CN117992893A - Power transformation data exception handling method and device - Google Patents
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Abstract
The invention discloses a transformation data exception handling method and device. Wherein the method comprises the following steps: acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period; determining target mean value data and target standard deviation data based on a plurality of equipment transformation data in a target transformation data sequence; determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data; and determining target abnormal data in the target power transformation data sequence based on the target threshold range, removing the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the removing processing. The technical problem that abnormal data in power transformation data cannot be effectively processed in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power transformation data exception handling method and device.
Background
The traditional manual experience overhaul mode can not meet the requirements of accurate monitoring and maintenance of the running state of high-voltage equipment, and the introduction of the digital diagnosis technology has important practical significance and wide application prospect.
With the advent of the big data age, data quality improvement and data cleaning become one of the current research hotspots.
However, conventional data cleaning means do not take targeted treatment measures for the characteristics of the power system. Therefore, effective data arrangement for the power transformation data cannot be achieved.
Disclosure of Invention
The invention provides a method and a device for processing abnormal data of power transformation, which are used for solving the technical problem that the abnormal data in the power transformation data cannot be effectively processed in the prior art.
According to an aspect of the present invention, there is provided a transformation data exception handling method, including:
acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period; determining target mean value data and target standard deviation data based on a plurality of equipment transformation data in a target transformation data sequence; determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data; and determining target abnormal data in the target power transformation data sequence based on the target threshold range, removing the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the removing processing.
According to another aspect of the present invention, there is provided a transformation data abnormality processing device including:
The power transformation data sequence acquisition module is used for acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period; the power transformation data sequence processing module is used for determining target mean value data and target standard deviation data based on a plurality of equipment power transformation data in the target power transformation data sequence; the target threshold range determining module is used for determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data; the abnormal data processing module is used for determining target abnormal data in the target power transformation data sequence based on the target threshold range, eliminating the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the eliminating processing.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the transformation data anomaly handling method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the transformation data anomaly handling method of any one of the embodiments of the present invention when executed.
According to the technical scheme, the target power transformation data sequence of the target power transformation equipment in the target period is obtained, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period, and the power transformation data sequence of the power transformation equipment at a plurality of time points in the specific period can be collected. Then, the target mean value data and the target standard deviation data are determined based on a plurality of equipment transformation data in the target transformation data sequence, and a data basis is provided for determining abnormal data in the transformation data sequence. Determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data; and determining target abnormal data in the target power transformation data sequence based on the target threshold range, removing the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the removing processing, thereby realizing abnormal data identification and abnormal data processing on the equipment power transformation data in a specific period. The technical problem that abnormal data in power transformation data cannot be effectively processed in the prior art is solved. The monitoring condition is reasonably adjusted based on the change trend of the data, the manual monitoring cost is reduced, and the accurate abnormal monitoring and processing beneficial effects of fitting the actual scene of the data are achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a power transformation data exception handling method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a power transformation data exception handling method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a transformation data exception handling apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a power transformation data exception handling method according to an embodiment of the present invention, where the method may be performed by a power transformation data exception handling device, the power transformation data exception handling device may be implemented in hardware and/or software, and the power transformation data exception handling device may be configured in an electronic device. As shown in fig. 1, the method includes:
S110, acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period.
In this embodiment, the target power transformation device may be a power transformation device such as a target transformer and a target circuit breaker, which perform power transformation data processing on the target power transformation device. The target period may be one or more specific operating time periods during which the target substation device is operating. The target power transformation data sequence may be a data sequence composed of power transformation data generated by the target power transformation device during the target period operation. Specifically, the target power transformation data sequence may include a plurality of device power transformation data collected at a plurality of time points in a target period, where the device power transformation data is power transformation data generated by the target power transformation device in a running process.
Optionally, acquiring the target transformation data sequence of the target transformation device in the target period includes: dividing a plurality of equipment power transformation data acquired in a target period into a plurality of candidate power transformation data sequences based on a time point; and calculating the variance of the candidate transformation data sequence, and determining the candidate transformation data sequence with the smallest variance as the target transformation data sequence.
