CN117272479B - High-strength geomembrane bursting strength prediction method based on load time course analysis - Google Patents
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Abstract
The invention provides a high-strength geomembrane bursting strength prediction method based on load time course analysis, and relates to the technical field of electric digital data processing. The method comprises the following steps: taking any one of sample data when the geomembrane is burst as target data, acquiring an association window of the target data, and determining the credibility of adjacent data of the target data; based on the credibility of the adjacent data, acquiring the importance degree of the target data, and further segmenting the sample data to obtain one or more target segments; re-reorganizing according to the target segment to obtain one or more reorganized sub-blocks of the sample data; and determining an optimal threshold according to the data in the reorganization sub-block, and acquiring compressed data based on the optimal threshold so as to further meet the prediction of the burst strength of the subsequent geomembrane. The method can solve the problem of poor compression effect of the prior Targelas-Prak algorithm, and can increase the compression rate of data as much as possible while ensuring the data precision.
Description
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a high-strength geomembrane bursting strength prediction method based on load time course analysis.
Background
Geomembranes are a thin film material used in civil engineering, typically made of High Density Polyethylene (HDPE) or Low Density Polyethylene (LDPE) and the like. It has good mechanical property and chemical stability, and can be widely used in the engineering of seepage prevention, isolation, reinforcement, etc. In civil engineering, geomembranes are often subjected to various static and dynamic loads, and especially in high-strength engineering, prediction of burst strength of the geomembrane becomes particularly important.
Data prediction requires a large amount of experimental data to support, and thus it is particularly important to store a large amount of experimental data in a limited memory space. At present, a Daphllas-Praex algorithm can be adopted for compressing large-scale data, the basic idea of the algorithm is to approximate an original curve by gradually deleting some unimportant points, so that the purpose of compressing the data is achieved, but the traditional Daphllas-Praex algorithm is to compress the whole curve at one time, so that different parts are excessively or inadequately fitted, and the compression effect is poor.
Disclosure of Invention
The invention aims to provide a high-strength geomembrane bursting strength prediction method based on load time course analysis, which adopts the following technical scheme:
The embodiment of the invention provides a high-strength geomembrane bursting strength prediction method based on load time course analysis, which comprises the following steps:
acquiring sample data when the geomembrane is burst;
taking any one of the sample data as target data, acquiring an association window of the target data, and determining the credibility of adjacent data of the target data according to the data in the association window;
acquiring the importance degree of the target data based on the credibility of the adjacent data;
segmenting the sample data according to the importance degree of each data in the sample data to obtain one or more target segments;
recombining the sample data according to the one or more target segments to obtain one or more recombined sub-blocks of the sample data;
and determining an optimal threshold according to the data in the rebuilt sub-block, acquiring compressed data of the sample data based on the optimal threshold, and predicting the geomembrane bursting strength according to the compressed data.
In one embodiment of the present application, the obtaining the association window of the target data includes:
acquiring a difference value between each data in the sample data and a normal value, and taking a ratio of the difference value of the target data to the maximum value of the difference value as a first ratio;
Determining a target association window size of the target data based on the first ratio and a preset association window size;
determining an association window of the target data according to the size of the target association window by taking adjacent data of the target data as a starting point, wherein the association window comprises the adjacent data of the target data; the adjacent data of the target data comprises left adjacent data and right adjacent data, and the association window of the target data comprises a left association window and a right association window.
In one embodiment of the present application, the determining, according to the data in the association window, the credibility of the adjacent data of the target data includes:
acquiring a target association window corresponding to target adjacent data, and calculating a data variance in the target association window;
determining the credibility of the target adjacent data corresponding to the target association window according to the data variance, wherein the credibility and the data variance are in a negative correlation relationship;
the target adjacent data is the left adjacent data or the right adjacent data of the target data, when the target adjacent data is the left adjacent data, the target associated window is the left associated window, and when the target adjacent data is the right adjacent data, the target associated window is the right associated window.
In an embodiment of the present application, the obtaining the importance level of the target data based on the credibility of the neighboring data includes:
obtaining a summation result of the credibility of the left adjacent data and the credibility of the right adjacent data;
when the summation result meets a first condition, a first weight coefficient and a second weight coefficient are obtained, and the first weight coefficient and the second weight coefficient are equal to a preset basic weight;
when the summation result meets a second condition, a first weight coefficient and a second weight coefficient are obtained according to the preset basic weight, and the first weight coefficient is larger than the second weight coefficient;
and determining the importance degree of the target data according to the first weight coefficient and the second weight coefficient.
In one embodiment of the present application, the determining the importance level of the target data according to the first weight coefficient and the second weight coefficient includes:
acquiring a first slope of the target data and left adjacent data thereof and a second slope of the target data and right adjacent data thereof;
determining a slope factor from the first slope and the second slope;
And respectively carrying out weighted summation on the first ratio and the slope factor based on the first weight coefficient and the second weight coefficient to obtain the importance degree of the target data.
In one embodiment of the present application, the segmenting the sample data according to the importance degree of each data in the sample data to obtain one or more target segments includes:
obtaining marking data of which the importance degree is greater than or equal to a preset importance degree threshold value in the sample data;
taking the first marking data in the sample data as a first starting point of a preset sliding window, and acquiring the first quantity of the marking data in the preset sliding window at present;
when the first quantity meets a preset condition, the last marking data in the current preset sliding window is used as a second starting point, and the position of the preset window is updated;
acquiring a second quantity of the marking data in the preset window currently;
when the second quantity does not meet the preset condition, determining the last marking data in the preset window as a first end point;
taking the data between the first starting point and the first ending point as a target segment;
Traversing the sample data based on the preset window to obtain one or more target segments of the sample data.
