CN113554527A - Electricity charge data processing method and device, terminal device and storage medium - Google Patents
Electricity charge data processing method and device, terminal device and storage medium Download PDFInfo
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Abstract
The invention discloses an electric charge data processing method, an electric charge data processing device, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring original power consumption data, and calculating the characteristic importance of the original power consumption data; sequencing the original power consumption data according to the feature importance, and deleting the sequenced power consumption data according to a preset proportion to obtain feature power consumption data; establishing a feature subset of the feature electricity utilization data, and calculating the out-of-bag error rate of the feature subset to determine a target feature set; and performing characteristic selection on the characteristic electricity utilization data according to the target characteristic set to generate an electricity charge data processing result. The method can delete a large amount of invalid and redundant electric charge data characteristics, and simultaneously reserve the most effective characteristics beneficial to electric charge error analysis, thereby effectively optimizing the characteristic set. The invention can reduce the difficulty of screening work, reduce the waste of computing resources and time cost, and simultaneously improve the efficiency of the screening work and the accuracy of results.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing electricity charge data, a terminal device, and a storage medium.
Background
In the electricity fee accounting work, it is generally necessary to perform an electricity fee error analysis on a large amount of user electricity consumption data. At present, an analysis method adopted by a power grid company is mainly a power rate error analysis method based on rule judgment, namely, a plurality of manually summarized power rate error rules are adopted to screen power rate data so as to select the power rate error data. However, such a power rate error analysis method based on rule judgment usually results in a large amount of redundant and invalid data in the original power rate data, and further results in poor hit rate of power rate screening. Meanwhile, the method also increases the difficulty of screening work, and causes the waste of computing resources and time cost.
Disclosure of Invention
The invention aims to provide a method and a device for processing electric charge data, terminal equipment and a storage medium, which are used for solving the problems of low efficiency, high error rate, high cost and high screening difficulty in the process of analyzing electric charge error data in the prior art.
In order to achieve the above object, the present invention provides an electricity fee data processing method, including:
acquiring original power consumption data, and calculating the characteristic importance of the original power consumption data;
sequencing the original power consumption data according to the feature importance, and deleting the sequenced power consumption data according to a preset proportion to obtain feature power consumption data;
establishing a feature subset of the feature electricity utilization data, and calculating an out-of-bag error rate of the feature subset to determine a target feature set;
and performing characteristic selection on the characteristic electricity utilization data according to the target characteristic set to generate an electricity charge data processing result.
Further, the calculating the feature importance of the raw electricity data includes:
randomly extracting a sample from the initial power utilization data by using a random forest algorithm, and calculating the out-of-bag error rate of the sample as a first out-of-bag error rate;
calculating the error rate outside the bag of the sample after adding random noise interference to the sample, and taking the error rate outside the bag as a second error rate outside the bag;
calculating a characteristic importance of the raw electricity usage data using the first out-of-bag error rate and the second out-of-bag error rate.
Further, random noise interference is added to the samples by using a permatation method.
Further, the performing feature selection on the feature electricity consumption data according to the target feature set includes:
and taking the minimum feature subset in the out-of-bag error rate of the feature subsets as a target feature subset, and taking the target feature subset as an electric charge data processing result.
The present invention also provides an electricity fee data processing apparatus, including:
the power consumption data acquisition unit is used for acquiring original power consumption data and calculating the characteristic importance of the original power consumption data;
the sorting unit is used for sorting the original power consumption data according to the feature importance, and deleting the sorted power consumption data according to a preset proportion to obtain feature power consumption data;
the target feature set determining unit is used for establishing a feature subset of the feature power utilization data and calculating the out-of-bag error rate of the feature subset to determine a target feature set;
and the characteristic selection unit is used for performing characteristic selection on the characteristic electricity utilization data according to the target characteristic set to generate an electricity charge data processing result.
