CN118069659A - Electronic commerce product information storage optimization management method - Google Patents
Electronic commerce product information storage optimization management method Download PDFInfo
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Abstract
The invention relates to the technical field of data storage, in particular to an electronic commerce product information storage optimization management method. Firstly, according to the uniformity of differences among all surrounding data points corresponding to the data points to be analyzed, obtaining an instability degree value of the data points to be analyzed; further segmenting to obtain historical time sequence segmented data of each product; according to the adjustment factors of the historical time sequence segmented data of each product, the original compression precision parameter values are adjusted, and the adjusted compression precision parameter values of the historical time sequence segmented data of each product are obtained; and then, the initial data of the historical time sequence of the product is compressed and stored by using a revolving door compression algorithm. According to the invention, through constructing proper and adjusted compression precision parameter values for different product history time sequence segmented data, high-efficiency compression is realized, the accuracy of decompressed data is ensured, and the information compression and storage effects of electronic commerce products are improved.
Description
Technical Field
The invention relates to the technical field of data storage, in particular to an electronic commerce product information storage optimization management method.
Background
The electronic commerce product information is of great significance to the merchant to know market demands and consumer preferences, and the merchant is facilitated to formulate more accurate market strategies, optimize pricing strategies and improve existing products by analyzing the electronic commerce product information. Since e-commerce product information is generally long in data and large in data size, when creating database storage to record e-commerce product information, in order to reduce the storage space occupied by the e-commerce product information, compression processing is required for the data. Because the electronic commerce product information contains a large amount of time sequence data, the revolving door compression algorithm has smaller calculation complexity, and can efficiently compress and store the large amount of time sequence data.
In the prior art, when the revolving door compression algorithm is utilized to compress the time sequence data of the electronic commerce product, a single compression precision parameter is integrally adopted, and the single compression precision parameter has relatively good compression effect on the whole time sequence data, however, because the time sequence data of the electronic commerce product presents seasonal fluctuation, such as holidays, seasonal promotion, annual final promotion and the like, the influence on the sales volume of the product is large, the data segments in the sales volume time sequence data of the electronic commerce product have different degrees of fluctuation, and for the data segments with large fluctuation, the higher compression precision parameter is needed to keep more change information so as to ensure the accuracy of the decompressed data; for data segments with small volatility, lower compression accuracy parameters are required to achieve higher compression rates. The single compression precision parameter is too small for the data segment with large volatility, so that the error rate of the decompressed data is large, and the accuracy of the decompressed data is difficult to ensure; the single compression precision parameter is too large for the data segment with small volatility, so that the compression efficiency is low, and higher compression rate is difficult to realize. The single compression precision parameter is difficult to ensure the data compression efficiency and the decompression data accuracy, and finally the information compression storage effect of the electronic commerce product is influenced.
Disclosure of Invention
In order to solve the technical problems that the time sequence data of an electronic commerce product is compressed by using a revolving door compression algorithm in the prior art, and the data compression efficiency and decompression data accuracy are difficult to ensure by a single compression precision parameter, so that the information compression and storage effects of the electronic commerce product are poor, the invention aims to provide an information storage optimization management method of the electronic commerce product, and the adopted technical scheme is as follows:
an electronic commerce product information storage optimization management method, the method comprising the following steps:
Acquiring product history time sequence initial data of a product;
Taking data points in the historical time sequence data of the product as data points to be analyzed; determining surrounding data points corresponding to a preset time interval of the data points to be analyzed, and acquiring an instability degree value of the data points to be analyzed according to the uniformity of differences among all the surrounding data points corresponding to the data points to be analyzed; segmenting the initial data of the product history time sequence according to the instability degree value of each data point to be analyzed to obtain segmented data of each product history time sequence;
Acquiring adjustment factors of each product history time sequence segmented data according to the conversion uniformity degree of all data points in each product history time sequence segmented data; acquiring an original compression precision parameter value of the historical time sequence initial data of the product; according to the adjustment factors of the historical time sequence segmented data of each product, the original compression precision parameter values are adjusted, and the adjusted compression precision parameter values of the historical time sequence segmented data of each product are obtained;
And according to the adjusted compression precision parameter values corresponding to all the product history time sequence segmented data, utilizing a revolving door compression algorithm to compress and store the product history time sequence initial data.
