CN115987294B - Multi-dimensional 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; sliding on the single-dimensional data sequence by utilizing windows with preset sizes to obtain information entropy of data positioned at the center of each window; obtaining an information entropy increment value of each data according to the information entropy of the adjacent data; acquiring a difference value change index of data positioned in the center of each window according to the difference value of every two data in each window; obtaining a smoothing coefficient of each data by using the information entropy and a difference value change index of each data in the single-dimension data sequence and the sum of information entropy increment values of each data in all the single-dimension data sequences; all data are smoothed with the smoothing coefficient of each data and compressed. The electric digital data processing method for the multi-dimensional data of the Internet of things can improve the repetition rate of the multi-dimensional 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 internet of things equipment monitors 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 internet of things equipment are often packed and compressed firstly, so that the purposes of reducing the bandwidth occupancy rate of the Internet and facilitating data management are achieved.
The existing various data compression methods can realize compression of monitoring data of various Internet of things devices. However, if the internet of things device is abnormal, the change of the monitoring data of the internet of things device is increased due to abnormal data, or the irregularity is increased, so that the repetition rate of the monitoring data of the internet of things device is reduced, the compression effect of the monitoring data of the internet of things device is poor due to unfavorable data compression, the data storage rate is increased, and finally, when the internet of things device is abnormal, accurate abnormal information cannot be timely obtained due to the fact that a large amount of useless data exists in the acquired compressed data, so that the abnormality of the internet of things device cannot be timely found.
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 due to the existence of abnormal data and cannot be effectively compressed, so that the data storage rate is increased, the invention aims to provide a multidimensional data processing method of the Internet of things, which adopts the following specific technical scheme:
the 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-data sequence;
sliding on the single-dimensional data sequence by utilizing windows with preset sizes to obtain information entropy of data positioned at 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 a difference change index of each data in the single-dimension data sequence and the sum of information entropy increment values of each data in all the single-dimension data sequences;
and carrying out smoothing processing on all the data according to the smoothing coefficient of each data, and compressing the multidimensional data after the smoothing processing.
Preferably, obtaining the entropy increment value of each data according to the entropy of each two adjacent data includes: and carrying out difference between the information entropy of the next data in the two adjacent data and the information entropy of the previous data and obtaining an absolute value to obtain the information entropy increment value of the previous data in the two adjacent data, thereby obtaining the information entropy increment value of each data.
Preferably, acquiring the difference change index of each data includes: obtaining an absolute value of a difference value of every two data in the window when the window with the preset size slides on the single-dimension data sequence; acquiring the absolute value of the maximum difference value as a reference value; the ratio of the absolute value of each difference value to the reference value is recorded 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 smallest average value is a 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 different categories is the difference change index of the data positioned at the center of the window, and then the difference change index of each data is obtained.
Preferably, the smoothing coefficient of each data is:
wherein,,a smoothing coefficient representing the nth data in the mth single-dimensional data sequence;an information entropy increment value representing nth data in the mth single-dimensional data sequence;a sum of information entropy increment values representing each data in all single-dimensional data sequences;a difference change index indicating the nth data in the mth single-dimensional data sequence;an exponential function based on a natural constant e;representing the normalization function.
Preferably, smoothing processing is performed on all data according to the smoothing coefficient of each data, including: and taking the smoothing coefficient of each data as the weight when the data is weighted and averaged to obtain each data after smoothing.
The embodiment of the invention has at least the following beneficial effects: the invention collects various types of data by using various types of Internet of things equipment sensors, each type of data forms a single-data sequence, and further obtains the information entropy of each data in the single-data sequence, and the information entropy is used for representing the confusion degree of the data around each data; the information entropy increment value of each data is obtained, and the smooth coefficient of each data is determined according to the information entropy increment value, so that the overall repetition rate of the data can be increased, and the compression rate is improved; further, the absolute value of the difference value of every two data in the window is analyzed to obtain a difference value change index of each data, the actual change size of the data is further determined on the basis of the information entropy increment value, and the smoothing coefficient of each data is determined by combining the information entropy increment value of each data, so that errors when the smoothing coefficient is determined only by the information entropy increment value can be avoided, better smoothness exists when the data is smoothed, the repetition rate after the data is smoothed 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 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 method flowchart of a multidimensional data processing method of the internet of things according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a multi-dimensional data processing method of the internet of things according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment. 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 multidimensional data processing method of the Internet of things provided by the invention with reference to the accompanying drawings.
Examples:
the main application scene of the invention is as follows: the intelligent energy data are acquired and compressed by different types of electric power Internet of things equipment, when the equipment works normally, the change of the data is small, when the data are compressed at the moment, the compression rate is very high, the transmission efficiency of the data is very high, but when the equipment is abnormal, abnormal data can appear, the repetition rate of the whole data can be reduced by the abnormal data, so that the compression rate can be reduced, the data storage rate is reduced, and therefore, the data needs to be smoothed.
Referring to fig. 1, a method flowchart of a multi-dimensional data processing method of the internet of things provided by an embodiment of the invention is shown, and the method includes the following steps:
step 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 equipment, and the data types of each type of internet of things equipment are different, different internet of things equipment sensors in the electric power machine room are required to be used for collecting various types of data to obtain multidimensional data, one type of data is one single-dimensional data, and the sampling frequencies for collecting the different types of data are the same, so that one type of data is formed into one single-dimensional data sequence, namely one internet of things equipment corresponds to one single-dimensional data sequence, and the arrangement of the data at each moment in the single-dimensional data sequence is required to be described according to a time sequence.
In a state that various devices of the electric power machine room work normally, all the Internet of things devices transmit collected data to a data compression module for packing and compression; when abnormal data occurs, the repetition rate of the data is lowered, so that the compression rate during compression is also lowered, and therefore, it is necessary to smooth the data to increase the repetition rate of the data.
S2, sliding to acquire information entropy of data positioned at the center of each window on a single-dimension data sequence by utilizing a window with a preset size; 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 data in the local time period is increased, the data with more different values appear in the local time period, and the data of the part should be particularly paid attention to when the data is smoothed, so that the increment of the information entropy of each data and the increment of the information entropy of all the data can be calculated first, then the increment of the information entropy of each data and the increment of the information entropy of all the data are analyzed to obtain the weight when the data is smoothed, and further the data smoothing is completed according to the weight when the data is smoothed.
The information entropy of the data is increased, which means that more data with different values appear in a local time period, firstly, a data sliding window is needed, 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 continuous data.
In this embodiment, since the single-dimensional data sequence is a sequence on time sequence, 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, and it should be noted that an implementer may also adjust the size of the window according to specific situations, but needs to ensure that the length of the window is odd, the width is 1, and the sliding step length of the window is 1.
In addition, when the window slides on the single-dimension data sequence, each data in the single-dimension data sequence is taken as the data of 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-dimension data sequence is obtained, the fourth data is taken as the data of the center of the window, namely, the 3 rd data in the window; in addition, when the window slides on the single-dimensional data sequence, when the first few bits of data and the last few bits of data of the single-dimensional data sequence pass, the data in the window need 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 taken as the data in the center of the window, at the moment, the first two bits of data of the window are not existed, so that the two data need to be supplemented into the window, the data in the window are 5, preferably, in the embodiment, the values of the supplemented data are all 0, an embodiment can also adjust the values of the supplemented data according to the data condition of the single-dimensional data sequence, and interpolation can also be utilized for interpolating the data in the window, so that the number of the data in the window always keeps 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 is obtained, then the information entropy of the data in the center of the window is obtained according to the frequency of each data in the window, and the information entropy is expressed as follows by a formula:
wherein,,information entropy representing the ith data in a single-dimensional data sequence;the method comprises the steps of representing the occurrence frequency of the ith data in a window corresponding to the ith data, wherein the larger the value is, the higher the occurrence number ratio of the ith data in the window corresponding to the ith data is, and in addition, the window corresponding to the ith data is the window when the window slides on a single-data sequence, and the ith data is positioned in the center of the window;a base 10 logarithmic function is shown. T represents the length of a window corresponding to the ith data in the single-data sequence, namely the quantity of the data in the window corresponding to the ith data in the single-data sequence, and T represents the traversal of the data in the window. Wherein, the information entropy formula is a well-known existing formula.The larger the value of (c) is, the larger the information amount of the data in the window corresponding to the i-th data point is, that is, the greater the degree of confusion is, and the fact that one window is regarded as one time period indicates that the data is greatly changed in the time period. When more-valued different data appear in the local time period, the value of the information entropy of the data local time period becomes larger, namely, when more-valued different data appear in the window, the information entropy of the data in the window becomes larger.
And finally, obtaining an information entropy increment value of each data in the single-dimension data sequence, carrying out difference between the information entropy of the next data in the two adjacent data and the information entropy of the previous data, and obtaining an absolute value to obtain the information entropy increment value of the previous data in the two adjacent data, wherein the information entropy increment value is expressed as follows by a formula:
wherein,,an information entropy increment value representing the i-th data in the single-data sequence,information entropy representing the (i+1) th data in the single-data sequence;information entropy representing the i-th data in the single-dimensional data sequence;the method is characterized in that the method is used for representing the result of taking the difference between the information entropy of the (i+1) th data and the information entropy of the (i) th data in the single-dimension data sequence and obtaining the absolute value, and the change condition of the information entropy of each data in the single-dimension data sequence can be represented.
Therefore, the information entropy increment value of each data in each single-data sequence can be obtained and used for expressing the change condition 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 data to change compared with other data is, and the data needs to be paid extra attention to when the data is smoothed.
Step S3, 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; the smoothing coefficient of each data is obtained based on the information entropy and the difference change index of each data in the single-data sequence and the sum of the information entropy increment values of each data in all the single-data sequences.
When transmitting data, firstly, the data to be transmitted is stored in an uplink area, after a certain period of time is separated, the data stored in the uplink area is packed and compressed to a network, and in order to reduce the bandwidth pressure of the network, each data in the multi-dimensional data is required to be smoothed to different degrees, so that the repetition rate of the data is improved, the compression rate of the data is further improved, and the data packet is made smaller.
In conventional smoothing weight distribution, the data is smoothed according to the duty ratio of the corresponding information entropy increment value under the data of different dimensions in the sum of the information entropy increment values corresponding to all the data, but only the duty ratio is considered as the smoothing weight, only the change of the data is considered, and the magnitude of the change degree of the data is not considered, so that the obtained smoothing weight is error, and the obtained result after the data smoothing is not necessarily ensured to obtain the expected maximum compression rate when the data is compressed, so that the difference value of the data in the single-data sequence is also required to be analyzed, and the final smoothing coefficient distribution weight of different data is obtained.
All data in each window are obtained, difference is carried out on every two data in the data, the absolute value of the difference value after difference is obtained, the absolute value of the largest difference value is selected as a reference value, the ratio of the absolute value of each difference value to the reference value is the difference degree, all the difference degrees corresponding to one window form a sequence which is marked as a window difference value sequence, and the change degree 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 the difference degree corresponding to the absolute value of the difference, so that the window difference sequence corresponding to the element positioned at the center of the window when the window slides on the single-data sequence can be obtained, namely, each data in each single-data sequence corresponds to one window difference sequence.
Further, clustering elements in a window difference sequence corresponding to a window, wherein the clustering is based on the size of the elements in the difference sequence, and is a known technique and will not be described in detail herein; the clustering can divide the elements in the window difference sequence into different categories, preferably, in the embodiment, a k-means clustering algorithm is selected for clustering, 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 numerical values in the difference sequence, and an implementer can set the number of the divided categories according to specific situations; and respectively calculating the average value of all the difference degrees in each category, recording the average value as a first average value and a second average value, comparing the sizes of the first average value and the second average value, and taking the smaller corresponding category as the first category, so that the first category corresponding to each data can be obtained.
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, namely the difference change index of the data positioned at the center of the window, and the difference change index is expressed as follows by a formula:
wherein,,representing a difference change index corresponding to the nth data in the mth single-dimension data sequence;representing the number of all the degrees of difference in the first category corresponding to the nth data in the mth single-dimension data sequence;representing the number of all degree of difference in the two categories corresponding to the nth data in the mth single-dimensional data sequence.The larger the value of (a) is, the smaller the degree of change of the data in the window corresponding to the nth data in the mth single-data sequence is, and when the weight is allocated for smoothing the data, the smaller weight is allocated, so that the smoothness of the data can be ensured.
Finally, the ratio of the information entropy increment value of each data to the sum of the information entropy increment values of all the data is obtained, and the difference change index of each data is combined for analysis to obtain a smoothing coefficient of each data, wherein the smoothing coefficient is expressed as follows by a formula:
wherein,,a smoothing coefficient representing the nth data in the mth single-dimensional data sequence;the larger the information entropy increment value of the nth data in the mth single-dimension data sequence is, the larger the information entropy increment value is used for indicating that the surrounding data of the nth data in the current mth single-dimension data sequence is changed, and the information quantity of the data is greatly increased;a sum of information entropy increment values representing each data in all single-dimensional data sequences;a difference change index indicating the nth data in the mth single-dimensional data sequence;an exponential function based on a natural constant e;representing the normalization function.
The larger the sum of the information entropy increment values in all the single-dimension data sequences corresponding to all the Internet of things equipment is, the more the value of the sum is, the data of all the current overall Internet of things equipment is changed, and the information quantity of the data is greatly increased.
The larger the value of the (n) th data in the single-dimension data sequence corresponding to the m-th internet of things equipment is, the larger the change of the n-th data in the single-dimension data sequence corresponding to the m-th internet of things equipment is, and the larger the influence on the change of the information amount of the whole data is, the more the information which should be reserved for the n-th data in the single-dimension data sequence corresponding to the m-th internet of things equipment is, and the larger the distributed smoothing coefficient is when the smoothing is performed.
But only if it isWhen the smoothing coefficient of the nth data in the single-data sequence corresponding to the mth internet of things device is used, the expected smoothing effect may not be achieved, and the difference value between the data is not considered when the value of the information entropy of the data is calculated, so that the repetition rate of the smoothed data is further improved. It is also necessary to combine the difference change index of each data to obtain the final smoothing coefficient of each data.
Wherein at data where the information entropy is small, there is a feature that: the difference between the data is often low, but is not complete, because the entropy value is calculated according to the repeated amount of the data only when calculating, and the difference between the data is not considered, and in order to enable better smoothness when smoothing, the smoothing force should be increased for the data where the difference is small. Therefore, the ratio of the number of elements in the category with smaller average value of the difference degree to the number of all elements in the two categories is selected, namely the difference change index pair is utilizedCorrecting to obtain a final smoothing coefficient;
the larger the difference degree in the window difference value sequence corresponding to the nth data point in the single-data sequence corresponding to the mth internet of things equipment is, the smaller the difference degree in the window difference value sequence corresponding to the nth data point in the single-data sequence corresponding to the mth internet of things equipment is, namely the smaller the difference degree ratio of the smaller value is, the smaller the smoothing coefficient value corresponding to the nth data point in the single-data sequence corresponding to the mth internet of things equipment is, so that the negative exponential function pair based on e is utilizedNegative correlation mapping is performed.
In addition, if calculated directlySmoothing the data as a weight at the time of smoothing, it is not guaranteed that the sum of weights at the time of smoothing of the respective data obtained is 1, that is, it is not guaranteed that the sum of smoothing coefficients of the respective data obtained is 1, and therefore it is necessary to perform smoothing onThe normalization process is carried out, the processing is carried out,representing a normalization function in the following manner: nth data using mth single-dimensional data sequenceComparing each data in all single-dimensional data sequencesObtaining a normalized value, namely obtaining a smoothing coefficient of the nth data of the mth single-dimension data sequence; the smoothing coefficient of each data can be obtained so far.
And S4, carrying out smoothing processing on all the data according to the smoothing coefficient of each data, and compressing the multidimensional data after the smoothing processing.
In step S3, a smoothing coefficient of each data in each single-data sequence is calculated, and smoothing processing is performed for each data by taking the smoothing coefficient of each data as a weight when the data is weighted and averaged. Compared with the original data repetition rate, the data repetition rate after the smoothing processing is improved, and the compression rate can be improved when the data transmission is carried out.
The embodiments use huffman coding to compress the smoothed multidimensional data, and then transmit the compressed data, and it should be noted that, since there are many algorithms for data compression, the practitioner can select other algorithms for data compression according to the actual situation.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. 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 are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements, etc. within the scope of the present invention should be included.
Claims (5)
1. The multi-dimensional data processing method of the Internet of things is characterized by comprising the following steps of:
acquiring various types of data, wherein one type of data forms a single-data sequence;
sliding on the single-dimensional data sequence by utilizing windows with preset sizes to obtain information entropy of data positioned at 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 a difference change index of each data in the single-dimension data sequence and the sum of information entropy increment values of each data in all the single-dimension data sequences;
smoothing all data according to the smoothing coefficient of each data, and compressing the multi-dimensional data after the smoothing;
the sliding obtaining the information entropy of the data positioned at the center of each window on the single-dimension data sequence by utilizing the window with the preset size comprises the following steps:
the length of the window is an odd number, the width of the window is 1, and the sliding step length of the window is 1; when the number of data in the window is inconsistent with the size of the window when the window slides on the single-data sequence due to the first few data and the last few data of the single-data sequence, supplementing the data on the corresponding data bit without the data in the window, wherein the value of the supplemented data is determined according to the data condition of the single-data sequence.
2. The method for processing multidimensional data of the internet of things according to claim 1, wherein the obtaining the information entropy increment value of each data according to the information entropy of each two adjacent data comprises: and carrying out difference between the information entropy of the next data in the two adjacent data and the information entropy of the previous data and obtaining an absolute value to obtain the information entropy increment value of the previous data in the two adjacent data, thereby obtaining 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 the difference value change index of the data located at the center of each window based on the difference value of every two data in each window comprises: obtaining an absolute value of a difference value of every two data in the window when the window with the preset size slides on the single-dimension data sequence; acquiring the absolute value of the maximum difference value as a reference value; the ratio of the absolute value of each difference value to the reference value is recorded 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 smallest average value is a 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 different categories is the difference change index of the data positioned at the center of the window, and then the difference change index of each data is obtained.
4. The multi-dimensional data processing method of the internet of things according to claim 1, wherein the smoothing coefficient of each data is:
wherein,,a smoothing coefficient representing the nth data in the mth single-dimensional data sequence; />An information entropy increment value representing nth data in the mth single-dimensional data sequence; />A sum of information entropy increment values representing each data in all single-dimensional data sequences; />A difference change index indicating the nth data in the mth single-dimensional data sequence; />An exponential function based on a natural constant e; />Representing the normalization function.
5. The method for processing multidimensional data of the internet of things according to claim 1, wherein the smoothing processing of all data according to the smoothing coefficient of each data comprises: and taking the smoothing coefficient of each data as the weight when the data is weighted and averaged to obtain each data after smoothing.
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| US9363510B2 (en) * | 2012-03-02 | 2016-06-07 | Qualcomm Incorporated | Scan-based sliding window in context derivation for transform coefficient coding |
| US12080384B2 (en) * | 2015-06-16 | 2024-09-03 | Gottfried Wilhelm Leibniz Universitaet Hannover | Method for compressing genomic data |
| CN106658004B (en) * | 2016-11-24 | 2019-05-17 | 浙江大学 | A kind of compression method and device based on image flat site feature |
| WO2018179851A1 (en) * | 2017-03-28 | 2018-10-04 | Sharp Kabushiki Kaisha | Systems and methods for determining a level of quantization |
| CN113035278B (en) * | 2021-04-08 | 2023-03-24 | 哈尔滨工业大学 | TPBWT-based sliding window compression method based on self-indexing structure |
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