CN118764033B - Data compression storage method of ultrasonic water meter of Internet of things - Google Patents
Data compression storage method of ultrasonic water meter of Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of data compression, in particular to a data compression storage method of an ultrasonic water meter of the Internet of things, which aims at any ultrasonic water meter, acquires N-dimensional monitoring data acquired by the ultrasonic water meter in a preset period, constructs a two-dimensional matrix of N x M, and acquires the data fluctuation degree of each ultrasonic water meter; the method comprises the steps of carrying out deep stacking on two-dimensional matrixes of all ultrasonic water meters according to the sequence of small fluctuation degrees of all data to obtain a stacking matrix, dividing the stacking matrix into two sub-stacking matrices, carrying out normalization processing on each element in each sub-stacking matrix to obtain a normalized matrix, acquiring a data sequence to be compressed according to an adaptive compression path of each normalized matrix, carrying out run-length coding on the data sequence to be compressed to obtain compressed data, and improving the compression efficiency of multidimensional monitoring data of the ultrasonic water meters based on higher repeatability in the data sequence to be compressed obtained by the adaptive compression path.
Description
Technical Field
The invention relates to the technical field of data compression, in particular to a data compression storage method of an ultrasonic water meter of the Internet of things.
Background
The ultrasonic water meter is an advanced water flow metering device and can provide accurate water flow data. The speed, the volume and the like of water flowing through the pipeline are measured through an ultrasonic technology, and measured water flow data are transmitted to a centralized management system through an Internet of things technology (such as NB-IoT, loRa, sigfox, GPRS and the like), so that remote monitoring and data analysis are realized. The historical data collected by the ultrasonic water meter needs to be stored for data calling and analysis, but because the measured data of the ultrasonic water meter is high in accuracy, and when the data of a plurality of ultrasonic water meters are stored in a centralized manner by adopting the Internet of things, the data size is extremely huge, and a high-efficiency data compression storage method is needed.
In the prior art, a run length encoding compression algorithm (RLE) can be generally adopted to compress the acquired data of the ultrasonic water meter, wherein the run length encoding reduces the data storage space by encoding continuous identical data, and has better data compression efficiency on the existence of more continuous repeated content, but the acquired data of the ultrasonic water meter contains a large amount of similar redundant data instead of repeated redundant data, so that the compression efficiency of the acquired data of the ultrasonic water meter by using the run length encoding is poor.
Therefore, how to improve the compression efficiency of the collected data of the ultrasonic water meter by using the run length code is a problem to be solved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a data compression storage method of an ultrasonic water meter of the internet of things, so as to solve the problem of how to improve the compression efficiency of collected data of the ultrasonic water meter by using run length codes.
The embodiment of the invention provides a data compression storage method of an ultrasonic water meter of the Internet of things, which comprises the following steps:
For any ultrasonic water meter, acquiring N-dimensional monitoring data acquired by the ultrasonic water meter in a preset period, constructing the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period into a two-dimensional matrix of N x M, and acquiring the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period;
Obtaining the data fluctuation degree of each ultrasonic water meter, carrying out deep stacking on the two-dimensional matrixes of all the ultrasonic water meters according to the sequence from small data fluctuation degrees to large data fluctuation degrees to obtain a stacking matrix, and dividing the stacking matrix into two sub-stacking matrixes according to the data fluctuation degree of each ultrasonic water meter;
respectively carrying out normalization processing on each element in each sub-stack matrix to obtain corresponding normalization matrixes, and respectively obtaining self-adaptive compression paths of each normalization matrix according to neighborhood similarity and overall data volatility of each data in each normalization matrix;
And acquiring a data sequence to be compressed according to the self-adaptive compression path of each normalization matrix, and performing run-length coding on the data sequence to be compressed to obtain corresponding compressed data.
Preferably, the acquiring the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired in the preset period includes:
Constructing a monitoring data change curve under any dimension of N-dimensional monitoring data acquired in the preset period, acquiring all peak points of the monitoring data change curve, normalizing the values of the peak points to obtain corresponding first normalized values, acquiring absolute values of slope difference values between the peak points and left and right adjacent coordinate points, and normalizing the absolute values of the slope difference values to obtain corresponding second normalized values;
Acquiring a first time interval between the peak point and a left adjacent peak point thereof and a second time interval between the peak point and a right adjacent peak point thereof, obtaining the inverse of the addition result of the first time interval and the second time interval, and acquiring the product of the first normalization value, the second normalization value and the inverse of the addition result as a fluctuation characteristic value of the peak point;
Acquiring accumulated values of fluctuation characteristic values of all peak points of the monitoring data change curve as data fluctuation indexes in the dimensions, and taking the accumulated values of the data fluctuation indexes of each dimension of the N-dimensional monitoring data acquired in the preset period as the data fluctuation degree of the ultrasonic water meter.