Further, the method for acquiring the product history time sequence initial data comprises the following steps:
Sequentially counting the product data values of all the historical moments of the historical reference interval according to the sequence of the historical moments, and taking the product data values as the data to be noise reduced of the product historical time sequence; and carrying out noise reduction operation on the data to be noise reduced of the historical time sequence to obtain initial data of the product historical time sequence.
Further, the method for acquiring the instability degree value comprises the following steps:
obtaining an instability degree value according to an instability degree value formula, wherein the instability degree value formula comprises:
; wherein/> The data point to be analyzed is an instability degree value; /(I)The total number of all surrounding data points corresponding to a preset time interval of the data points to be analyzed; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)Is a denominator regulating factor; /(I)Is a sine function; /(I)Is a hyperbolic function; /(I)To/>Is an exponential function of the base.
Further, the method for acquiring the product history time sequence segmented data comprises the following steps:
In the historical time sequence data of the product, taking each data point to be analyzed, of which the instability degree value is larger than a preset dividing threshold value, as each dividing point; and segmenting the product history time sequence initial data by utilizing all the dividing points to obtain product history time sequence segmented data.
Further, the method for obtaining the adjustment factor comprises the following steps:
Obtaining an adjustment factor according to an adjustment factor formula, wherein the adjustment factor formula comprises:
; wherein/> An adjustment factor for the product history timing segment data; /(I)The method comprises the steps of obtaining historical time corresponding to historical time sequence segment data of a product; /(I)The final value of the historical moment corresponding to the product historical time sequence segmented data; /(I)Starting point values of historical moments corresponding to the product historical time sequence segmented data; /(I)Segmenting the total number of all data points in the data for the product history time sequence; in order to produce historical time sequence segment, historical time/> A corresponding data point; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; To/>, in the product history timing segment data Data points; /(I)To/>, in the product history timing segment dataData points; /(I)A first adjustment factor that is denominator; /(I)A second regulator that is denominator; /(I)Is a sine function; Is a hyperbolic function; /(I) To/>Is an exponential function of the base.
Further, the method for acquiring the adjusted compression precision parameter value comprises the following steps:
Acquiring an adjusted compression precision parameter value according to an adjusted compression precision parameter value formula, wherein the adjusted compression precision parameter value formula comprises:
；/> For/> The adjusted compression precision parameter values of the individual product history time sequence segmented data; /(I)For/>Original compression precision parameter values of individual product history time sequence segmented data; For/> Adjusting factors of individual product history time sequence segment data; /(I)Rounding down the symbol; /(I)To/>Is an exponential function of the base.
Further, the method for acquiring the preset time interval includes:
The starting point of the preset time interval is the historical moment corresponding to the data point to be analyzed; the length of the preset time interval is the preset time size.
Further, the method for obtaining the original compression precision parameter value comprises the following steps:
Based on a heuristic method, according to the initial data of the historical time sequence of the product, an original compression precision parameter value is obtained by using a revolving door compression algorithm.
Further, the preset time size has a value of 10.
Further, the preset dividing threshold value is 0.95.
The invention has the following beneficial effects:
In order to improve the storage effect of the product history time sequence initial data, firstly, the seasonal fluctuation of the product history time sequence initial data is considered, the compression requirement is difficult to meet by a single compression precision parameter, and the compression storage effect of the product history time sequence initial data is improved by constructing proper compression precision parameters for different data segments.
In order to effectively segment the historical time sequence initial data of the product, a revolving door compression algorithm is considered to be an effective compression method for time sequence data, and is particularly suitable for data which are consistent in time change and have small fluctuation within a certain threshold range. Because the initial data of the historical time sequence of the product presents volatility along with time, according to the uniformity of differences among all surrounding data points corresponding to the data points to be analyzed, the instability degree value of the data points to be analyzed is obtained; the instability level value reflects the instability level of a preset time interval of the data point to be analyzed. According to the instability degree value of each data point to be analyzed, the product history time sequence initial data are segmented, so that the data in each product history time sequence segmented data are relatively stable, the data are suitable for compression by a revolving door compression algorithm, and therefore, the storage effect of the product history time sequence initial data is improved by constructing a proper compression precision parameter for the product history time sequence segmented data.