In this embodiment, the target transformation data sequence of the target transformation device in the target period may be obtained by dividing the target period into a plurality of time periods based on the time point. The plurality of device transformation data acquired in the target period may then be sliced into a plurality of transformation data sets based on a number of time periods as candidate transformation data sequences. The variance of each candidate transformation data sequence may be calculated, and the candidate transformation data sequence with the smallest variance is determined as the target transformation data sequence. By acquiring a section of data with the smallest fluctuation amplitude in the transformation data based on the variance as the reference data, the target transformation data sequence can be enabled to exclude the interference of noise data, so that the accuracy of determining the normal data threshold range in the follow-up process is ensured.
S120, determining target mean value data and target standard deviation data based on a plurality of device transformation data in the target transformation data sequence.
In this embodiment, the target mean value data may be mean value data obtained by averaging a plurality of device power transformation data in the target power transformation data sequence. The target standard deviation data may be standard deviation data obtained by calculating standard deviations of a plurality of device power transformation data in the target power transformation data sequence.
S130, determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data.
In this embodiment, the target threshold range may be a threshold range corresponding to normal data in the target power transformation data sequence. The determining the target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data may be determining the target threshold range corresponding to the target power transformation data sequence based on a mathematical relationship between the target mean value data and the target standard deviation data. For example, the upper limit of the target threshold range may be determined based on the sum of the target mean data and the target standard deviation data, and the lower limit of the target threshold range may be determined based on the difference between the target mean data and the target standard deviation data.
Optionally, determining the target threshold range corresponding to the target transformation data sequence based on the target mean data and the target standard deviation data includes: determining a target threshold adjustment parameter based on the target standard deviation data; determining the difference value between the target mean value data and the target threshold value adjusting parameter as the lower limit value of the target threshold value; determining the sum of the target mean value data and the target threshold value adjusting parameter as the upper limit value of the target threshold value; and determining a target threshold range corresponding to the target power transformation data sequence based on the lower limit value of the target threshold and the upper limit value of the target threshold.
In this embodiment, the target threshold adjustment parameter may be determined based on the target standard deviation data, and is used to adjust the target mean data to determine the adjustment parameter of the target threshold range. For example, the target threshold adjustment parameter may be the target standard deviation data itself or a multiple of the target standard deviation data.
Specifically, the target threshold range corresponding to the target power transformation data sequence is determined based on the target mean value data and the target standard deviation data, and the target threshold adjustment parameter may be determined based on a multiple of the target standard deviation data. The difference between the target mean data and the target threshold adjustment parameter may then be determined as a lower value of the target threshold and the sum of the target mean data and the target threshold adjustment parameter may be determined as an upper value of the target threshold. And finally, determining a target threshold range corresponding to the target power transformation data sequence based on the data range corresponding to the lower limit value of the target threshold and the upper limit value of the target threshold.
Optionally, determining the target threshold adjustment parameter based on the target standard deviation data includes: and determining a triple value of the target standard deviation data as a target threshold adjustment parameter.
In this embodiment, the target threshold adjustment parameter may be determined to be a triple value of the target standard deviation data based on the 3σ principle. Wherein, sigma in the 3 sigma principle is the target standard deviation data, and 3 sigma is the triple value of the target standard deviation data. Thus, the data range corresponding to the target threshold range may be a numerical range [ μ -3σ, μ+3σ ], where μ is the target mean data. Because the target transformation data sequence can meet normal distribution, the normal data range in the target transformation data sequence can be accurately determined based on the 3 sigma principle so as to identify abnormal data.
And S140, determining target abnormal data in the target power transformation data sequence based on the target threshold range, eliminating the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the eliminating processing.
In this embodiment, the abnormal data in the target abnormal data target transformation data sequence may be noise data generated by the transformation device, for example. Data outside the target threshold range in the target power transformation data sequence may be determined based on the target threshold range as target abnormal data. Then, the target abnormal data can be removed from the target transformation data sequence. Finally, for the blank data in the target transformation data sequence subjected to the rejection processing, the filling processing can be performed based on the data. The data filling processing may be to fill the blank data based on a mean value of data before and after the blank data in the target power transformation data sequence. The data filling process may be performed by using expected values of all data before and after the time point corresponding to the vacant data as filling data.