Preferably, the dividing the stacking matrix into two sub-stacking matrices according to the data fluctuation degree of each ultrasonic water meter includes:
Respectively carrying out normalization processing on the data fluctuation degree of each ultrasonic water meter to obtain a corresponding normalization value, acquiring a preset normalization threshold value, taking a two-dimensional matrix of the ultrasonic water meter in the stacking matrix as a two-dimensional matrix with small data fluctuation if the normalization value corresponding to any ultrasonic water meter is smaller than or equal to the normalization threshold value, otherwise taking the two-dimensional matrix of the ultrasonic water meter in the stacking matrix as a two-dimensional matrix with large data fluctuation if the normalization value corresponding to any ultrasonic water meter is larger than the normalization threshold value;
And forming a first sub-stacking matrix by the two-dimensional matrix with small data fluctuation in the stacking matrix, and forming a second sub-stacking matrix by the two-dimensional matrix with large data fluctuation in the stacking matrix.
Preferably, the obtaining the adaptive compression path of each normalized matrix according to the neighborhood similarity and the overall data volatility of each data in each normalized matrix includes:
Constructing a three-dimensional space of the normalization matrix aiming at the normalization matrix corresponding to the first sub-stacking matrix to obtain three space planes, acquiring the absolute value of a data difference value between each piece of data in the space plane and each piece of neighborhood data in a preset neighborhood range of the space plane aiming at any space plane to obtain an accumulated value of the absolute value of the data difference value of each piece of data in the space plane, and carrying out inverse proportion normalization on the sum of the accumulated values of the absolute values of the data difference values of all pieces of data by using an exponential function taking a natural constant as a base, wherein the obtained result is used as an integral similarity characteristic value of the space plane;
And according to the global similar characteristic value of each space plane, acquiring the space plane corresponding to the maximum global similar characteristic value as a traversing plane, and taking the traversing plane as a self-adaptive compression path of the normalization matrix corresponding to the first sub-stack matrix according to a traversing mode of a transverse Z shape.
Preferably, the obtaining the adaptive compression path of each normalized matrix according to the neighborhood similarity and the overall data volatility of each data in each normalized matrix includes:
For any ultrasonic water meter in a normalization matrix corresponding to the second sub-stacking matrix, respectively calculating pearson correlation coefficients between normalized data sequences of every two dimensions according to normalized data sequences of each dimension respectively corresponding to N-dimensional monitoring data of the ultrasonic water meter in the normalization matrix, and obtaining a difference accumulated value as a multidimensional correlation characteristic value of the ultrasonic water meter according to a difference value between each pearson correlation coefficient and a constant 1;
according to the multidimensional association characteristic values of each ultrasonic water meter in the normalization matrix corresponding to the second sub-stack matrix, obtaining accumulated values of the multidimensional association characteristic values, and performing negative mapping on the accumulated values of the multidimensional association characteristic values by using an exponential function with a natural constant as a base number, wherein the obtained mapping value is used as an overall data association index of the normalization matrix corresponding to the second sub-stack matrix;
and acquiring the self-adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix according to the overall data association index of the normalized matrix corresponding to the second sub-stack matrix.
Preferably, the obtaining the adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix according to the overall data association index of the normalized matrix corresponding to the second sub-stack matrix includes:
Acquiring a preset integral data association index threshold, and taking the integral data association index of the normalization matrix corresponding to the second sub-stack matrix as a self-adaptive compression path of the normalization matrix corresponding to the second sub-stack matrix according to a longitudinal Z-shaped traversal mode on a preset traversal plane if the integral data association index of the normalization matrix corresponding to the second sub-stack matrix is larger than or equal to the integral data association index threshold;
If the overall data association index of the normalized matrix corresponding to the second sub-stack matrix is smaller than the overall data association index threshold, acquiring a traversing plane of the normalized matrix corresponding to the second sub-stack matrix, and taking the traversing plane of the normalized matrix corresponding to the second sub-stack matrix as an adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix in a traversing manner of transverse Z-shaped.
Preferably, the obtaining the data sequence to be compressed according to the adaptive compression path of each normalization matrix includes:
For any normalization matrix, sequentially traversing the data in the normalization matrix according to the self-adaptive compression path of the normalization matrix to obtain a traversing data sequence, performing first-order difference on the traversing data sequence to obtain a difference sequence, and placing the difference sequence behind the first data in the traversing data sequence to form a data sequence to be compressed.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
The method comprises the steps of acquiring N-dimensional monitoring data acquired by any ultrasonic water meter in a preset period, constructing the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period into N-X M two-dimensional matrixes, acquiring the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period, acquiring the data fluctuation degree of each ultrasonic water meter, carrying out deep stacking on the two-dimensional matrixes of all the ultrasonic water meters according to the sequence of all the data fluctuation degrees from small to large to obtain stacked matrixes, dividing the stacked matrixes into two sub-stacked matrixes according to the data fluctuation degree of each ultrasonic water meter, respectively carrying out normalization processing on each element in each sub-stacked matrix to obtain corresponding normalized matrixes, respectively acquiring the self-adaptive compression path of each normalized matrix according to the similarity and the overall data fluctuation of each data in each normalized matrix, acquiring a data sequence to be compressed according to the self-adaptive compression path of each normalized matrix, and carrying out run-length coding on the data sequence to be compressed to obtain corresponding compressed data sequence. The method comprises the steps of carrying out data fluctuation analysis on N-dimensional monitoring data of a plurality of ultrasonic water meters connected through the Internet of things, carrying out depth stacking on two-dimensional matrixes of all the ultrasonic water meters to obtain a stacking matrix, namely forming a multidimensional space, dividing the stacking matrix into two sub-stacking matrixes with large data fluctuation and small data fluctuation based on the multidimensional space where the stacking matrix is located, and obtaining compression paths with high adjacent similarity in a differential mode subsequently, so that the possibility that the differential data are repeated data is higher, and therefore carrying out data similarity or overall data fluctuation analysis on each sub-stacking matrix to obtain an adaptive compression path of each sub-stacking matrix, so that the repeatability in a data sequence to be compressed obtained based on the adaptive compression path is higher, and the compression efficiency of run-length coding on the data sequence to be compressed is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present 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 of a method for data compression storage of an ultrasonic water meter of internet of things according to an embodiment of the invention.