In one embodiment of the present application, the reorganizing the sample data according to the one or more target segments, to obtain one or more reorganized sub-blocks of the sample data, including:
configuring variable windows, and sequentially accumulating the lengths of the variable windows from a preset initial value to obtain candidate variable windows with different lengths; wherein the maximum value of the candidate variable window is the number of the sample data;
dividing the sample data into a plurality of candidate sub-blocks with candidate variable windows of any length, and acquiring the number of target candidate sub-blocks comprising target segments and the length of the target segments included in the target candidate word blocks;
determining the preference degree of the candidate variable window with any length according to the number of the target candidate sub-blocks and the length of the target segment included in the target candidate word block;
selecting the length of the candidate variable window with the maximum preference degree as the optimal length;
dividing the sample data into a plurality of preferred sub-blocks based on the optimal length, and adjusting the positions of the preferred sub-blocks to reorganize the sample data to obtain one or more reorganized sub-blocks of the sample data.
In one embodiment of the present application, the adjusting the position of the preferred sub-block reorganizes the sample data to obtain one or more reorganized sub-blocks of the sample data, including:
numbering each preferred sub-block from small to large, and acquiring a histogram of each preferred sub-block;
acquiring a first overlapping degree of each preferred sub-block and a first preferred sub-block corresponding to the minimum number based on the histogram;
acquiring the preferred sub-block with the first overlapping degree being greater than or equal to a preset overlapping threshold value as a first matched sub-block of the first preferred sub-block;
moving the first matched sub-block to the first preferred sub-block according to the serial number sequence, and forming a first recombined sub-block;
acquiring a second preferred sub-block corresponding to the minimum number outside the first recombined sub-block;
calculating a second degree of overlap between each of the preferred sub-blocks and the second preferred sub-block other than the first rebinned sub-block; acquiring the preferred sub-block with the second overlapping degree being greater than or equal to the preset overlapping threshold value as a second matched sub-block of the second preferred sub-block;
moving the second matched sub-block to the second preferred sub-block according to the number sequence and forming a second reconstructed sub-block; and the same is repeated until all the preferred sub-blocks in the sample data are traversed, and one or more recombined sub-blocks corresponding to the sample data are obtained.
In one embodiment of the present application, the determining the optimal threshold according to the data in the reorganized sub-block includes:
determining a change curve based on the data in each recombination sub-block, and acquiring the area of a region with the change curve higher than a preset intensity threshold;
acquiring the ratio of the area of each recombined sub-block to the theoretical area, wherein the ratio is used as a second ratio, and the effective range of the second ratio is not more than 1;
and calculating a product result of the second ratio and an empirical threshold, wherein the difference value between the empirical threshold and the product result is the optimal threshold.
In one embodiment of the present application, the acquiring compressed data of the sample data based on the optimal threshold value includes:
taking the optimal threshold value of each reorganization sub-block as a judging threshold value of a Fabry-Perot algorithm;
and compressing the data of each recombined sub-block by using the Douglas-Pocke algorithm to obtain compressed data of the sample data.
The application has at least the following beneficial effects: by analyzing each target data in the sample data, determining the importance degree of the target data based on the reliability of adjacent data of the target data, judging whether the target data is important data or not according to the difference or similar situation of the target data and the adjacent data, and therefore, the accuracy is higher, the referenceability of the importance degree of the target data is higher, the primary segmentation is carried out based on the importance degree of the target data, a plurality of target segments are obtained, the recombination is carried out again based on the target segments, the recombination sub-blocks are obtained, the error of direct grouping according to a single index is avoided, the division of the recombination sub-blocks has more practical significance, the self-adaptive optimal threshold value is determined by analyzing the data in each recombination sub-block, the compression is carried out according to the optimal threshold value, the data compression effect and the data compression efficiency of each recombination sub-block can be guaranteed, the problem that the compression effect of the conventional Dalgar-Prime algorithm is poor is solved, and the compression rate of the data is increased as much as possible while the data accuracy is guaranteed.
Drawings
FIG. 1 is a flow chart of a high strength geomembrane burst strength prediction method based on load time course analysis provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a target association window corresponding to target data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a first sliding window and marking data provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a second sliding window and marking data provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a third sliding window and marking data provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of a first target segment provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of an iterative acquisition target segment provided by an embodiment of the present invention;
FIG. 8 is a schematic diagram of obtaining a reorganized sub-block according to an embodiment of the present invention;
FIG. 9 is a schematic illustration of a variation provided by an embodiment of the present invention;
fig. 10 is a schematic diagram of a simplified curve of a douglas-pock algorithm according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above 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 invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
The following describes a high-strength geomembrane bursting strength prediction method based on load time course analysis according to an embodiment of the invention with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for predicting bursting strength of a high-strength geomembrane based on load time course analysis, which is provided in an embodiment of the present invention, as shown in fig. 1, and includes the following steps:
s101, sample data when the geomembrane is burst is obtained.
Geomembranes are commonly used to withstand various loading actions, and prediction of the burst strength of the geomembrane is an essential link for the geomembrane to work properly, so a large amount of sample data is required to support the burst strength prediction of the geomembrane.
Alternatively, load time course data, geomembrane performance parameters, geomembrane burst strength data, and stress-strain data may be included in the sample data.
In some embodiments, the magnitude and change law of the load applied to the geomembrane can be monitored on site using sensors, load cells, accelerometers, and the like. Meanwhile, the related load data can be obtained by utilizing reference data such as historical data, design specifications and the like.
In some embodiments, the performance parameters of the geomembrane may be measured by an indoor test, for example, the strength parameters may be obtained by a tensile test, the extensibility parameters may be obtained by a tensile or shear test, and the aging resistance may be obtained by a manual accelerated aging test.
In some embodiments, burst strength data for geomembranes under different loading conditions may be obtained through indoor testing or numerical simulation. For the indoor test, a proper test can be designed and carried out, the load condition in the actual engineering is simulated, and the bursting strength of the geomembrane is measured. For numerical simulation, finite element analysis and other methods can be used to establish a numerical model of the geomembrane, simulate behaviors under different load conditions, and extract burst strength data.
