CN116743179B - Ammeter data optimization acquisition processing method - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to an ammeter data optimization acquisition processing method, which comprises the following steps: all the electricity consumption is formed into a matrix; obtaining the length and width of an initial window on a matrix; obtaining redundancy of the initial window according to the length and the width of the initial window; obtaining correction parameters according to the redundancy, and obtaining a correction window according to the correction parameters; and compressing the power consumption in different correction windows according to the area of the correction windows to obtain compressed data of the matrix, and storing the compressed data of the matrix. The invention obtains the correction window of the matrix of the electricity consumption according to the electricity consumption change, thereby enhancing the effect of bit layered compression data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an ammeter data optimization acquisition processing method.
Background
In order to record electricity consumption data of enterprises or individual users so as to facilitate user management of the power supply enterprises, electricity consumption data on each user ammeter needs to be collected; because the users are more, and the electricity consumption data that each user needs to collect is more, the collected electricity consumption data needs to be compressed and stored, and the existing storage method, such as a storage method based on bit layering, can reduce the storage space of the data, but when the electricity consumption data is compressed by bit layering, only a single data stream is compressed, so that excessive grouping can be caused, the compression time is greatly increased, and multiple groups of data are compressed together by adopting multiple groups of data combination compression. When multiple groups of data are compressed together, the existing algorithm only considers grouping the data according to the size fluctuation of the data, so that the redundancy after grouping still cannot achieve an ideal effect, and therefore, in order to solve the problem that a large amount of electricity consumption data occupy too much storage space in the acquisition process, the electricity consumption data needs to be further analyzed and processed on the basis of the existing compression method, so that the purpose of efficient compression and storage is achieved.
Disclosure of Invention
The invention provides an ammeter data optimization acquisition processing method, which aims to solve the existing problems.
The invention relates to an ammeter data optimization acquisition processing method which adopts the following technical scheme:
the embodiment of the invention provides an ammeter data optimization acquisition processing method, which comprises the following steps:
collecting electricity consumption at a plurality of moments, and forming a matrix by all the electricity consumption;
presetting a compression window on a matrix, and obtaining the length and the width of an initial window according to the difference of adjacent electricity consumption of each row and the difference of adjacent electricity consumption of each column in the compression window;
obtaining redundancy of the initial window according to the length and width of the initial window and the number of binary repeated bits of all power consumption in the initial window; obtaining correction parameters according to the redundancy, and obtaining correction windows and areas of the correction windows according to the correction parameters and the length and width of the initial window;
and compressing the power consumption in different correction windows in sequence according to the area size of the correction windows and the intersection condition of the correction windows to obtain compressed data of the matrix, and storing the compressed data of the matrix.
Preferably, the method comprises obtaining the difference of adjacent electricity consumption of each row and the difference of adjacent electricity consumption of each column in the compression window
The length and width of the initial window comprise the following specific steps:
for a 3*3 compression window with the element of the ith row and the jth column as the upper left corner, the length and width calculation formula for obtaining the initial window according to the 3*3 compression window is as follows:
x ij 、y ij representing the length and width, k, of an initial window corresponding to a compression window having the element of the ith row and jth column as the upper left corner m,j 、k m,j+1 、k m,j+2 The electricity consumption in the jth column of the mth row, the jth+1th column of the mth row and the jth+2th column of the mth row is represented; k (k) i,n 、k i+1,n 、k i+2,n Represents the ith row and the nth column, and the (i+1) th rown columns, i+2th row and n column electricity consumption; alpha 1 For a preset initial window length-related parameter, alpha 2 For a preset association parameter of the width of the initial window,representing an upward rounding.