According to the technical scheme, firstly, a plurality of equipment power transformation data acquired in a target period are segmented into a plurality of candidate power transformation data sequences based on a time point; and calculating the variance of the candidate transformation data sequence, and determining the candidate transformation data sequence with the smallest variance as a target transformation data sequence, so that the interference of noise data in the target transformation data sequence can be eliminated, and the accuracy of the subsequent determination of the normal data threshold range is ensured. Then determining target mean value data and target standard deviation data based on a plurality of equipment transformation data in a target transformation data sequence; determining a triple value of the target standard deviation data as a target threshold adjustment parameter; determining the difference value between the target mean value data and the target threshold value adjusting parameter as the lower limit value of the target threshold value; determining the sum of the target mean value data and the target threshold value adjusting parameter as the upper limit value of the target threshold value; the target threshold range corresponding to the target power transformation data sequence is determined based on the lower limit value of the target threshold and the upper limit value of the target threshold, and the target threshold range is determined based on the three-time standard deviation principle, so that the normal data range in the target power transformation data sequence can be accurately determined. And finally, determining target abnormal data in the target power transformation data sequence based on the target threshold range, eliminating the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the eliminating processing. The abnormal data identification and the abnormal data processing of the equipment power transformation data in a specific period are realized. The technical problem that abnormal data in power transformation data cannot be effectively processed in the prior art is solved. The monitoring condition is reasonably adjusted based on the change trend of the data, the manual monitoring cost is reduced, and the accurate abnormal monitoring and processing beneficial effects of fitting the actual scene of the data are achieved.
Example two
Fig. 2 is a flowchart of a power transformation data exception handling method according to a second embodiment of the present invention, where the method of data filling processing is specifically described based on the above embodiments. Reference is made to the description of this example for a specific implementation. The technical features that are the same as or similar to those of the foregoing embodiments are not described herein. As shown in fig. 2, the method includes:
s210, acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period.
S220, determining target mean value data and target standard deviation data based on a plurality of device transformation data in the target transformation data sequence.
S230, determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data.
S240, determining target transformation data falling outside a target threshold range as target abnormal data.
In this embodiment, the target transformation data that falls outside the target threshold range may be determined as the target abnormal data so as to remove noise data in the target transformation data sequence.
S250, eliminating the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence based on a time point corresponding to the target abnormal data and equipment power transformation data of the target power transformation equipment.
In this embodiment, since the target transformation data sequence may include a plurality of device transformation data acquired at a plurality of time points within the target period, each target abnormality data may also correspond to one time point. The equipment power transformation data can be obtained by inquiring in a database such as an equipment power transformation data storage device or a cloud database based on the equipment number of the target power transformation equipment. The device power transformation data before and after the time point corresponding to the target abnormal data can be obtained. And carrying out data filling processing on the target power transformation data sequence based on the time point corresponding to the target abnormal data and the equipment power transformation data of the target power transformation equipment, acquiring one or more pieces of equipment power transformation data around the time point based on the time point corresponding to the target abnormal data as reference data, and then carrying out data filling processing on the target power transformation data sequence based on the reference data.
Optionally, performing data filling processing on the target transformation data sequence based on a time point corresponding to the target abnormal data and equipment transformation data of the target transformation equipment, including: determining a power transformation data mean value based on equipment power transformation data of the target power transformation equipment, and filling the target power transformation data sequence based on the power transformation data mean value and a time point corresponding to the target abnormal data; and/or performing interpolation processing on the target transformation data sequence after the rejection processing according to the time point corresponding to the target abnormal data.
In this embodiment, the power transformation data mean may be mean data determined based on data remaining after the target abnormal data is removed from the target power transformation data sequence. For example, the power transformation data average value may be a power transformation data reflecting the data average value such as an expected value and an average value. And filling the target power transformation data sequence based on the power transformation data mean value and the time point corresponding to the target abnormal data, wherein the power transformation data mean value can be filled into a data blank area in the target power transformation data sequence by taking the time point corresponding to the target abnormal data as an index.
The interpolation processing may be a processing method of determining and interpolating a missing value from existing data. For example, the interpolation processing may be a processing method such as linear interpolation, polynomial interpolation, and spline interpolation. And carrying out interpolation processing on the target transformation data sequence after the rejection processing according to the time point corresponding to the target abnormal data, namely carrying out function fitting on the missing values based on the data before and after the time point corresponding to the target abnormal data, determining interpolation values based on fitting results, and carrying out interpolation processing on the target transformation data sequence after the rejection processing based on the interpolation values.
