CN116151933A - International trade information data digital supervision system and method based on big data - Google Patents
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Abstract
The invention relates to the technical field of international trade information data digital supervision, in particular to a large data-based international trade information data digital supervision system and a large data-based international trade information data digital supervision method, wherein the large data-based international trade information data digital supervision system comprises an international trade data acquisition module, an evaluation index extraction module, a grading module, an actual effective data analysis module and an evaluation result analysis module; the international trade data acquisition module is used for acquiring trade data and operation modes recorded in the international trade database for selling different types of commodities by merchants; the evaluation index extraction module is used for extracting an evaluation index corresponding to the commodity based on the trade data; the grade classification module is used for setting the score grade of the evaluation index; the actual effective data analysis module is used for analyzing and confirming the actual effective data of the evaluation index under different operation data; the evaluation result analysis module is used for analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
Description
Technical Field
The invention relates to the technical field of international trade information data digital supervision, in particular to a large data-based international trade information data digital supervision system and a large data-based international trade information data digital supervision method.
Background
With the rise of the internet e-commerce industry, cross-border e-commerce also gradually goes deep into consumer groups in daily life, but unlike domestic e-commerce operation environments, the cross-border e-commerce has no logistics industry chain like domestic maturity, and because a delivery place and a receiving place belong to two countries, a plurality of problems such as long transportation timeliness, clearance detention and the like are often generated and are difficult to predict; the operation market of cross-border electronic commerce is provided with two operation modes FBM and FBA which can meet the selection requirement of the merchant, wherein the FBM is a self-delivery mode, the seller has a self-delivery source channel, and the store sends the self-delivery source channel to the hands of foreign customers through international express packages after the store has a customer order; the FBA represents a warehouse dispatching mode, and the warehouse needs to be prepared in advance to a specified warehouse; although the two operation modes solve the operation difficulty of the merchant, due to the difference of commodity types, various types and diversity of users, the commodity is differentiated in different operation modes, and the merchant cannot digitally analyze and judge the difference and effectively select a reasonable operation mode for the sold commodity, so that the merchant generates the problems of high cost, unstable users and the like in the operation of the cross-border electronic merchant.
Disclosure of Invention
The invention aims to provide an international trade information data digital supervision system and method based on big data so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the international trade information data digital supervision method based on big data comprises the following analysis steps:
step S1: acquiring trade data and operation modes of selling different types of commodities by a merchant, wherein the trade data comprises transportation data and sales feedback data; the operation modes include an FBA mode and an FBM mode; the transportation data comprise the transportation period, the number of transfer stations and the environmental protection index of express packages of the goods in the transportation process; the sales feedback data refers to evaluation data of commodities by buyers after the international trade transaction is completed;
step S2: based on the trade data in the step S1, extracting the evaluation index corresponding to the commodity, and establishing the grading of the commodity operation mode evaluation index data;
step S3: analyzing and confirming actual effective data corresponding to the evaluation index in the step S2 under different operation data;
step S4: based on the actual effective data in the step S3, analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
Further, step S2 includes the following analysis steps:
extracting j-th evaluation indexes pj, pj= { p1, p2, p3, p4}, corresponding to the same type of commodity, in trade data; p1 is a transportation period, p2 is a buyer forward evaluation proportion, p3 is the number of transfer stations, and p4 is a packaging environmental protection level; setting the number of score grades of the evaluation index as n; the buyer forward evaluation proportion is obtained from the sales feedback data;
obtaining the maximum value max [ Tp1] and the minimum value min [ Tp1] of the transportation period in trade data; calculating a period difference value as T0, wherein T0=max [ Tp1] -min [ Tp1], and dividing the corresponding fraction level of the output transportation period into: more than "T0/n" of min+ (n-1), [ min+ (n-2), "T0/n", min+ (n-1), "T0/n", [ min [ Tp1] + "T0/n", [ min+2 "+" T0/n ", [ min [ Tp1] +" T0/n "]; wherein "T0/n" represents rounding T0/n; the transportation period is extracted as an evaluation index because the transportation period reflects the transportation efficiency, and the shorter the transportation time length is, the higher the transportation efficiency is;
