CN115940959B

CN115940959B - Low-power consumption electric energy data acquisition management system

Info

Publication number: CN115940959B
Application number: CN202310119723.2A
Authority: CN
Inventors: 王清; 荆臻; 张志�; 王平欣; 朱红霞; 李琮琮; 陈祉如; 赵曦; 曹彤; 马俊
Original assignee: Marketing Service Center of State Grid Shandong Electric Power Co Ltd
Current assignee: Marketing Service Center of State Grid Shandong Electric Power Co Ltd
Priority date: 2023-02-16
Filing date: 2023-02-16
Publication date: 2023-06-23
Anticipated expiration: 2043-02-16
Also published as: CN115940959A

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a low-power consumption electric energy data acquisition management system, which comprises: the data acquisition module, the self-adaptive segmentation module, the abnormality degree analysis module, the determination module, the similarity analysis module, the similarity insertion updating module and the compression storage module can realize the following steps through the mutual coordination among the modules: acquiring an electric energy data set through a sensor and performing self-adaptive segmentation; performing anomaly degree analysis processing on the electric energy data set; determining the degree of abnormality of the data; performing similarity analysis processing on the target sliding window and the electric energy data behind the target sliding window; performing similar insertion update processing on the electric energy data set; and compressing and storing the electric energy similar data set. The invention improves the efficiency of compressing and storing the electric energy data by carrying out electric digital data processing on the electric energy data set, and is mainly applied to the acquisition and management of the electric energy data.

Description

Low-power consumption electric energy data acquisition management system

Technical Field

The invention relates to the technical field of electric digital data processing, in particular to a low-power consumption electric energy data acquisition management system.

Background

Whether the power grid runs safely often relates to enterprise production and folk life, and the running state of a circuit participating in the power grid often needs to be monitored when judging whether the power grid runs safely. The power data may be electrically related data affecting the operational state of the circuit, for example, the power data may be current. Therefore, the operation state of the circuit participating in the power grid can be monitored through a large amount of collected electric energy data in a preset time period. Because the amount of collected electrical energy data is relatively large, the collected electrical energy data often needs to be compressed in order to reduce the occupation of the storage space. Currently, when data is stored in a compressed manner, the following methods are generally adopted: and acquiring data through a sensor, and compressing and storing the acquired data based on an LZ77 algorithm.

However, when the collected electric energy data is compressed and stored based on the LZ77 algorithm, there are often the following technical problems:

because the electric energy data that gathers at each moment often is real-time change, so adopt LZ77 algorithm, when compressing the electric energy data that gathers, the electric energy data in the sliding window often is bigger with the electric energy data that corresponds in the forward buffer, consequently adopt LZ77 algorithm, when compressing the electric energy data that gathers, often lead to compressing the dynamics of electric energy data that gathers not enough to lead to compressing the efficiency of electric energy data that gathers lower, and then occupy more space when leading to the storage, thereby lead to storing the efficiency of electric energy data that gathers lower.

Disclosure of Invention

The summary of the invention is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. The summary of the invention 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.

In order to solve the technical problem of low efficiency of compressing and storing electric energy data, the invention provides a low-power consumption electric energy data acquisition and management system.

The invention provides a low-power consumption electric energy data acquisition management system, which comprises:

the data acquisition module is used for acquiring an electric energy data set of the circuit to be monitored;

the self-adaptive segmentation module is used for carrying out self-adaptive segmentation on the electric energy data set to obtain an electric energy data set;

the abnormality degree analysis module is used for carrying out abnormality degree analysis processing on each electric energy data set in the electric energy data set to obtain a target abnormality degree corresponding to the electric energy data set;

the determining module is used for presetting a target sliding window corresponding to each electric energy data in each electric energy data set, and determining the data abnormality degree corresponding to the electric energy data according to the target sliding window corresponding to the electric energy data and the target abnormality degree corresponding to the electric energy data set where the electric energy data is located;

The similarity analysis module is used for carrying out similarity analysis processing on the target sliding window corresponding to the electric energy data and the electric energy data behind the target sliding window according to the data abnormality degree corresponding to each electric energy data to obtain the data similarity corresponding to the electric energy data;

the similarity insertion updating module is used for carrying out similarity insertion updating processing on the electric energy data sets according to the data similarity corresponding to each electric energy data in each electric energy data set, determining the electric energy similarity data set corresponding to the electric energy data set and obtaining an electric energy similarity data set;

and the compression storage module is used for carrying out compression storage on the electric energy similar data set through a target sliding window according to an LZ77 algorithm.

Further, the adaptively segmenting the electric energy data set to obtain an electric energy data set includes:

sequencing the electric energy data in the electric energy data set according to the acquisition time of the electric energy data to obtain an electric energy data sequence;

for each electric energy data in the electric energy data sequence, determining the absolute value of the slope between the electric energy data and the next electric energy data of the electric energy data according to the coordinates of the electric energy data and the next electric energy data of the electric energy data under a target coordinate system, wherein the target coordinate system takes the acquisition time of the electric energy data as a horizontal axis and takes the electric energy data as a vertical axis as a target slope corresponding to the electric energy data;

When the target slope corresponding to the electric energy data in the electric energy data sequence is larger than a preset slope threshold, determining the electric energy data as abnormal electric energy data;

when the target slope corresponding to the electric energy data in the electric energy data sequence is smaller than or equal to the slope threshold value, determining the electric energy data as stable electric energy data;

combining abnormal electric energy data with continuous positions in the electric energy data sequence into an electric energy data group;

and combining stable electric energy data with continuous positions in the electric energy data sequence into an electric energy data group.