And carrying out data filling processing on the target power transformation data sequence based on the time point corresponding to the target abnormal data and the equipment power transformation data of the target power transformation equipment, filling the missing value in the target power transformation data sequence based on the actual equipment power transformation data, further ensuring the data integrity and continuity of the target power transformation data sequence, and improving the data reliability of the target power transformation data sequence.
Optionally, determining the power transformation data mean value based on the device power transformation data of the target power transformation device includes: and acquiring equipment power transformation data of the target power transformation equipment at a plurality of historical moments, and determining the average value or the median of the equipment power transformation data at the plurality of historical moments as a power transformation data average value.
In this embodiment, the device power transformation data at a plurality of historical moments may be selected from the target power transformation data sequence, and then the device power transformation data at the plurality of historical moments may be averaged or median processed to determine an average or median of the device power transformation data at the plurality of historical moments. The average or median of the acquired device transformation data may then be determined as the transformation data average.
Optionally, performing interpolation processing on the target transformation data sequence after the rejection processing according to a time point corresponding to the target abnormal data, including: taking the equipment power transformation data in the target power transformation data sequence after the elimination processing as reference power transformation data, and carrying out interpolation processing on the target power transformation data sequence based on a preset interpolation algorithm, a time point corresponding to the target abnormal data and the reference power transformation number; the preset interpolation algorithm comprises at least one of a linear interpolation method, a polynomial interpolation method and a spline interpolation method.
In this embodiment, the reference transformation data may be a plurality of pieces of equipment transformation data remaining in the target transformation data sequence after the rejection processing. The preset interpolation algorithm may be one or more of linear interpolation, polynomial interpolation, and spline interpolation. And carrying out interpolation processing on the target transformation data sequence based on a preset interpolation algorithm, a time point corresponding to the target abnormal data and the reference transformation number, wherein a function fitting value corresponding to blank data where the time point corresponding to the abnormal data is located can be determined based on the preset interpolation algorithm and the reference transformation data. And then, based on a time point corresponding to the target abnormal data, inserting a function fitting value corresponding to the time point into the target transformation data sequence, so that accurate data interpolation processing of the fitting data actual scene is realized.
According to the technical scheme, the target power transformation data sequence of the target power transformation equipment in the target period is obtained, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period. The target mean value data and the target standard deviation data are determined based on a plurality of device transformation data in the target transformation data sequence. And determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data. And determining the target transformation data falling outside the target threshold range as target abnormal data. Removing the target abnormal data from the target power transformation data sequence, determining a power transformation data mean value based on equipment power transformation data of the target power transformation equipment, and filling the target power transformation data sequence based on the power transformation data mean value and a time point corresponding to the target abnormal data; and/or performing interpolation processing on the target transformation data sequence after the rejection processing according to the time point corresponding to the target abnormal data. Abnormal data rejection of equipment power transformation data in a specific period is achieved, and filling processing and interpolation processing are reasonably carried out on blank data based on reference data in the equipment power transformation data. The technical problem that abnormal data in power transformation data cannot be effectively processed in the prior art is solved. The monitoring condition is reasonably adjusted based on the change trend of the data, the manual monitoring cost is reduced, and the accurate abnormal monitoring and processing beneficial effects of fitting the actual scene of the data are achieved.
Example III
Fig. 3 is a schematic structural diagram of a transformation data exception handling apparatus according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: the system comprises a transformation data sequence acquisition module 310, a transformation data sequence processing module 320, a target threshold range determination module 330 and an abnormal data processing module 340.
The power transformation data sequence obtaining module 310 is configured to obtain a target power transformation data sequence of a target power transformation device in a target period, where the power transformation data sequence includes a plurality of device power transformation data collected at a plurality of time points in the target period; the transformation data sequence processing module 320 is configured to determine target mean value data and target standard deviation data based on a plurality of device transformation data in the target transformation data sequence; a target threshold range determining module 330, configured to determine a target threshold range corresponding to the target transformation data sequence based on the target mean data and the target standard deviation data; the abnormal data processing module 340 is configured to determine target abnormal data in the target transformation data sequence based on the target threshold range, reject the target abnormal data from the target transformation data sequence, and perform data filling processing on the target transformation data sequence after the rejection processing.
