CN115987294A - Multidimensional data processing method of Internet of things - Google Patents
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Abstract
The invention relates to the technical field of electric digital data processing, in particular to a multidimensional data processing method of the Internet of things. The method comprises the following steps: one type of data constitutes a single-dimensional data sequence; utilizing a window with a preset size to slide on the single-dimensional data sequence to obtain the information entropy of the data positioned in the center of each window; obtaining an information entropy increment value of each data according to the information entropy of adjacent data; obtaining a difference value change index of the data positioned in the center of each window according to the difference value of every two data in each window; obtaining a smooth coefficient of each data by using the information entropy and difference change index of each data in the single-dimensional data sequence and the sum of the information entropy increment values of each data in all the single-dimensional data sequences; and smoothing and compressing all the data by using the smoothing coefficient of each data. The electric digital data processing method for the multidimensional data of the Internet of things can improve the repetition rate of the multidimensional data of the Internet of things, and further improve the data compression efficiency.
Description
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a multidimensional data processing method of the Internet of things.
Background
When various electric power thing networking devices are monitoring electric power data, a large amount of multidimensional data can be generated, and in order to improve the resource storage rate, the monitoring data of various electric power thing networking devices are packed and compressed at first, so that the purposes of reducing the internet bandwidth occupancy rate and facilitating data management are achieved.
Various data compression methods are available, and compression of monitoring data of various internet of things devices can be achieved. However, if the internet of things equipment is abnormal, due to the fact that abnormal data can cause change of monitoring data of the internet of things equipment to be enlarged or irregular increase, the repetition rate of the monitoring data of the internet of things equipment can be reduced at the moment, data compression is not facilitated, the compression effect of the monitoring data of the internet of things equipment is poor, the data storage rate is increased, finally, when the internet of things equipment is abnormal, accurate abnormal information cannot be timely acquired due to the fact that a large amount of useless data exist in the acquired compressed data, and therefore the abnormality of the internet of things equipment cannot be timely discovered.
Disclosure of Invention
In order to solve the problem that when the internet of things equipment is abnormal, the repetition rate of monitoring data of the internet of things equipment is reduced and cannot be effectively compressed due to the existence of abnormal data, and the data storage rate is increased, the invention aims to provide a multidimensional data processing method of the internet of things, and the adopted technical scheme is as follows:
one embodiment of the invention provides a multidimensional data processing method of the Internet of things, which comprises the following steps:
acquiring various types of data, wherein one type of data forms a single-dimensional data sequence;
utilizing windows with preset sizes to slide on the single-dimensional data sequence to obtain information entropy of data located in the center of each window; obtaining an information entropy increment value of each data according to the information entropy of every two adjacent data;
acquiring a difference value change index of data positioned in the center of each window based on the difference value of every two data in each window; obtaining a smoothing coefficient of each data based on the information entropy and difference change index of each data in the single-dimensional data sequence and the sum of the information entropy increment values of each data in all the single-dimensional data sequences;
and smoothing all the data according to the smoothing coefficient of each data, and compressing the multi-dimensional data after smoothing.
Preferably, the obtaining of the entropy increment value of each data according to the entropy of each two adjacent data comprises: and (3) performing difference between the information entropy of the next data and the information entropy of the previous data in the two adjacent data, and calculating an absolute value to obtain an information entropy increment value of the previous data in the two adjacent data, so as to obtain the information entropy increment value of each data.
Preferably, obtaining the difference change index of each data comprises: acquiring the absolute value of the difference value of every two data in the data in a window when the window with the preset size slides on the single-dimensional data sequence; obtaining the absolute value of the maximum difference value as a reference value; recording the ratio of the absolute value of each difference value to the reference value as the difference degree; clustering the difference degree of every two data in the window to obtain different categories; respectively obtaining the average value of all the difference degrees in each category, wherein the category with the minimum average value is a first category; the ratio of the number of all the difference degrees of the first category to the number of all the difference degrees in different categories is a difference change index of the data located in the center of the window, and then the difference change index of each data is obtained.
