CN116303409B - Industrial production time sequence data transparent compression method with ultrahigh compression ratio - Google Patents
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Abstract
The invention provides a transparent compression method for industrial production time sequence data with ultrahigh compression ratio, which comprehensively adopts lossless compression and lossy compression to effectively compress written data, and meanwhile, after the lossless compression is finished, the value of the data is judged according to the reading frequency of the data, and the lossy compression mode is distinguished in a targeted way on the basis of the judgment, so that the contradiction and entanglement of users in the aspects of compression ratio and distortion degree are relieved to the greatest extent, the data is directly cleared under the condition that the data is determined to be worthless, and the storage space of a database is saved to the greatest extent.
Description
Technical Field
The invention relates to an information processing technology, in particular to an industrial production time sequence data transparent compression method with ultrahigh compression ratio.
Background
The data volume of the digitized multimedia information, especially digital video and audio signals, is particularly huge; it is difficult to obtain practical application without effective compression. Thus, data compression technology has become a key commonplace technology in today's digital communications, broadcasting, storage, and multimedia entertainment.
Data compression techniques are generally divided into two general categories, lossy compression and lossless compression. However, it is currently apparent on the market that both lossy compression and lossless compression are integrated within a single piece of software. The lossy compression and the lossless compression have different working principles and very different realization effects, although the lossy compression and the lossless compression have only a single difference. It can even be said that the two are "mutually exclusive".
Lossy compression takes advantage of the human insensitivity to certain frequency components in data (e.g., image data or audio data), allowing some information to be lost during compression; while the original data cannot be fully restored, the lost portion has reduced impact on understanding the original image, but has replaced with a much larger compression ratio.
While lossless compression uses statistical redundancy of the data to compress, the original data can be fully recovered without causing any distortion, the compression rate is generally not too much limited by the theory of statistical redundancy of the data.
Therefore, the lossy compression is easy to distort and has high compression rate, the lossless compression is fidelity and has insufficient compression rate, and the lossy compression and the lossless compression are not compatible, so that it has been difficult to find a proper software capable of carrying out lossy compression and lossless compression on the original data at the same time.
Further, due to the advantages and disadvantages of the two compression methods, the selection of the lossy compression and the lossless compression by many users is extremely entangled, so that on one hand, it is desired to ensure that the data is not distorted as much as possible after the data is compressed, and on the other hand, it is desired to obtain the compression ratio as large as possible to save the storage space, however, in reality, both the lossy compression and the lossless compression are not available due to mutual exclusion.
Disclosure of Invention
The industrial production time sequence data transparent compression method with ultrahigh compression ratio can effectively balance the advantages and disadvantages of lossy compression and lossless compression, effectively mix two compression modes which seem to be mutually exclusive into the whole compression process according to actual conditions, and effectively solve a plurality of defects existing in the prior art.
Specifically, the invention provides an industrial production time sequence data transparent compression method with ultrahigh compression ratio, which is used for carrying out lossless compression on instant data written into a database, and converting the instant data into historical data with time after the lossless compression is finished; taking a time point passing through a investigation period T after lossless compression is finished as an investigation time starting point, and tracing back one investigation period T from each time point starting from the investigation time starting point, calculating the reading times N of the historical data in the investigation period T, thereby obtaining the reading frequency f=N/T in the investigation period T traced back from the time point, setting a first threshold k1 and a second threshold k2 for the reading frequency, wherein k1> k2>0, and if the reading frequency f is more than or equal to k1, keeping the historical data as it is; if the reading frequency is in the interval that k1> f is more than or equal to k2, adopting first-order polynomial fitting to perform lossy compression on the historical data; if the reading frequency is in a section with k2> f >0, adopting a second order polynomial fitting lossy compression to the historical data, wherein the first order polynomial fitting lossy compression is higher than the second order polynomial fitting lossy compression in fitting order; if the reading frequency f=n/t=0 is calculated in a computer sense, the history data is cleared from the database.
Preferably, the first order polynomial fit lossy compression and/or the second order polynomial fit lossy compression comprises the steps of: step one: acquiring initial historical data and initial investigation time points of the initial historical data, and setting the type of a fitting curve passing through the initial historical data; step two: acquiring an initial feasible region of initial historical data according to the fitted curve; step three: acquiring first historical data of a set time interval after an initial investigation time point, and acquiring a first feasible region of the first historical data according to a fitting curve; step four: judging whether an intersection exists between the initial feasible region and the first feasible region, if so, executing the fifth step, and if not, executing the sixth step; step five: taking the intersection as an initial feasible region, taking the first historical data as initial historical data, and executing a step three; step six: and (3) acquiring coordinate values in an initial feasible region, taking the coordinate values as coefficients of a fitting curve, storing the coordinate values and initial historical data acquired at an initial time point in the first step, taking the first historical data as the initial historical data, and re-executing the first step.
More preferably, the fitted curve function is a quadratic function, and the equation is expressed as: y=a2t2+a1t+a0, where t is time and y is the amplitude of the history data acquired at time t.
Preferably, when lossless compression is applied to the instant data, a differential compression method is applied to the time stamps, i.e. after storing the first time stamp in absolute value, each time stamp is stored thereafter in the form of a difference from the previous time stamp, expressed by the formula: deltan=Tn-Tn-1, where Tn represents the absolute value of the nth timestamp and Tn-1 represents the absolute value of the nth-1 timestamp, and therefore Deltan represents the difference between the nth timestamp and the absolute value of the nth-1 timestamp.
Preferably, when lossless compression is adopted for the instant data, a dual difference compression method is adopted for the time stamp, namely, firstly, the difference between the front and rear adjacent time stamps is calculated as a first step value, then, the difference between the front and rear adjacent first step values is calculated and stored as a dual difference value, and the dual difference value is expressed as follows: deltan= (Tn-Tn-1) - (Tn-Tn-1), where Tn represents the absolute value of the nth timestamp, tn-1 represents the absolute value of the nth-1 timestamp, and Tn-2 represents the absolute value of the nth-2 timestamp.
The invention comprehensively adopts lossless compression and lossy compression to effectively compress the written data, and simultaneously, after the lossless compression is finished, the data value is judged according to the data reading frequency, and the lossy compression mode is distinguished in a targeted way on the basis of the judgment, so that the contradiction and entanglement of users in the compression ratio and the distortion degree are relieved to the greatest extent, the data is directly cleared under the condition that the data is determined to have no value, and the storage space of a database is saved to the greatest extent.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following discussion will discuss the embodiments or the drawings required in the description of the prior art, and it is obvious that the technical solutions described in connection with the drawings are only some embodiments of the present invention, and that other embodiments and drawings thereof can be obtained according to the embodiments shown in the drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an example timing diagram of a trigger phase of an ultra high compression ratio industrial process timing data transparent compression method according to an embodiment of the invention;
FIG. 2 illustrates another example timing diagram of the trigger phase of an ultra high compression ratio industrial process timing data transparent compression method according to an embodiment of the invention;
FIG. 3 illustrates an exemplary flow chart of an ultra high compression ratio industrial process time series data transparent compression method in a lossy compression stage according to an embodiment of the invention;
FIG. 4 shows a schematic diagram of viable field compression in a lossy compression stage for a super high compression ratio industrial process time series data transparent compression method according to an embodiment of the invention;
fig. 5 shows a full flow diagram of an ultra-high compression ratio industrial production time series data transparent compression method according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made in detail with reference to the accompanying drawings, wherein it is apparent that the embodiments described are only some, but not all embodiments of the present invention. All other embodiments, which can be made by a person of ordinary skill in the art without the need for inventive faculty, are within the scope of the invention, based on the embodiments described in the present invention.
The super high compression ratio industrial production time sequence data transparent compression method provided by the invention references the memory characteristics of human brain. For the human brain, the information just entering the brain is most fidelity and fit to the reality, and is of course most importance for the processing of the current event, however, the information memorized in the human brain is gradually blurred and gradually distorted with the passage of time. The invention considers that the data is subjected to lossless compression when information is written in, thereby ensuring the fidelity to the greatest extent, then, after the instant data is changed into the historical data to be stored along with the time, a specific trigger mechanism is adopted to change the lossless compression into the lossy compression, and the importance of the historical data is often reduced compared with that of the instant data, so that the unimportant historical data is subjected to the lossy compression, even if distortion occurs, the lossless compression is not damaged and elegant, but the higher compression ratio can be ensured, and the compression space is saved.
In other words, the industrial production time sequence data transparent compression method with ultrahigh compression ratio provided by the invention is divided into three stages of a lossless compression stage, a triggering stage and a lossy compression stage. Hereinafter, these three stages will be described in detail.
The lossless compression phase is first introduced. In this stage, the data written in the database is subjected to lossless compression.
It should be noted that there may be an infinite variety of types of lossless compression in theory. The lossless compression can be referred to as lossless compression as long as it is ensured that the data can be reversibly restored (decompressed) after being compressed, and that the restored data is completely undistorted compared to the data before restoration. For the above lossless compression of these initial measurement data, a two-stage compression may be employed. First, in the first stage, a proper lossless compression algorithm is selected according to the data type and characteristics. After the compression algorithm is determined in the first stage, the data is formally losslessly compressed in the second stage using the selected lossless compression algorithm. And finally, storing the compressed data.
For example, it may be considered to compress the time stamp data of these write data first.
The time stamps are typically stored in long format, such as 1571889600000, 1571889600010, 1571889600025, 1571889600030, which are visually very long, and a large number of time stamps in such format can be expected to consume a large amount of storage space. For these time stamps, therefore, a differential compression method can be employed, i.e. after storing the first time stamp in absolute form, each time stamp is thereafter stored in the form of a difference from the previous time stamp, expressed by the formula Delta (n) =t (n) -T (n-1), for example as shown in table 1:
as can be seen from the above table, the storage space of the difference is much smaller than the storage space required by the absolute value of the timestamp, and the compression is reversible, and when the difference is decompressed, the original absolute value of the timestamp can still be returned by conversion of the difference.
Further, in order to further compress the storage space of the time stamps, a double difference compression method may be further considered, that is, first, a difference between the front and rear adjacent time stamps is calculated as a first step value, and then a difference between the front and rear adjacent first step values is calculated and stored as a double difference value, expressed as Deltan=Tn-Tn-1, for example, as shown in Table 2:
according to the inventor statistics, in the same case, if the compression ratio under the differential compression method is 10:1, the compression ratio under the dual differential compression can reach 15:1.
Next, a trigger phase is described, which is a transition phase between a lossless compression phase and a lossy compression phase, and also a transition phase of the real-time data to the history data.
After lossless compression of the instant data written to the database, the instant data will transition to historical data over time. It is also considered that if the number of times the history data is read in a certain time is very small, the trigger mechanism may determine the history data as non-important data, and the distortion of the data may be insensitive due to the reduction of importance, or may be considered to be a lossy compression with a higher compression ratio.
For example, when the written data is stored in the database for one month, the trigger mechanism determines that the written data is read only twice during the month, thereby determining that the data is non-important data, and may instead employ lossy compression.
Specifically, once data is written in the database, after the data is subjected to lossless compression, i.e., the parameter of the total number of times of reading N of the line construction cycle is counted as the first data reading when the lossless compression is completed, and therefore the total number of times of reading N of the cycle is automatically set to 0 initially, and in one observation period T after the completion of the lossless compression, the written data becomes history data with the lapse of time, and the total number of times of reading N of the cycle is automatically increased by 1 every time the history data is read. The total number of reads N of the period after the lossless compression is completed can thus be counted. From this, it can be calculated that the reading frequency in one investigation period T after the completion of the lossless compression is f=n/T.
For example, in the example shown in fig. 1, the reading is performed twice in one investigation period T since the lossless compression is ended, whereby the total number of readings N is superimposed by two times "1", and thus the total number of readings n=2 in the investigation period T, whereby the reading frequency is f=2/T in the investigation period T.
Therefore, a point in time after the completion of the lossless compression, at which one investigation period T passes, is taken as an investigation time start point, and each point in time from this start point is traced back one investigation period T for investigating the reading frequency.
For example, in fig. 2, backtracking one investigation cycle T from a certain time point 1 after the start of the investigation time, the number of readings is investigated, and it will be found that the first reading in fig. 1 has been missed, whereby only one reading is performed during the backtracking investigation cycle T, and therefore, the number of readings during one investigation cycle T is backtracked to be 1 at the time point 1, and accordingly, n=1, and the reading frequency f=n/t=1/T.
A first threshold k1 and a second threshold k2 can be set for the read frequency, wherein k1> k2>0, if the read frequency f is greater than or equal to k1 at time 1, this means that the read frequency is still relatively high during this time period T and therefore the data value is also high, and therefore the data remains unchanged. If the reading frequency at the time point 1 is in the interval that k1> f is larger than or equal to k2, the data still has a certain value, but the compression ratio needs to be increased, the lossy compression is considered by adopting a first-order polynomial fit to the data, if k2> f is 0, the data value is further reduced, and therefore the higher compression ratio is needed, the lossy compression is considered by adopting a second-order polynomial fit to the data, wherein the first-order polynomial fit lossy compression is higher than the second-order polynomial fit lossy compression in the fitting order.
It should be noted here that lossy compression also encompasses a number of ways, of which a step polynomial fit is typical. In the step polynomial fitting, the higher order polynomial fitting tends to result in lower data distortion. Thus, in the foregoing, for relatively more valuable data, a higher order polynomial fit is employed, while for relatively less valuable data, a lower order polynomial fit is employed.
If f=n/t=0 in the computer sense, it indicates that the data is "almost" free of human body fluids, and the data is deleted from the database, thereby saving the data storage space of the database to the maximum.
It should be noted that "equal to 0" in the computer sense and "equal to 0" in the mathematical sense are not the same. In a mathematical sense, if N/t=0, n=0 must be obtained, whereby the data can be extrapolated back to an absolute absence of human reading during this period.
However, it is not necessarily the case that even if n+.0, where N is very small and T is very large, N/T may be calculated to obtain a very small fraction, and any data format of the computer has a certain accuracy, if the fraction is smaller than this accuracy, f=n/t=0 is also considered in the computer calculation. This is where the word "almost" is referred to in the expression "almost" without the human body fluids above.
The first order polynomial fit lossy compression and/or the second order polynomial fit lossy compression mentioned above may employ a viable domain compression method specific to the present invention. This will be described below.
Fig. 3 shows a schematic flow chart of a lossy compression stage of an ultra-high compression ratio industrial production time series data transparent compression method according to an embodiment of the invention. As shown in fig. 3, the cycle period of the lossy compression phase comprises the steps of:
step 101, acquiring initial historical data and initial investigation time points of the initial historical data, and setting the type of a fitting curve passing through the initial historical data;
102, acquiring an initial feasible region of initial historical data according to a fitting curve;
step 103, acquiring first historical data of a set time interval after an initial investigation time point, and acquiring a first feasible region of the first historical data according to a fitting curve;
step 104, judging whether an intersection exists between the initial feasible region and the first feasible region, if yes, executing step 105, and if not, executing step 106;
step 105, taking the intersection set as an initial feasible region, taking the first historical data as initial historical data, and executing step 103;
and 106, acquiring coordinate values in an initial feasible region, taking the coordinate values as coefficients of a fitting curve, storing the coordinate values and initial historical data acquired at the initial time point in the step 101, taking the first historical data as initial measurement data, and executing the step 101.
As can be seen from the above steps, step 104 is a critical one-step determination step, if step 104 always determines that there is an intersection between the initial feasible region and the first feasible region, the distance is always in the loop of steps 103-105, in which case, a plurality of historical data sequentially spaced from the initial historical data by the set time interval are all within the range of the first feasible region of the fitted curve. The loop is continued until there is no intersection of the historical data at the acquisition time point encountered in step 104 with the first feasible region, and the entire loop period is skipped from step 106. Thus, step 106 may be considered as either the end of one cycle or the restart of the next cycle.
Therefore, the general numerical condition of the whole cycle can be reflected only by storing initial historical data and a fitting curve in the whole cycle. Of course, in this way, an error loss of data accuracy does occur, but since the historical data of all the acquisition time points in the period are within the feasible range, the error loss is effectively controlled within the controllable range.
The cycle period of the above-described lossy compression will be described below by way of example. As shown in fig. 4, fig. 4 shows an exemplary schematic of a lossy compression phase of the compression method according to the invention. In an example, the fitted curve is set as a conic, i.e., a parabola. The equation for the conic is:
y＝a2t2+a1t+a0
where t is time and y is the amplitude of the historical data acquired at time t.
The distance between two adjacent measurement data is set as a time interval T, the set deviation is epsilon, the time travel of the measurement data is N, in the area limited by the parabola and the set deviation epsilon, the first measurement data to the Nth measurement data are all positioned in the area, and when the (n+1) th measurement data PN+1 (T, Y) is positioned outside the set deviation epsilon, the calculation of the parameters (a 2, a1, a 0) corresponding to the new parabola is restarted from the (n+1) th measurement data.
In fig. 4, P (t 0, Y0) indicates that the initial acquisition time point of the present compression cycle is t0, the initial history data is Y0, and the initial feasible region can be obtained from the fitting curve (i.e., y=a2t2+a1t+a0) and the set deviation epsilon in the present example. Thus, a first feasible region of the next time point of P (t 0, Y0) is examined again, and if the first feasible region has an intersection with the initial feasible region, the measurement data corresponding to the next time point is indicated to be within the range of the set deviation epsilon. By pushing the above until the n+1th measurement data jumps out of the range of the set deviation epsilon, ending the compression cycle, wherein the compression cycle only stores the initial measurement data, namely P (t 0, Y0), and the fitting curve equation (y=a2t2+a1t+a0) can represent the data of all the acquisition time points in the cycle within the controllable range epsilon.
Subsequently, as can be seen from fig. 4, the n+1th measurement data jumps out of the range of the set deviation epsilon, and therefore, the next cycle is started with the n+1th measurement data as the initial measurement data.
Thus, according to the flow shown in fig. 3, the cyclic feasible region compression can be performed on the mass data periodically, and the respective initial acquisition time point data and the respective fitting curve equation are reserved in each compression period. This feasible region compression mode can exhibit a powerful compression function.
The transparent compression method of the industrial production time sequence data with the ultrahigh compression ratio is basically introduced. Fig. 5 presents an overall flow chart of an ultra-high compression ratio industrial production time series data transparent compression method according to the invention. The invention comprehensively adopts lossless compression and lossy compression to effectively compress the written data, and simultaneously, after the lossless compression is finished, the data value is judged according to the data reading frequency, and the lossy compression mode is distinguished in a targeted way on the basis of the judgment, so that the contradiction and entanglement of users in the compression ratio and the distortion degree are relieved to the greatest extent, the data is directly cleared under the condition that the data is determined to have no value, and the storage space of a database is saved to the greatest extent.
As mentioned above, the invention is in concert with the reference of the memory characteristics of the human brain. The data just written into the database is often the most fidelity and important corresponding to the human brain, but after the history memory is formed, the data can be gradually blurred by lossy compression or even finally cleared.
The foregoing description of the exemplary embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any modifications, equivalents, and variations which fall within the spirit and scope of the invention are intended to be included in the scope of the invention.
Claims (3)
1. A transparent compression method for industrial production time sequence data with ultrahigh compression ratio is characterized in that,
performing lossless compression on the instant data written into the database, and converting the instant data into historical data with time after the lossless compression is completed;
when lossless compression is adopted for the instant data, a difference compression method is adopted for the time stamp, namely after the first time stamp is stored in the form of absolute value, each time stamp is stored in the form of a difference value from the previous time stamp, and the method is expressed as follows: delta (Delta) n = T n - T n-1 Wherein T is n Represents the absolute value of the nth timestamp, T n-1 Represents the absolute value of the n-1 th timestamp, and therefore Delta n Representing the difference in absolute value of the nth timestamp from the n-1 th timestamp,
a time point after completion of lossless compression passing through a review period T is taken as a review time start point, each time point from which a review period T is traced back forward by one review period T, the number of times N of reading of the history data in the review period T is calculated, thereby deriving a reading frequency f=n/T in the review period T traced back from the time point,
a first threshold k1 and a second threshold k2 are set for the read frequency, where k1> k2>0,
if the reading frequency f is more than or equal to k1, the historical data is kept as it is;
if the reading frequency is in the interval that k1> f is more than or equal to k2, adopting first-order polynomial fitting to perform lossy compression on the historical data;
if the reading frequency is in a section with k2> f >0, adopting a second order polynomial fitting lossy compression to the historical data, wherein the first order polynomial fitting lossy compression is higher than the second order polynomial fitting lossy compression in fitting order;
if the reading frequency f=n/t=0 is calculated in a computer sense, the history data is cleared from the database.
2. The ultra-high compression ratio industrial production time series data transparent compression method according to claim 1, wherein,
the first order polynomial fit lossy compression and/or the second order polynomial fit lossy compression comprises the steps of:
step one: acquiring initial historical data and initial investigation time points of the initial historical data, and setting the type of a fitting curve passing through the initial historical data;
step two: acquiring an initial feasible region of initial historical data according to the fitted curve;
step three: acquiring first historical data of a set time interval after an initial investigation time point, and acquiring a first feasible region of the first historical data according to a fitting curve;
step four: judging whether an intersection exists between the initial feasible region and the first feasible region, if so, executing the fifth step, and if not, executing the sixth step;
step five: taking the intersection as an initial feasible region, taking the first historical data as initial historical data, and executing a step three;
step six: and (3) acquiring coordinate values in an initial feasible region, taking the coordinate values as coefficients of a fitting curve, storing the coordinate values and initial historical data acquired at an initial time point in the first step, taking the first historical data as the initial historical data, and re-executing the first step.
3. The transparent compression method for industrial production time series data with ultra-high compression ratio according to claim 2, wherein the fitted curve function is a quadratic function, and the equation is expressed as:
y＝a 2 t 2 +a 1 t+a 0 wherein t is time, and y is the amplitude of the historical data acquired at the moment t.
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