According to the technical scheme, firstly, a plurality of equipment power transformation data acquired in a target period are segmented into a plurality of candidate power transformation data sequences based on a time point; and calculating the variance of the candidate transformation data sequence, and determining the candidate transformation data sequence with the smallest variance as a target transformation data sequence, so that the interference of noise data in the target transformation data sequence can be eliminated, and the accuracy of the subsequent determination of the normal data threshold range is ensured. Then determining target mean value data and target standard deviation data based on a plurality of equipment transformation data in a target transformation data sequence; determining a triple value of the target standard deviation data as a target threshold adjustment parameter; determining the difference value between the target mean value data and the target threshold value adjusting parameter as the lower limit value of the target threshold value; determining the sum of the target mean value data and the target threshold value adjusting parameter as the upper limit value of the target threshold value; the target threshold range corresponding to the target power transformation data sequence is determined based on the lower limit value of the target threshold and the upper limit value of the target threshold, and the target threshold range is determined based on the three-time standard deviation principle, so that the normal data range in the target power transformation data sequence can be accurately determined. And finally, determining target abnormal data in the target power transformation data sequence based on the target threshold range, eliminating the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the eliminating processing. The abnormal data identification and the abnormal data processing of the equipment power transformation data in a specific period are realized. The technical problem that abnormal data in power transformation data cannot be effectively processed in the prior art is solved. The monitoring condition is reasonably adjusted based on the change trend of the data, the manual monitoring cost is reduced, and the accurate abnormal monitoring and processing beneficial effects of fitting the actual scene of the data are achieved.
Based on the above technical solution, further, the transformation data sequence obtaining module 310 is specifically configured to: dividing a plurality of equipment power transformation data acquired in a target period into a plurality of candidate power transformation data sequences based on a time point; and calculating the variance of the candidate transformation data sequence, and determining the candidate transformation data sequence with the smallest variance as the target transformation data sequence.
Based on the above technical solution, further, the target threshold range determining module 330 is specifically configured to: determining a target threshold adjustment parameter based on the target standard deviation data; determining the difference value between the target mean value data and the target threshold value adjusting parameter as the lower limit value of the target threshold value; determining the sum of the target mean value data and the target threshold value adjusting parameter as the upper limit value of the target threshold value; and determining a target threshold range corresponding to the target power transformation data sequence based on the lower limit value of the target threshold and the upper limit value of the target threshold.
Based on the above technical solution, further, the abnormal data processing module 340 is specifically configured to: and determining the target transformation data falling outside the target threshold range as target abnormal data.
Based on the above technical solution, further, the abnormal data processing module 340 is specifically configured to: and carrying out data filling processing on the target power transformation data sequence based on the time point corresponding to the target abnormal data and the equipment power transformation data of the target power transformation equipment.
Based on the above technical solution, further, the abnormal data processing module 340 includes a filling processing unit and/or an interpolation processing unit.
The filling processing unit is used for determining a power transformation data mean value based on equipment power transformation data of the target power transformation equipment and filling the target power transformation data sequence based on the power transformation data mean value and a time point corresponding to the target abnormal data;
And the interpolation processing unit is used for carrying out interpolation processing on the target transformation data sequence after the elimination processing according to the time point corresponding to the target abnormal data.
On the basis of the above technical solution, further, the filling processing unit is specifically configured to: and acquiring equipment power transformation data of the target power transformation equipment at a plurality of historical moments, and determining the average value or the median of the equipment power transformation data at the plurality of historical moments as a power transformation data average value.
Based on the above technical solution, further, the interpolation processing unit is specifically configured to: taking the equipment power transformation data in the target power transformation data sequence after the elimination processing as reference power transformation data, and carrying out interpolation processing on the target power transformation data sequence based on a preset interpolation algorithm, a time point corresponding to the target abnormal data and the reference power transformation number; the preset interpolation algorithm comprises at least one of a linear interpolation method, a polynomial interpolation method and a spline interpolation method.
On the basis of the above technical solution, further, the target threshold range determining module 330 is specifically configured to determine a triple value of the target standard deviation data as the target threshold adjustment parameter.