Further, the performing the anomaly degree analysis processing on each electric energy data set in the electric energy data set to obtain the target anomaly degree corresponding to the electric energy data set includes:

determining the quantity of abnormal electric energy data in the electric energy data set as a first quantity corresponding to the electric energy data set;

determining an average value of target slopes corresponding to the electric energy data in the electric energy data set as a first average value of slopes corresponding to the electric energy data set;

determining the product of the first quantity corresponding to the electric energy data set and the first slope average value as a first abnormality degree corresponding to the electric energy data set;

Normalizing the first abnormality degree corresponding to the electric energy data set to obtain the target abnormality degree corresponding to the electric energy data set.

Further, the determining the data anomaly degree corresponding to the electric energy data according to the target sliding window corresponding to the electric energy data and the target anomaly degree corresponding to the electric energy data group where the electric energy data is located includes:

determining each electric energy data in a target sliding window corresponding to the electric energy data as sliding window electric energy data, and obtaining a sliding window electric energy data sequence corresponding to the electric energy data;

for each sliding window electric energy data in a sliding window electric energy data sequence corresponding to the electric energy data, determining the absolute value of the difference value of reference data corresponding to the sliding window electric energy data and the sliding window electric energy data as a first difference index corresponding to the sliding window electric energy data, wherein the reference data corresponding to the sliding window electric energy data is electric energy data with the same serial number in a first data sequence as that of the sliding window electric energy data in the sliding window electric energy data sequence, and the first data sequence is a sequence formed by electric energy data after the sliding window electric energy data sequence corresponding to the electric energy data in the electric energy data group;

For each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data, determining the product of a first difference index corresponding to the sliding window electric energy data and a target slope as a second difference index corresponding to the sliding window electric energy data;

determining the sum of second difference indexes corresponding to each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data as a third difference index corresponding to the electric energy data;

determining a product of a third difference index corresponding to the electric energy data and a target abnormality degree corresponding to an electric energy data set where the electric energy data are located as a fourth difference index corresponding to the electric energy data;

for each sliding window electric energy data in a sliding window electric energy data sequence corresponding to the electric energy data, determining the ratio of the sliding window electric energy data to reference data corresponding to the sliding window electric energy data as a first ratio corresponding to the sliding window electric energy data;

for each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data, determining an absolute value of a difference value of a first ratio value corresponding to constant 1 and the sliding window electric energy data as a fifth difference index corresponding to the sliding window electric energy data;

Determining the sum of fifth difference indexes corresponding to each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data as a sixth difference index corresponding to the electric energy data;

and determining the product of a fourth difference index and a sixth difference index corresponding to the electric energy data as the data abnormality degree corresponding to the electric energy data.

Further, according to the degree of data abnormality corresponding to each piece of electric energy data, performing similarity analysis processing on the target sliding window corresponding to the electric energy data and the electric energy data after the target sliding window to obtain data similarity corresponding to the electric energy data, including:

determining the sum of first difference indexes corresponding to each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data as a seventh difference index corresponding to the electric energy data;

determining a product of a seventh difference index corresponding to the electric energy data and a data abnormality degree as an eighth difference index corresponding to the electric energy data;

determining the opposite number of the eighth difference index corresponding to the electric energy data as a first opposite number corresponding to the electric energy data;

and determining the first inverse power of the power corresponding to the electric energy data of the natural constant as the data similarity corresponding to the electric energy data.

Further, the performing a similar insertion update process on the electric energy data sets according to the data similarity corresponding to each electric energy data in each electric energy data set, and determining the electric energy similar data set corresponding to the electric energy data set includes:

updating the electric energy data sets according to the data similarity corresponding to the first electric energy data in the electric energy data sets to obtain a first similar data set corresponding to the first electric energy data in the electric energy data sets;

updating the first similar data set corresponding to the first electric energy data in the electric energy data sets according to the data similarity corresponding to the first similar data set corresponding to the first electric energy data in the electric energy data sets and the second electric energy data in the electric energy data sets, so as to obtain the second similar data set corresponding to the second electric energy data in the electric energy data sets;

and updating the second similar data set corresponding to the second electric energy data in the electric energy data sets according to the second similar data set corresponding to the second electric energy data in the electric energy data sets and the data similarity corresponding to the third electric energy data in the electric energy data sets, so as to obtain the third similar data set corresponding to the third electric energy data in the electric energy data sets, and the like, determining that each electric energy data in the electric energy data sets corresponds to the similar data set, and determining the similar data set corresponding to the last electric energy data in the electric energy data sets as the electric energy similar data set corresponding to the electric energy data sets.

Further, the updating the electric energy data set according to the data similarity corresponding to the first electric energy data in the electric energy data set to obtain a first similar data set corresponding to the first electric energy data in the electric energy data set includes:

when the data similarity corresponding to the first electric energy data in the electric energy data sets is larger than a preset similarity threshold, judging whether the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the first electric energy data in the electric energy data sets is identical to the reference data corresponding to the sliding window electric energy data, inserting data identical to the sliding window electric energy data in front of the reference data corresponding to the sliding window electric energy data when the sliding window electric energy data is different from the reference data corresponding to the sliding window electric energy data, and determining the electric energy data sets subjected to data insertion as the first similar data sets corresponding to the first electric energy data in the electric energy data sets;

and when the data similarity corresponding to the first electric energy data in the electric energy data sets is smaller than or equal to a similarity threshold value, determining the electric energy data sets as the first similar data set corresponding to the first electric energy data in the electric energy data sets.