In some embodiments, stress-strain data for geomembranes under different loads may be obtained using experimentation or numerical simulation. Different loads can be applied by setting proper test parameters or establishing a numerical model, and the strain and stress of the geomembrane can be measured; it will be appreciated that the stress-strain data may be used to determine stress-strain characteristics of the geomembrane to facilitate subsequent predictions of geomembrane burst strength.
It can be understood that the sample data may not be immediately predicted for the geomembrane burst strength after real-time collection, so that the sample data needs to be stored, so that the sample data can be directly obtained for use in the subsequent prediction, and the sample data is usually compressed and stored in consideration of huge data volume of the sample data, but the conventional compressed effect of the conventional Dallas-Prime algorithm for compressing large-scale data is poor, so that the subsequent accurate prediction for the geomembrane strength is not facilitated, and therefore, in the embodiment of the application, the Dallas-Prime algorithm is partially improved to improve the data compression effect.
S102, taking any one of the sample data as target data, acquiring an association window of the target data, and determining the credibility of adjacent data of the target data according to the data in the association window.
It can be understood that the sample data are geomembrane related data collected sequentially in time sequence, when the performance of the geomembrane is normal, various parameter indexes are stable, the data difference in the similar time collected by the sensor is small, when an abnormality occurs at a certain moment, the difference between the data and the data at the adjacent moment is large, at the moment, the importance degree of the data point is large, and when the data point is subjected to lossy compression, the loss degree of the data point is small, so that the importance degree of the data point can be analyzed to determine the corresponding compression condition.
Considering that the sample data comprises various types of data of the geomembrane test, the value range of each type of data is different, so that the data of different types are respectively analyzed, and any type is taken as an example for analysis, for example, the burst strength data at each moment in the sample data is obtained for analysis.
Alternatively, a difference value between each data in the sample data and a normal value may be obtained, and a ratio of the difference value of the target data and a maximum value of the difference values is taken as a first ratio; determining a target association window size of the target data based on the first ratio and a preset association window size; determining an associated window of the target data according to the size of the target associated window by taking adjacent data of the target data as a starting point, wherein the associated window comprises the adjacent data of the target data; the adjacent data of the target data comprises left adjacent data and right adjacent data, and the association window of the target data comprises a left association window and a right association window.
It can be understood that each geomembrane has a normal bursting strength range, a difference value between each bursting strength data and a normal value is obtained based on the normal bursting strength range, and a ratio of the difference value corresponding to the currently analyzed target data to the maximum value in all the difference values is used as a first ratio, so that the size of an associated window corresponding to each target data is obtained in a self-adaptive manner, and the corresponding associated window is obtained for subsequent analysis.
For example, assuming that the current normal burst intensity range is [ b1, b2], the burst intensity value corresponding to the target data is c, if the burst emphasis value c of the target data is within the range of [ b1, b2], the difference value a=0 corresponding to the target data, if c > b2, the difference value a=c-b 2 corresponding to the target data, and if c < b1, the difference value a=b1-c corresponding to the target data, that is, the difference value a corresponding to the target data is determined according to the relationship between the burst intensity value and the normal value range of the target data.
Obtaining difference values corresponding to all data, and selecting the maximum value a of the difference values in all data max With the difference value corresponding to the target data and the maximum value a of the difference value max Obtaining a first ratioFurther, based on the first ratio and the preset association window size, determining the target association window size of the target data, that is, the target association window size of the target data may be:
wherein d represents a target associated window size of the target data; d, d max Representing a preset associated window size, d in the embodiment of the present application max ＝10；Representing a rounding down operation.
Optionally, in an embodiment of the present application, an association window size minimum value, for example, an association window size minimum value d may also be set min =4, when the calculated target correlation window size of the target data is smaller than the correlation window size minimum d min When determining that the target associated window size of the target data is the associated window size minimum d min That is, when d<d min When taking d=d min 。
It is understood that the range of the target association window size for each target data in the embodiment of the present application is within the range of [4,10 ].
Further, with the adjacent data of the target data as a starting point, that is, with the left adjacent data and the right adjacent data of the target data as starting points, the left associated window and the right associated window of the target data are determined according to the target associated window size corresponding to the target data, as shown in fig. 2, which shows the target left adjacent point and the target right adjacent point corresponding to the target point, that is, the left adjacent data and the right adjacent data of the target data, and the left associated window and the right associated window are determined based on the left adjacent data and the right adjacent data and the target associated window size, it is understood that the size of the target associated size of the target data shown in fig. 2 is 4.
After the left association window and the right association window corresponding to the target data are determined, the credibility of the left adjacent data and the right adjacent data is analyzed based on the data in the left association window and the right association window, so that the importance degree of the target data is analyzed according to the credibility of the left adjacent data and the right adjacent data.
Further, a target association window corresponding to the target adjacent data can be obtained, and the data variance in the target association window is calculated; determining the credibility of the target adjacent data corresponding to the target association window according to the data variance, wherein the credibility and the data variance are in a negative correlation relationship; the target adjacent data is left adjacent data or right adjacent data of the target data, when the target adjacent data is left adjacent data, the target associated window is a left associated window, and when the target adjacent data is right adjacent data, the target associated window is a right associated window.
Alternatively, taking the left adjacent data as an example, the reliability of the left adjacent data may be analyzed based on a left association window where the left adjacent data is located, and the calculation of the reliability may be:
wherein f 1 Representing the credibility of the left adjacent data; Δc i The i-th data value in the left association window where the left adjacent data is located is represented, and in the embodiment of the present application, the data number takes the left adjacent data as a starting point;representing the data mean in the left associated window; d represents the target associated window size of the target data, i.e. the amount of data in the left associated window; />Representing the variance of the data within the left associated window; exp represents an exponential function operation based on a natural constant e.