Detailed Description
Embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, are described in detail below. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.
It should be noted that the terms "first," "second," and the like in the description of the present disclosure and the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of the present disclosure.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
Referring to fig. 1, a method flowchart of a data compression storage method of an ultrasonic water meter of internet of things according to an embodiment of the present invention is shown in fig. 1, where the method may include:
Step S101, aiming at any ultrasonic water meter, acquiring N-dimensional monitoring data acquired by the ultrasonic water meter in a preset time period, constructing the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset time period into a two-dimensional matrix of N.times.M, and acquiring the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset time period.
After the multi-dimensional monitoring data are collected by each ultrasonic water meter distributed at multiple places, the collected monitoring data are transmitted to a centralized management system through the internet of things technology, in the embodiment of the invention, the data monitored by the ultrasonic water meter in 24 hours are collected through a preset sampling frequency according to any ultrasonic water meter with the preset time period, the types of the mainly collected data are instantaneous flow, water temperature and water pressure, namely, the instantaneous flow, the water temperature and the water pressure are respectively monitored in 24 hours by collecting the ultrasonic water meter, one sampling moment collects one instantaneous flow, one water temperature and one water pressure, namely N=3, and then all the instantaneous flow, the water temperature and the water pressure collected in 24 hours form a two-dimensional matrix 3*M, wherein the first behavior of the two-dimensional matrix is the instantaneous flow, the second behavior of the water temperature and the third behavior of the water pressure, and the column of the two-dimensional matrix refers to M sampling moments in 24 hours. Preferably, the sampling frequency is set to 1 minute, corresponding to m=1440.
And similarly, acquiring data monitored by each ultrasonic level within 24 hours, and constructing a corresponding 3*M two-dimensional matrix, namely a two-dimensional matrix corresponding to one ultrasonic water meter.
The effect of the run length encoding on the data with larger repetition degree is better, but the purpose of the embodiment of the invention is to improve the compression efficiency of the follow-up run length encoding on the data in the two-dimensional matrix of all the ultrasonic water meters by converting the data distribution in the two-dimensional matrix, so that the data fluctuation degree of the ultrasonic water meters is obtained according to the data change of the 3-dimensional monitoring data acquired in a preset period of time for any one ultrasonic water meter, and the method for obtaining the data fluctuation degree of the ultrasonic water meters is as follows:
Constructing a monitoring data change curve under any dimension of N-dimensional monitoring data acquired in the preset period, acquiring all peak points of the monitoring data change curve, normalizing the values of the peak points to obtain corresponding first normalized values, acquiring absolute values of slope difference values between the peak points and left and right adjacent coordinate points, and normalizing the absolute values of the slope difference values to obtain corresponding second normalized values;
Acquiring a first time interval between the peak point and a left adjacent peak point thereof and a second time interval between the peak point and a right adjacent peak point thereof, obtaining the inverse of the addition result of the first time interval and the second time interval, and acquiring the product of the first normalization value, the second normalization value and the inverse of the addition result as a fluctuation characteristic value of the peak point;
Acquiring accumulated values of fluctuation characteristic values of all peak points of the monitoring data change curve as data fluctuation indexes in the dimensions, and taking the accumulated values of the data fluctuation indexes of each dimension of the N-dimensional monitoring data acquired in the preset period as the data fluctuation degree of the ultrasonic water meter.
In an embodiment, taking the p ultrasonic water meter as an example, for the j dimension of the p ultrasonic water meter, constructing a corresponding monitoring data change curve for all monitoring data of the dimension, and obtaining all peak points on the monitoring data change curve by using an AMPD algorithm, where the AMPD algorithm belongs to the prior art, and is not described in detail herein, and the calculation expression for obtaining the data fluctuation degree of the p ultrasonic water meter is as follows:
Wherein, The data fluctuation degree of the p ultrasonic water meter is represented, N represents the number of dimensions,Representing the number of peak points in the j-th dimension,Representing the time interval (abscissa interval) between the ith peak point and its left adjacent peak point in the jth dimension,Representing the time interval (abscissa interval) between the ith peak point and its right adjacent peak point in the jth dimension, norm () represents the normalization function,A numerical value (ordinate value) representing the ith peak point in the jth dimension,And the absolute value of the difference between the slope value between the ith peak point and the left adjacent coordinate point and the slope value between the ith peak point and the right adjacent coordinate point in the jth dimension is represented.