In order to improve the storage effect of the product history time sequence initial data, the invention constructs proper compression precision parameters for different product history time sequence segmented data, thereby improving the compression storage effect of the product history time sequence initial data. Considering that the more regular the data change is in the product history time sequence segmented data, the lower the fluctuation degree of the data change is, which indicates that the fewer the features contained in the data in the product history time sequence segmented data are, the fewer the features need to be reserved when the data are compressed, and the lower the corresponding compression precision parameter value is, so that the compression efficiency is improved and the data features are not lost. According to the transformation uniformity degree of all data points in the product history time sequence segmented data, regulating factors of each product history time sequence segmented data are obtained, the regulating factors can reflect the degree of regularity of data transformation in the history time sequence segmented data, and the lower the transformation rule is, the lower the fluctuation degree of the change is, and the regulating factors are higher. And adjusting the original compression precision parameter value according to the adjustment factors of the historical time sequence segmented data of each product to obtain the adjusted compression precision parameter value of the historical time sequence segmented data of each product. The more regular the data transformation is in the historical time sequence segmented data, the lower the fluctuation degree of the change is, and the smaller the corresponding adjusted compression precision parameter value is; the more irregular the data change is in the historical time sequence segmented data, the larger the change fluctuation degree is, and the correspondingly adjusted compression precision parameter value is relatively larger; and constructing proper adjusted compression precision parameter values for different product history time sequence segmented data. And then according to the compression precision parameter values after the adjustment corresponding to all the product history time sequence segmented data, the compression and storage of the product history time sequence initial data are carried out by utilizing a revolving door compression algorithm, so that the compression efficiency is improved, the data characteristics are not lost, the high-efficiency compression is realized, the accuracy of decompressed data is ensured, the information compression effect of electronic commerce products is improved, and the aim of optimizing the data storage is fulfilled.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an electronic commerce product information storage optimization management method according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to the specific implementation, structure, characteristics and effects of an electronic commerce product information storage optimization management method according to the invention, which is provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the electronic commerce product information storage optimization management method provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an electronic commerce product information storage optimization management method according to an embodiment of the invention is shown, the method includes the following steps:
step S1, obtaining initial data of product history time sequence of a product.
Specifically, in order to optimize the storage compression storage effect of the product information of the electronic commerce, first, the product history time sequence initial data of the electronic commerce product is required. Considering that product information of electronic commerce includes data of various data types, the data types include: price of the product, stock quantity of the product, scoring of the product, etc. Aiming at any data type, taking the data value of the system cloud end at the last moment of the preset time interval as the product data value of the preset time interval; further obtaining product data values of each preset time interval in the historical reference interval; taking the preset time interval as a historical moment, and sequentially counting the product data values of all the historical moments of the historical reference interval according to the sequence of the historical moments to serve as the product historical time sequence to-be-noise reduction data corresponding to the data types; because noise exists in the collected historical time sequence data to be noise reduced, the noise can affect subsequent operation, so that the noise reduction operation is carried out on the historical time sequence data to be noise reduced, and the initial data of the product historical time sequence is obtained. In one embodiment of the present invention, the preset time interval is from the beginning to the end of one day, the history reference interval is 365 days, and the implementer can set the preset time interval according to the implementation scenario. The embodiment of the invention adopts median filtering to reduce the noise of the data, and an implementer can set the data according to actual conditions.
It should be noted that, in order to facilitate the operation, all index data involved in the operation in the embodiment of the present invention is subjected to data preprocessing, so as to cancel the dimension effect. The specific means for removing the dimension influence is a technical means well known to those skilled in the art, and is not limited herein.
Step S2, taking data points in the historical time sequence data of the product as data points to be analyzed; determining surrounding data points corresponding to a preset time interval of the data points to be analyzed, and acquiring an instability degree value of the data points to be analyzed according to the uniformity of differences among all the surrounding data points corresponding to the data points to be analyzed; and segmenting the initial data of the product history time sequence according to the instability degree value of each data point to be analyzed, and obtaining segmented data of each product history time sequence.