As can be seen from the calculation of the above-mentioned reliability, when the data variance in the associated window is larger, that is, the fluctuation of the data value is larger, the performance of the geomembrane is more unstable, and the possible abnormal value is more, so that the reliability of the corresponding adjacent data is lower, that is, the reliability and the data variance are in a negative correlation.
It can be understood that the reliability of the right neighboring data of the target data can be calculated based on the above method for obtaining the reliability corresponding to the left neighboring data, and the reliability of the right neighboring data is consistent with the reliability calculation method of the left neighboring data, that is, the data variance in the right neighboring window is calculated, and the reliability of the right neighboring data is obtained based on the data variance and the above calculation formula, so as to facilitate the distinction, in the embodiment of the present application, the reliability of the left neighboring data is marked as f 1 Right adjacentThe credibility of the data is marked as f 2 。
S103, acquiring importance degree of the target data based on the credibility of the adjacent data.
The importance degree of the target data is greatly influenced by the adjacent data of the target data, so that the importance degree of the target data can be acquired according to the reliability of the adjacent data, and the analysis of the importance degree of the target data is more accurate.
Alternatively, the result of summing the credibility of the left adjacent data and the credibility of the right adjacent data may be obtained; when the summation result meets a first condition, acquiring a first weight coefficient and a second weight coefficient, wherein the first weight coefficient and the second weight coefficient are equal to a preset basic weight; when the summation result meets a second condition, acquiring a first weight coefficient and a second weight coefficient according to a preset basic weight, wherein the first weight coefficient is larger than the second weight coefficient; and determining the importance degree of the target data according to the first weight coefficient and the second weight coefficient.
That is, calculating the confidence level f of the left adjacent data of the target data 1 And confidence level f of right adjacent data 2 According to the summation result satisfying the first condition or the second condition, determining the first weight coefficient and the second weight coefficient, thereby solving the importance degree of the target data.
In some embodiments, the first condition may be satisfied by the average value of the summation result being equal to or greater than a preset confidence threshold, i.e. (f 1 +f 2 ) And (2) is larger than or equal to Deltaf, wherein Deltaf represents a preset credibility threshold, the value can be 0.7 in the embodiment of the application, and the preset credibility threshold can be determined according to actual conditions in other embodiments. When (f) 1 +f 2 ) When/2. Gtoreq.DELTA.f, i.e. when the summation result satisfies the first condition, then 1 ＝β 2 ＝Δβ，β 1 And beta 2 Respectively representing a first weight coefficient and a second weight coefficient, wherein Δβ represents a preset base weight, and in the embodiment of the present application, Δβ may take a value of 0.5.
In some embodiments, the second condition may be that the average of the summed results is less than a preset confidence thresholdThe value is (f) 1 +f 2 ) When the second condition is satisfied, the calculation of the first weight coefficient and the second weight coefficient may be:
β 1 ＝Δβ+(f 1 +f 2 )/Δf
β 2 ＝Δβ-(f 1 +f 2 )/Δf
wherein beta is 1 And beta 2 Respectively representing a first weight coefficient and a second weight coefficient; Δf represents a preset confidence threshold, and in the embodiment of the present application, the value is 0.7; Δβ represents a preset base weight, and in the embodiment of the present application, the Δβ takes a value of 0.5; f (f) 1 Representing the credibility of the left adjacent data; f (f) 2 Representing the trustworthiness of the right neighbor data.
After the first weight coefficient and the second weight coefficient are determined, the importance degree of the target data is solved according to the first weight coefficient and the second weight coefficient.
Optionally, a first slope of the target data and its left neighboring data, and a second slope of the target data and its right neighboring data may be obtained; determining a slope factor from the first slope and the second slope; and respectively carrying out weighted summation on the first ratio and the slope factor based on the first weight coefficient and the second weight coefficient to obtain the importance degree of the target data.
The slope obtaining formula in the embodiment of the present application may beThe coordinates of the target data i are [ x ] i ，c i ]The coordinates of the left adjacent data and the right adjacent data are respectively [ x ] i-1 ，c i-1 ]，[x i+1 ，c i+1 ]，c i The value of the target data i is obtained, so that the first slope k of the target data and the left adjacent data thereof can be obtained according to the slope acquisition formula 1 Second slope k of target data and right adjacent data thereof 2 . It will be appreciated that the first slope k 1 And a second slope k 2 The actual value of (2) is the difference between the target data and the left adjacent data, the difference between the target data and the right adjacent data, taking the first into considerationThe values of the slope and the second slope may be negative, so in order to facilitate calculation, in this embodiment of the present application, the absolute values of the first slope and the second slope are taken, and subsequent analysis is performed.
In some embodiments, an empirical maximum slope value k may be set max In the embodiment of the application, k is set max =2, i.e. the absolute value of the first slope or the second slope is always less than or equal to the empirical maximum slope value, when |k 1 I or k 2 I is greater than the empirical maximum slope value k max Let |k 1 I or k 2 I is equal to the empirical maximum slope value k max 。
Further, after determining the first slope and the second slope, an average value of the first slope and the second slope may be calculated, and the ratio of the average value to the empirical maximum slope value is used as a slope factor, i.e., the slope factor is
After determining the slope factor, respectively carrying out weighted summation on the first ratio and the slope factor based on the first weight coefficient and the second weight coefficient to obtain the importance degree of the target data, wherein the calculation of the importance degree of the target data can be as follows:
wherein G represents the importance of the target data;representing a first ratio; />Representing a slope factor; beta 1 And beta 2 Representing a first weight coefficient and a second weight coefficient, respectively.
The larger the difference between the target data and the adjacent data, namely the larger the first slope and the second slope, the larger the corresponding slope factor, and the smaller the similarity between the target data and the adjacent data; the larger the difference value a between the target data and the normal value is, that is, the more the target data deviates from the normal value, the larger the importance degree corresponding to the target data is, that is, the more important analysis should be performed on the target data, and the smaller the loss degree corresponding to the target data should be when lossy compression is performed.