The more the number of peak points in each dimension, the more the data points indicating that the monitoring data acquired in the dimension has fluctuation, the higher the corresponding data fluctuation degree, and for each peak point,The larger the value of (c) the more isolated the peak point is, the lower the contribution to the overall waviness is, without forming continuous waviness; For the vertical axis span from the ith peak point to the lowest minimum value point in the two sides, the larger the value of the peak point is, the more intense the fluctuation of the peak point is, the harder the similar value is, and the larger the contribution to the overall fluctuation characteristic degree is; The larger the value of the (p) ultrasonic water meter is, the sharper and more intense the change at the peak point is, the harder the similar value is, and the larger the contribution to the overall fluctuation characteristic degree is, so that the fluctuation characteristic degree of the (p) ultrasonic water meter in N dimensions is accumulated to be used as the data fluctuation degree of the (p) ultrasonic water meter, namely the data fluctuation characteristic in the two-dimensional matrix of the (p) ultrasonic water meter.
And in the same way, the data fluctuation degree of each ultrasonic water meter can be obtained.
Step S102, obtaining the data fluctuation degree of each ultrasonic water meter, carrying out deep stacking on the two-dimensional matrixes of all the ultrasonic water meters according to the sequence from small data fluctuation degrees to large data fluctuation degrees to obtain a stacking matrix, and dividing the stacking matrix into two sub-stacking matrixes according to the data fluctuation degree of each ultrasonic water meter.
The step S101 is utilized to obtain the data fluctuation degree and the two-dimensional matrix of each ultrasonic water meter, and further the two-dimensional matrices of all the ultrasonic water meters are deeply stacked according to the sequence from small to large data fluctuation degrees to obtain a stacked matrix, the stacked matrix at the moment belongs to a multi-dimensional space form, and the stacked matrix is divided into a gradual change process from small data fluctuation to large data fluctuation in the vertical direction, and because the two-dimensional matrix areas with small data fluctuation have larger adjacent similarity after adjacent aggregation, the stacked matrix is divided into two sub-stacked matrices according to the data fluctuation degree of each ultrasonic water meter, so that the situation that the adjacent similarity degree of data is low due to the staggered superposition of the data with small fluctuation and the data with large fluctuation is avoided.
Preferably, the stacking matrix is divided into two sub-stacking matrices according to the data fluctuation degree of each ultrasonic water meter, including:
Respectively carrying out normalization processing on the data fluctuation degree of each ultrasonic water meter to obtain a corresponding normalization value, acquiring a preset normalization threshold value, taking a two-dimensional matrix of the ultrasonic water meter in the stacking matrix as a two-dimensional matrix with small data fluctuation if the normalization value corresponding to any ultrasonic water meter is smaller than or equal to the normalization threshold value, otherwise taking the two-dimensional matrix of the ultrasonic water meter in the stacking matrix as a two-dimensional matrix with large data fluctuation if the normalization value corresponding to any ultrasonic water meter is larger than the normalization threshold value;
And forming a first sub-stacking matrix by the two-dimensional matrix with small data fluctuation in the stacking matrix, and forming a second sub-stacking matrix by the two-dimensional matrix with large data fluctuation in the stacking matrix.
In one embodiment, the data fluctuation degree of each ultrasonic water meter is normalized by using a normalization function norm to obtain a corresponding normalization value, and the normalization value of the p-th ultrasonic water meter is recorded asThe normalization threshold is set to be 0.3, and floating adjustment can be specifically performed according to scene requirements without limitation, and then the stacked matrix is divided into two parts according to the normalization threshold, wherein one part is a first sub-stacked matrix formed by two-dimensional matrixes with normalization values smaller than or equal to 0.3, namely a part with small data fluctuation in the stacked matrix, and the other part is a second sub-stacked matrix formed by two-dimensional matrixes with normalization values larger than 0.3, namely a part with large data fluctuation in the stacked matrix. Briefly, the stacking matrix is divided into two parts in the vertical direction according to a normalized threshold value of 0.3.
To this end, the stacking matrix is divided into a first sub-stacking matrix and a second sub-stacking matrix.
Step S103, respectively carrying out normalization processing on each element in each sub-stack matrix to obtain corresponding normalization matrixes, and respectively obtaining self-adaptive compression paths of each normalization matrix according to neighborhood similarity and overall data volatility of each data in each normalization matrix.
In order to avoid the influence of data dimension, each element in each sub-stack matrix is normalized to obtain a corresponding normalized matrix. Because the normalized matrix corresponding to the first sub-stack matrix belongs to a part with small data fluctuation, and the adjacent similarity degree of the data is higher, the self-adaptive compression path of the normalized matrix corresponding to the first sub-stack matrix can be acquired according to the neighborhood similarity of each data in the normalized matrix corresponding to the first sub-stack matrix, and the specific acquisition method is as follows:
Constructing a three-dimensional space of the normalization matrix aiming at the normalization matrix corresponding to the first sub-stacking matrix to obtain three space planes, acquiring the absolute value of a data difference value between each piece of data in the space plane and each piece of neighborhood data in a preset neighborhood range of the space plane aiming at any space plane to obtain an accumulated value of the absolute value of the data difference value of each piece of data in the space plane, and carrying out inverse proportion normalization on the sum of the accumulated values of the absolute values of the data difference values of all pieces of data by using an exponential function taking a natural constant as a base, wherein the obtained result is used as an integral similarity characteristic value of the space plane;
And according to the global similar characteristic value of each space plane, acquiring the space plane corresponding to the maximum global similar characteristic value as a traversing plane, and taking the traversing plane as a self-adaptive compression path of the normalization matrix corresponding to the first sub-stack matrix according to a traversing mode of a transverse Z shape.