The invention mainly aims at improving the storage effect of the product history time sequence initial data, firstly considers that the product history time sequence initial data has seasonal fluctuation, and the single compression precision parameter is difficult to meet the compression requirement.
In order to effectively segment the historical time sequence initial data of the product, a revolving door compression algorithm is considered to be an effective compression method for time sequence data, and is particularly suitable for data which are consistent in time change and have small fluctuation within a certain threshold range. Because the initial data of the historical time sequence of the product presents volatility along with time, according to the uniformity of differences among all surrounding data points corresponding to the data points to be analyzed, the instability degree value of the data points to be analyzed is obtained; the instability level value reflects the instability level of a preset time interval of the data point to be analyzed. According to the instability degree value of each data point to be analyzed, the product history time sequence initial data are segmented, so that the data in each product history time sequence segmented data are relatively stable, the data are suitable for compression by a revolving door compression algorithm, and therefore, the storage effect of the product history time sequence initial data is improved by constructing a proper compression precision parameter for the product history time sequence segmented data.
Preferably, considering that the initial data of the product history time sequence needs to be segmented, the segmentation needs to consider the instability degree of the data, and in order to analyze the instability degree around the data point to be analyzed, a preset time interval of the data point to be analyzed is constructed. In one embodiment of the present invention, the method for acquiring the preset time interval includes:
Taking the time corresponding to the data point to be analyzed as a starting point, taking the length backwards as a preset time size, and constructing a preset time interval; wherein the end point of the preset time interval is behind the start point. That is, a predetermined time interval with a predetermined time dimension is determined along the time sequence direction by taking a historical time corresponding to the data point to be analyzed as a starting time. In one embodiment of the present invention, the preset time size is 10, and the practitioner can set the preset time according to the implementation scenario.
Preferably, in order to analyze the degree of instability of surrounding data of the data point to be analyzed, surrounding data points of the data point to be analyzed are determined. In one embodiment of the present invention, a method for acquiring surrounding data points includes:
and taking each data point as each surrounding data point of the data point to be analyzed in a preset time interval of the data point to be analyzed.
Preferably, considering that the compression effect of the revolving door compression algorithm on stable data is good, the initial data of the product history time sequence shows fluctuation along with time, and the instability degree value of the data point to be analyzed is obtained by analyzing the fluctuation of all surrounding data points corresponding to the data point to be analyzed, so that the product history time sequence segmented data with relatively stable data can be obtained later. In one embodiment of the present invention, the method for obtaining the instability degree value includes:
Obtaining an instability degree value according to an instability degree value formula, wherein the instability degree value formula comprises:
; wherein/> The data point to be analyzed is an instability degree value; /(I)The total number of all surrounding data points corresponding to a preset time interval of the data points to be analyzed; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)Is a denominator regulating factor; /(I)Is a sine function; /(I)Is a hyperbolic function; /(I)To/>Is an exponential function of the base. It should be noted that, considering that, in the preset time interval of the data point to be analyzed, the last surrounding data point has no corresponding last two surrounding data points, in one embodiment of the present invention, the last two surrounding data points of the last surrounding data point in the preset time interval of the data point to be analyzed are fitted by using a difference fitting method. In one embodiment of the present invention, the denominator adjustment factor is 0.001, and the practitioner can set itself according to the implementation scenario. It is noted that hyperbolic function/>Will beNormalization, value is at/>. It should be noted that, the surrounding data points and the data points to be analyzed are data points in the initial data of the product history time sequence, namely, refer to the product data values.
In the formula of the value of the degree of instability,Reflect the/>Peripheral data points and/>Differences between the surrounding data points; /(I)Reflect the/>Peripheral data points and/>Differences between the surrounding data points; /(I)The more uniform the uniformity reflecting the variation of the difference, the more the ratio approaches 1; the uniformity of the variation of the difference value is reflected, and the value is larger as the uniformity is; The change uniformity of all surrounding data points in a preset time interval of the data points to be analyzed is reflected, and the more uniform the change is, the larger the value is, and the smaller the instability degree value is. The instability metric integrates the degree of variation unevenness of all surrounding data points in a preset time interval of the data point to be analyzed, and the larger the instability metric is, the more unstable the variation is in the preset time interval of the data point to be analyzed.