Further, the power consumption data acquiring unit is further configured to:
randomly extracting a sample from the initial power utilization data by using a random forest algorithm, and calculating the out-of-bag error rate of the sample as a first out-of-bag error rate;
calculating the error rate outside the bag of the sample after adding random noise interference to the sample, and taking the error rate outside the bag as a second error rate outside the bag;
calculating a characteristic importance of the raw electricity usage data using the first out-of-bag error rate and the second out-of-bag error rate.
Further, the power consumption data acquisition unit is further configured to add random noise interference to the sample by using a permatation method.
Further, the feature selection unit is further configured to:
and taking the minimum feature subset in the out-of-bag error rate of the feature subsets as a target feature subset, and taking the target feature subset as an electric charge data processing result.
The present invention also provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the electricity fee data processing method as described in any one of the above.
The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the electricity fee data processing method according to any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses an electric charge data processing method, which comprises the following steps: acquiring original power consumption data, and calculating the characteristic importance of the original power consumption data; sequencing the original power consumption data according to the feature importance, and deleting the sequenced power consumption data according to a preset proportion to obtain feature power consumption data; establishing a feature subset of the feature electricity utilization data, and calculating the out-of-bag error rate of the feature subset to determine a target feature set; and performing characteristic selection on the characteristic electricity utilization data according to the target characteristic set to generate an electricity charge data processing result.
The method can delete a large amount of invalid and redundant electric charge data characteristics, and simultaneously reserve the most effective characteristics beneficial to electric charge error analysis, thereby effectively optimizing the characteristic set. The invention can reduce the difficulty of screening work, reduce the waste of computing resources and time cost, and simultaneously improve the efficiency of the screening work and the accuracy of results.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for processing electricity charge data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electric charge data processing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, an embodiment of the present invention provides an electricity charge data processing method, including the following steps:
and S10, acquiring the original power utilization data, and calculating the characteristic importance of the original power utilization data.
In the present embodiment, the original electricity consumption data includes, but is not limited to, characteristics such as electricity consumption type, metering method, electricity quantity information, and metering electricity fee.
In an optional embodiment, calculating the feature importance of the raw electricity data comprises the following steps:
1.1) randomly extracting samples from the initial power utilization data by using a random forest algorithm, and calculating the out-of-bag error rate of the samples to serve as a first out-of-bag error rate.
Specifically, the first out-of-bag error rate Eoob1The calculation formula of (2) is as follows:
in the formula, NiFor the selected number of samples of the ith tree, I (-) is an indication function, namely 1 is taken for true time in the function and 0 is taken for false time; y ispAs a true result of the p-th sample,
the prediction of the p sample for the ith tree.
1.2) calculating the out-of-bag error rate of the sample after random noise interference is added to the sample, and taking the out-of-bag error rate as a second out-of-bag error rate.
Wherein, the calculation formula of the out-of-bag error rate (second out-of-bag error rate) after adding random noise interference is:
in the formula, NiFor a selected number of samples in the ith tree, I (-) is an indicator function, i.e., if the value in the function is true, it takes 1, andif yes, taking 0; y ispAs a true result of the p-th sample,
and predicting the result of the p sample of the ith tree after the noise interference is added.
1.3) calculating the characteristic importance of the raw electricity data using the first out-of-bag error rate and the second out-of-bag error rate.
Specifically, the feature importance of the original electricity consumption data features Xj is calculated
The calculation formula is as follows:
in a specific embodiment, random noise interference is added to the samples by using a persistence mode (full array), that is, the ith eigenvalue of all N samples is redistributed, so that the distribution of the eigenvalue substitute is approximate to that of the original characteristic.
And S20, sequencing the original power consumption data according to the feature importance, and deleting the sequenced power consumption data according to a preset proportion to obtain feature power consumption data.
Specifically, the original electricity consumption data is sorted in descending order according to the feature importance calculated in step S10, and then the unimportant features except the corresponding ratio are deleted in the current feature according to the preset ratio q to obtain the feature electricity consumption data.
S30, establishing a feature subset of the feature electricity utilization data, and calculating the out-of-bag error rate of the feature subset to determine a target feature set.
In the step, a new random forest model is established according to the characteristic electricity utilization data, and the characteristic importance of each characteristic in the characteristic subset is calculated; steps S20-S30 are then repeated until there are m features left in the subset of features. It should be noted that q in step S20 and m in step S30 are both preset system values.
And S40, performing characteristic selection on the characteristic electricity consumption data according to the target characteristic set to generate an electricity charge data processing result.
Specifically, the feature subset with the minimum out-of-bag error rate of the feature subsets is used as a target feature subset, and the target feature subset is used as an electric charge data processing result.
The electric charge data processing method provided by the embodiment of the invention can delete a large number of invalid and redundant electric charge data characteristics and simultaneously reserve the most effective characteristics beneficial to electric charge error analysis, thereby effectively optimizing the characteristic set. The invention can reduce the difficulty of screening work, reduce the waste of computing resources and time cost, and simultaneously improve the efficiency of the screening work and the accuracy of results.
Referring to fig. 2, an embodiment of the present invention further provides an electricity fee data processing apparatus, including:
the power consumption data acquisition unit 01 is used for acquiring original power consumption data and calculating the characteristic importance of the original power consumption data;
the sorting unit 02 is used for sorting the original power consumption data according to the feature importance, and deleting the sorted power consumption data according to a preset proportion to obtain feature power consumption data;
the target feature set determining unit 03 is configured to establish a feature subset of the feature power consumption data, and calculate an out-of-bag error rate of the feature subset to determine a target feature set;
and the feature selection unit 04 is configured to perform feature selection on the feature electricity consumption data according to the target feature set, and generate an electricity charge data processing result.
In an embodiment, the electricity data acquiring unit 01 is further configured to:
randomly extracting a sample from the initial power utilization data by using a random forest algorithm, and calculating the out-of-bag error rate of the sample as a first out-of-bag error rate;
calculating the error rate outside the bag of the sample after adding random noise interference to the sample, and taking the error rate outside the bag as a second error rate outside the bag;
calculating a characteristic importance of the raw electricity usage data using the first out-of-bag error rate and the second out-of-bag error rate.
In an embodiment, the electrical data acquisition unit 01 is further configured to add random noise interference to the samples by using a permatation method.
In an embodiment, the feature selecting unit 04 is further configured to:
and taking the minimum feature subset in the out-of-bag error rate of the feature subsets as a target feature subset, and taking the target feature subset as an electric charge data processing result.
The electric charge data processing device provided by the embodiment of the invention is used for executing the electric charge data processing method in any embodiment, and the embodiment can delete a large number of invalid and redundant electric charge data characteristics and simultaneously reserve the most effective characteristics beneficial to electric charge error analysis, thereby effectively optimizing the characteristic set. The invention can reduce the difficulty of screening work, reduce the waste of computing resources and time cost, and simultaneously improve the efficiency of the screening work and the accuracy of results.
Referring to fig. 3, an embodiment of the present invention further provides a terminal device, which includes one or more processors; a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the electricity fee data processing method as described above.
The processor is used for controlling the whole operation of the terminal device so as to complete all or part of the steps of the electricity fee data processing method. The memory is used to store various types of data to support operation at the terminal device, and these data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the electricity charge data Processing method according to any one of the above embodiments, and achieve technical effects consistent with the above methods.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions, which when executed by a processor, implement the steps of the electricity fee data processing method according to any one of the above embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions, which are executable by the processor of the terminal device to perform the electricity fee data processing method according to any one of the above-mentioned embodiments, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. An electricity charge data processing method, characterized by comprising:
acquiring original power consumption data, and calculating the characteristic importance of the original power consumption data;
sequencing the original power consumption data according to the feature importance, and deleting the sequenced power consumption data according to a preset proportion to obtain feature power consumption data;
establishing a feature subset of the feature electricity utilization data, and calculating an out-of-bag error rate of the feature subset to determine a target feature set;
and performing characteristic selection on the characteristic electricity utilization data according to the target characteristic set to generate an electricity charge data processing result.