In an embodiment, since the normalization matrix corresponding to the first sub-stacked matrix is a three-dimensional two-dimensional matrix, a three-dimensional space of the normalization matrix is constructed, where the three-dimensional space refers to a direction x and a direction y of a row and a direction z of a column of a last two-dimensional matrix in the normalization matrix, and three spatial planes are a plane xy, a plane xz, and a plane yz. For a plane xy, respectively acquiring neighborhood data of each data in the normalized matrix in eight adjacent domains on the plane xy, and further acquiring integral similar characteristic values of all data of the normalized matrix on the plane xy according to numerical value differences between each data and the neighborhood data, wherein the calculation expression of the integral similar characteristic values is as follows:
Wherein, Representing the global similarity feature value of the q-th spatial plane of the normalized matrix,The value representing the ith data of the normalized matrix,A value representing the j-th data in the octant of the i-th data of the normalized matrix,Represents the data amount of the ith data of the normalized matrix in the eighth neighborhood on the qth spatial plane, w represents the data amount in the normalized matrix, |represents the absolute value sign, exp () represents the exponential function with the natural constant as the base.
It should be noted that the number of the substrates,The smaller the value of the corresponding two data is, the smaller the difference between the corresponding two data is, the higher the similarity is, the smaller the accumulated value is by accumulating the numerical value difference between all the data in the normalized matrix and each data in the eight adjacent areas of the normalized matrix, the smaller the difference between the data in the normalized matrix is, the greater the overall similarity of all the data of the corresponding normalized matrix on the q-th space plane is, and the greater the overall similarity characteristic value of the q-th space plane of the corresponding normalized matrix is.
Similarly, according to the numerical value difference between each data in the normalized matrix and each data in the eight adjacent domains in different space planes, the integral similar characteristic value of all data of the normalized matrix on each space plane can be obtained respectively, and then the space plane corresponding to the largest integral similar characteristic value is taken as the traversing plane of the normalized matrix, and carrying out data traversal on the normalized matrix according to the traversal plane and the transverse Z-shaped on the traversal plane, wherein the traversal mode obtains the strongest adjacent similarity among the traversal data, and the larger the repeatability of the differential data obtained by the subsequent differential based on the traversal data is, so that the adaptive compression path of the normalized matrix corresponding to the first sub-stack matrix is the traversal mode according to the transverse Z-shaped on the traversal plane.
Further, for the normalized matrix corresponding to the second sub-stacking matrix, the adjacent similarity degree on all planes is lower for the part with large data fluctuation, so that the data distribution with large fluctuation is evaluated by combining the multidimensional relevance of the acquired data of the ultrasonic water meter.
When the water temperature is increased, the volume of the water is expanded, the water pressure is increased due to the volume expansion of the pipeline monitored by the water meter, and otherwise, when the water temperature is reduced, the volume of the water is contracted, and the water pressure is reduced. On the other hand, the water pressure also affects the flow rate of water, and the high pressure generally affects the heat transfer and heat dissipation of water in the pipeline, so that the heat loss of the water flowing at a high speed is reduced, the water temperature is kept higher, and the heat dissipation of the water flowing at a low speed is easier, and the temperature is reduced. Therefore, the multidimensional monitoring data collected by the single ultrasonic water meter have a certain degree of relevance, when the inside of the water pipe monitored by the ultrasonic water meter is narrower, the internal space is smaller, and the relevance among the water temperature, the water pressure and the flow velocity is easier to generate. The higher the fluctuation similarity of the data in three dimensions of instantaneous flow, water temperature and water pressure is, the stronger the relevance of the corresponding multidimensional data sequences is, when the more the ultrasonic water meters with strong relevance are, the more the type of monitoring scenes are contained in the ultrasonic water meters connected with the Internet of things, the data traversing is carried out by adopting the longitudinal Z shape of the space plane xy, and the adjacent similarity degree of the data with large fluctuation can be improved, therefore, the self-adaptive compression path of the normalization matrix is obtained according to the neighborhood similarity and the integral data fluctuation of each data in the normalization matrix aiming at the normalization matrix corresponding to the second sub-stack matrix, and the specific obtaining method is as follows:
For any ultrasonic water meter in a normalization matrix corresponding to the second sub-stacking matrix, respectively calculating pearson correlation coefficients between normalized data sequences of every two dimensions according to normalized data sequences of each dimension respectively corresponding to N-dimensional monitoring data of the ultrasonic water meter in the normalization matrix, and obtaining a difference accumulated value as a multidimensional correlation characteristic value of the ultrasonic water meter according to a difference value between each pearson correlation coefficient and a constant 1;
according to the multidimensional association characteristic values of each ultrasonic water meter in the normalization matrix corresponding to the second sub-stack matrix, obtaining accumulated values of the multidimensional association characteristic values, and performing negative mapping on the accumulated values of the multidimensional association characteristic values by using an exponential function with a natural constant as a base number, wherein the obtained mapping value is used as an overall data association index of the normalization matrix corresponding to the second sub-stack matrix;
and acquiring the self-adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix according to the overall data association index of the normalized matrix corresponding to the second sub-stack matrix.