Further, the updating the first similar data set corresponding to the first electric energy data in the electric energy data sets according to the data similarity corresponding to the first similar data set corresponding to the first electric energy data in the electric energy data sets and the second similar data set corresponding to the second electric energy data in the electric energy data sets to obtain the second similar data set corresponding to the second electric energy data in the electric energy data sets includes:

updating the electric energy data set into a first similar data set corresponding to first electric energy data in the electric energy data sets;

when the data similarity corresponding to the second electric energy data in the electric energy data sets is larger than a similarity threshold, judging whether the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the second electric energy data in the electric energy data sets is identical to the reference data corresponding to the sliding window electric energy data, and when the sliding window electric energy data is not identical to the reference data corresponding to the sliding window electric energy data, inserting the same data as the sliding window electric energy data in front of the reference data corresponding to the sliding window electric energy data, and determining the electric energy data sets after data insertion as second similar data sets corresponding to the second electric energy data in the electric energy data sets;

And when the data similarity corresponding to the second electric energy data in the electric energy data sets is smaller than or equal to a similarity threshold value, determining the second electric energy data in the electric energy data sets as a second similar data set corresponding to the second electric energy data in the electric energy data sets.

The invention has the following beneficial effects:

according to the low-power consumption electric energy data acquisition management system, through carrying out electric digital data processing on the electric energy data set, the technical problem that the efficiency of compressing and storing the electric energy data is low is solved, and the efficiency of compressing and storing the electric energy data is improved. Firstly, acquiring an electric energy data set of a circuit to be monitored through a data acquisition module. The electric energy data set can be conveniently compressed and stored later. And then, the self-adaptive segmentation of the electric energy data set is realized through a self-adaptive segmentation module, so that an electric energy data set is obtained. The electric energy data set is adaptively segmented, so that the electric energy data set can be accurately processed later. And then, carrying out abnormality degree analysis processing on each electric energy data set in the electric energy data set through an abnormality degree analysis module to obtain a target abnormality degree corresponding to the electric energy data set. Generally, if the electric energy data set is abnormal, the circuit to be monitored may be abnormal in the acquisition time period corresponding to the electric energy data set, so that the electric energy data set is analyzed and processed to be abnormal, and the operation state of the circuit to be monitored can be monitored conveniently. And continuously, presetting a target sliding window corresponding to each electric energy data in each electric energy data group through a determining module, and determining the data abnormality degree corresponding to the electric energy data according to the target sliding window corresponding to the electric energy data and the target abnormality degree corresponding to the electric energy data group in which the electric energy data is located. The accuracy of determining the data abnormality degree corresponding to the electric energy data can be improved by comprehensively considering the target sliding window corresponding to the electric energy data and the target abnormality degree corresponding to the electric energy data group where the electric energy data are located. And then, carrying out similarity analysis processing on the target sliding window corresponding to the electric energy data and the electric energy data behind the target sliding window according to the data abnormality degree corresponding to each electric energy data by a similarity analysis module to obtain the data similarity corresponding to the electric energy data. Generally, when the LZ77 algorithm is adopted to compress the collected electric energy data, if the electric energy data in the target sliding window is the same as the electric energy data corresponding to the forward buffer area, the electric energy data in the target sliding window and the electric energy data corresponding to the forward buffer area can be compressed to the greatest extent at this time. Therefore, the similarity analysis processing is carried out on the target sliding window corresponding to the electric energy data and the electric energy data behind the target sliding window, so that the compression degree of compressing the electric energy data in the target sliding window and the electric energy data corresponding to the forward buffer area can be conveniently determined. And then, performing similar insertion updating processing on the electric energy data sets according to the data similarity corresponding to each electric energy data in each electric energy data set through a similar insertion updating module, and determining the electric energy similar data sets corresponding to the electric energy data sets to obtain an electric energy similar data set. And finally, implementing compression storage of the electric energy similar data set through a target sliding window according to an LZ77 algorithm through a compression storage module. After the similar insertion update processing is performed, the similarity degree of the electric energy data in the target sliding window and the corresponding electric energy data in the forward buffer area in the electric energy data set can be improved, so that the efficiency of compression storage is often improved when the electric energy similar data set is compressed and stored through the LZ77 algorithm and the target sliding window in the follow-up process, compared with the process of directly compressing and storing the electric energy data set.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a low-power consumption power data collection management system according to the present invention;

FIG. 2 is a schematic diagram of a power data set and a target sliding window according to the present invention;

fig. 3 is a schematic diagram of a power data set update process according to the present invention.

Wherein, the reference numerals include: sliding window 201, first power data set 301, and second power data set 302.

Detailed Description

In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description is given below of the specific implementation, structure, features and effects of the technical solution according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

and the compression storage module is used for carrying out compression storage on the electric energy similar data set through the target sliding window according to the LZ77 algorithm.

Referring to fig. 1, a schematic diagram of a low-power consumption power data acquisition management system according to the present invention is shown. The low-power consumption electric energy data acquisition management system comprises:

the data acquisition module 101 is configured to acquire a set of electrical energy data of a circuit to be monitored.

In some embodiments, a power data set of a circuit to be monitored may be obtained.

The circuit to be monitored may be a circuit to be monitored for an operation state. For example, the operating state of the circuit may be smooth operation or abnormal operation. When the circuit is stable, the circuit can be considered to operate smoothly. When the circuit fails or the short circuit causes large fluctuation of the power data, the circuit can be considered to operate abnormally. The power data in the power data set may be electrically related data affecting the operational state of the circuit. For example, the power data in the power data set may be a current. The power data in the power data set may be a current of the circuit to be monitored for a period of time. The circuit to be monitored may be a circuit of a certain cell.

It should be noted that, if the circuit to be monitored runs stably, the difference between the electric energy data in the electric energy data set is small, and the change of the electric energy data is stable. If the circuit to be monitored is abnormally operated, the difference between the electric energy data in the electric energy data set is large, and the variation fluctuation of the electric energy data is large. Therefore, the electric energy data set is collected, and the operation state of the circuit to be monitored can be conveniently monitored according to the change fluctuation of the electric energy data. Because the quantity of the electric energy data in the electric energy data set is often relatively large, the electric energy data set of the circuit to be monitored is obtained, and the electric energy data set can be conveniently compressed and stored later.