Preferably, the smoothing factor for each datum is:
wherein the content of the first and second substances,
a smoothing coefficient representing the nth data in the mth one-dimensional data sequence;
representing the information entropy increment value of the nth data in the mth single-dimensional data sequence;
representing the sum of the information entropy increment values of all data in the single-dimensional data sequence;
representing a difference change index of the nth data in the mth single-dimensional data sequence;
expressing an exponential function with a natural constant e as a base;
a normalization function is represented.
Preferably, the smoothing process is performed on all data according to the smoothing coefficient of each data, and includes: and taking the smoothing coefficient of each data as the weight when each data is weighted and averaged, and obtaining each data after smoothing processing.
The embodiment of the invention at least has the following beneficial effects: various types of data are collected by various types of Internet of things equipment sensors, each type of data forms a single-dimensional data sequence, and the information entropy of each data in the single-dimensional data sequence is further obtained and used for representing the chaos degree of the data around each data; therefore, the information entropy increment value of each datum is obtained, and the smooth coefficient of each datum is determined through the information entropy increment value, so that the overall repetition rate of the datum can be increased, and the compression rate is improved; furthermore, the absolute value of the difference value of every two data positioned in the window is analyzed to obtain the difference value change index of each data, the change size of the actual data is further determined on the basis of the information entropy increment value, the smooth coefficient of each data is determined by combining the information entropy increment value of each data, the error caused when the smooth coefficient is determined only by the information entropy increment value can be avoided, the data is smooth, the repetition rate after the data is smooth is further improved, and the compressibility of the data is increased.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a method flowchart of a multidimensional data processing method of an internet of things according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, structures, features and effects of a multidimensional data processing method of the internet of things according to the present invention are provided with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
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 describes a specific scheme of the multidimensional data processing method of the internet of things provided by the invention in detail with reference to the accompanying drawings.
The embodiment is as follows:
the main application scenarios of the invention are as follows: heterogeneous electric power thing networking device carries out data acquisition and compression to wisdom energy data, at equipment normal operating, the change of data is very little, when compressing data this moment, the compression ratio is very big, the transmission efficiency of data is very high, nevertheless when equipment appears unusually, unusual data can appear, the repetition rate that these unusual data can reduce whole data reduces, thereby also can reduce the compression ratio, make the data storage rate reduce, consequently, need carry out smooth processing to data.
Referring to fig. 1, a method flowchart of a multidimensional data processing method of the internet of things according to an embodiment of the present invention is shown, where the method includes the following steps:
s1, acquiring various types of data; one type of data constitutes a single-dimensional data sequence.
Because there are different types of internet of things devices and the data type of each type of internet of things device is also different, it is necessary to acquire various types of data by using different internet of things device sensors in an electric power room to obtain multidimensional data, one type of data is one type of single-dimensional data, and the sampling frequencies for acquiring the different types of data are the same, so that one type of data forms a single-dimensional data sequence, that is, one internet of things device corresponds to one single-dimensional data sequence, where it is to be noted that the arrangement of data at each time in the single-dimensional data sequence is arranged according to a time sequence order.
Under the condition that various devices of the electric power machine room work normally, the collected data are transmitted to a data compression module by the Internet of things devices for packing and compressing; when abnormal data occurs, the repetition rate of the data is reduced, so that the compression rate during compression is also reduced, and therefore, the data needs to be smoothed, and the repetition rate of the data is improved.
S2, utilizing a window with a preset size to slide on the single-dimensional data sequence to obtain the information entropy of the data positioned in the center of each window; and obtaining the information entropy increment value of each data according to the information entropy of every two adjacent data.
When the information entropy of each piece of data in the local time period becomes larger, the data with a plurality of different values appears in the local time period, and the data should be particularly paid attention to the part of data when smoothing the data, so that the increment of the information entropy of each piece of data and the sum of the increments of the information entropies of all pieces of data can be firstly obtained, then the weight of each piece of data when smoothing is obtained by analyzing the sum of the increment of the information entropy of each piece of data and the increment of the information entropies of all pieces of data, and the data smoothing is completed according to the weight of each piece of data when smoothing is performed.
The information entropy of the data is increased to show that more data with different values appear in a local time period, firstly, a data sliding window is needed to be processed, the information entropy value of each data is obtained through calculation according to the data sliding window, and then the information entropy increment value is obtained according to the change value of the information entropy between the previous continuous data and the next continuous data.