Based on the method for acquiring the same importance degree of the target data, the importance degree of each data in the sample data can be acquired, and subsequent analysis can be performed according to the importance degree of the data.
S104, segmenting the sample data according to the importance degree of each data in the sample data to obtain one or more target segments.
It can be understood that the greater the importance degree of the data point is, the smaller the loss degree of the data point is, so that time sequence data under a certain type in sample data can be grouped, the data with the great importance degree is divided into the same group as much as possible, and when the lossy compression is performed, the group with the great importance degree selects smaller threshold parameters, thereby ensuring the data precision; the group with small importance degree selects larger threshold parameters, thereby ensuring the compression effect and compression efficiency of the data.
Optionally, marking data with an importance degree greater than or equal to a preset importance degree threshold value in the sample data can be obtained; taking the first marking data in the sample data as a first starting point of a preset sliding window, and acquiring the first quantity of marking data in the current preset sliding window; when the first quantity meets the preset condition, the last marking data in the current preset sliding window is used as a second starting point, and the position of the preset window is updated; acquiring a second quantity of marking data in a current preset window; when the second quantity does not meet the preset condition, determining the last marking data in the current preset window as a first end point; taking the data between the first starting point and the first ending point as a target segment; traversing the sample data based on a preset window to obtain one or more target segments of the sample data.
In this embodiment of the present application, the preset importance threshold Δg may be a value of 0.43, and in other embodiments, the value may be changed, which is not limited herein.
When the importance degree of the data is larger than the preset importance degree threshold value, the importance degree of the data can be considered to be larger, so that the data with larger importance degree is marked to obtain marked data in time sequence data in a certain type of sample data; and performing preliminary segmentation on the time sequence data according to the marking data to obtain one or more target segments, and re-organizing the sample data based on the target segments to obtain more accurate grouping.
For example, as shown in fig. 3, a period of time series data is shown, in which a black part is marked data, a white part is normal data, and a first marked data in the time series data is used as a first start point of a preset sliding window, and the preset sliding window length may be set to 3, so as to obtain a first sliding window with a length of 3, that is, the first marked point and the first sliding window in fig. 3. The first quantity of the marking data in the first sliding window is obtained to be 2, and the first quantity of the marking data in the first sliding window meets the preset condition on the assumption that the quantity of the marking data is more than or equal to 2, so that the last marking data in the first sliding window is used as a second starting point, and a second sliding window with the length of 3 is obtained.
As shown in fig. 4, obtaining a second number of marking data in the second sliding window, judging whether the second number meets a preset condition, if yes, establishing a third sliding window by taking the current preset window, namely the last marking data in the second sliding window, as shown in fig. 5, obtaining a third number of marking data in the third sliding window, judging the third number, iterating until the number of marking data does not meet the preset condition, for example, the third number shown in fig. 5 is 1, and if not, taking the last marking data in the third sliding window as a first end point, taking the data between the first start point and the first end point as a target segment, and particularly, as shown in fig. 6, the data in the dotted line frame is the first target segment of the primary analysis; according to the same method, iterative analysis is performed to obtain a first or multiple target segments in the sample data, for example, as shown in fig. 7, the remaining first marker data which is not analyzed is used as a new first starting point, a first sliding window is constructed, that is, a first marker point and a first sliding window, whether the number of marker data in the first sliding window meets a preset condition is further judged, and further iterative analysis is performed to obtain subsequent target segments.
It should be noted that the length 3 of the preset sliding window and the number 2 of satisfaction of the marking data in the preset condition are only one example of the embodiments of the present application, and may be set according to the actual situation in other embodiments, which is not limited herein.
S105, reorganizing the sample data according to one or more target segments to obtain one or more reorganized sub-blocks of the sample data.
After one or more target segments in the sample data are acquired, the sample data are analyzed and recombined again according to the length of the target segments, so that more accurate and reliable recombined sub-blocks are obtained, and the subsequent data compression effect is improved.
Optionally, variable windows can be configured, and the lengths of the variable windows are accumulated sequentially from a preset initial value to obtain candidate variable windows with different lengths; wherein the maximum value of the candidate variable window is the number of sample data. That is, a variable window with variable length is configured, and the lengths of the variable windows can be sequentially accumulated from a preset initial value, so as to obtain candidate variable windows with different lengths. Preferably, in the embodiment of the present application, the preset initial value is set to be 5, and the accumulation is performed each time with 1 as a step, that is, candidate variable windows with lengths of 5, 6, 7 and other lengths sequentially increasing in sequence can be obtained.
It will be appreciated that the candidate variable window is used to analyze the sample data so that the length of the candidate variable window does not exceed the amount of sample data; in view of the fact that in the embodiments of the present application, the analysis is performed on the time series sequences of different types of data in the sample data, the length of the candidate variable window in the embodiments of the present application may also be understood as not being greater than the length of the time series data sequence.
Further, the sample data is divided into a plurality of candidate sub-blocks with a candidate variable window of any length, and the number of target candidate sub-blocks including the target segment and the length of the target segment included in the target candidate block are acquired. That is, the sample data is divided by a candidate variable window of any length, for example, length 5, and then the length of each candidate sub-block into which the sample data is divided is 5, thereby obtaining a plurality of candidate sub-blocks; and determining target candidate sub-blocks of all the candidate sub-blocks including the target segment.
In some embodiments, the candidate sub-blocks for which the target segment exists may be denoted as target candidate sub-blocks, and the number of all target candidate sub-blocks and the length of the target segment included in each target candidate sub-block may be counted.
In some embodiments, more than one target segment may be included in the target candidate sub-block, and when more than one target segment is included in the target candidate sub-block, the length of the target segment included in the target candidate block is the total length of the plurality of target segments in the current target candidate sub-block.
Further, determining the preference degree of the candidate variable window with any length according to the number of the target candidate sub-blocks and the length of the target segment included in the target candidate word block; selecting the length of the candidate variable window with the maximum preference degree as the optimal length; dividing the sample data into a plurality of preferred sub-blocks based on the optimal length, and adjusting the positions of the preferred sub-blocks to reorganize the sample data to obtain one or more reorganized sub-blocks of the sample data.
Alternatively, the calculation of the preference degree of the candidate variable window may be:
wherein Q is r Indicating the preference degree of the candidate variable window with the length r; s represents the number of target candidate sub-blocks containing the target segment; ΔL j Representing the length of a target segment in a j-th target candidate sub-block containing the target segment, the target candidate sub-block possibly including a complete target segment or a partial target segment, so that the length of the target segment included in the target candidate sub-block is the length of the complete/partial target segment existing only in the target candidate sub-block; r is the current weather The length of the variable window is selected, and the length of the target candidate sub-block can be understood.
Based on the method for acquiring the preference degree of the candidate variable window when the length is r, acquiring the preference degree of the candidate variable window under each length, wherein the larger the preference degree is, the better the candidate variable window has a good candidate sub-block effect on sample data division at the moment, so that the length of the candidate variable window with the largest preference degree can be selected as the optimal length; and dividing the sample data according to the optimal length to obtain a plurality of optimal sub-blocks, wherein the length of each optimal sub-block is the optimal length, and carrying out recombination analysis on the sample data again based on the optimal sub-block to obtain a plurality of final recombination sub-blocks.
Alternatively, each preferred sub-block may be numbered from small to large and a histogram of each preferred sub-block may be obtained. That is, preferred sub-blocks are numbered sequentially, e.g., 1,2,3,4, …, n; n is the number of preferred sub-blocks. After numbering the preferred sub-blocks, obtaining a histogram of each preferred sub-block, wherein the histogram is used for counting the data amount corresponding to each data value in the preferred sub-block, so as to obtain a histogram of the data amount corresponding to each value in the preferred sub-block, and the histogram can intuitively see the distribution condition of the data values in the preferred sub-block.
Further, based on the histogram, acquiring a first overlapping degree of each preferred sub-block and a first preferred sub-block corresponding to the minimum number; acquiring a preferred sub-block with the first overlapping degree being greater than or equal to a preset overlapping threshold value as a first matched sub-block of the first preferred sub-block; the first matching sub-block is shifted to the first preferred sub-block in numbered order and constitutes a first reorganized sub-block.
Illustratively, the first preferred sub-block corresponding to the minimum number in the embodiment of the present application is the preferred sub-block with the number 1, and thus the first degree of overlap between any preferred sub-block and the first preferred sub-block corresponding to the number 1 is calculated based on the histogram. Alternatively, the method for calculating the first overlapping degree may be:
wherein,representing a first degree of overlap; m represents the union range of the horizontal axes of the two histograms, i.e. the number of all different data values in the union range, e.g. the horizontal axes of the two histograms are [1,2,3 ] respectively]And [3,4,5 ]]The union range of the horizontal axes of the two histograms is 5; z 1 Representing the number of data corresponding to the mth data value in the histogram of the first preferred sub-block numbered 1; z 2 Representing the number of data corresponding to the mth data value in the histogram of any preferred sub-block.
The greater the first degree of overlap between the histogram of the first preferred sub-block numbered 1 and the histogram of any other preferred sub-block, the more similar the data distribution in any preferred sub-block and the first preferred sub-block, and the more likely it is that the two preferred sub-blocks will be divided into one rebinned sub-block.
Optionally, a preset overlap threshold is setPreset overlap threshold +.>The empirical value of (2) may be +.>In other embodiments, different values may be provided; if the first overlapping degree of the histogram of the first preferred sub-block with the number 1 and the histogram of any other preferred sub-block is greater than or equal to the preset overlapping threshold +.>Then, using any one of the preferred sub-blocks as the first matched sub-block of the first preferred sub-block, moving the first matched sub-block to the first preferred sub-block in the order of number, and recording the number, as shown in FIG. 8, if preferred sub-blocks of number 3, number 6 and number 9 are calculatedAnd if the first overlapping degree between the block and the first preferred sub-block (number 1) meets the threshold requirement, the preferred sub-blocks numbered 3, 6 and 9 are first matched sub-blocks of the first preferred sub-block, and the first matched sub-block is sequentially moved to the first preferred sub-block (number 1) according to the number of the first matched sub-block to obtain the first recombined sub-block.
Further, a second preferred sub-block corresponding to the minimum number outside the first recombined sub-block is obtained; that is, a second preferred sub-block corresponding to the smallest number other than number 1, number 3, number 6 and number 9 (preferred sub-block corresponding to number 2), a second degree of overlap between each preferred sub-block other than the first reorganized sub-block and the second preferred sub-block is calculated; acquiring a preferred sub-block with the second overlapping degree being greater than or equal to a preset overlapping threshold value as a second matched sub-block of the second preferred sub-block; moving the second matched sub-block to the second preferred sub-block according to the number sequence and forming a second recombinant sub-block; and the same is repeated until all the preferred sub-blocks in the sample data are traversed, so as to obtain one or more recombined sub-blocks corresponding to the sample data.
S106, determining an optimal threshold according to the data in the reorganization sub-block, acquiring compressed data of the sample data based on the optimal threshold, and predicting the bursting strength of the geomembrane according to the compressed data.
Based on the steps, one or more recombined sub-blocks corresponding to the sample data are obtained, and each sub-block with similar data distribution in the recombined sub-blocks can be subjected to self-adaptive compression, so that the compression effect is improved.
In some embodiments, a change curve may be determined based on the data in each reorganized sub-block and an area of the region where the change curve is above a preset intensity threshold is obtained. For example, as shown in fig. 9, when the data is burst intensity data, a corresponding change curve is constructed based on burst intensity values and data sequence numbers (sequence numbers of data acquisition sequences) in the reorganization sub-blocks, and an area of the change curve greater than a preset intensity threshold, that is, an area of the area greater than the burst intensity threshold is obtained, and an adaptive threshold corresponding to each reorganization sub-block is obtained according to the area of the area.
Optionally, a ratio of the area of the region corresponding to each reorganization sub-block to the theoretical area can be obtained as a second ratio, and an effective range of the second ratio is not more than 1; and calculating a product result of the second ratio and the empirical threshold, wherein the difference value between the empirical threshold and the product result is the optimal threshold. The calculation of the optimal threshold may be:
wherein Δγγ v Representing an optimal threshold value corresponding to the v-th reorganization sub-block; gamma ray max Representing an empirical threshold, which in the embodiment of the application is 25; t (T) v Indicating the area of the region of the v-th recombination sub-block, wherein the change curve of the region is larger than a preset intensity threshold value; Δt represents the theoretical area, and the theoretical area in the embodiment of the present application takes a value of 74; For a second ratio of T v >Delta T, let T v The effective range of the second ratio is not greater than 1.
Based on the method for acquiring the optimal threshold corresponding to the v-th recombination sub-block, acquiring the optimal threshold corresponding to each recombination sub-block in the sample data, wherein the optimal threshold is the adaptive threshold parameter value of the Fabry-Perot corresponding to the recombination sub-block. The optimal threshold value of each recombined sub-block is used as a judging threshold value of the Fabry-Perot algorithm; and compressing the data of each recombined sub-block by utilizing a Fabry-Perot algorithm to obtain compressed data of the sample data.
Specifically, the data value in the reorganized sub-block can be simplified and adjusted by using a daglas-pramipexole algorithm, wherein the running process of the daglas-pramipexole algorithm is as follows: selecting a starting point P and an ending point Q on a bursting emphasis curve corresponding to the reorganization sub-block, and adding the starting point P and the ending point Q into a result point set; the distances from all points on the curve to the line segment PQ are calculated, and the point W with the largest distance is found. If the distance of W is smaller than the preset judgment threshold value delta gamma v Then consider that the whole curve is sufficiently simpleAnd (5) ending the algorithm. If the distance of W is greater than or equal to the preset judgment threshold value delta gamma v Then W is added to the result point set.
Further, the curve is divided into two sections, one section is from a starting point P to a point W, and the other section is from the point W to an ending point Q; the two curves are respectively recursively applied with the Targelas-Prak algorithm, the recursively obtained result point sets are combined to obtain the final simplified curve, as shown in FIG. 10, the process of simplifying the curve is shown, the starting point is 1, the ending point is 8, the distance between each point and the line segment 1-8 is calculated, the point 4 with the largest distance is obtained, and the distance is larger than the judgment threshold value delta gamma v Dividing the curve into 1-4,4-8 in (2), sequentially analyzing the two curves 1-4 and 4-8 to obtain the distance from the point 2, the point 3 to the line segment 1-4, wherein the maximum distance point is not more than the judgment threshold delta gamma v The curves 1-4 are sufficiently simplified, the algorithm ends, the curves 4-8 are analyzed, the point 6 with the largest distance is determined, and the distance is greater than the judgment threshold delta gamma v The curve at this time becomes 1-4,4-6,6-8 in (3), and since 1-4 is sufficiently simplified, the curves 4-6 and 6-8 are analyzed again, resulting in the final simplified curve in (4) being 1-4,4-6,6-7,7-8.
Further, all the reorganization sub-blocks are analyzed, and the judgment threshold delta gamma corresponding to the reorganization sub-blocks is based v And the moraxella-pramipexole algorithm obtains the burst emphasis value of each recombined sub-block after simplification and stores the burst emphasis value. In the embodiment of the present invention, the number of each preferred sub-block in the reorganized sub-block needs to be stored during storage, for example, the preferred sub-blocks of number 1, number 3, number 6 and number 9 included in the reorganized sub-block in fig. 8, and the determination threshold corresponding to the reorganized sub-block, that is, the optimal threshold corresponding to the reorganized sub-block, and the optimal length in step S105, so that the compressed data is decompressed according to the number, the determination threshold and the optimal length to obtain the sample data, and when the geomembrane burst strength prediction is required to be performed subsequently, the compressed data is directly processed to obtain the sample data to perform training prediction, thereby achieving good data storage.
In summary, the embodiment of the invention obtains the sample data when the geomembrane is broken; taking any one of the sample data as target data, acquiring an association window of the target data, and determining the credibility of adjacent data of the target data according to the data in the association window; acquiring importance degree of target data based on credibility of adjacent data; segmenting the sample data according to the importance degree of each data in the sample data to obtain one or more target segments; recombining the sample data according to the target segment to obtain one or more recombined sub-blocks of the sample data; and determining an optimal threshold according to the data in the reorganization sub-block, acquiring compressed data of the sample data based on the optimal threshold, and predicting the geomembrane bursting strength according to the compressed data. And acquiring the reorganization sub-block through each index, and acquiring the self-adaptive optimal threshold value of the reorganization sub-block for compression, so that the effect of large-scale data compression is improved, and meanwhile, the efficiency of large-scale data compression is ensured.
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. 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 foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (1)
1. The method for predicting the bursting strength of the high-strength geomembrane based on load time course analysis is characterized by comprising the following steps of:
acquiring sample data when the geomembrane is burst;
taking any one of the sample data as target data, acquiring an association window of the target data, and determining the credibility of adjacent data of the target data according to the data in the association window;
Acquiring the importance degree of the target data based on the credibility of the adjacent data;
segmenting the sample data according to the importance degree of each data in the sample data to obtain one or more target segments;
recombining the sample data according to the one or more target segments to obtain one or more recombined sub-blocks of the sample data;
determining an optimal threshold according to the data in the rebuilt sub-block, acquiring compressed data of the sample data based on the optimal threshold, and predicting the bursting strength of the geomembrane according to the compressed data;
wherein, the obtaining the association window of the target data includes: acquiring a difference value between each data in the sample data and a normal value, and taking a ratio of the difference value of the target data to the maximum value of the difference value as a first ratio; determining a target association window size of the target data based on the first ratio and a preset association window size; determining an association window of the target data according to the size of the target association window by taking adjacent data of the target data as a starting point, wherein the association window comprises the adjacent data of the target data; the adjacent data of the target data comprises left adjacent data and right adjacent data, and the associated window of the target data comprises a left associated window and a right associated window;
The determining the credibility of the adjacent data of the target data according to the data in the association window comprises the following steps: acquiring a target association window corresponding to target adjacent data, and calculating a data variance in the target association window; determining the credibility of the target adjacent data corresponding to the target association window according to the data variance, wherein the credibility and the data variance are in a negative correlation relationship; the target adjacent data is the left adjacent data or the right adjacent data of the target data, when the target adjacent data is the left adjacent data, the target associated window is the left associated window, and when the target adjacent data is the right adjacent data, the target associated window is the right associated window;
the obtaining the importance degree of the target data based on the credibility of the adjacent data includes: obtaining a summation result of the credibility of the left adjacent data and the credibility of the right adjacent data; when the summation result meets a first condition, a first weight coefficient and a second weight coefficient are obtained, and the first weight coefficient and the second weight coefficient are equal to a preset basic weight; when the summation result meets a second condition, a first weight coefficient and a second weight coefficient are obtained according to the preset basic weight, and the first weight coefficient is larger than the second weight coefficient; determining the importance degree of the target data according to the first weight coefficient and the second weight coefficient;
The determining the importance degree of the target data according to the first weight coefficient and the second weight coefficient comprises the following steps: acquiring a first slope of the target data and left adjacent data thereof and a second slope of the target data and right adjacent data thereof; determining a slope factor from the first slope and the second slope; respectively carrying out weighted summation on the first ratio and the slope factor based on the first weight coefficient and the second weight coefficient to obtain the importance degree of the target data;
the step of segmenting the sample data according to the importance degree of each data in the sample data to obtain one or more target segments comprises the following steps: obtaining marking data of which the importance degree is greater than or equal to a preset importance degree threshold value in the sample data; taking the first marking data in the sample data as a first starting point of a preset sliding window, and acquiring the first quantity of the marking data in the preset sliding window at present; when the first quantity meets a preset condition, the last marking data in the preset sliding window is used as a second starting point, and the position of the preset sliding window is updated; acquiring a second quantity of the marking data in the preset sliding window at present; when the second quantity does not meet the preset condition, determining the last marking data in the preset sliding window as a first end point; taking the data between the first starting point and the first ending point as a target segment; traversing the sample data based on the preset sliding window to obtain one or more target segments of the sample data;
The reorganizing the sample data according to the one or more target segments to obtain one or more reorganized sub-blocks of the sample data, including: configuring variable windows, and sequentially accumulating the lengths of the variable windows from a preset initial value to obtain candidate variable windows with different lengths; wherein the maximum value of the candidate variable window is the number of the sample data; dividing the sample data into a plurality of candidate sub-blocks with candidate variable windows of any length, and acquiring the number of target candidate sub-blocks comprising target segments and the length of the target segments included in the target candidate word blocks; determining the preference degree of the candidate variable window with any length according to the number of the target candidate sub-blocks and the length of the target segment included in the target candidate word block; selecting the length of the candidate variable window with the maximum preference degree as the optimal length; dividing the sample data into a plurality of preferred sub-blocks based on the optimal length, and adjusting the positions of the preferred sub-blocks to reorganize the sample data to obtain one or more reorganized sub-blocks of the sample data;
the adjusting the position of the preferred sub-block reorganizes the sample data to obtain one or more reorganized sub-blocks of the sample data, including: numbering each preferred sub-block from small to large, and acquiring a histogram of each preferred sub-block; acquiring a first overlapping degree of each preferred sub-block and a first preferred sub-block corresponding to the minimum number based on the histogram; acquiring the preferred sub-block with the first overlapping degree being greater than or equal to a preset overlapping threshold value as a first matched sub-block of the first preferred sub-block; moving the first matched sub-block to the first preferred sub-block according to the serial number sequence, and forming a first recombined sub-block; acquiring a second preferred sub-block corresponding to the minimum number outside the first recombined sub-block; calculating a second degree of overlap between each of the preferred sub-blocks and the second preferred sub-block other than the first rebinned sub-block; acquiring the preferred sub-block with the second overlapping degree being greater than or equal to the preset overlapping threshold value as a second matched sub-block of the second preferred sub-block; moving the second matched sub-block to the second preferred sub-block according to the number sequence and forming a second reconstructed sub-block; and so on until all the preferred sub-blocks in the sample data are traversed, obtaining one or more recombined sub-blocks corresponding to the sample data;
The determining an optimal threshold according to the data in the reorganization sub-block comprises the following steps: determining a change curve based on the data in each recombination sub-block, and acquiring the area of a region with the change curve higher than a preset intensity threshold; acquiring the ratio of the area of each recombined sub-block to the theoretical area, wherein the ratio is used as a second ratio, and the effective range of the second ratio is not more than 1; calculating a product result of the second ratio and an empirical threshold, wherein the difference value between the empirical threshold and the product result is the optimal threshold;
the obtaining compressed data of the sample data based on the optimal threshold value includes: taking the optimal threshold value of each reorganization sub-block as a judging threshold value of a Fabry-Perot algorithm; and compressing the data of each recombined sub-block by using the Douglas-Pocke algorithm to obtain compressed data of the sample data.
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