Preferably, considering that in the product history time series data of the price of the e-commerce product, when the preset time interval of the data point to be analyzed does not contain important time nodes, such as a last-year promotion, a holiday and a seasonal promotion, the price fluctuation is less changed or remains unchanged along with time, the instability degree value of the corresponding data point to be analyzed is less, and when the preset time interval of the data point to be analyzed contains important time nodes, such as a last-year promotion, a holiday and a seasonal promotion, the higher the instability degree value of the data point to be analyzed is, the more unstable data change condition exists in the preset time interval of the data point to be analyzed is represented. In one embodiment of the present invention, a method for acquiring time-series segmented data of a product history includes:
The greater the instability measurement value is, the more unstable the change is in a preset time interval of the data points to be analyzed, and in the historical time sequence data of the product, each data point to be analyzed with the instability measurement value larger than a preset dividing threshold value is used as each dividing point; and segmenting the initial data of the product history time sequence by utilizing all the dividing points to obtain segmented data of the product history time sequence. The data in the product history time sequence segmented data are relatively stable, and the method is suitable for compression by a revolving door compression algorithm. In one embodiment of the present invention, the preset dividing threshold is 0.95, and the implementer can set according to the implementation scenario. It should be noted that, each dividing point is located between corresponding adjacent product history time series segmented data, and each dividing point is divided into the previous product history time series segmented data, which is not limited herein.
Step S3, according to the conversion uniformity degree of all data points in each product history time sequence segmented data, obtaining the adjustment factors of each product history time sequence segmented data; acquiring an original compression precision parameter value of the historical time sequence initial data of the product; and adjusting the original compression precision parameter value according to the adjustment factors of the historical time sequence segmented data of each product to obtain the adjusted compression precision parameter value of the historical time sequence segmented data of each product.
In order to improve the storage effect of the product history time sequence initial data, the invention constructs proper compression precision parameters for different product history time sequence segmented data, thereby improving the compression storage effect of the product history time sequence initial data. Considering that the more regular the data change is in the product history time sequence segmented data, the lower the fluctuation degree of the data change is, which indicates that the fewer the features contained in the data in the product history time sequence segmented data are, the fewer the features need to be reserved when the data are compressed, and the lower the corresponding compression precision parameter value is, so that the compression efficiency is improved and the data features are not lost. According to the transformation uniformity degree of all data points in the product history time sequence segmented data, regulating factors of each product history time sequence segmented data are obtained, the regulating factors can reflect the degree of regularity of data transformation in the history time sequence segmented data, and the lower the transformation rule is, the lower the fluctuation degree of the change is, and the regulating factors are higher. And adjusting the original compression precision parameter value according to the adjustment factors of the historical time sequence segmented data of each product to obtain the adjusted compression precision parameter value of the historical time sequence segmented data of each product. The more regular the data transformation is in the historical time sequence segmented data, the lower the fluctuation degree of the change is, and the smaller the corresponding adjusted compression precision parameter value is; the more irregular the data change is in the historical time sequence segmented data, the larger the change fluctuation degree is, and the correspondingly adjusted compression precision parameter value is relatively larger; the method realizes the construction of proper compression precision parameter values after adjustment for different product history time sequence segmented data for subsequent compression.
Preferably, in order to construct proper compression precision parameters for different product history time sequence segmented data, firstly, analyzing the degree of data change regularity in the product history time sequence segmented data, and constructing an adjusting factor for adjusting the original compression precision parameter value subsequently. In one embodiment of the present invention, a method for acquiring an adjustment factor includes:
obtaining an adjustment factor according to an adjustment factor formula, wherein the adjustment factor formula comprises:
; wherein/> An adjustment factor for the product history timing segment data; /(I)The method comprises the steps of obtaining historical time corresponding to historical time sequence segment data of a product; /(I)The final value of the historical moment corresponding to the product historical time sequence segmented data; /(I)Starting point values of historical moments corresponding to the product historical time sequence segmented data; /(I)Segmenting the total number of all data points in the data for the product history time sequence; in order to produce historical time sequence segment, historical time/> A corresponding data point; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; To/>, in the product history timing segment data Data points; /(I)To/>, in the product history timing segment dataData points; /(I)A first adjustment factor that is denominator; /(I)A second regulator that is denominator; /(I)Is a sine function; Is a hyperbolic function; /(I) To/>Is an exponential function of the base. It should be noted that, considering that the last data point does not correspond to the next data point in the product history time series segmented data, in one embodiment of the present invention, the next data point of the last data point of the product history time series segmented data is fitted by using a difference fitting method. In one embodiment of the present invention, the value of the first denominator adjustment factor is 0.01, the value of the second denominator adjustment factor is 0.001, and the operator can set the values according to the implementation scenario. It is noted that/>The acquisition method of (1) comprises the following steps: firstly, all history moments corresponding to the product history time sequence segmented data are acquired, and when two intermediate values exist in the history moments, one of the intermediate values is selected as/>; When there is one intermediate value of the history time, it is regarded as/>。
In the formula of the adjustment factor, considering that the product price is stable from the beginning to the end in the product history time sequence segmented data of the price of the e-commerce product, no surge or dip occurs in a certain half segment, and the product history time sequence segmented data has trends and regularity.Differences of first half segment data reflecting product history time sequence segmented data,/>Reflecting the difference of the second half segment data of the product history time sequence segmented data; The approach degree of the difference between the first half data and the second half data of the product history time sequence segmented data is reflected, the closer the approach degree is, the more trend and regularity the product history time sequence segmented data is, the larger the value is, and the larger the formula of the regulating factor is; considering that the product price is uniformly increased or decreased in the product history time sequence segmented data of the e-commerce product price, the product price is more trend and regularity in the product history time sequence segmented data. /(I) The overall difference degree between the data of the product history time sequence segmented data is reflected; The approach degree reflecting the difference between the data and the overall difference between the data is closer to the description, the data is evenly increased or decreased, the trend and regularity of the product history time sequence segmented data are described, and the larger the value is, the larger the regulating factor formula is. The adjustment factor can reflect the degree of regularity of data transformation in the historical time sequence segmented data, and the more regular the transformation is, the larger the adjustment factor is.
Preferably, in order to construct a proper compression precision parameter value for different product history time sequence segmented data, a proper compression precision parameter value for the whole product history time sequence initial data is first determined, and an original compression precision parameter value of the product history time sequence initial data is obtained. In one embodiment of the present invention, the method for obtaining the original compression precision parameter value includes:
Based on a heuristic method, according to the initial data of the historical time sequence of the product, an original compression precision parameter value is obtained by using a revolving door compression algorithm. It should be noted that the revolving door compression algorithm is a prior art well known to those skilled in the art, and is not described herein.
It should be noted that, the heuristic method is a prior art well known to those skilled in the art, and only a brief process of obtaining an original compression precision parameter value based on the heuristic method and according to the initial data of the product history time sequence by using a revolving door compression algorithm is briefly described herein: the compression precision parameter value selects different values, the compression rate and reconstruction precision effects of the different values are obtained by using a revolving door compression algorithm according to the historical time sequence initial data of the product, and the compression precision parameter value with the best effect is taken as the original compression precision parameter value.
Preferably, in order to construct proper compression precision parameters for different product history time sequence segmented data, the original compression precision parameter values are adjusted according to the adjustment factors of each product history time sequence segmented data, and the adjusted compression precision parameter values of each product history time sequence segmented data are obtained. In one embodiment of the present invention, the method for acquiring the adjusted compression precision parameter value includes:
acquiring an adjusted compression precision parameter value according to an adjusted compression precision parameter value formula, wherein the adjusted compression precision parameter value formula comprises:
；/> For/> The adjusted compression precision parameter values of the individual product history time sequence segmented data; /(I)For/>Original compression precision parameter values of individual product history time sequence segmented data; For/> Adjusting factors of individual product history time sequence segment data; /(I)Rounding down the symbol; /(I)To/>Is an exponential function of the base.
In the adjusted compression precision parameter value formula, the adjusting factor can reflect the degree of regularity of data transformation in historical time sequence segmented data, and the more regular the transformation is, the larger the adjusting factor is. The original compression precision parameter value is adjusted through the adjusting factor of the historical time sequence segmented data of the product, so that the more regular the data transformation in the historical time sequence segmented data is, the smaller the corresponding adjusted compression precision parameter value is; the more irregular the data transformation is in the historical time sequence segmented data, the larger the corresponding adjusted compression precision parameter value is; the method realizes the construction of proper compression precision parameter values after adjustment for different product history time sequence segmented data for subsequent compression.
And S4, according to the adjusted compression precision parameter values corresponding to all the product history time sequence segmented data, utilizing a revolving door compression algorithm to compress and store the initial data of the product history time sequence.
By the steps, proper adjusted compression precision parameter values are constructed for different product history time sequence segmented data, and then the initial data of the product history time sequence is compressed and stored by utilizing a revolving door compression algorithm according to the adjusted compression precision parameter values corresponding to all the product history time sequence segmented data, so that the compression efficiency is improved, the data characteristics are not lost, the high-efficiency compression is realized, the accuracy of decompressed data is ensured, the information compression effect of electronic commerce products is improved, and the aim of optimizing the data storage is fulfilled.
It should be noted that the revolving door compression algorithm is a well-known prior art for those skilled in the art, and only a brief method for compressing and storing initial data of a product history time sequence by using the revolving door compression algorithm according to adjusted compression precision parameter values corresponding to all the segmented data of the product history time sequence is briefly described herein:
Firstly initializing parameters, traversing each data in the product history time sequence segmented data for each product history time sequence segmented data, and compressing according to the adjusted compression precision parameter value corresponding to the product history time sequence segmented data until all the data in the product history time sequence segmented data are traversed. At the beginning and end of each product history time series segment data, since the sliding window cannot be completely covered, it is possible to select to retain this portion of data, or to perform interpolation or approximation processing according to the specific case. After processing one product history time sequence segment data, switching to the next product history time sequence segment data, updating the adjusted compression precision parameter value corresponding to the product history time sequence segment data until all the product history time sequence segment data in the product history time sequence initial data are traversed, and outputting a product history time sequence initial data to carry out compression storage result.
In summary, the embodiment of the invention provides an electronic commerce product information storage optimization management method, firstly, according to the uniformity of differences among all surrounding data points corresponding to data points to be analyzed, an instability degree value of the data points to be analyzed is obtained; segmenting the initial data of the product history time sequence according to the instability degree value of each data point to be analyzed to obtain segmented data of each product history time sequence; acquiring adjustment factors of each product history time sequence segmented data according to the conversion uniformity degree of all data points in each product history time sequence segmented data; according to the adjustment factors of the historical time sequence segmented data of each product, the original compression precision parameter values are adjusted, and the adjusted compression precision parameter values of the historical time sequence segmented data of each product are obtained; and then, the initial data of the historical time sequence of the product is compressed and stored by using a revolving door compression algorithm. According to the invention, through constructing proper adjusted compression precision parameter values for different product history time sequence segmented data, high-efficiency compression is realized, the accuracy of decompressed data is ensured, the information compression effect of electronic commerce products is improved, and the aim of optimizing data storage is fulfilled.
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. The processes depicted in the accompanying drawings 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.
Claims (10)
1. An electronic commerce product information storage optimization management method is characterized by comprising the following steps:
Acquiring product history time sequence initial data of a product;
Taking data points in the historical time sequence data of the product as data points to be analyzed; determining surrounding data points corresponding to a preset time interval of the data points to be analyzed, and acquiring an instability degree value of the data points to be analyzed according to the uniformity of differences among all the surrounding data points corresponding to the data points to be analyzed; segmenting the initial data of the product history time sequence according to the instability degree value of each data point to be analyzed to obtain segmented data of each product history time sequence;
Acquiring adjustment factors of each product history time sequence segmented data according to the conversion uniformity degree of all data points in each product history time sequence segmented data; acquiring an original compression precision parameter value of the historical time sequence initial data of the product; according to the adjustment factors of the historical time sequence segmented data of each product, the original compression precision parameter values are adjusted, and the adjusted compression precision parameter values of the historical time sequence segmented data of each product are obtained;
And according to the adjusted compression precision parameter values corresponding to all the product history time sequence segmented data, utilizing a revolving door compression algorithm to compress and store the product history time sequence initial data.