As an example, the total current of the cell may be collected as the electric energy data by the current sensor from 2023, 01, 17, 00, to 2023, 01, 17, 24, 00, and then once every 3 seconds, and the electric energy data set is obtained.

The adaptive segmentation module 102 is configured to adaptively segment the electric energy data set to obtain an electric energy data set.

In some embodiments, the above-mentioned power data set may be adaptively segmented to obtain a power data set.

As an example, this step may include the steps of:

the first step, sorting the electric energy data in the electric energy data set according to the acquisition time of the electric energy data to obtain an electric energy data sequence.

And a second step of determining, for each of the electric energy data in the electric energy data sequence, an absolute value of a slope between the electric energy data and the next electric energy data of the electric energy data as a target slope corresponding to the electric energy data according to coordinates of the electric energy data and the next electric energy data of the electric energy data in a target coordinate system.

The target coordinate system may be a coordinate system with coordinates (0, 0) as an origin, a time of collecting the electric energy data as a horizontal axis, and the electric energy data as a vertical axis.

For example, for the first electric energy data in the electric energy data sequence, the absolute value of the slope between the first electric energy data and the second electric energy data can be determined as the target slope corresponding to the first electric energy data according to the coordinates of the first electric energy data and the second electric energy data in the target coordinate system.

And thirdly, when the target slope corresponding to the electric energy data in the electric energy data sequence is larger than a preset slope threshold value, determining the electric energy data as abnormal electric energy data.

The slope threshold may be a maximum target slope allowed when the preset power data is stable. For example, the slope threshold may be 0.2.

It should be noted that the abnormal power data may be power data with larger fluctuation, and the larger fluctuation may be caused by abnormal operation of the circuit to be monitored. Therefore, the abnormal power data may be abnormal power data generated when the circuit to be monitored performs abnormal operation.

And fourthly, determining the electric energy data as stable electric energy data when the target slope corresponding to the electric energy data in the electric energy data sequence is smaller than or equal to the slope threshold value.

It should be noted that the stationary power data may be power data having small fluctuation variation and relatively stationary. Thus, the stationary power data may be stable power data generated when the circuit to be monitored is stationary.

And fifthly, combining abnormal electric energy data with continuous positions in the electric energy data sequence into an electric energy data set.

And sixthly, combining stable electric energy data with continuous positions in the electric energy data sequence into an electric energy data group.

For example, the sequence of power data may be { first stationary power data, second stationary power data, third stationary power data, first abnormal power data, second abnormal power data, third abnormal power data, fourth stationary power data, fifth stationary power data, sixth stationary power data }. The first stable electric energy data, the second stable electric energy data and the third stable electric energy data are stable electric energy data with continuous positions, and can form an electric energy data set. The first abnormal power data, the second abnormal power data, the third abnormal power data and the fourth abnormal power data are abnormal power data with continuous positions, and can form a power data set. The fourth stationary power data, the fifth stationary power data and the sixth stationary power data are stationary power data with continuous positions, and may constitute one power data set.

It should be noted that, if the circuit to be monitored runs stably, the difference between the electric energy data in the electric energy data set is small, and the change of the electric energy data is stable. The larger the corresponding target slope of the electric energy data is, the larger the fluctuation of the electric energy data is, and the unstable change of the electric energy data is. Therefore, the larger the corresponding target slope of the electrical energy data, the more likely the electrical energy data is abnormal. According to the target slope corresponding to the electric energy data, the electric energy data sequence is divided into electric energy data sets, and the electric energy data sets can be accurately processed conveniently.

The abnormality degree analysis module 103 is configured to perform abnormality degree analysis processing on each electric energy data set in the electric energy data set, so as to obtain a target abnormality degree corresponding to the electric energy data set.

In some embodiments, the anomaly degree analysis may be performed on each electrical energy data set in the electrical energy data set to obtain the target anomaly degree corresponding to the electrical energy data set.

As an example, this step may include the steps of:

and a first step of determining the quantity of abnormal electric energy data in the electric energy data set as a first quantity corresponding to the electric energy data set.

Wherein, when the electric energy data set is composed of stationary electric energy data, the number of abnormal electric energy data in the electric energy data set is 0. When the electrical energy data set is composed of abnormal electrical energy data, the number of elements in the electrical energy data set may be equal to the number of abnormal electrical energy data in the electrical energy data set.

And secondly, determining the average value of the target slope corresponding to the electric energy data in the electric energy data set as the first slope average value corresponding to the electric energy data set.

And thirdly, determining the product of the first quantity corresponding to the electric energy data set and the first slope average value as a first abnormality degree corresponding to the electric energy data set.

And step four, normalizing the first abnormality degree corresponding to the electric energy data set to obtain the target abnormality degree corresponding to the electric energy data set.

For example, the formula for determining the target abnormality degree corresponding to the electric energy data set may be:

wherein, the liquid crystal display device comprises a liquid crystal display device,

is the first of the above-mentioned sets of electrical energy datarTarget abnormality degrees corresponding to the electric energy data sets.rIs the serial number of the electric energy data group in the electric energy data group set. />

Is the first of the above-mentioned sets of electrical energy datarThe number of abnormal power data in the power data group, i.e. the first rA first number of the electrical energy data sets. />

Is the first of the above-mentioned sets of electrical energy datarNumber of power data in a power data set。/>

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiElectric energy data and the firstiSlope between +1 power data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe target slope corresponding to the individual power data.iIs the firstrAnd the serial numbers of the electric energy data in the electric energy data groups. />

Is the first of the above-mentioned sets of electrical energy datarThe average value of the target slopes corresponding to the electric energy data in the electric energy data sets, namelyrAnd the first slope average value corresponding to each electric energy data set. />

Is the first of the above-mentioned sets of electrical energy datarAnd the first degree of abnormality corresponding to the electric energy data sets. />

Is to

Normalization was performed. />

Is->

Is the absolute value of (c).