the buyer forward evaluation proportion refers to the proportion of the buyer to forward evaluate the commodity after confirming the receipt, and the forward evaluation refers to default good evaluation under the premise of not generating bad evaluation or carrying out goods returning and changing due to the commodity transportation and packaging problem; setting the classification of the score grades corresponding to the buyer forward evaluation proportion as follows: below "100%/n", [ "100%/n", "2" 100%/n ", ], -, [ (n-2)," 100%/n "," (n-1), "100%/n", -, above; the forward evaluation proportion of the buyers is analyzed, so that the feedback of the buyers to the commodities can effectively judge the operation modes of the commodities, and when a certain operation mode of a certain commodity is improper, the buying experience brought to the buyers is also bad; easily causing the loss of customer groups;
the classification of the corresponding score grades of the environmental protection level of the package is set as follows: n, n-1, n-2, 1; the analysis package environmental-protection level is that the FBM spontaneous goods mode and the FBA mode are different in the logistics data recorded in international trade on the autonomy of express package, the affected package environment-protection degree is different, and the environment-protection concept is currently followed as an evaluation index of the commodity operation mode to realize the greening of express logistics; because the merchant has own sales channels in the FBM spontaneous mode, the customers in the store send the sales channels into foreign customers through international express packages after ordering, and the commodity package depends on the merchant; the FBA mode is that the warehouse is used for transporting the same stock package, the package of the package is not diversified, and the standard is uniform;
obtaining the average transfer station number u in trade data, and setting the division of the score grades corresponding to the transfer station number as follows: u+b above, [ u, u+b ], [ u- (n-3) b, u ], [ u- (n-2) b, u- (n-3) b ]; b represents the number of transfer stations at the set interval. The more transfer stations, the greater the risk of express delivery in the transfer process, and the greater the possibility that the commodity is damaged after the buyer receives the commodity.
Further, step S3 includes the following analysis steps:
acquiring an average transportation period w1, a buyer forward evaluation proportion w2 and an average transfer station number w3 of the same type of commodity in the monitoring period in an FBM mode of operation; acquiring an average transportation period f1, a buyer forward evaluation proportion f2 and the number f3 of average transfer stations of the same type of commodity in the monitoring period under the FBA mode of operation;
the method comprises the steps of obtaining the recyclable material proportion b of express packages of the same type of commodity and the number q of the express packages, and calculating an environmental protection index z by using a formula, wherein z=b×q; when z=100% q, the output package environmental protection level is 1; when z belongs to [75 x q,100 x q ], the output package environmental protection level is 2, and the like, when z is less than 50% q, the output package environmental protection level is n;
acquiring a packaging environmental protection level w4 of the same type of commodity in a monitoring period in an FBM mode and an environmental protection index f4 in an FBA mode;
actual effective data { w1, w2, w3, w4} in the FBM mode and actual effective data { f1, f2, f3, f4} in the FBA mode are generated.
Further, step S4 includes the following analysis steps:
determining a factor set and a corresponding weight vector;
the factor set is U, u= { p1, p2, p3, p4}; and sets the corresponding weight vector as a, a= {0.25,0.2,0.3,0.25};
set comment set V, v= {0,1,..n-1 }; the comment set represents the score grade of the evaluation index;
determining membership function C of each factor to comment set 0...n-1 (U),
C 0...n-1 (U)={C 0 (U),C 2 (U),...,C n-1 (U)}
C 0...n-1 (U)={C 0...n-1 (p1),C 0...n-1 (p2),C 0...n-1 (p3),C 0...n-1 (p4)}
Wherein C is n-1 (U) represents a membership function of the factor set U to the nth comment set; c (C) 0...n-1 (p 1) represents a factor p1, i.e. the membership function of the transport cycle to the 1 st to n th panel sets;
drawing an image of the membership function by utilizing Matlab;
establishing a fuzzy judgment matrix, and substituting actual effective data { w1, w2, w3, w4} in the FBM mode and actual effective data { f1, f2, f3, f4} in the FBA mode into corresponding membership functions respectively, wherein { w1, w2, w3, w4}, { f1, f2, f3, f4} and factor types in factor sets { p1, p2, p3, p4} are in one-to-one correspondence;
calculating a corresponding fuzzy judgment matrix;
r 1...n =[C 0 (pj),C 1 (pj),...,C n-1 (pj)]
p1={w1,f1};p2={w2,f2};p3={w3,f3};p4={w4,f4};
ri represents a fuzzy judgment matrix corresponding to actual effective data in an ith operation mode; r is (r) 1...n Four are shownThe actual effective data belongs to the membership degree of the comment set;
based on the fuzzy evaluation matrix, calculating the total evaluation value Bi of the actual effective data in the ith operation mode by using M (·, +) operator, wherein Bi=ARi, and carrying out normalization treatment on Bi; the M (·, +) operator refers to a weighted average fuzzy operator;
based on the total evaluation value and the maximum membership principle, outputting a corresponding evaluation result;
when the corresponding evaluation results in the two modes are the same, calculating a total score si, si= Σ (Bi x V) under the corresponding scheme based on the total evaluation value and the comment set; as the comment is a scoring, the total score of the two schemes is calculated, and the total score is more reasonable as an evaluation standard;
based on the total scores, judging that the scheme corresponding to the highest total score is the optimal operation mode of the monitoring type commodity, and performing early warning recommendation on the operation mode under the same type commodity. By the method, qualitative evaluation in the international trade transportation express is converted into quantitative evaluation, so that the most suitable operation mode of a type of commodity in the international trade transaction is effectively analyzed, and the high satisfaction degree of customers, the low trend of transportation cost, the minimization of transportation risk and the saving and environmental protection of packaging are realized.
