equipment data of a plurality of points to the gateway through a wireless Lora node, and the Lora gateway transmits the data to the server through the Ethernet or the 4G network after processing the data.
In some optional embodiments, the anomaly detection module 202 is configured to process the raw power data according to a time sequence to obtain time-series power data, and form a first sequence
，i=1, 2, 3, …, t, t represents time points (e.g. days) of a time series; according to a first sequence
Determining the median med of the electric quantity data
And determining each electric quantity data and median med in the first sequence
Ratio of
(ii) a Setting the ratio threshold to
，
If, if
Or is or
If the electric quantity data is differentReplacing the abnormal value with 0, processing the replaced electric quantity data according to the time sequence to obtain time sequence electric quantity data, and forming a second sequence
(ii) a According to the second numerical sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a third number sequence
(ii) a The third array is subjected to the ensemble empirical mode decomposition algorithm
Decomposing to obtain n components, arranging according to frequency from high to low, discarding high-frequency components, and correspondingly summing m low-frequency components to obtain a fourth sequence
. Here, the value of m is closely related to the detection of an abnormal value, when the value of m is unreasonable, false detection or missed detection is easily caused, and according to practical experience, n: when m =5:4, the detection effect is better; based on fourth sequence of numbers
And the third array
Defining the degree of deviation of
(ii) a Hypothesis abnormal deviation rate threshold
If the abnormal deviation rate is larger than the abnormal deviation rate threshold value, the abnormal value is obtained, and the second sequence is used
Is replaced by 0 to obtain a fifth sequence
。
The abnormal value correction module 203 is used for correcting the abnormal value according to the fifth sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a sixth number sequence
(ii) a The sixth array is subjected to a set empirical mode decomposition algorithm
Decomposing to obtain
The components are arranged from high to low according to frequency, the high frequency component is discarded, and
corresponding summation of low-frequency components to obtain the seventh sequence
Seventh series of numbers
As the trend term, among others,
：
trend term when =3:2
Can well represent the sixth array
The trend of change of (c); according to the fifth sequence
Whether or not the unevenness is changed is corrected by an abnormal value: if the fifth sequence
The concave-convex property of the surface is not changed, and the curve fitting is directly carried out. The positive number according to the fifth sequence is screened out to obtain the number of samples
，
Indicating the position where a positive number occurs, in number of samples
And (6) performing curve fitting. If the fifth sequence
When the concave-convex property is changed, the piecewise curve is fitted, and the fitting results of the piecewise curve are spliced according to the time sequence; and correcting the abnormal value according to the curve fitting result.
In some optional embodiments, the review modification module 204 is configured to extract a characteristic curve based on the basic electric quantity data based on a non-parametric kernel density estimation method; obtaining historical electric quantity data, and determining the maximum value and the minimum value of the electric quantity data at the same moment; determining an upper limit value and a lower limit value of a historical data domain by comparing the characteristic curve with the maximum value of the historical electric quantity data; determining a threshold coefficient allowing change according to historical data and determining an upper limit value and a lower limit value of a feasible region of electric quantity data; detecting basic electric quantity data, wherein the basic electric quantity data is a normal value when the data to be detected is positioned between an upper limit value and a lower limit value of a feasible region, and the basic electric quantity data is an abnormal value if the data to be detected is not positioned between the upper limit value and the lower limit value of the feasible region; and determining a scaling ratio according to the total power consumption of the section corresponding to the abnormal value of the characteristic curve, and correcting the abnormal value according to the scaling ratio.
In some optional embodiments, the alarm module 205 is configured to perform a feedback alarm on data in which a difference between the original power amount data and the modified power amount data reaches an alarm threshold.
Referring now to FIG. 3, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage device 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 303. The communication means 303 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 308, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring original electric quantity data; detecting abnormal data existing in the original electric quantity data; correcting abnormal data, and taking the corrected data as basic electric quantity data; and feeding back and alarming the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the name of a module in some cases does not constitute a limitation on the module itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (9)
1. A method for monitoring power data, the method comprising:
acquiring original electric quantity data;
detecting abnormal data existing in the original electric quantity data;
correcting abnormal data, and taking the corrected data as basic electric quantity data;
performing feedback alarm on the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value;
the abnormal data for detecting the existence of the original electric quantity data comprises:
processing the original electric quantity data according to the time sequence to obtain the time sequence electric quantity data to form a first sequence
，i=1, 2, 3, …, t, t representing time points of a time series;
according to a first sequence
Determining the median med of the electric quantity data
And determining each electric quantity data and median med in the first sequence
Ratio of
；
Setting the ratio threshold to
，
If, if
Or is or
If the electric quantity data is an abnormal value, replacing the abnormal value with 0, processing the replaced electric quantity data according to the time sequence to obtain time sequence electric quantity data, and forming a second sequence
；
According to the second numerical sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a third number sequence
；
The third array is subjected to the ensemble empirical mode decomposition algorithm
Decomposing to obtain n components, arranging according to frequency from high to low, discarding high-frequency components, and correspondingly summing m low-frequency components to obtain a fourth sequence
；
Based onFour arrays
And the third array
Defining the degree of deviation of
，
Hypothesis abnormal deviation rate threshold
If the abnormal deviation rate is larger than the abnormal deviation rate threshold value, the abnormal value is obtained, and the second sequence is used
Is replaced by 0 to obtain a fifth sequence
。
2. The electrical quantity data monitoring method according to claim 1, wherein n: m =5: 4.
3. The electric quantity data monitoring method according to claim 2, wherein the step of correcting abnormal data and using the corrected data as basic electric quantity data comprises the steps of:
according to the fifth sequence
The positive numbers in each electric quantity data are screened and arranged according to the original sequence to form a sixth number sequence
；
According to a set empirical mode decomposition algorithmSix arrays of
Decomposing to obtain
The components are arranged from high to low according to frequency, the high frequency component is discarded, and
corresponding summation of low-frequency components to obtain the seventh sequence
Seventh series of numbers
As a trend term;
according to the fifth sequence
Whether or not the unevenness is changed is corrected by an abnormal value: if the fifth sequence
If the concavity and convexity of (2) is not changed, the curve is fitted directly, if the fifth array is
When the concave-convex property is changed, the piecewise curve is fitted, and the fitting results of the piecewise curve are spliced according to the time sequence;
and correcting the abnormal value according to the curve fitting result.
4. The electrical quantity data monitoring method according to claim 3,
：
=3：2。
5. the electrical quantity data monitoring method according to claim 4, wherein if the fifth sequence is the same as the first sequence, the method further comprises
The concave-convex property of the surface is not changed, and then the direct curve fitting is carried out, specifically:
screening out according to the positive number of the fifth sequence to obtain the number of samples
，
Indicating the position where a positive number occurs, in number of samples
And (6) performing curve fitting.
6. An electric quantity data monitoring system is characterized by comprising
The acquisition module is used for acquiring original electric quantity data;
the anomaly detection module is used for detecting abnormal data existing in the original electric quantity data;
the abnormal value correction module is used for correcting abnormal data and taking the corrected data as basic electric quantity data;
and the alarm module is used for carrying out feedback alarm on the data of which the difference value between the original electric quantity data and the corrected electric quantity data reaches an alarm threshold value.
7. The electric quantity data monitoring system according to claim 6, wherein the acquisition module is configured to transmit the electric quantity data to the data monitoring platform through the LoRA gateway according to a preset time and a preset frequency.
8. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
9. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114710369A (en) * | 2022-06-06 | 2022-07-05 | 山东云天安全技术有限公司 | Abnormal data detection method and device, computer equipment and storage medium |
| CN117349778A (en) * | 2023-12-04 | 2024-01-05 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107273234A (en) * | 2017-05-26 | 2017-10-20 | 中国航天***科学与工程研究院 | A kind of time series data rejecting outliers and bearing calibration based on EEMD |
| CN107463633A (en) * | 2017-07-17 | 2017-12-12 | 中国航天***科学与工程研究院 | A kind of real time data rejecting outliers method based on EEMD neutral nets |
| CN110047014A (en) * | 2019-01-04 | 2019-07-23 | 国网浙江省电力有限公司电力科学研究院 | A kind of user's electricity data restorative procedure based on load curve and history electricity |
| CN110135612A (en) * | 2018-07-05 | 2019-08-16 | 国网江苏省电力有限公司物资分公司 | The monitoring of material supply quotient's production capacity and abnormity early warning method based on analysis of electric power consumption |
| CN112381433A (en) * | 2020-11-20 | 2021-02-19 | 昆明电力交易中心有限责任公司 | Practical electric quantity data processing method for spot market settlement |
| CN112818297A (en) * | 2021-02-05 | 2021-05-18 | 国网安徽省电力有限公司合肥供电公司 | Data anomaly detection method in cloud environment |
| CN113009406A (en) * | 2021-02-26 | 2021-06-22 | 珠海格力电器股份有限公司 | Electric quantity measurement correction method and device and air conditioning system |
| CN113128867A (en) * | 2021-04-20 | 2021-07-16 | 四川瑞康智慧能源有限公司 | Electricity selling deviation management system and method |
| US20210264272A1 (en) * | 2018-07-23 | 2021-08-26 | The Fourth Paradigm (Beijing) Tech Co Ltd | Training method and system of neural network model and prediction method and system |
-
2021
- 2021-11-25 CN CN202111409521.9A patent/CN113837324A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107273234A (en) * | 2017-05-26 | 2017-10-20 | 中国航天***科学与工程研究院 | A kind of time series data rejecting outliers and bearing calibration based on EEMD |
| CN107463633A (en) * | 2017-07-17 | 2017-12-12 | 中国航天***科学与工程研究院 | A kind of real time data rejecting outliers method based on EEMD neutral nets |
| CN110135612A (en) * | 2018-07-05 | 2019-08-16 | 国网江苏省电力有限公司物资分公司 | The monitoring of material supply quotient's production capacity and abnormity early warning method based on analysis of electric power consumption |
| US20210264272A1 (en) * | 2018-07-23 | 2021-08-26 | The Fourth Paradigm (Beijing) Tech Co Ltd | Training method and system of neural network model and prediction method and system |
| CN110047014A (en) * | 2019-01-04 | 2019-07-23 | 国网浙江省电力有限公司电力科学研究院 | A kind of user's electricity data restorative procedure based on load curve and history electricity |
| CN112381433A (en) * | 2020-11-20 | 2021-02-19 | 昆明电力交易中心有限责任公司 | Practical electric quantity data processing method for spot market settlement |
| CN112818297A (en) * | 2021-02-05 | 2021-05-18 | 国网安徽省电力有限公司合肥供电公司 | Data anomaly detection method in cloud environment |
| CN113009406A (en) * | 2021-02-26 | 2021-06-22 | 珠海格力电器股份有限公司 | Electric quantity measurement correction method and device and air conditioning system |
| CN113128867A (en) * | 2021-04-20 | 2021-07-16 | 四川瑞康智慧能源有限公司 | Electricity selling deviation management system and method |
Non-Patent Citations (1)
| Title |
|---|
| 方海泉等: "基于EEMD的水资源监测数据异常值检测与校正", 《农业机械学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114710369A (en) * | 2022-06-06 | 2022-07-05 | 山东云天安全技术有限公司 | Abnormal data detection method and device, computer equipment and storage medium |
| CN114710369B (en) * | 2022-06-06 | 2022-08-16 | 山东云天安全技术有限公司 | Abnormal data detection method and device, computer equipment and storage medium |
| CN117349778A (en) * | 2023-12-04 | 2024-01-05 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
| CN117349778B (en) * | 2023-12-04 | 2024-02-20 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
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