Here, a window with a preset size is set, preferably, because the single-dimensional data sequence is a sequence in a time sequence, in this embodiment, the length of the window is the length of data acquired in each packing time interval during data packing and compression, for example, 5 data are acquired in one packing time interval, the size of the window is 1*5, if 11 data are acquired, the length of the window is 1 × 11, it should be noted that an implementer may also adjust the size of the window according to a specific situation, but it needs to be ensured that the length of the window is odd, the width of the window is 1, and the step length of window sliding is 1.
In addition, when the window slides on the single-dimensional data sequence, each data in the single-dimensional data sequence is used as data at the center of the window, for example, the size of the window is 1*5, and when the information entropy of the fourth data in the single-dimensional data sequence is obtained, the fourth data is used as data at the center of the window, that is, the 3 rd data in the window; in addition, when the window slides on the single-dimensional data sequence, when the window passes through the first several bits of data and the last several bits of data of the single-dimensional data sequence, the data in the window needs to be supplemented, for example, when the window with the size of 1*5 passes through the first data in the single-dimensional data sequence, that is, the first data is used as the data in the center of the window, the first two bits of data in the window do not exist at this time, so that two data need to be supplemented into the window, so that the number of data in the window is 5, preferably, in this embodiment, the value of the supplemented data is 0, an implementer may also adjust the value of the supplemented data according to the data condition of the single-dimensional data sequence, and may also interpolate the data in the window by using an interpolation method, so that the number of data in the window is always consistent with the size of the window when the window slides on the single-dimensional data sequence.
Further, the information entropy of the data in the center of the window is obtained by using the data in the window, taking a window as an example, the frequency of each data in the window appearing in the window is obtained, and then the information entropy of the data in the center of the window is obtained according to the frequency of each data appearing in the window, and is expressed by a formula:
wherein the content of the first and second substances,
representing the information entropy of the ith data in a single-dimensional data sequence;
the window corresponding to the ith data is a window when the window slides on the single-dimensional data sequence, and the window corresponding to the ith data is positioned in the center of the window;
a logarithmic function with base 10 is shown. T represents the length of a window corresponding to the ith data in the single-dimensional data sequence, that is, the amount of data in the window corresponding to the ith data in the single-dimensional data sequence, and T represents the traversal of the data in the window. Wherein the information entropy formula is a known existing formula.
The larger the value of (d) is, the larger the information amount indicating the data in the window corresponding to the ith data point is, and the larger the degree of confusion is, and it can be said that one window is regarded as one time slot, and it is explained that the data has changed greatly in the time slot. If more data with different values appear in the local time period, the information entropy of the data in the local time period may become larger, that is, if more data with different values appear in the window, the information entropy of the data in the window may become larger.
And finally, obtaining the information entropy increment value of each data in the single-dimensional data sequence, carrying out difference on the information entropy of the next data and the information entropy of the previous data in two adjacent data, and solving an absolute value to obtain the information entropy increment value of the previous data in two adjacent data, wherein the formula is represented as follows:
wherein, the first and the second end of the pipe are connected with each other,
an entropy increment value representing the ith data in the one-dimensional data sequence,
representing the information entropy of the (i + 1) th data in the single-dimensional data sequence;
representing the information entropy of the ith data in the single-dimensional data sequence;
the difference between the information entropy of the (i + 1) th data and the information entropy of the ith data in the single-dimensional data sequence is calculated, and the absolute value is obtained, so that the change condition of the information entropy of each data in the single-dimensional data sequence can be represented.
Therefore, the information entropy increment value of each data in each single-dimensional data sequence can be obtained and used for representing the change situation of the data of different internet of things devices, the larger the entropy increment of one data is, the greater the possibility degree of the change of the data compared with other data is, and the data needs to be paid extra attention when being smoothed.
S3, acquiring a difference value change index of the data positioned in the center of each window based on the difference value of every two data in each window; and obtaining a smoothing coefficient of each data based on the information entropy and the difference value change index of each data in the single-dimensional data sequence and the sum of the information entropy increment values of all the data in the single-dimensional data sequence.
When data is transmitted, the data to be transmitted is stored in an uplink region, and after a certain period of time, the data stored in the uplink region is packed and compressed to be transmitted to a network.