2. The electric charge data processing method according to claim 1, wherein the calculating the characteristic importance of the raw electricity consumption data includes:
randomly extracting a sample from the initial power utilization data by using a random forest algorithm, and calculating the out-of-bag error rate of the sample as a first out-of-bag error rate;
calculating the error rate outside the bag of the sample after adding random noise interference to the sample, and taking the error rate outside the bag as a second error rate outside the bag;
calculating a characteristic importance of the raw electricity usage data using the first out-of-bag error rate and the second out-of-bag error rate.
3. The electric charge data processing method according to claim 2, wherein random noise interference is added to the samples using a permatation method.
4. The electric charge data processing method according to claim 1, wherein the performing feature selection on the feature electricity consumption data according to the target feature set includes:
and taking the minimum feature subset in the out-of-bag error rate of the feature subsets as a target feature subset, and taking the target feature subset as an electric charge data processing result.
5. An electricity fee data processing apparatus characterized by comprising:
the power consumption data acquisition unit is used for acquiring original power consumption data and calculating the characteristic importance of the original power consumption data;
the sorting unit is used for sorting the original power consumption data according to the feature importance, and deleting the sorted power consumption data according to a preset proportion to obtain feature power consumption data;
the target feature set determining unit is used for establishing a feature subset of the feature power utilization data and calculating the out-of-bag error rate of the feature subset to determine a target feature set;
and the characteristic selection unit is used for performing characteristic selection on the characteristic electricity utilization data according to the target characteristic set to generate an electricity charge data processing result.
6. The electric fee data processing device according to claim 5, wherein the electric fee data acquisition unit is further configured to:
randomly extracting a sample from the initial power utilization data by using a random forest algorithm, and calculating the out-of-bag error rate of the sample as a first out-of-bag error rate;
calculating the error rate outside the bag of the sample after adding random noise interference to the sample, and taking the error rate outside the bag as a second error rate outside the bag;
calculating a characteristic importance of the raw electricity usage data using the first out-of-bag error rate and the second out-of-bag error rate.
7. The electric charge data processing apparatus according to claim 6, wherein the electric charge data acquisition unit is further configured to add random noise interference to the samples by using a persistence method.
8. The electric charge data processing apparatus according to claim 5, wherein the feature selection unit is further configured to:
and taking the minimum feature subset in the out-of-bag error rate of the feature subsets as a target feature subset, and taking the target feature subset as an electric charge data processing result.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the electricity fee data processing method according to any one of claims 1 to 4.
10. A computer-readable storage medium on which a computer program is stored, the computer program implementing the electricity fee data processing method according to any one of claims 1 to 4 when executed by a processor.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108960436A (en) * | 2018-07-09 | 2018-12-07 | 上海应用技术大学 | Feature selection approach |
| CN109933605A (en) * | 2019-03-08 | 2019-06-25 | 广东电网有限责任公司 | Electricity charge mistake checks method, apparatus and electronic equipment |
| CN111738297A (en) * | 2020-05-26 | 2020-10-02 | 平安科技（深圳）有限公司 | Feature selection method, device, equipment and storage medium |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108960436A (en) * | 2018-07-09 | 2018-12-07 | 上海应用技术大学 | Feature selection approach |
| CN109933605A (en) * | 2019-03-08 | 2019-06-25 | 广东电网有限责任公司 | Electricity charge mistake checks method, apparatus and electronic equipment |
| CN111738297A (en) * | 2020-05-26 | 2020-10-02 | 平安科技（深圳）有限公司 | Feature selection method, device, equipment and storage medium |
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