It should be noted that the number of the substrates,

the larger is, tend to illustrate the firstrThe greater the number of abnormal power data in the power data sets, the more often the description of the firstrThe greater the difference between the power data in the individual power data sets. />

The larger is, tend to illustrate the firstrThe greater the data fluctuation between the power data in the individual power data sets. In general, if the difference in data fluctuation between power data is large without performing circuit maintenance, a circuit may malfunction. Thus (S) >

The larger the circuit, the more often the circuit to be monitored is monitored laterrThe more important the individual power data sets tend to be. And->

Can realize->

Normalization is performed to make the value range of the target abnormality degree be [0,1 ]]And the subsequent treatment can be facilitated.

The determining module 104 is configured to preset a target sliding window corresponding to each electric energy data in each electric energy data set, and determine a data abnormality degree corresponding to the electric energy data according to the target sliding window corresponding to the electric energy data and a target abnormality degree corresponding to the electric energy data set where the electric energy data is located.

In some embodiments, a target sliding window corresponding to each electric energy data may be preset in each electric energy data set, and the data abnormality degree corresponding to the electric energy data is determined according to the target sliding window corresponding to the electric energy data and the target abnormality degree corresponding to the electric energy data set where the electric energy data is located.

The target sliding window may be a one-dimensional sliding window set in advance. For example, the target sliding window may be a 1×6 sliding window. The degree of abnormality of the data corresponding to the electrical energy data can represent the degree of abnormality of the electrical energy data in the target sliding window corresponding to the electrical energy data. When the sliding window slides to the electric energy data, the sliding window at the moment can be used as a target sliding window corresponding to the electric energy data. As shown in fig. 2, the sliding window 201 has been slid to C, and the sliding window 201 may be regarded as a target sliding window corresponding to C. A, B, C, a, b, c and d in fig. 2 may be power data.

As an example, this step may include the steps of:

first, presetting a target sliding window corresponding to each electric energy data in each electric energy data group.

For example, the target sliding window corresponding to each power data may be set to a sliding window of 1×6.

And step two, determining each electric energy data in the target sliding window corresponding to the electric energy data as sliding window electric energy data, and obtaining a sliding window electric energy data sequence corresponding to the electric energy data.

And thirdly, determining the absolute value of the difference value of the sliding window electric energy data and the reference data corresponding to the sliding window electric energy data as a first difference index corresponding to the sliding window electric energy data for each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data.

The reference data corresponding to the sliding window electric energy data may be electric energy data with the same serial number as that of the sliding window electric energy data in the sliding window electric energy data sequence, where the serial number corresponds to the first data sequence. The first data sequence may be a sequence of electrical energy data after a sliding window electrical energy data sequence corresponding to the electrical energy data in the electrical energy data set.

For example, as shown in fig. 2, since the sliding window 201 may be a target sliding window corresponding to C, the sliding window power data sequence corresponding to C may be { a, B, C }, and the sequence numbers of A, B, C in the sliding window power data sequence may be 1, 2, and 3, respectively. The first data sequence may be { a, b, c, d, d }. a. b, c, d, d the sequence numbers in the first data sequence may be 1, 2, 3, 4, 5, respectively. Since the sequence numbers of a and a, B and B, and C are the same, the reference data corresponding to a may be the reference data corresponding to a and B, and the reference data corresponding to B and C may be C. Wherein A, B, C, a, b, c and d can be electrical energy data.

Fourth, for each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data, determining the product of the first difference index corresponding to the sliding window electric energy data and the target slope as a second difference index corresponding to the sliding window electric energy data.

And fifthly, determining the sum of the second difference indexes corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data as a third difference index corresponding to the electric energy data.

And a sixth step of determining a product of a third difference index corresponding to the electric energy data and a target abnormality degree corresponding to an electric energy data group where the electric energy data is located as a fourth difference index corresponding to the electric energy data.

Seventh, for each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data, determining a ratio of the sliding window electric energy data to reference data corresponding to the sliding window electric energy data as a first ratio corresponding to the sliding window electric energy data.

Eighth, for each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data, determining an absolute value of a difference value of a first ratio corresponding to the sliding window electric energy data and a constant 1 as a fifth difference index corresponding to the sliding window electric energy data.

And a ninth step of determining a sum of fifth difference indexes corresponding to each sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data as a sixth difference index corresponding to the electric energy data.

And tenth, determining the product of a fourth difference index and a sixth difference index corresponding to the electric energy data as the data abnormality degree corresponding to the electric energy data.

For example, the formula for determining the degree of abnormality of the data corresponding to the power data may be:

is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiData difference corresponding to the electric energy dataTo a constant extent.rIs the serial number of the electric energy data group in the electric energy data group set.iIs the firstrAnd the serial numbers of the electric energy data in the electric energy data groups. />

Is the first of the above-mentioned sets of electrical energy datarTarget abnormality degrees corresponding to the electric energy data sets. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiAnd the number of sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequence jAnd the sliding window electric energy data corresponds to the target slope.jIs the firstiAnd the serial numbers of the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejSliding window power data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejAnd the reference data corresponds to the sliding window electric energy data. />

Is->

Is the absolute value of (c). />

Is the aboveThe first of the set of electrical energy data setsrThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejAnd the first difference index corresponds to the sliding window electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejAnd a second difference index corresponding to the sliding window electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiAnd a third difference index corresponding to the electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data sets iFourth difference index corresponding to the electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejAnd a first ratio corresponding to the sliding window power data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejFifth difference index corresponding to the sliding window electric energy data.