In the conventional smooth weight distribution, data is smoothed according to the proportion of corresponding information entropy increment values under data of different dimensions in the sum of information entropy increment values corresponding to all data in the whole, but only the proportion is taken as a smooth weight, only the change of the data is considered, and the degree of change of the data is not considered, so that the obtained smooth weight is in error, and the expected maximum compression rate can not be obtained when the data is compressed by using the obtained result of data smoothing, so that the difference of the data in a single-dimensional data sequence is further analyzed to obtain the final smooth coefficient distribution weight of different data.
And obtaining all data in each window, performing difference on every two data in the data to obtain the absolute value of the difference after difference, selecting the absolute value of the largest difference as a reference value, wherein the ratio of the absolute value of each difference to the reference value is the difference degree, all the difference degrees corresponding to one window form a sequence which is recorded as a window difference sequence, and the change degrees of all the data in the window can be analyzed through the values of elements in the sequence. Each element in the window difference sequence is a difference degree corresponding to the absolute value of the difference, so that a window difference sequence corresponding to an element located in the center of the window when the window slides on the single-dimensional data sequence can be obtained, that is, each data in each single-dimensional data sequence corresponds to one window difference sequence.
Further, clustering elements in a window difference sequence corresponding to a window according to the size of the elements in the difference sequence, wherein the clustering is a known technology and is not described in detail herein; the clustering can divide elements in the window difference sequence into different categories, preferably, a k-means clustering algorithm is selected for clustering in the embodiment, and the elements in the window difference sequence are set to be divided into two categories, which is to embody the characteristics of a part of elements with smaller values in the difference sequence, and an implementer can set the number of the divided categories according to specific conditions; and respectively calculating the mean value of all the difference degrees in each category, recording as a first mean value and a second mean value, comparing the first mean value with the second mean value, and obtaining the first category corresponding to each data by taking the smaller corresponding category as the first category.
The ratio of the number of all the difference degrees in the first category to the number of all the difference degrees in the two categories is the difference change index corresponding to the window, that is, the difference change index of the data located in the center of the window, and is expressed by a formula:
wherein the content of the first and second substances,
representing a difference value change index corresponding to the nth data in the mth single-dimensional data sequence;
representing the number of all the difference degrees in the first category corresponding to the nth data in the mth single-dimensional data sequence;
and the quantity of all the difference degrees in the two categories corresponding to the nth data in the mth single-dimensional data sequence is represented.
The larger the value of (d) is, the smaller the change degree of the data in the window corresponding to the nth data in the mth single-dimensional data sequence is, and when a weight value for smoothing is assigned to the data, the smaller weight value should be assigned to ensure the smoothness of the data.
And finally, acquiring the ratio of the information entropy increment value of each datum to the sum of the information entropy increment values of all the data, analyzing by combining the difference change index of each datum, and acquiring the smoothing coefficient of each datum, wherein the smoothing coefficient is expressed by a formula as follows:
wherein, the first and the second end of the pipe are connected with each other,
a smoothing coefficient representing the nth data in the mth one-dimensional data sequence;
the larger the information entropy increment value of the nth data in the mth single-dimensional data sequence is, the larger the change of the data around the nth data in the current mth single-dimensional data sequence is, and the information amount of the data is increased greatly;
representing the sum of the information entropy increment values of all data in the single-dimensional data sequence;
representing a difference value change index of the nth data in the mth single-dimensional data sequence;
expressing an exponential function with a natural constant e as a base;
representing a normalization function.
But only if so
AsWhen the smoothing coefficient of the nth data in the single-dimensional data sequence corresponding to the mth internet of things device is calculated, the difference value between the data is not considered, so that the repetition rate of the smoothed data is further increased. And obtaining a final smoothing coefficient of each data by combining the difference change indexes of each data.