The power transformation data exception handling device provided by the embodiment of the invention can execute the power transformation data exception handling method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, for example, the transformation data abnormality processing method.
In some embodiments, the transformation data exception handling method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the transformation data abnormality processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the transformation data exception handling method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The power transformation data exception handling method is characterized by comprising the following steps of:
Acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period;
Determining target mean value data and target standard deviation data based on a plurality of the device transformation data in the target transformation data sequence;
Determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data;
and determining target abnormal data in the target power transformation data sequence based on the target threshold range, removing the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the removing processing.
2. The method of claim 1, wherein the obtaining the target transformation data sequence for the target transformation device over the target period comprises:
Dividing a plurality of equipment power transformation data acquired in a target period into a plurality of candidate power transformation data sequences based on a time point;
And calculating the variance of the candidate transformation data sequence, and determining the candidate transformation data sequence with the smallest variance as a target transformation data sequence.
3. The method of claim 1, wherein the determining a target threshold range corresponding to the target transformation data sequence based on the target mean data and the target standard deviation data comprises:
Determining a target threshold adjustment parameter based on the target standard deviation data;
determining the difference value between the target mean value data and the target threshold value adjusting parameter as the lower limit value of the target threshold value;
Determining the sum of the target mean value data and the target threshold value adjusting parameter as an upper limit value of the target threshold value;
And determining a target threshold range corresponding to the target power transformation data sequence based on the lower limit value of the target threshold and the upper limit value of the target threshold.
4. The method of claim 1, wherein the determining the target anomaly data in the target substation data sequence based on the target threshold range comprises:
and determining the target power transformation data falling outside the target threshold range as the target abnormal data.
5. The method according to claim 1, wherein the performing data filling processing on the target transformation data sequence after the rejection processing includes:
And carrying out data filling processing on the target power transformation data sequence based on the time point corresponding to the target abnormal data and the equipment power transformation data of the target power transformation equipment.
6. The method according to claim 5, wherein the data filling processing of the target power transformation data sequence based on the time point corresponding to the target abnormal data and the device power transformation data of the target power transformation device includes:
determining a power transformation data mean value based on equipment power transformation data of the target power transformation equipment, and filling the target power transformation data sequence based on the power transformation data mean value and the time point corresponding to the target abnormal data;
And/or the number of the groups of groups,
And carrying out interpolation processing on the target transformation data sequence after the rejection processing according to the time point corresponding to the target abnormal data.
7. The method of claim 6, wherein the determining the power transformation data mean based on the device power transformation data of the target power transformation device comprises:
And acquiring equipment power transformation data of the target power transformation equipment at a plurality of historical moments, and determining the average value or the median of the equipment power transformation data at the plurality of historical moments as a power transformation data average value.
8. The method according to claim 6, wherein the interpolating the target transformation data sequence after the rejection processing according to the time point corresponding to the target abnormal data includes:
taking the equipment transformation data in the target transformation data sequence after the elimination processing as reference transformation data, and carrying out interpolation processing on the target transformation data sequence based on a preset interpolation algorithm, the time point corresponding to the target abnormal data and the reference transformation number; the preset interpolation algorithm comprises at least one of a linear interpolation method, a polynomial interpolation method and a spline interpolation method.
9. A method according to claim 3, wherein said determining a target threshold adjustment parameter based on said target standard deviation data comprises:
and determining the triple value of the target standard deviation data as a target threshold adjustment parameter.
10. An apparatus for processing power transformation data abnormality, comprising:
The power transformation data sequence acquisition module is used for acquiring a target power transformation data sequence of target power transformation equipment in a target period, wherein the power transformation data sequence comprises a plurality of equipment power transformation data acquired at a plurality of time points in the target period;
the power transformation data sequence processing module is used for determining target mean value data and target standard deviation data based on a plurality of equipment power transformation data in the target power transformation data sequence;
The target threshold range determining module is used for determining a target threshold range corresponding to the target power transformation data sequence based on the target mean value data and the target standard deviation data;
And the abnormal data processing module is used for determining target abnormal data in the target power transformation data sequence based on the target threshold range, eliminating the target abnormal data from the target power transformation data sequence, and performing data filling processing on the target power transformation data sequence after the eliminating processing.
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