Is->

Is the absolute value of (c). />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiAnd a sixth difference index corresponding to the electric energy data.

It should be noted that, the degree of data abnormality corresponding to the electrical energy data may represent the degree of difference between each sliding window electrical energy data in the sliding window electrical energy data sequence corresponding to the electrical energy data and the corresponding reference data.

The larger is, tend to illustrate the firstrThe greater the difference between the power data in the power data sets. />

The larger is, tend to illustrate the firstrThe first of the electric energy data setsiThe greater the difference between each sliding window power data in the sliding window power data sequence corresponding to the respective power data and the corresponding reference data. />

The more toward 0, tend to describe the first rThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejThe smaller the difference between the individual sliding window power data and the corresponding reference data. Thus (S)>

The larger is, tend to illustrate the firstrThe first of the electric energy data setsiThe greater the difference between each sliding window power data in the sliding window power data sequence corresponding to each power data and the corresponding reference data, the more often the description of the firstiThe more the reference data corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the individual electric energy data has analysis value, the more the description is more unlikely to change the firstiAnd the change trend of the reference data corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data.

The similarity analysis module 105 is configured to perform similarity analysis processing on the target sliding window corresponding to the electric energy data and the electric energy data after the target sliding window according to the data anomaly degree corresponding to each electric energy data, so as to obtain data similarity corresponding to the electric energy data.

In some embodiments, according to the degree of data abnormality corresponding to each piece of electric energy data, similarity analysis processing may be performed on the target sliding window corresponding to the electric energy data and the electric energy data after the target sliding window, so as to obtain the data similarity corresponding to the electric energy data.

As an example, this step may include the steps of:

and determining the sum of first difference indexes corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data as a seventh difference index corresponding to the electric energy data.

And a second step of determining the product of the seventh difference index corresponding to the electric energy data and the data abnormality degree as an eighth difference index corresponding to the electric energy data.

And thirdly, determining the opposite number of the eighth difference index corresponding to the electric energy data as a first opposite number corresponding to the electric energy data.

And fourthly, determining the first inverse power of the power corresponding to the electric energy data with the natural constant as the data similarity corresponding to the electric energy data.

For example, the formula for determining the data similarity corresponding to the power data may be:

is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiAnd data similarity corresponding to the electric energy data.rIs the serial number of the electric energy data group in the electric energy data group set.iIs the firstrAnd the serial numbers of the electric energy data in the electric energy data groups. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data sets iCorresponding to the electric energy dataDegree of data anomaly. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejSliding window power data.jIs the firstiAnd the serial numbers of the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiThe sliding window corresponding to the electric energy data is the first one in the electric energy data sequencejAnd the first difference index corresponds to the sliding window electric energy data. />

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiAnd a seventh difference index corresponding to the electric energy data.

Is the first of the above-mentioned sets of electrical energy datarThe first of the electric energy data setsiAnd an eighth difference index corresponding to the electric energy data. />

Is the first of the above-mentioned sets of electrical energy data rThe first of the electric energy data setsiThe first electric energy dataAn opposite number. />

Is of natural constant

To the power. />

Is->

Is the absolute value of (c).

It should be noted that, the data similarity corresponding to the electrical energy data may represent a degree of similarity between each sliding window electrical energy data in the sliding window electrical energy data sequence corresponding to the electrical energy data and the corresponding reference data. When (when)

The smaller the time, the more description of the firstrThe first of the electric energy data setsiThe smaller the difference between each sliding window power data in the sliding window power data sequence corresponding to the respective power data and the corresponding reference data, and thus the subsequent pair ofiWhen data is inserted into reference data corresponding to sliding window electric energy data in a sliding window electric energy data sequence corresponding to the electric energy data, the more will not change the first timeiAnd the change trend of the reference data corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the electric energy data. />

Smaller, tend to describe the firstrThe first of the electric energy data setsiThe smaller the variation between each sliding window power data in the sliding window power data sequence corresponding to the respective power data and the corresponding reference data. Thus, when->

The larger the tends to explain the firstrThe first of the electric energy data sets iThe greater the degree of similarity between each sliding window power data in the sliding window power data sequence corresponding to the respective power data and the corresponding reference data, and thus the subsequent pairiPersonal electric applianceWhen the data is inserted into the reference data corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the data, the more will not change the first timeiThe less the influence of subsequent data insertion is often caused by the trend of the change of the reference data corresponding to the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the individual electric energy data.

And the similarity insertion updating module 106 is configured to perform similarity insertion updating processing on the electric energy data sets according to the data similarity corresponding to each electric energy data in each electric energy data set, determine the electric energy similarity data set corresponding to the electric energy data set, and obtain an electric energy similarity data set.

In some embodiments, the similar insertion update process may be performed on the electric energy data sets according to the data similarity corresponding to each electric energy data in each electric energy data set, so as to determine the electric energy similar data set corresponding to the electric energy data set, and obtain an electric energy similar data set.

As an example, this step may include the steps of:

and a first step of updating the electric energy data sets according to the data similarity corresponding to the first electric energy data in the electric energy data sets to obtain a first similar data set corresponding to the first electric energy data in the electric energy data sets.

For example, updating the electric energy data set according to the data similarity corresponding to the first electric energy data in the electric energy data set to obtain the first similar data set corresponding to the first electric energy data in the electric energy data set may include the following substeps:

and a first sub-step of judging whether the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the first electric energy data in the electric energy data group is identical to the reference data corresponding to the sliding window electric energy data or not when the data similarity corresponding to the first electric energy data in the electric energy data group is larger than a preset similarity threshold value, and inserting the same data as the sliding window electric energy data in front of the reference data corresponding to the sliding window electric energy data when the sliding window electric energy data is different from the reference data corresponding to the sliding window electric energy data, and determining the electric energy data group after data insertion as the first similar data group corresponding to the first electric energy data in the electric energy data group.