Wherein at the data with smaller information entropy, the following characteristics are provided: the difference between the data is often low, but not completely because the information entropy is calculated only according to the repetition amount of the data, and the difference between the data is not considered, and in order to obtain better smoothness when smoothing, the smoothing strength should be increased for the data with the smaller difference. Therefore, the ratio of the number of the elements in the category with smaller average difference degree to the number of all the elements in the two categories is selected, namely, the difference change index pair is utilized
Correcting to obtain a final smoothing coefficient;
Otherwise, if calculated directly
Smoothing the data as weights during smoothing, the sum of the weights during smoothing of each data obtained cannot be guaranteed to be 1, that is, notThe sum of the smoothing coefficients of the obtained data is 1, so that the sum of the smoothing coefficients of the obtained data is required to be 1
The normalization treatment is carried out, and the normalization treatment is carried out,
expressing a normalization function in a way that: using the nth data of the mth one-dimensional data sequence
Comparing data in all single-dimensional data sequences
Obtaining a normalized value, namely obtaining a smoothing coefficient of the nth data of the mth single-dimensional data sequence; so far, the smoothing coefficient of each data can be obtained.
And S4, smoothing all the data according to the smoothing coefficient of each data, and compressing the multi-dimensional data after smoothing.
In step S3, the smoothing coefficient of each data in each single-dimensional data sequence is calculated, and the smoothing coefficient of each data is used as a weight when the data is weighted and averaged, and smoothing is performed on each data. The data repetition rate after the smoothing process is increased compared with the original data repetition rate, and the compression rate can be improved when data transmission is carried out.
In the embodiment, huffman coding is selected to compress the smoothed multidimensional data, and then the compressed data is transmitted, and it should be noted that since there are many data compression algorithms, an implementer may select another algorithm for data compression according to actual situations.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the scope of the present invention are intended to be included therein.
Claims (5)
1. A multidimensional data processing method of the Internet of things is characterized by comprising the following steps:
acquiring various types of data, wherein one type of data forms a single-dimensional data sequence;
utilizing a window with a preset size to slide on the single-dimensional data sequence to obtain the information entropy of the data positioned in the center of each window; obtaining an information entropy increment value of each data according to the information entropy of every two adjacent data;
acquiring a difference value change index of data positioned in the center of each window based on the difference value of every two data in each window; obtaining a smoothing coefficient of each data based on the information entropy and difference change index of each data in the single-dimensional data sequence and the sum of the information entropy increment values of each data in all the single-dimensional data sequences;
and smoothing all the data according to the smoothing coefficient of each data, and compressing the multi-dimensional data after smoothing.
2. The method for processing the multidimensional data of the internet of things according to claim 1, wherein the obtaining the entropy increment value of each data according to the entropy of each two adjacent data comprises: and (3) performing difference between the information entropy of the next data and the information entropy of the previous data in the two adjacent data, and calculating an absolute value to obtain an information entropy increment value of the previous data in the two adjacent data, so as to obtain the information entropy increment value of each data.
3. The method for processing multidimensional data of the internet of things according to claim 1, wherein the obtaining of the difference change index of each data comprises: acquiring the absolute value of the difference value of every two data in the data in a window when the window with the preset size slides on the single-dimensional data sequence; obtaining the absolute value of the maximum difference value as a reference value; recording the ratio of the absolute value of each difference value to the reference value as the difference degree; clustering the difference degree of every two data in the window to obtain different categories; respectively obtaining the mean value of all the difference degrees in each category, wherein the category with the minimum mean value is a first category; the ratio of the number of all the difference degrees of the first category to the number of all the difference degrees in different categories is a difference change index of the data located in the center of the window, and then the difference change index of each data is obtained.
4. The method for processing multidimensional data of the internet of things as claimed in claim 1, wherein the smoothing coefficient of each data is:
wherein the content of the first and second substances,
a smoothing coefficient representing the nth data in the mth one-dimensional data sequence; />
Representing the information entropy increment value of the nth data in the mth single-dimensional data sequence; />
Representing the sum of information entropy increment values of all data in the single-dimensional data sequence; />
Representing a difference change index of the nth data in the mth single-dimensional data sequence; />
Expressing an exponential function with a natural constant e as a base; />
A normalization function is represented.
5. The method for processing multidimensional data of the internet of things as claimed in claim 1, wherein the smoothing of all data according to the smoothing coefficient of each data comprises: and taking the smoothing coefficient of each data as the weight when each data is weighted and averaged to obtain each data after smoothing treatment.
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| CN116828070B (en) * | 2023-08-28 | 2023-11-07 | 无锡市锡容电力电器有限公司 | Intelligent power grid data optimization transmission method |
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