In an embodiment, the calculation expression of the overall data association index of the normalized matrix corresponding to the second sub-stack matrix is:
Wherein W represents the overall data association index of the normalization matrix corresponding to the second sub-stack matrix, exp () represents an exponential function based on a natural constant, N represents the number of dimensions of the monitoring data, m represents the number of ultrasonic meters corresponding to the normalization matrix corresponding to the second sub-stack matrix, A normalized data sequence representing the a-th dimension of the p-th ultrasonic meter,A normalized data sequence representing the b dimension of the p-th ultrasonic meter,The pearson correlation coefficient between the normalized data sequences of the two dimensions representing the p-th ultrasonic meter, 1 representing a constant.
It should be noted that, for any ultrasonic water meter in the normalized matrix, by the correlation between the data sequences between every two dimensions of the ultrasonic water meter, whether the two dimensions are correlated is evaluated, that is, by acquiring the pearson correlation coefficient, the closer the pearson correlation coefficient is to 1, the stronger the positive correlation between the data sequences corresponding to the two dimensions is, since the pearson correlation coefficient has a negative value, the utilization is made ofThe quantization result is positive, and further indicates that the smaller the result is, the stronger the positive correlation of the two-dimensional data sequence is. And accumulating positive correlations of data sequences among dimensions of all the ultrasonic water meters in the normalized matrix, and taking the accumulated result as an integral data association index of the normalized matrix corresponding to the second sub-stack matrix.
After obtaining the overall data association index of the normalized matrix corresponding to the second sub-stack matrix, obtaining the adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix according to the overall data association index of the normalized matrix corresponding to the second sub-stack matrix, wherein the specific obtaining method comprises the following steps:
Acquiring a preset integral data association index threshold, and taking the integral data association index of the normalization matrix corresponding to the second sub-stack matrix as a self-adaptive compression path of the normalization matrix corresponding to the second sub-stack matrix according to a longitudinal Z-shaped traversal mode on a preset traversal plane if the integral data association index of the normalization matrix corresponding to the second sub-stack matrix is larger than or equal to the integral data association index threshold;
If the overall data association index of the normalized matrix corresponding to the second sub-stack matrix is smaller than the overall data association index threshold, acquiring a traversing plane of the normalized matrix corresponding to the second sub-stack matrix, and taking the traversing plane of the normalized matrix corresponding to the second sub-stack matrix as an adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix in a traversing manner of transverse Z-shaped.
In an embodiment, setting the overall data association index threshold to be 0.6, and performing adaptive adjustment according to the scene, if the overall data association index of the normalized matrix corresponding to the second sub-stack matrix is greater than or equal to 0.6, it is indicated that the correlation between the multidimensional data in the normalized matrix corresponding to the second sub-stack matrix is relatively strong, and traversing the data in the normalized matrix by adopting the longitudinal zigzag of the spatial plane xy, that is, the adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix is in the traversing manner of the longitudinal zigzag on the spatial plane xy. Otherwise, if the overall data association index of the normalized matrix corresponding to the second sub-stack matrix is smaller than 0.6, according to the method for acquiring the adaptive compression path of the normalized matrix corresponding to the first sub-stack matrix, acquiring a corresponding traversal plane by analyzing the overall similarity characteristic value of each space plane in the normalized matrix corresponding to the second sub-stack matrix, and further taking the traversal plane of the normalized matrix corresponding to the second sub-stack matrix as the adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix in a transverse Z-shaped traversal mode.
Thus, the adaptive compression path of each normalized matrix is obtained.
Step S104, according to the self-adaptive compression path of each normalized matrix, a data sequence to be compressed is obtained, and run-length encoding is carried out on the data sequence to be compressed, so that corresponding compressed data is obtained.
After the self-adaptive path compression path of each normalization matrix is obtained, a data sequence to be compressed is obtained according to the self-adaptive compression path of the normalization matrix for any normalization matrix, wherein the data in the normalization matrix is sequentially traversed according to the self-adaptive compression path of the normalization matrix for any normalization matrix to obtain a traversing data sequence, the traversing data sequence is subjected to first-order difference to obtain a differential sequence, and the differential sequence is placed behind the first data in the traversing data sequence to form the data sequence to be compressed.
In one embodiment, if the adaptive compression path of the normalized matrix is a traversing manner of traversing the plane by using a transverse zigzag manner, a specific traversing process is to traverse the plane from the upper left corner of the corresponding uppermost traversing plane in the normalized matrix, traverse the plane by using the transverse zigzag manner until the plane is traversed, and continue traversing the next plane, thereby iterating until all data in the normalized matrix is traversed, and storing the data in a traversing data sequence according to the traversing sequence. If the adaptive compression path of the normalized matrix is a traversing mode adopting a longitudinal Z shape of the spatial plane xy, a specific traversing process is that traversing is started from the upper left corner of the uppermost layer corresponding to the plane xy of the normalized matrix, the plane is traversed by the longitudinal Z shape until the plane traversing is finished, and the next layer of plane is continuously traversed, so that iteration is performed until all data in the normalized matrix are traversed, and the data are stored in a traversing data sequence according to the traversing sequence. So far, the traversal data sequences obtained by carrying out data traversal on each normalization matrix can be obtained, the adjacent similarity of the data between the traversal data sequences obtained at the moment is higher, and then the repeatability of the data is improved by carrying out differential processing on the traversal data sequences on each traversal data sequence, specifically, the traversal data sequences are subjected to first-order differential to obtain differential sequences, and the differential sequences are placed behind the first data in the traversal data sequences to form the data sequence to be compressed. It should be noted that, the data sequence to be compressed also corresponds to a symbol sequence, and for the differential value of negative number in the sequence to be compressed, the sign of the differential value is set to be 0, and if not, the sign of the differential value is set to be 1.
Further, after the data sequence to be compressed is obtained, the data sequence to be compressed is compressed by using an RLE compression algorithm to obtain compressed data, the purpose of compressing the monitoring data of all the ultrasonic water meters is achieved, the repetition degree of the adjacent data is improved through the data sequence to be compressed obtained by improving the sequence difference of the adjacent similarity, and the compression efficiency of the RLE compression algorithm on the multidimensional data collected by the ultrasonic water meters of the Internet of things is improved.
In summary, the embodiment of the invention aims at any ultrasonic water meter, acquires N-dimensional monitoring data acquired by the ultrasonic water meter in a preset period, constructs the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period into N-M two-dimensional matrixes, acquires the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period, acquires the data fluctuation degree of each ultrasonic water meter, deep stacks the two-dimensional matrixes of all the ultrasonic water meters according to the sequence of all the data fluctuation degrees from small to large to obtain stacked matrixes, divides the stacked matrixes into two sub-stacked matrixes according to the data fluctuation degree of each ultrasonic water meter, normalizes each element in each sub-stacked matrix to obtain corresponding normalized matrixes, acquires the adaptive compression path of each normalized matrix according to the similarity and the whole data fluctuation of each data in each normalized matrix, acquires the data sequence to be compressed according to the adaptive compression path of each normalized matrix, and compresses the data sequence to obtain the corresponding compressed data sequence. The method comprises the steps of carrying out data fluctuation analysis on N-dimensional monitoring data of a plurality of ultrasonic water meters connected through the Internet of things, carrying out depth stacking on two-dimensional matrixes of all the ultrasonic water meters to obtain a stacking matrix, namely forming a multidimensional space, dividing the stacking matrix into two sub-stacking matrixes with large data fluctuation and small data fluctuation based on the multidimensional space where the stacking matrix is located, and obtaining compression paths with high adjacent similarity in a differential mode subsequently, so that the possibility that the differential data are repeated data is higher, and therefore carrying out data similarity or overall data fluctuation analysis on each sub-stacking matrix to obtain an adaptive compression path of each sub-stacking matrix, so that the repeatability in a data sequence to be compressed obtained based on the adaptive compression path is higher, and the compression efficiency of run-length coding on the data sequence to be compressed is improved.
The foregoing embodiments are merely for illustrating the technical solution of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solution described in the foregoing embodiments may be modified or substituted for some of the technical features thereof, and that these modifications or substitutions should not depart from the spirit and scope of the technical solution of the embodiments of the present invention and should be included in the protection scope of the present invention.
Claims (3)
1. The data compression storage method of the ultrasonic water meter of the Internet of things is characterized by comprising the following steps of:
For any ultrasonic water meter, acquiring N-dimensional monitoring data acquired by the ultrasonic water meter in a preset period, constructing the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period into a two-dimensional matrix of N x M, and acquiring the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired by the ultrasonic water meter in the preset period;
Obtaining the data fluctuation degree of each ultrasonic water meter, carrying out deep stacking on the two-dimensional matrixes of all the ultrasonic water meters according to the sequence from small data fluctuation degrees to large data fluctuation degrees to obtain a stacking matrix, and dividing the stacking matrix into two sub-stacking matrixes according to the data fluctuation degree of each ultrasonic water meter;
respectively carrying out normalization processing on each element in each sub-stack matrix to obtain corresponding normalization matrixes, and respectively obtaining self-adaptive compression paths of each normalization matrix according to neighborhood similarity and overall data volatility of each data in each normalization matrix;
According to the self-adaptive compression path of each normalization matrix, a data sequence to be compressed is obtained, run-length coding is carried out on the data sequence to be compressed, and corresponding compressed data is obtained;
The method for dividing the stacking matrix into two sub-stacking matrices according to the data fluctuation degree of each ultrasonic water meter comprises the following steps:
Respectively carrying out normalization processing on the data fluctuation degree of each ultrasonic water meter to obtain a corresponding normalization value, acquiring a preset normalization threshold value, taking a two-dimensional matrix of the ultrasonic water meter in the stacking matrix as a two-dimensional matrix with small data fluctuation if the normalization value corresponding to any ultrasonic water meter is smaller than or equal to the normalization threshold value, otherwise taking the two-dimensional matrix of the ultrasonic water meter in the stacking matrix as a two-dimensional matrix with large data fluctuation if the normalization value corresponding to any ultrasonic water meter is larger than the normalization threshold value;
forming a first sub-stacking matrix from two-dimensional matrixes with small data fluctuation in the stacking matrix, and forming a second sub-stacking matrix from two-dimensional matrixes with large data fluctuation in the stacking matrix;
the obtaining the self-adaptive compression path of each normalized matrix according to the neighborhood similarity and the overall data volatility of each data in each normalized matrix comprises the following steps:
Constructing a three-dimensional space of the normalization matrix aiming at the normalization matrix corresponding to the first sub-stacking matrix to obtain three space planes, acquiring the absolute value of a data difference value between each piece of data in the space plane and each piece of neighborhood data in a preset neighborhood range of the space plane aiming at any space plane to obtain an accumulated value of the absolute value of the data difference value of each piece of data in the space plane, and carrying out inverse proportion normalization on the sum of the accumulated values of the absolute values of the data difference values of all pieces of data by using an exponential function taking a natural constant as a base, wherein the obtained result is used as an integral similarity characteristic value of the space plane;
According to the integral similar characteristic value of each space plane, acquiring a space plane corresponding to the maximum integral similar characteristic value as a traversing plane, and taking the traversing plane as a self-adaptive compression path of a normalization matrix corresponding to the first sub-stacking matrix in a traversing manner of transverse Z shape;
For any ultrasonic water meter in a normalization matrix corresponding to the second sub-stacking matrix, respectively calculating pearson correlation coefficients between normalized data sequences of every two dimensions according to normalized data sequences of each dimension respectively corresponding to N-dimensional monitoring data of the ultrasonic water meter in the normalization matrix, and obtaining a difference accumulated value as a multidimensional correlation characteristic value of the ultrasonic water meter according to a difference value between each pearson correlation coefficient and a constant 1;
according to the multidimensional association characteristic values of each ultrasonic water meter in the normalization matrix corresponding to the second sub-stack matrix, obtaining accumulated values of the multidimensional association characteristic values, and performing negative mapping on the accumulated values of the multidimensional association characteristic values by using an exponential function with a natural constant as a base number, wherein the obtained mapping value is used as an overall data association index of the normalization matrix corresponding to the second sub-stack matrix;
Acquiring a preset integral data association index threshold, and taking the integral data association index of the normalization matrix corresponding to the second sub-stack matrix as a self-adaptive compression path of the normalization matrix corresponding to the second sub-stack matrix according to a longitudinal Z-shaped traversal mode on a preset traversal plane if the integral data association index of the normalization matrix corresponding to the second sub-stack matrix is larger than or equal to the integral data association index threshold;
If the overall data association index of the normalized matrix corresponding to the second sub-stack matrix is smaller than the overall data association index threshold, acquiring a traversing plane of the normalized matrix corresponding to the second sub-stack matrix, and taking the traversing plane of the normalized matrix corresponding to the second sub-stack matrix as an adaptive compression path of the normalized matrix corresponding to the second sub-stack matrix in a traversing manner of transverse Z-shaped.
2. The data compression storage method of the internet of things ultrasonic water meter according to claim 1, wherein the acquiring the data fluctuation degree of the ultrasonic water meter according to the data change of the N-dimensional monitoring data acquired in the preset period of time comprises:
Constructing a monitoring data change curve under any dimension of N-dimensional monitoring data acquired in the preset period, acquiring all peak points of the monitoring data change curve, normalizing the values of the peak points to obtain corresponding first normalized values, acquiring absolute values of slope difference values between the peak points and left and right adjacent coordinate points, and normalizing the absolute values of the slope difference values to obtain corresponding second normalized values;
Acquiring a first time interval between the peak point and a left adjacent peak point thereof and a second time interval between the peak point and a right adjacent peak point thereof, obtaining the inverse of the addition result of the first time interval and the second time interval, and acquiring the product of the first normalization value, the second normalization value and the inverse of the addition result as a fluctuation characteristic value of the peak point;
Acquiring accumulated values of fluctuation characteristic values of all peak points of the monitoring data change curve as data fluctuation indexes in the dimensions, and taking the accumulated values of the data fluctuation indexes of each dimension of the N-dimensional monitoring data acquired in the preset period as the data fluctuation degree of the ultrasonic water meter.
3. The method for data compression and storage of an ultrasonic water meter of the internet of things according to claim 1, wherein the obtaining the data sequence to be compressed according to the adaptive compression path of each normalization matrix comprises:
For any normalization matrix, sequentially traversing the data in the normalization matrix according to the self-adaptive compression path of the normalization matrix to obtain a traversing data sequence, performing first-order difference on the traversing data sequence to obtain a difference sequence, and placing the difference sequence behind the first data in the traversing data sequence to form a data sequence to be compressed.
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