The similarity threshold may be a maximum allowed data similarity when the preset target sliding window corresponding to the electric energy data is not similar to the data after the target sliding window. For example, the similarity threshold may be 0.7. When the sliding window electric energy data is the same as the reference data corresponding to the sliding window electric energy data, the data identical to the sliding window electric energy data does not need to be inserted in front of the reference data corresponding to the sliding window electric energy data.

And a second sub-step of determining the electric energy data set as a first similar data set corresponding to the first electric energy data in the electric energy data set when the data similarity corresponding to the first electric energy data in the electric energy data set is smaller than or equal to a similarity threshold.

And a second step of updating the first similar data set corresponding to the first electric energy data in the electric energy data sets according to the data similarity corresponding to the first similar data set corresponding to the first electric energy data in the electric energy data sets and the second similar data set corresponding to the second electric energy data in the electric energy data sets, so as to obtain the second similar data set corresponding to the second electric energy data in the electric energy data sets.

For example, updating the first similar data set corresponding to the first electric energy data in the electric energy data sets according to the data similarity corresponding to the first similar data set corresponding to the first electric energy data in the electric energy data sets and the second electric energy data in the electric energy data sets, to obtain the second similar data set corresponding to the second electric energy data in the electric energy data sets may include the following substeps:

and a first sub-step of updating the electric energy data set to a first similar data set corresponding to the first electric energy data in the electric energy data sets.

And a second sub-step of judging whether the sliding window electric energy data in the sliding window electric energy data sequence corresponding to the second electric energy data in the electric energy data group is identical to the reference data corresponding to the sliding window electric energy data or not when the data similarity corresponding to the second electric energy data in the electric energy data group is larger than a similarity threshold value, and inserting the same data as the sliding window electric energy data into the front of the reference data corresponding to the sliding window electric energy data when the sliding window electric energy data is different from the reference data corresponding to the sliding window electric energy data, and determining the electric energy data group after data insertion as the second similar data group corresponding to the second electric energy data in the electric energy data group.

And a third sub-step of determining the second electric energy data in the electric energy data set as a second similar data set corresponding to the second electric energy data in the electric energy data set when the data similarity corresponding to the second electric energy data in the electric energy data set is smaller than or equal to a similar threshold value.

And thirdly, updating the second similar data set corresponding to the second electric energy data in the electric energy data sets according to the second similar data set corresponding to the second electric energy data in the electric energy data sets and the data similarity corresponding to the third electric energy data in the electric energy data sets, so as to obtain the third similar data set corresponding to the third electric energy data in the electric energy data sets, and so on, determining the similar data sets corresponding to the electric energy data in the electric energy data sets, and determining the similar data set corresponding to the last electric energy data in the electric energy data sets as the electric energy similar data set corresponding to the electric energy data sets.

For example, the target sliding window may be a 1×4 sliding window. The second similar data set corresponding to the second one of the power data sets may be {2,3,6,2,6,9,3}. The power data set is updated to 2,3,6,2,6,9,3. If the data similarity corresponding to the 3 rd power data in {2,3,6,2,6,9,3} is greater than the similarity threshold, data insertion may be performed on {2,3,6,2,6,9,3}, and since the target sliding window is a 1×4 sliding window, the first 3 power data in {2,3,6,2,6,9,3} are all within the target sliding window. {2,3,6} in {2,3,6,2,6,9,3} may be a sliding window power data sequence corresponding to the third power data. {2,6,9,3} of {2,3,6,2,6,9,3} can be the first data sequence. The reference data corresponding to "2" in {2,3,6} may be "2" in {2,6,9,3}, which is the same, without data insertion preceding "2" in {2,6,9,3 }. The reference data corresponding to "3" in {2,3,6} may be "6" in {2,6,9,3}, which are different, and "3" needs to be inserted in front of "6" in {2,6,9,3}, where the sequence number of "6" in {2,6,9,3} becomes 3, and the reference data corresponding to "6" in {2,3,6} may be "6" in {2,3,6,9,3}, which are the same, and no data insertion is required. Thus, the third similar data set for the third power data may be {2,3,6,2,3,6,9,3}. The process may be as shown in fig. 3, and the first power data set 301 may be a second similar data set corresponding to a second power data in the power data sets. The sliding window on the first power data set 301 may be a target sliding window corresponding to a third power data in the power data set. The second power data set 302 may be a third power data set corresponding to a third power data in the power data sets.

When the collected electric energy data is compressed by adopting the LZ77 algorithm, if the electric energy data in the target sliding window is the same as the electric energy data corresponding to the forward buffer area, the electric energy data in the target sliding window and the electric energy data corresponding to the forward buffer area can be compressed to the greatest extent. Therefore, the electric energy data group where the electric energy data with the data similarity larger than the similarity threshold value is located is updated, the change trend of the electric energy data in the electric energy data group is not changed, the frequency of the electric energy data in the target sliding window, which is identical to that of the electric energy data corresponding to the electric energy data in the forward buffer area, is improved, and the compression effect is improved. And secondly, the larger the data similarity is, the less data needs to be inserted, so the data insertion is carried out on the electric energy data group where the electric energy data with the data similarity larger than the similarity threshold value is located, the frequency of the electric energy data in the target sliding window which is identical with the corresponding electric energy data in the forward buffer zone can be improved, namely, although the data is increased through the data insertion, compared with the increased compression degree when the LZ77 algorithm is adopted for compression, the increased data quantity is usually negligible through the data insertion, and therefore the compression effect is often improved. The LZ77 algorithm can search the same data points in the buffer data through a sliding window, is an iterative traversal algorithm, can realize data compression without carrying out a large amount of operations, has a large compression ratio, and is a low-power-consumption data compression algorithm.