Preferably, the binary repetition bit number of all the electricity consumption in the initial window is obtained according to the length and the width of the initial window
The redundancy of the initial window includes the following specific formulas:
m＝x*y
wherein m represents the total number of data in the initial block, and x and y represent the length and width of the initial window;
the acquisition method of N is as follows: converting all the electricity consumption data in the initial window into binary data, counting the number of repeated bits from left to right in the binary data, and recording the number as N;
C i representing the length of the binary of the ith power usage data within the initial window, and R represents the redundancy of the initial window.
Preferably, the specific formula for obtaining the correction parameter according to the redundancy is as follows:
h represents correction parameter, A min Representing the minimum value of the redundancy acceptance range, A max Represents the maximum value of the redundancy acceptance range, R represents the redundancy of the initial block, exp () represents an exponential function based on a natural constant.
Preferably, the correction window and the area of the correction window are obtained according to the correction parameters and the length and width of the initial window,
the method comprises the following specific steps:
the product of the length of the initial window and the correction parameter is recorded as the length of the correction window, the product of the width of the initial window and the correction parameter is recorded as the width of the correction window, the correction window is obtained by the length and the width of the correction window, and the area of the correction window is the product of the length and the width of the positive window.
Preferably, the compressing the power consumption in different correction windows according to the area size of the correction window and the intersection condition of the correction windows to obtain the compressed data of the matrix includes the following specific steps:
respectively acquiring correction windows taking all elements in the matrix as corresponding correction windows, acquiring the correction window with the largest area, recording the correction window as a first correction window, converting all power consumption in the first correction window into binary data, and compressing the binary data by using bit layering; then in other correction windows which do not intersect with the first correction window, obtaining the correction window with the largest area, recording the correction window as a second correction window, converting all the electricity consumption in the second correction window into binary data and compressing the binary data by using bit layering; then, in other correction windows which are not intersected with the first correction window and the second correction window, the correction window with the largest area is obtained and recorded as a third correction window, all the electricity consumption in the third correction window is converted into binary data, and bit layering is utilized for compression; and so on until there are no other correction windows without intersections;
and the rest other correction windows sequentially convert the electricity consumption in the correction windows into binary data according to the sequence from large to small in area, and compress the binary data by using bit layering until all the electricity consumption in the matrix is completely compressed, so as to obtain the compressed data of the matrix.
The technical scheme of the invention has the beneficial effects that: and calculating the size of the initial grouping window of the data according to the data change, and then calculating the correction parameters of the grouping window through the redundancy of the data after grouping, correcting the size of the grouping window, and enhancing the effect of compressing the data by using the bit layering of the data window after correction. When the data is directly compressed by utilizing bit layering, only a single data stream is compressed, so that excessive packets are caused. When multiple groups of data are compressed together, the existing algorithm only considers grouping the data according to the size fluctuation of the data, so that the redundancy after grouping still cannot achieve an ideal effect.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of an optimized acquisition and processing method for ammeter data.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of an ammeter data optimization acquisition processing method according to the 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.
The following specifically describes a specific scheme of the method for optimizing, collecting and processing ammeter data.
Referring to fig. 1, a flowchart of steps of an electric meter data optimization acquisition processing method according to an embodiment of the invention is shown, and the method includes the following steps:
step S001: and acquiring a power consumption matrix of the user through the ammeter.
Most of the existing electric meters are used for counting electricity consumption data through a counter, and the value of the electricity consumption data is increased in the process of using electric energy. The electricity consumption habits of users are similar, so that the electricity consumption data differences of the users at the same time difference at the same time point every day are the same, the difference of the electricity consumption data of the previous time point subtracted from the whole time point is recorded as electricity consumption, the time interval of 1 hour is used as time interval, and the electricity consumption of each whole time point of a certain user in continuous multiple days is counted for analysis because part of continuous electricity consumption has certain local similarity.
And for any user, acquiring electricity data of the user at different times of continuous days. The user has small changes in the electricity consumption data at all times of the day, and in order to facilitate data compression, the electricity consumption data of the next hour and the previous hour need to be subtracted, so that the electricity consumption amount with the time interval of one hour is obtained, and the electricity consumption amount is 24 hours per day, so that 24 electricity consumption data acquired per day are acquired, and in this embodiment, q days are continuously acquired as an example, and an original time sequence of 24 x q values acquired in the q days is described.
The original time series sequence is converted into a memory structure of a matrix. Taking the acquired number of days q as a row of the matrix, taking the acquired number of times of 24 whole points as columns of the matrix, obtaining a q-24 matrix, taking the electricity consumption as an element of each matrix, and taking q=30 as an example in the embodiment.
The following table shows the power usage data for one of the days:
these power usage changes only slightly, requiring differences between their neighbors to be made, only the differences remain, for ease of compression. The following table:
the matrix described in this embodiment is obtained according to the power consumption of the adjacent term after the difference is made, so that the subsequent analysis and calculation are facilitated, wherein the first element of the matrix is made the difference with 1010.
It should be noted that the above data in the table is only schematic, and the power consumption data obtained by the embodiment for different power consumption scenarios may have a large difference, but the method steps of the embodiment are applicable to any power consumption scenario, such as power consumption scenario of an enterprise user or a personal home user.
Step S002: and calculating the length and width of the initial window according to the difference between the electricity consumption in the matrix.
Since bit-layered compression is applicable to data with relatively small differences. The power consumption of the user at the same moment on different dates is similar, and the power consumption can be used as matrix elements to block the matrix to obtain a compression window, so that the power consumption calculation times are reduced, and the calculation amount and time of data compression are reduced.
And carrying out bit layered compression on the power consumption data of the user at all times of different dates, and firstly, acquiring the value range of the size of the compression window by the implementation. The size of the compression window is very important, and the compression rate of bit layered compression of the proper power consumption of the compression window is good; if the size of the compression window is not properly selected, the compression rate is poor after bit-layered compression is performed on the data.
A compression window of fixed size 3*3, hereinafter abbreviated as 3*3 window, is established. And calculating the side length of the initial window according to the electricity consumption change rate of the 3*3 window.
For a 3*3 window with the element of the ith row and jth column as the upper left corner, the method for calculating the length and width of the initial window is described by taking the window as an example. The method comprises the following steps:
the length and width calculation formula of the initial window:
x ij 、y ij represents a 3*3 window with the element of the ith row and the jth column as the upper left corner, the window corresponds to the length and width, k of the initial window m,j 、k m,j+1 、k m,j+2 The electricity consumption in the jth column of the mth row, the jth+1th column of the mth row and the jth+2th column of the mth row is represented; k (k) i,n 、k i+1,n 、k i+2,n The power consumption of the ith row and the nth column, the (i+1) th row and the (i+2) th row and the nth column are shown.
α 1 Alpha, a long correlation parameter for the initial window 1 The larger the value of (a), the longer the initial window, alpha 1 In particular, the related parameters of the same time of different dates are expressed as alpha in the embodiment 1 Let 1.5 be described as an example; alpha 2 Alpha is the broad correlation parameter of the initial window 2 The larger the value of (a), the larger the width of the initial window, alpha 2 The specific meaning of the parameter is related parameters of different moments of the same date, and alpha is adopted in the embodiment 2 Let 2 be described as an example.
The absolute value of the power consumption change rate in the 3*3 window is recorded as a long fluctuation degree, which is equivalent to calculating the sum of the power consumption difference values of every two adjacent time in each day of the 3*3 window, and the larger the value is, the larger the power consumption fluctuation degree in the compression window is, and the smaller the length coefficient of the initial window is; the smaller the value, the smaller the degree of fluctuation of the power consumption in the initial window, and the larger the length coefficient of the initial window.
The absolute value of the power consumption change rate in the 3*3 window is recorded as a wide fluctuation degree, which is equivalent to calculating the sum of the power consumption difference values of the same two adjacent dates in the 3*3 window, and the larger the value is, the larger the power consumption fluctuation degree in the initial window is, and the smaller the wide coefficient of the initial window is; the smaller the value, the smaller the degree of fluctuation of the power consumption in the initial window, and the larger the wide coefficient of the initial window.
Representing an upward rounding.
Thus, an initial window with the element of the ith row and the jth column as the upper left corner is obtained, and the length and the width of the window are x ij 、y ij . The length and width of the initial window with any element as the upper left corner can be obtained by the same method and is recorded as x and y.
Step S003: redundancy is calculated according to the length and width of the initial window.
After the power consumption is segmented according to the local variation characteristics of the power consumption, the power consumption of each initial segment cannot be guaranteed to meet the condition of bit layered compression (redundancy threshold is set), so that the redundancy after the initial segment needs to be calculated, and whether the redundancy meets the preset condition is judged.
In addition, after the power consumption is partitioned once according to the fluctuation degree in the initial window, the power consumption cannot be determined, and the power consumption compression after the primary partitioning can ensure that the bit layered compression effect is good. Thus, redundancy in the amount of electricity used needs to be calculated.
Because the electric quantity is required to be compressed in a bit layering mode, the electric quantity value is required to be converted into binary electric quantity, and because one decimal exists in the electric quantity value, the decimal has the problem of retaining precision when converting into binary, and four decimal binary decimal points are uniformly retained for the purpose of facilitating compression.
The redundancy calculation formula of the initial window is as follows:
m＝x*y
in the formula, redundancy is calculated for any initial block, m represents the total number of data in the initial block, and x and y represent the length and width of an initial window;
the acquisition method of N is as follows: converting all power consumption data in the initial window into binary data, counting the number of repeated bits from left to right in the binary data, and recording the number as N; i.e. the first N bits of these binary data are identical; for example, 101, 101101, 101001, where N is equal to 3.
C i Representing the length of the binary of the ith power usage data within the initial window, R represents the redundancy with which the initial window is compressed using bit compression layering.
The more the number of continuous repetition of the data in the initial window is, the greater the redundancy of the initial window is, and the better the effect of bit layered compression of the data in the initial window is.
So far, the redundancy of all power consumption data in any initial window is obtained.
Step S004: and obtaining a correction window according to the redundancy of the initial window.
The calculated initial window does not necessarily meet the preset redundancy threshold condition, if the redundancy in the initial window is too large (the first repeated item from left to right is too many), although the compression effect is good, the number of the initial blocks is still relatively large at the moment, and the compression time is also relatively long; if the redundancy in the initial window is too small (the number of repeated items is too small), the compression time is small, but the compression rate is high, and it is difficult to achieve a good compression effect.
Therefore, the correction parameters are calculated through the local redundancy obtained by the initial window side length parameters, and the unreasonable initial window size is corrected.
The redundancy R of each initial window is calculated in the previous step, in the implementation, the receiving range R= [0.3,0.7] of the redundancy is taken as an example, and the smaller the redundancy is, the fewer the repeated items of the data are indicated, and the worse the compression effect of the data is; the greater the redundancy, the more repeated items of data are indicated, and the better the compression effect of the data is.
If the redundancy of any one initial block is smaller than 0.3, the redundancy of the initial block is too low, the repeated data items in the initial block are too few, the compression effect is poor, the length or width of the initial block is too large, and the range of the initial block is required to be reduced to increase the redundancy; if the data redundancy of any one initial block is greater than 0.7, the data redundancy of the initial block is too large, the data repetition of the initial block is too many, the compression effect is good, but the length or width of the initial block is too small, the number of the initial blocks is too many, and the compression time is increased (each block needs to be compressed once), so that the range of the data blocks is required to be enlarged, the grouping is reduced, and the compression time is shortened.
The specific calculation formula of the correction parameters is as follows:
h represents correction parameter, A min Representing the minimum value of the redundancy acceptance range, A max Represents the maximum value of the redundancy acceptance range, A in this embodiment max =0.7, at a min Let us say by way of example that =0.3, R represents the redundancy of the initial block, exp () represents an exponential function based on a natural constant.
The redundancy of the initial block is within the receiving range, and the correction parameter is 1, namely the redundancy of the initial block meets the requirement, and the initial block is not corrected; if the calculated initial block redundancy is smaller than 0.3, indicating that the initial block is too large, and reducing the initial block is needed; if the calculated block redundancy is greater than 0.7, the block is indicated to be too small, and the block is enlarged. The smaller the calculated block redundancy R is, the smaller the correction parameter is, and the larger the block redundancy R is, the larger the correction parameter is.
The calculation formula of the side length parameter of the correction window is as follows:
where x represents the length of the initial window, y represents the width of the window, h represents the correction parameter, u represents the length of the correction window, and v represents the width of the correction window. The larger the correction parameter h is, the larger the corrected window is; the smaller the correction parameter γ, the smaller the corrected window.
All the steps obtain the operation process: obtaining correction parameters through the difference calculation of the redundancy of the initial window and the redundancy acceptance range, and correcting the initial window with the redundancy not within the acceptance range; and calculating the side length of the initial window according to the fluctuation degree of the electricity consumption, calculating the redundancy of the initial window according to the redundancy of the initial window, calculating the correction parameters of the initial window according to the difference between the redundancy of the initial window and the receiving range of the redundancy, and correcting the size of the initial window to obtain a correction window.
Step S005: and carrying out bit layered compression on the correction window to obtain compressed data and storing the compressed data.
Step S002 to step S004 show the method of obtaining the correction window with any one element in the matrix as the upper left corner.
Respectively acquiring correction windows taking all elements in the matrix as the upper left corner, acquiring the correction window with the largest area, marking the correction window as a first correction window, converting all power consumption in the first correction window into binary data, and compressing by using bit layering; then in other correction windows which do not intersect with the first correction window, obtaining the correction window with the largest area, recording the correction window as a second correction window, converting all the electricity consumption in the second correction window into binary data and compressing the binary data by using bit layering; then, in other correction windows which are not intersected with the first correction window and the second correction window, the correction window with the largest area is obtained and recorded as a third correction window, all the electricity consumption in the third correction window is converted into binary data, and bit layering is utilized for compression; and so on until there are no other correction windows without intersections. At this time, the remaining other correction windows sequentially convert the electricity consumption in the correction windows into binary data according to the sequence from large to small in area, and compress the binary data by using bit layering, so that the compressed electricity consumption is not repeatedly compressed until all the electricity consumption in the matrix is completely compressed.
Wherein the correction window area is the product of the length and width of the correction window.
It should be noted that the elements in the matrix are numbered from small to large in the order from left to right and from top to bottom; if the correction window area is the same, the compression processing is performed from small to large according to the number of the element at the upper left corner of the correction window. In addition, if the correction window exceeds the matrix boundary, the area of the correction window refers to the area of the intersection of the correction window and the matrix. If a portion of the data within a correction window has been compressed, the correction window is not considered for the compressed data when calculating area.
The matrix is continuously compressed to obtain compressed data, the compressed data is stored, the storage space is effectively saved, and the length, the width and the position of a correction window corresponding to each compressed data are stored, so that the compressed data are restored to the electricity consumption of the matrix. It should be noted that, because the embodiment uses bit layering for compression, which is a known technique, the decompression is also performed by using bit layering during decompression, and the specific process is not repeated in this embodiment.
The compressed storage of the electricity data is completed by processing the electricity data.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. The method for optimally collecting and processing the ammeter data is characterized by comprising the following steps:
collecting electricity consumption at a plurality of moments, and forming a matrix by all the electricity consumption;
presetting a compression window on a matrix, and obtaining the length and the width of an initial window according to the difference of adjacent electricity consumption of each row and the difference of adjacent electricity consumption of each column in the compression window;
obtaining redundancy of the initial window according to the length and width of the initial window and the number of binary repeated bits of all power consumption in the initial window; obtaining correction parameters according to the redundancy, and obtaining correction windows and areas of the correction windows according to the correction parameters and the length and width of the initial window;
compressing the power consumption in different correction windows in sequence according to the area size of the correction windows and the intersection condition of the correction windows to obtain compressed data of the matrix, and storing the compressed data of the matrix;
the method for obtaining the length and the width of the initial window according to the difference of adjacent power consumption of each row and the difference of adjacent power consumption of each column in the compression window comprises the following specific steps:
for a 3*3 compression window with the element of the ith row and the jth column as the upper left corner, the length and width calculation formula for obtaining the initial window according to the 3*3 compression window is as follows:
；
；
、/>representing the length and width of the initial window corresponding to the compression window with the element of the ith row and jth column as the upper left corner,/->、、/>Is indicated at +.>Line->Column, th->Line->+1 column, < ->Line->+2 columns of electricity usage; />、/>、The electricity consumption of the ith row and the nth column, the (i+1) th row and the (i+2) th row and the nth column is represented; />For the long associated parameter of the preset initial window, +.>For the wide associated parameters of the preset initial window, +.>Representing an upward rounding;
the redundancy of the initial window is obtained according to the length and width of the initial window and the binary repetition bit number of all the electricity consumption in the initial window, and the specific formulas are as follows:
；
；
in the middle ofRepresenting the total number of data in the initial block, and x and y represent the length and width of the initial window; the initial block is a region corresponding to the initial window in the matrix;
the acquisition method of (1) comprises the following steps: converting all the electricity consumption data in the initial window into binary data, counting the number of repeated bits from left to right in the binary data, and recording the number as N;
representing +.>The binary length of the individual power consumption data, R represents the redundancy of the initial window benefit;
the specific formulas for obtaining correction parameters according to the redundancy are as follows:
；
representing correction parameters->Representing the minimum value of the redundancy window, < ->Represents the maximum value of the redundancy window, +.>Representing redundancy of the original block, +.>An exponential function based on a natural constant is represented.
2. The method for optimizing collection and processing of ammeter data according to claim 1, wherein the correction window and the area of the correction window are obtained according to the correction parameters and the length and width of the initial window, comprising the following specific steps:
the product of the length of the initial window and the correction parameter is recorded as the length of the correction window, the product of the width of the initial window and the correction parameter is recorded as the width of the correction window, the correction window is obtained by the length and the width of the correction window, and the area of the correction window is the product of the length and the width of the positive window.
3. The method for optimized acquisition and processing of ammeter data according to claim 1, wherein the method sequentially compresses the electricity consumption in different correction windows according to the area size of the correction window and the intersection condition of the correction windows to obtain the compressed data of the matrix, and comprises the following specific steps:
respectively acquiring correction windows taking all elements in the matrix as corresponding correction windows, acquiring the correction window with the largest area, recording the correction window as a first correction window, converting all power consumption in the first correction window into binary data, and compressing the binary data by using bit layering; then in other correction windows which do not intersect with the first correction window, obtaining the correction window with the largest area, recording the correction window as a second correction window, converting all the electricity consumption in the second correction window into binary data and compressing the binary data by using bit layering; then, in other correction windows which are not intersected with the first correction window and the second correction window, the correction window with the largest area is obtained and recorded as a third correction window, all the electricity consumption in the third correction window is converted into binary data, and bit layering is utilized for compression; and so on until there are no other correction windows without intersections;
and the rest other correction windows sequentially convert the electricity consumption in the correction windows into binary data according to the sequence from large to small in area, and compress the binary data by using bit layering until all the electricity consumption in the matrix is completely compressed, so as to obtain the compressed data of the matrix.
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