2. The method for optimizing and managing information storage of electronic commerce products according to claim 1, wherein the method for acquiring the initial data of the history time series of the products comprises the steps of:
Sequentially counting the product data values of all the historical moments of the historical reference interval according to the sequence of the historical moments, and taking the product data values as the data to be noise reduced of the product historical time sequence; and carrying out noise reduction operation on the data to be noise reduced of the historical time sequence to obtain initial data of the product historical time sequence.
3. The method for optimizing and managing information storage of electronic commerce products according to claim 2, wherein the method for acquiring the instability level value comprises the steps of:
obtaining an instability degree value according to an instability degree value formula, wherein the instability degree value formula comprises:
; wherein/> The data point to be analyzed is an instability degree value; /(I)The total number of all surrounding data points corresponding to a preset time interval of the data points to be analyzed; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)For the first/>, in a preset time interval of the data point to be analyzedA plurality of surrounding data points; /(I)Is a denominator regulating factor; /(I)Is a sine function; /(I)Is a hyperbolic function; /(I)To/>Is an exponential function of the base.
4. The method for optimizing and managing information storage of electronic commerce products according to claim 1, wherein the method for acquiring the time-series segmented data of the product history comprises the steps of:
In the historical time sequence data of the product, taking each data point to be analyzed, of which the instability degree value is larger than a preset dividing threshold value, as each dividing point; and segmenting the product history time sequence initial data by utilizing all the dividing points to obtain product history time sequence segmented data.
5. The method for optimizing and managing information storage of electronic commerce products according to claim 2, wherein the method for acquiring the adjustment factors comprises:
Obtaining an adjustment factor according to an adjustment factor formula, wherein the adjustment factor formula comprises:
; wherein/> An adjustment factor for the product history timing segment data; /(I)The method comprises the steps of obtaining historical time corresponding to historical time sequence segment data of a product; /(I)The final value of the historical moment corresponding to the product historical time sequence segmented data; /(I)Starting point values of historical moments corresponding to the product historical time sequence segmented data; /(I)Segmenting the total number of all data points in the data for the product history time sequence; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; /(I)In order to produce historical time sequence segment, historical time/>A corresponding data point; /(I)To/>, in the product history timing segment dataData points; /(I)To/>, in the product history timing segment dataData points; /(I)A first adjustment factor that is denominator; /(I)A second regulator that is denominator; /(I)Is a sine function; /(I)Is a hyperbolic function; /(I)To/>Is an exponential function of the base.
6. The method for optimizing and managing information storage of electronic commerce products according to claim 1, wherein the method for acquiring the adjusted compression precision parameter value comprises the steps of:
Acquiring an adjusted compression precision parameter value according to an adjusted compression precision parameter value formula, wherein the adjusted compression precision parameter value formula comprises:
；/> For/> The adjusted compression precision parameter values of the individual product history time sequence segmented data; /(I)For/>Original compression precision parameter values of individual product history time sequence segmented data; /(I)For/>Adjusting factors of individual product history time sequence segment data; /(I)Rounding down the symbol; /(I)To/>Is an exponential function of the base.
7. The method for optimizing and managing information storage of electronic commerce products according to claim 1, wherein the method for acquiring the preset time interval comprises the following steps:
The starting point of the preset time interval is the historical moment corresponding to the data point to be analyzed; the length of the preset time interval is the preset time size.
8. The method for optimizing and managing information storage of electronic commerce products according to claim 1, wherein the method for acquiring the original compression accuracy parameter value comprises the steps of:
Based on a heuristic method, according to the initial data of the historical time sequence of the product, an original compression precision parameter value is obtained by using a revolving door compression algorithm.
9. The method for optimizing management of information storage of electronic commerce product according to claim 7, wherein the preset time size has a value of 10.
10. The method for optimizing management of information storage of electronic commerce product according to claim 4, wherein the preset division threshold value is 0.95.
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