The compression storage module 107 is configured to compress and store the set of similar electric energy data sets through the target sliding window according to the LZ77 algorithm.

In some embodiments, the set of electrical energy-like data sets may be stored in compression through a target sliding window according to the LZ77 algorithm.

As an example, the above-described electric energy similar data set may be compressed and stored according to the LZ77 algorithm, with a sliding window at the time of processing the LZ77 algorithm set as a target sliding window.

Optionally, the compressed electric energy similar data set can be decompressed through a decoding rule of the LZ77 coding algorithm, and the decompressed electric energy similar data set can be obtained.

In summary, firstly, if the circuit to be monitored runs stably, the difference between the electric energy data in the electric energy data set is small, and the change of the electric energy data is stable. If the circuit to be monitored is abnormally operated, the difference between the electric energy data in the electric energy data set is large, and the variation fluctuation of the electric energy data is large. Therefore, the electric energy data set is collected, and the operation state of the circuit to be monitored can be conveniently monitored according to the change fluctuation of the electric energy data. Because the quantity of the electric energy data in the electric energy data set is often relatively large, the electric energy data set of the circuit to be monitored is obtained, and the electric energy data set can be conveniently compressed and stored later. The abnormal power data may then be power data with a large fluctuation, which may be caused by abnormal operation of the circuit to be monitored. Therefore, the abnormal power data may be abnormal power data generated when the circuit to be monitored performs abnormal operation. The stationary power data may be power data having small fluctuation changes and relatively stationary. Thus, the stationary power data may be stable power data generated when the circuit to be monitored is stationary. Then, the larger the corresponding target slope of the electric energy data, the larger the fluctuation of the electric energy data is, and the less stable the change of the electric energy data is. Therefore, the larger the corresponding target slope of the electrical energy data, the more likely the electrical energy data is abnormal. According to the target slope corresponding to the electric energy data, the electric energy data sequence is divided into electric energy data sets, and the electric energy data sets can be accurately processed conveniently. In general, if the electric energy data set is abnormal, the circuit to be monitored may be abnormal in the corresponding acquisition time period of the electric energy data set, so that the electric energy data set is analyzed and processed to be abnormal, and the operation state of the circuit to be monitored can be monitored conveniently. And then, comprehensively considering the target sliding window corresponding to the electric energy data and the target abnormality degree corresponding to the electric energy data group where the electric energy data are, and improving the accuracy of determining the data abnormality degree corresponding to the electric energy data. Then, the larger the data similarity is, the less data needs to be inserted, so the data insertion is performed on the electric energy data group where the electric energy data with the data similarity larger than the similarity threshold value is located, the same frequency of the electric energy data in the target sliding window and the corresponding electric energy data in the forward buffer zone can be improved, namely, although the data is increased through the data insertion, compared with the increased compression degree when the LZ77 algorithm is adopted for compression, the increased data amount is usually negligible through the data insertion, and therefore the compression effect can be improved. Finally, the electric energy similar data set collection is compressed and stored through an LZ77 algorithm and a target sliding window, so that the occupation of storage space can be reduced.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the scope of the embodiments of the present application, and are intended to be included within the scope of the present application.

Claims

1. A low power consumption electrical energy data collection management system, the system comprising:

the compression storage module is used for carrying out compression storage on the electric energy similar data set through a target sliding window according to an LZ77 algorithm;

performing similar insertion update processing on the electric energy data sets according to the data similarity corresponding to each electric energy data in each electric energy data set, and determining the electric energy similar data set corresponding to the electric energy data sets, including:

updating the second similar data set corresponding to the second electric energy data in the electric energy data sets according to the second similar data set corresponding to the second electric energy data in the electric energy data sets and the data similarity corresponding to the third electric energy data in the electric energy data sets, obtaining a third similar data set corresponding to the third electric energy data in the electric energy data sets, and so on, determining each electric energy data in the electric energy data sets to correspond to the similar data set, and determining the similar data set corresponding to the last electric energy data in the electric energy data sets to correspond to the electric energy similar data set;

The updating of the electric energy data set according to the data similarity corresponding to the first electric energy data in the electric energy data set to obtain a first similar data set corresponding to the first electric energy data in the electric energy data set includes:

when the data similarity corresponding to the first electric energy data in the electric energy data sets is smaller than or equal to a similarity threshold value, determining the electric energy data sets as first similar data sets corresponding to the first electric energy data in the electric energy data sets;

The updating the first similar data set corresponding to the first electric energy data in the electric energy data sets according to the data similarity corresponding to the first similar data set corresponding to the first electric energy data in the electric energy data sets and the second electric energy data in the electric energy data sets to obtain the second similar data set corresponding to the second electric energy data in the electric energy data sets includes:

2. The low-power consumption power data collection management system according to claim 1, wherein said adaptively segmenting the power data set to obtain a power data set includes:

3. The low-power consumption electric energy data collection management system according to claim 2, wherein the performing the anomaly degree analysis processing on each electric energy data set in the electric energy data set to obtain the target anomaly degree corresponding to the electric energy data set includes:

4. The system for collecting and managing low-power consumption electric energy data according to claim 2, wherein the determining the degree of data abnormality corresponding to the electric energy data according to the target sliding window corresponding to the electric energy data and the target degree of abnormality corresponding to the electric energy data group in which the electric energy data is located comprises:

5. The system for collecting and managing low-power consumption electric energy data according to claim 4, wherein the performing similarity analysis processing on the target sliding window corresponding to the electric energy data and the electric energy data after the target sliding window according to the data abnormality degree corresponding to each electric energy data to obtain the data similarity corresponding to the electric energy data comprises: