CN116881242B - Intelligent storage system for purchasing data of fresh agricultural product electronic commerce - Google Patents
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Abstract
The invention relates to the technical field of data analysis, in particular to an intelligent storage system for purchasing data of fresh agricultural products by electronic commerce, which comprises the following components: acquiring residual components according to the purchasing data of fresh agricultural product electronic commerce, constructing a residual sequence according to the residual components, and recording each data in the residual sequence as residual data; acquiring a stability factor of residual data according to the stability of adjacent residual data; acquiring suspected non-noise data in residual data according to the stability factor of the residual data; and acquiring non-noise data in the suspected non-noise data according to the periodicity of the suspected non-noise data in the residual data. According to the invention, through analyzing the residual data in the purchasing data of the fresh agricultural product electronic commerce, the noise data in the residual data is deleted to realize targeted and accurate compression, so that the target data volume stored in the compression is reduced.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an intelligent storage system for purchasing data of fresh agricultural products by electronic commerce.
Background
The fresh agricultural product electronic commerce platform provides places for communication, display and transaction for buyers and sellers; consumers can easily browse and purchase fresh agricultural products through mobile phones, computers and other devices at any time and any place, and time and energy required by purchasing in a physical store are saved. The electronic commerce platform has richer commodity alternatives, can manage inventory more effectively, cooperates with high-quality suppliers, strengthens links such as logistics distribution, improves efficiency and reduces cost.
In the e-commerce mode, the purchasing data information has higher predictive value, and the future market trend, consumer demand, price fluctuation and other aspects can be predicted by analyzing the purchasing data stored by the system, so that supply chain management is optimized, inventory risk is reduced, and a more targeted marketing strategy is formulated. For example: sales volume, price, inventory data and the like are changed in a trend along with factors such as market supply and demand, seasons and the like, so that time sequence decomposition can be used for compression storage processing in a targeted manner when the purchase data with predictive value is stored, and the method is not an algorithm for data compression, but can be used as an effective processing step aiming at the current scene to help extract compressible data characteristics, so that the data volume is reduced and data compression is realized.
However, in general, STL time sequence decomposition is generally used for purchasing data of fresh agricultural product electronic commerce, the current time sequence data is split into trend, season and residual components and is respectively compressed, and the residual components are noise and irregular components in the data, so that the data can be selected to be deleted, namely, the compression is not performed; however, since there is a model deviation in which the algorithm fails to completely capture the actual variation of the trend or the seasonal component in the actual time series decomposition process, non-noise data information may be included in the residual component, resulting in a risk of data loss.
Therefore, the invention provides an intelligent storage system for purchasing data of fresh agricultural products by electronic commerce; evaluating the retention degree of each data information in residual components by carrying out data characteristic analysis on residual items in the purchase data after time sequence decomposition; therefore, important information is prevented from being lost in time sequence decomposition processing, and more accurate and efficient data compression processing is realized.
Disclosure of Invention
The invention provides an intelligent storage system for purchasing data of fresh agricultural products by electronic commerce, which aims to solve the existing problems.
The intelligent storage system for the purchasing data of the fresh agricultural product electronic commerce adopts the following technical scheme:
the embodiment of the invention provides an intelligent storage system for purchasing data of fresh agricultural products by electronic commerce, which comprises the following modules:
and a data acquisition module: STL time sequence decomposition is used for purchasing data of fresh agricultural product electronic commerce to obtain trend, season and residual error components, a residual error sequence is constructed according to the residual error components, and each data in the residual error sequence is recorded as residual error data;
data stability analysis module: acquiring a first stability factor of data acquired by an electronic commerce of fresh agricultural products according to the stability of adjacent residual data; acquiring a second stability factor of the data acquired by the electronic commerce of the fresh agricultural products according to the stability of the local residual data; acquiring the stability factor of the data acquired by the fresh agricultural product electronic commerce according to the first stability factor of the data acquired by the fresh agricultural product electronic commerce and the second stability factor of the data acquired by the fresh agricultural product electronic commerce; acquiring suspected non-noise data in residual data according to the stability factor;
and a data periodicity analysis module: acquiring the period length of each suspected non-noise data according to the suspected non-noise data; calculating the periodicity factor of each piece of suspected non-noise data according to the period length of each piece of suspected non-noise data and each piece of suspected non-noise data; acquiring non-noise data in the suspected non-noise data according to the periodicity factor of the suspected non-noise data;
and the data compression storage module is used for: and carrying out compression storage processing according to the non-noise data in the trend component, the season component and the residual component.
Preferably, the method for obtaining the first stability factor of the data collected by the electronic commerce of the fresh agricultural products comprises the following specific steps:
taking any residual data as a center, acquiring all residual data in a preset range, recording the residual data positioned in the center as center data, calculating the absolute value of the center data and the average value of all residual data, and recording the first stability factor of the residual data as the first stability factor of the data acquired by the electronic commerce of fresh agricultural products according to the absolute value.
Preferably, the method for obtaining the second stability factor of the data collected by the electronic commerce of the fresh agricultural product according to the stability of the local residual data comprises the following specific steps:
equally dividing residual data in a residual sequence into a plurality of clusters of residual data according to a time sequence, wherein a calculation formula of a second stability factor of the residual data in each cluster of residual data is as follows:
in the method, in the process of the invention,and (3) withRespectively represent the firstThe first in the clusterThe residual data value is the firstA plurality of residual data values;and (3) withRespectively represent the firstThe first in the clusterThe residual data value is the firstA plurality of residual data values;representing the amount of residual data in each cluster;an exponential function representing a base natural number;represent the firstA second stability factor of the cluster, also representing the firstAnd (3) recording the second stability factor of the residual data in the cluster as the second stability factor of the data acquired by the fresh agricultural product electronic commerce.
Preferably, the method for obtaining the stability factor of the data collected by the electronic commerce of the fresh agricultural products comprises the following specific steps:
firstly, multiplying a first stability factor of the data acquired by the electronic commerce of the fresh agricultural products by a preset weight of the first stability factor of the data acquired by the electronic commerce of the fresh agricultural products, and recording the obtained product as a first product; then multiplying a second stability factor of the data acquired by the fresh agricultural product electronic commerce with a preset weight of the second stability factor of the data acquired by the fresh agricultural product electronic commerce to obtain a second product; and finally, taking the sum of the first product and the second product as a stability factor of the data acquired by the electronic commerce of the fresh agricultural products.
Preferably, the acquiring suspected non-noise data in the residual data includes the specific method that:
by presetting a screening thresholdWhen the stability factor of the residual data is greater than or equal toAnd if not, the residual data is noise data.
Preferably, the method for obtaining the period length of each suspected non-noise data includes the following specific steps:
first, the same-sized suspected non-noise data is recorded as the same-sized suspected non-noise data, the average value of the number of residual data between adjacent same-sized suspected non-noise data in the residual sequence is calculated, and the cycle length of the same-sized suspected non-noise data is recorded asWhereinIs the firstThe period length of the suspected non-noise data.
Preferably, the calculating the periodicity factor of each suspected non-noise data includes the following specific calculation formula:
in the method, in the process of the invention,represent the firstA periodicity factor of the suspected non-noise data,represent the firstA data value of the respective suspected non-noise data,represent the firstThe period length of the individual suspected non-noise data,represent the firstA data value of the respective suspected non-noise data,represent the firstA data value of the respective suspected non-noise data,an exponential function based on a natural number is represented.
Preferably, the acquiring non-noise data in the suspected non-noise data includes the specific method that:
by presetting a screening thresholdWhen the periodicity factor of the suspected non-noise data is greater than or equal toAnd when the suspected non-noise data is non-noise data, otherwise, the suspected non-noise data is noise data.
The technical scheme of the invention has the beneficial effects that: under the general condition, STL time sequence decomposition is generally used for purchasing data of fresh agricultural product electronic commerce, the current time sequence data is split into trend, season and residual components and is respectively compressed, and the residual components in the current time sequence data are noise and irregular components in the data, so that the data can be selected to be deleted, namely the compression is not performed; however, since there is a model deviation in which the algorithm fails to completely capture the actual variation of the trend or the seasonal component in the actual time series decomposition process, non-noise data information may be included in the residual component, resulting in a risk of data loss.
Therefore, the invention evaluates the retention degree of each data information in residual components by carrying out data characteristic analysis on residual items in the purchase data after time sequence decomposition; therefore, important information is prevented from being lost in time sequence decomposition processing, and more accurate and efficient data compression processing is realized.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a system for intelligent storage of electronic commerce procurement data of fresh agricultural products according to 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 is a detailed description of the specific implementation, structure, characteristics and effects of the intelligent storage system for purchasing data of fresh agricultural products by electronic commerce 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 invention provides a specific scheme of an intelligent purchasing data storage system of fresh agricultural products by an electronic commerce.
Referring to fig. 1, a block diagram of an intelligent storage system for purchasing data of fresh agricultural products by an electronic commerce is shown, which comprises the following modules:
and a data acquisition module: and acquiring purchase data with predictive value, performing STL time sequence decomposition, and acquiring a residual sequence.
It should be noted that, the data information with predictive value in the purchasing data of the fresh agricultural product electronic commerce is often necessary to be transmitted or stored in a key way, and the fluctuation or change of the data often directly predicts the future market trend, consumer demand, price fluctuation and other aspects, so as to optimize the supply chain management, reduce the inventory risk and develop a more targeted marketing strategy. Data information having predictive value can be acquired through STL timing decomposition.
The method is characterized in that STL time sequence decomposition is used for purchasing data of fresh agricultural products and the structure and mode of the purchasing data of the fresh agricultural products are obtained as trend, season and residual components, so that the purchasing data of the fresh agricultural products and the purchasing data of the fresh agricultural products can be understood; meanwhile, the trend and seasonal components have a certain rule, and can be compressed by using methods such as function approximation, periodic compression and the like; however, the residual component may contain non-noise data information because there is a model deviation that the algorithm fails to completely capture the trend or the real change of the seasonal component in the actual STL time sequence decomposition process; thus requiring separate analysis of the residual components.
Specifically, acquiring the purchasing data of the fresh agricultural product electronic commerce and constructing a time sequence characteristic curve of the purchasing data of the fresh agricultural product electronic commerce; taking the current sales volume as an example, the horizontal axis in the characteristic curve represents progressive date, usually month is taken as the minimum construction unit, and the vertical axis represents sales volume; thereby reflecting the fluctuating changes in the data into the timing curve.
Because residual error components in the raw and fresh agricultural product e-commerce purchase data can be influenced by factors such as market variation, economic fluctuation and the like, noise data appear in the residual error components, and the noise data do not have predictive value, the noise data in the residual error components need to be removed, so that the aim of reducing the target data volume stored in a compressed mode is fulfilled.
STL time sequence decomposition is carried out on the current time sequence data; the sequence from which the residual component is derived is denoted as the residual sequence.
To this end, a residual sequence is obtained, and each data in the residual sequence is recorded as residual data.
Data stability analysis module: and constructing a stability factor of the residual data according to the stability of the residual data, and acquiring suspected non-noise data according to the stability factor of the residual data.
1. And acquiring a first stability factor of the data acquired by the electronic commerce of the fresh agricultural products.
It should be noted that, residual components obtained in the process of performing STL time sequence decomposition on the raw and fresh agricultural product electronic commerce purchase data with a predictive value may include non-noise data information due to factors such as model deviation; the distribution of the non-noise data in the residual component and the noise data in the residual sequence is different to a certain extent, namely the non-noise data in the residual component generally has higher stability; the noise interference data may show random distribution, i.e. low stability; and the abnormal data or abrupt data that is generally present in the residual component is also more likely to be non-noise data. The stability factor of the residual component can be constructed by the stability of the data in the residual component.
Specifically, the first stability factor of the residual data is calculated by each residual data in the residual sequence and residual data around each residual data, and a specific calculation formula is as follows:
in the method, in the process of the invention,representing the first of the residual componentsA first stability factor for the individual data;representing the first in the residual sequenceA plurality of residual data values;representing the first in the residual sequenceBefore and after the dataResidual data values for the individual data;representing selected residualsData surrounding in a compositionData of individual, whereinFor the preset selection range, can be set according to the specific conditionsThe value of (2) is not particularly limited in this embodiment, but is as follows in this embodimentCalculating;an exponential function based on a natural number is represented.
It should be further noted that, when the first of the calculated residual componentsThe greater the first stability factor of the data value, the more indicative of the first of the residual componentsThe higher the stability of the data, i.e. the first in the residual componentThe greater the likelihood that the individual data is non-noise data.
So far, a first stability factor of residual data is obtained, and the first stability factor of the residual data is recorded as a first stability factor of data acquired by fresh agricultural product electronic commerce.
2. And acquiring a second stability factor of the data acquired by the electronic commerce of the fresh agricultural products.
It should be noted that, through residual data around each residual data in the residual sequence, the first stability factor of the calculated residual data cannot comprehensively evaluate the residual data; in order to evaluate the stability more comprehensively and at multiple angles, the analysis of the variation degree of the adjacent residual data difference is also needed to be used as a second stability evaluation model for comprehensively analyzing and judging, and a second stability factor of the residual data is calculated.
Specifically, the residual data in the residual sequence are equally divided into a plurality of clusters of residual data according to a time sequence order, wherein each cluster of residual data sharesResidual data ofFor the preset selection range, can be set according to the specific conditionsThe value of (2) is not particularly limited in this embodiment, but is as follows in this embodimentCalculating; calculating a second stability factor of each cluster of residual components by means of an absolute value between each residual data and the previous residual data in each cluster of residual components and an average value of absolute values between each residual data and the previous residual data in each cluster of residual components, wherein a specific calculation formula is as follows:
in the method, in the process of the invention,represent the firstA second stability factor for the cluster residual component;and (3) withRespectively represent the firstThe first of the cluster residual componentsThe residual data value is the firstA plurality of residual data values;and (3) withRespectively represent the firstThe first of the cluster residual componentsThe residual data value is the firstA plurality of residual data values;representing the amount of residual data in each cluster of residual componentsThe value of (2) is not particularly limited in this embodiment, and can be set according to the specific situationIn the present embodiment byCalculating;an exponential function based on a natural number is represented.
It should be further noted that since the first residual data in each cluster residual component has no previous data, calculation from the second residual data in each cluster residual component is required, and thereforeAnd (3) withStarting from a value of 2,represent the firstThe first cluster residual componentThe difference size of the residual data and the previous residual data;represent the firstThe means of the differences of the residual data except the first one and the previous one in the cluster residual components; when calculating the firstThe larger the second stability factor of the cluster residual component, the description of the secondThe more stable the cluster residual data.
So far, the second stability factor of each cluster of residual components is obtained, meanwhile, the second stability factor of each cluster of residual components also characterizes the second stability factors of all residual data in each cluster of residual components, and the second stability factors of the residual data are recorded as the second stability factors of the data acquired by fresh agricultural product electronic commerce.
It should be noted that, because the second stability factor of the data collected by the electronic commerce of the fresh agricultural product reflects the trend change of the adjacent data more specifically and deeply, the influence of the second stability factor of the data collected by the electronic commerce of the fresh agricultural product on the residual data is greater than the influence of the first stability factor of the data collected by the electronic commerce of the fresh agricultural product on the residual data; and then, based on the influence of the second stability factor of the data acquired by the fresh agricultural product electronic commerce on the residual error data, the influence of the first stability factor of the data acquired by the fresh agricultural product electronic commerce on the residual error data is larger than that of the first stability factor of the data acquired by the fresh agricultural product electronic commerce, and the stability factor of the data acquired by the fresh agricultural product electronic commerce is acquired.
Specifically, the stability factor of residual data is calculated by presetting the weight of a first stability factor and a second stability factor of the data collected by the fresh agricultural product electronic commerce, wherein the weight of the first stability factor of the data collected by the fresh agricultural product electronic commerce is larger than the weight of the second stability factor of the data collected by the fresh agricultural product electronic commerce, and a specific calculation formula is as follows:
in the method, in the process of the invention,is the firstThe stability factor of the individual residual data,and (3) withRespectively the firstThe first stability factor and the second stability factor of the data collected by the fresh agricultural product electronic commerce of the residual error data,and (3) withRespectively the firstThe weight of the first stability factor and the weight of the second stability factor of the data acquired by the fresh agricultural product electronic commerce of the residual data, whereinAnd (3) withAll are preset weights and can be set according to specific conditionsAnd (3) withThe value of (2) is not specifically required in the embodiment, but is required to satisfyAnd is also provided with=1, in this embodiment to，And (5) performing calculation.
It should be further noted that, when the stability factor of the residual data is larger, the stability representing the residual data is higher, that is, the residual data is more unlikely to be noise data.
So far, the stability factor of the residual data is obtained through the method, and the stability factor of the residual data is recorded as the stability factor of the data acquired by the fresh agricultural product electronic commerce.
And acquiring suspected non-noise data in residual components by acquiring stability factors of data through fresh agricultural product electronic commerce.
Specifically, by presetting a screening thresholdWhen the stability factor of the residual data is greater than or equal toWhen the residual data is considered to be suspected non-noise data; whereas the residual data is noise data, whereinThe value of (2) can be determined according to the specific situation, and the embodiment does not require any specific requirement, in the embodimentDescribing, namely when the stability factor of the residual data is more than or equal to 0.8, the residual data is considered to be suspected non-noise data; otherwise, the residual data is noise data.
So far, suspected non-noise data in the residual components are obtained.
And a data periodicity analysis module: and acquiring non-noise data in the suspected non-noise data according to the periodicity of the suspected non-noise data.
The periodicity and stability of the non-noise data in the residual component are higher than those of the noise data in the residual component, and the residual data with high stability in the residual component is filtered out as the suspected non-noise data through the data stability analysis module, so that the residual data with high periodicity in the suspected non-noise data is filtered out as the non-noise data.
First, the same-sized suspected non-noise data is recorded as the same-sized suspected non-noise data, the average value of the number of residual data between adjacent same-sized suspected non-noise data in the residual sequence is calculated, and the cycle length of the same-sized suspected non-noise data is recorded asWhereinIs the firstThe cycle length of the suspected non-noise data;
then, selecting any suspected non-noise data, marking the period length of the selected suspected non-noise data as a target period length, and counting the suspected non-noise data with the target period length after the selected suspected non-noise data and the suspected non-noise data with the two target period lengths after the selected suspected non-noise data;
finally, calculating the periodicity factor of the selected suspected non-noise data according to the selected suspected non-noise data, the suspected non-noise data with the target period length after the selected suspected non-noise data and the suspected non-noise data with the two target period lengths after the selected suspected non-noise data, wherein the specific calculation process is as follows:
in the method, in the process of the invention,represent the firstA periodicity factor of the suspected non-noise data,represent the firstA data value of the respective suspected non-noise data,represent the firstThe period length of the individual suspected non-noise data,represent the firstA data value of the respective suspected non-noise data,represent the firstA data value of the respective suspected non-noise data,an exponential function based on a natural number is represented.
It should be further noted that, when the periodicity factor of the suspected non-noise data is larger, the periodicity of the suspected non-noise data is higher, that is, the periodicity factor of the suspected non-noise data is larger, the suspected non-noise data is more likely to be non-noise data.
Thus, the periodicity factor of the suspected non-noise data is obtained.
And then acquiring non-noise data in the suspected non-noise data through the periodicity factor of the suspected non-noise data.
Specifically, by presetting a screening thresholdWhen the periodicity factor of the suspected non-noise data is greater than or equal toWhen=0.3, the suspected non-noise data is considered to be non-noise data, whereas the suspected non-noise data is noise data, whereinThe value of (2) can be determined according to the specific situation, and the embodiment does not require any specific requirement, in the embodimentWhen the periodicity factor of the suspected non-noise data is more than or equal to 0.3, the suspected non-noise data is considered to be non-noise data; otherwise, the suspected non-noise data is noise data.
Thus, non-noise data in the suspected non-noise data is acquired.
And the data compression storage module is used for: and carrying out targeted independent compression storage processing on all the component data.
Fitting and compressing the trend component scores by using methods such as moving average, linear regression and the like; calculating and extracting a compression seasonal component by using a method such as a seasonal decomposition method or a seasonal index method in a periodic compression method for the seasonal component data; and deleting the noise data in the calculated residual data, and compressing and storing the noise data in the calculated non-residual data according to the corresponding time node sequence.
Therefore, the targeted accurate compression is realized, and the aim of reducing the target data volume stored by compression is fulfilled.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (4)
1. The intelligent storage system for the purchasing data of the fresh agricultural product electronic commerce is characterized by comprising the following modules:
and a data acquisition module: STL time sequence decomposition is used for purchasing data of fresh agricultural product electronic commerce to obtain trend components, season components and residual components, a residual sequence is constructed according to the residual components, and each data in the residual sequence is recorded as residual data;
data stability analysis module: acquiring a first stability factor of data acquired by an electronic commerce of fresh agricultural products according to the stability of adjacent residual data; acquiring a second stability factor of the data acquired by the electronic commerce of the fresh agricultural products according to the stability of the local residual data; acquiring the stability factor of the data acquired by the fresh agricultural product electronic commerce according to the first stability factor of the data acquired by the fresh agricultural product electronic commerce and the second stability factor of the data acquired by the fresh agricultural product electronic commerce; acquiring suspected non-noise data in residual data according to the stability factor;
and a data periodicity analysis module: acquiring the period length of each suspected non-noise data according to the suspected non-noise data; calculating the periodicity factor of each piece of suspected non-noise data according to the period length of each piece of suspected non-noise data and each piece of suspected non-noise data; acquiring non-noise data in the suspected non-noise data according to the periodicity factor of the suspected non-noise data;
and the data compression storage module is used for: compressing and storing non-noise data in the trend component, the season component and the residual component;
the method for acquiring the first stability factor of the data acquired by the electronic commerce of the fresh agricultural products comprises the following specific steps:
taking any residual data as a center, acquiring all residual data in a preset range, recording the residual data positioned in the center as center data, calculating the absolute value of the average value of the center data and all the residual data, and recording the first stability factor of the residual data as the first stability factor of the data acquired by the fresh agricultural product electronic commerce according to the absolute value;
according to the stability of the local residual data, a second stability factor of the data acquired by the electronic commerce of the fresh agricultural products is acquired, and the method comprises the following specific steps:
equally dividing residual data in a residual sequence into a plurality of clusters of residual data according to a time sequence, wherein a calculation formula of a second stability factor of the residual data in each cluster of residual data is as follows:
；
in the method, in the process of the invention,and->Respectively represent +.>First->Residual data value and->A plurality of residual data values; />And->Respectively represent +.>First->Residual data value and->A plurality of residual data values;representing the amount of residual data in each cluster; />An exponential function representing a base natural number; />Indicate->A second stability factor of the cluster, which at the same time also represents +.>The second stability factor of the residual data in the cluster is recorded as the second stability factor of the data acquired by the fresh agricultural product electronic commerce;
the method for acquiring the period length of each suspected non-noise data comprises the following specific steps:
first, the same-sized suspected non-noise data is recorded as the same-sized suspected non-noise data, the average value of the number of residual data between adjacent same-sized suspected non-noise data in the residual sequence is calculated, and the cycle length of the same-sized suspected non-noise data is recorded asWherein->Is->The cycle length of the suspected non-noise data;
the calculating the periodicity factor of each suspected non-noise data comprises the following specific calculation formulas:
；
in the method, in the process of the invention,indicate->A periodicity factor of the suspected non-noise data, < >>Indicate->Data value of the suspected non-noise data, +.>Indicate->Period length of the suspected non-noise data, +.>Indicate->Data value of the suspected non-noise data, +.>Indicate->Data value of the suspected non-noise data, +.>An exponential function based on a natural number is represented.
2. The intelligent storage system for purchasing data of fresh agricultural products by electronic commerce according to claim 1, wherein the method for obtaining the stability factor of the data collected by the electronic commerce of the fresh agricultural products comprises the following specific steps:
firstly, multiplying a first stability factor of the data acquired by the electronic commerce of the fresh agricultural products by a preset weight of the first stability factor of the data acquired by the electronic commerce of the fresh agricultural products, and recording the obtained product as a first product; then multiplying a second stability factor of the data acquired by the fresh agricultural product electronic commerce with a preset weight of the second stability factor of the data acquired by the fresh agricultural product electronic commerce to obtain a second product; and finally, taking the sum of the first product and the second product as a stability factor of the data acquired by the electronic commerce of the fresh agricultural products.
3. The intelligent storage system for purchasing data of fresh agricultural products by electronic commerce according to claim 1, wherein the method for acquiring suspected non-noise data in residual data comprises the following specific steps:
by presetting a screening thresholdWhen the stability factor of the residual data is greater than or equal to +.>And if not, the residual data is noise data.
4. The intelligent storage system for purchasing data of fresh agricultural products by electronic commerce according to claim 1, wherein the acquiring non-noise data in suspected non-noise data comprises the following specific methods:
by presetting a screening thresholdWhen the periodicity factor of the suspected non-noise data is greater than or equal to +.>And when the suspected non-noise data is non-noise data, otherwise, the suspected non-noise data is noise data.
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| CN103370880A (en) * | 2011-02-16 | 2013-10-23 | 日本电信电话株式会社 | Encoding method, decoding method, encoding apparatus, decoding apparatus, program and recording medium |
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| CN110060011A (en) * | 2019-04-12 | 2019-07-26 | 山东师范大学 | A kind of fresh logistics data management method, apparatus and system |
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| CN103370880A (en) * | 2011-02-16 | 2013-10-23 | 日本电信电话株式会社 | Encoding method, decoding method, encoding apparatus, decoding apparatus, program and recording medium |
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