The international trade information data digital supervision system based on big data comprises an international trade data acquisition module, an evaluation index extraction module, a grading module, an actual effective data analysis module and an evaluation result analysis module;
the international trade data acquisition module is used for acquiring trade data and operation modes recorded in the international trade database for selling different types of commodities by merchants;
the evaluation index extraction module is used for extracting an evaluation index corresponding to the commodity based on the trade data; the evaluation indexes comprise a transportation period, a buyer forward evaluation proportion, the number of transfer stations and a packaging environmental protection level;
the grade classification module is used for setting the score grade of the evaluation index;
the actual effective data analysis module is used for analyzing and confirming the actual effective data of the evaluation index under different operation data;
the evaluation result analysis module is used for analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
Further, the grading module comprises a grading quantity setting unit and a grading analysis unit;
the grade number setting unit is used for setting the grade number of the score based on the evaluation indexes corresponding to the commodities of the same type;
the grading analysis unit is used for analyzing grading of the transportation period, grading of the forward evaluation proportion of the buyers and grading corresponding to the packaging environmental protection grade based on the number of the score grades.
Further, the actual effective data analysis module comprises an average value analysis unit and an environmental protection index calculation unit;
the average value calculation unit is used for obtaining the average transportation period, the forward evaluation proportion of buyers and the average number of transfer stations of the same type of commodities in the monitoring period in the FBM mode of the operation mode; and the average transportation period, the forward evaluation proportion of buyers and the average number of transfer stations in the FBA mode of operation;
the environment protection index calculating unit is used for obtaining the proportion of recyclable materials in the express packages of the same type of commodities and the number of the recyclable packages used for express in a monitoring period, calculating the environment protection index and outputting the corresponding package environment protection level according to a set rule.
Further, the evaluation result analysis module comprises a factor set determination unit, a weight vector determination unit, a comment set setting unit, a membership function analysis unit, a fuzzy judgment matrix establishment unit and a total evaluation value analysis unit;
the factor set determining unit is used for determining a factor set;
the weight vector determining unit is used for determining weight vectors of the corresponding factor sets;
the comment set setting unit is used for setting a comment set corresponding to the factor set;
the membership function analysis unit is used for analyzing membership functions of the corresponding comment sets of all factors and calculating corresponding membership;
the fuzzy judgment matrix establishing unit is used for establishing a fuzzy judgment matrix based on membership;
the total evaluation value analysis unit is used for analyzing the total evaluation value and the total score, outputting a scheme corresponding to the highest total score as an optimal operation mode of the monitoring type commodity, and performing early warning recommendation on the operation mode under the same type commodity.
Compared with the prior art, the invention has the following beneficial effects: according to the method, recorded data generated by cross-border electronic commerce in international trade data are analyzed, evaluation indexes in four directions of transportation cost, user stability, transportation risk, saving and environmental protection are extracted to analyze optimization and rationalization of commodity operation modes, and meanwhile, fuzzy comprehensive evaluation is carried out on the data to quantify the data, so that the operation modes applicable to the same type of commodity can be effectively, clearly and digitally analyzed, early warning and reminding can be carried out on the operation modes of merchants systematically, the problem that some merchants are difficult to select in the operation modes of international trade is solved, and the problems that the cost is too high, the user is unstable and the like are generated in the operation of the cross-border electronic commerce; and meanwhile, the international trade development tends to be positive and green.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic diagram of the structure of the international trade information data digital supervision system based on big data of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: the international trade information data digital supervision method based on big data comprises the following analysis steps:
step S1: acquiring trade data and operation modes of selling different types of commodities by a merchant, wherein the trade data comprises transportation data and sales feedback data; the operation modes include an FBA mode and an FBM mode; and trade data of the commodity is generated and recorded in both the FBA mode and the FBM mode; the merchant selling different types of commodities refers to that the analysis of the invention is based on the overall analysis of the same type of commodity and is not the data analysis of one commodity alone;
the transportation data comprise the transportation period, the number of transfer stations and the environmental protection index of express packages of the goods in the transportation process; the sales feedback data refers to evaluation data of commodities by buyers after the international trade transaction is completed;
the shipping period refers to the period of time that the commodity is sent to the buyer to receive the commodity after the buyer determines to purchase; the international trade database refers to a trade data platform established by a merchant performing international trade;
step S2: based on the trade data in the step S1, extracting the evaluation index corresponding to the commodity, and establishing the grading of the commodity operation mode evaluation index data;
step S3: analyzing and confirming actual effective data corresponding to the evaluation index in the step S2 under different operation data;
step S4: based on the actual effective data in the step S3, analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
Step S2 comprises the following analysis steps:
extracting j-th evaluation indexes pj, pj= { p1, p2, p3, p4}, corresponding to the same type of commodity, in trade data; p1 is a transportation period, p2 is a buyer forward evaluation proportion, p3 is the number of transfer stations, and p4 is a packaging environmental protection level; setting the number of score grades of the evaluation index as n; the buyer forward evaluation proportion is obtained from the sales feedback data; if the number of the score grades can be set to be 4, the scores respectively correspond to 0,1,2 and 3;
obtaining the maximum value max [ Tp1] and the minimum value min [ Tp1] of the transportation period in trade data; calculating a period difference value as T0, wherein T0=max [ Tp1] -min [ Tp1], and dividing the corresponding fraction level of the output transportation period into: more than "T0/n" of min+ (n-1), [ min+ (n-2), "T0/n", min+ (n-1), "T0/n", [ min [ Tp1] + "T0/n", [ min+2 "+" T0/n ", [ min [ Tp1] +" T0/n "]; wherein "T0/n" represents rounding T0/n; the transportation period is extracted as an evaluation index because the transportation period reflects the transportation efficiency, and the shorter the transportation time length is, the higher the transportation efficiency is;
as shown in the examples: minimum value is 3 days, maximum value is 30 days, then T0 is 27 days, then "T0/n" = "27/4" = "6; the division of the transport period into corresponding fractional grades is: 9 below, 9-15, 15-21, 21 above; the unit is day;
the buyer forward evaluation proportion refers to the proportion of the buyer to forward evaluate the commodity after confirming the receipt, and the forward evaluation refers to default good evaluation under the premise of not generating bad evaluation or carrying out goods returning and changing due to the commodity transportation and packaging problem; setting the classification of the score grades corresponding to the buyer forward evaluation proportion as follows: below "100%/n", [ "100%/n", "2" 100%/n ", ], -, [ (n-2)," 100%/n "," (n-1), "100%/n", -, above; the forward evaluation proportion of the buyers is analyzed, so that the feedback of the buyers to the commodities can effectively judge the operation modes of the commodities, and when a certain operation mode of a certain commodity is improper, the buying experience brought to the buyers is also bad; easily causing the loss of customer groups; the score grades corresponding to the buyer forward rating scale are divided into: less than 25 percent [25 percent, 50 percent ], [50 percent, 75 percent ], [75 percent ] more than 75 percent;
the classification of the corresponding score grades of the environmental protection level of the package is set as follows: n, n-1, n-2, 1; the environment-friendly packaging grade refers to that the smaller the grade under the condition of the corresponding score grade, the smaller the influence on the environment is, and the environment is more friendly; the analysis package environmental-protection level is that the FBM spontaneous goods mode and the FBA mode are different in the logistics data recorded in international trade on the autonomy of express package, the affected package environment-protection degree is different, and the environment-protection concept is currently followed as an evaluation index of the commodity operation mode to realize the greening of express logistics; because the merchant has own sales channels in the FBM spontaneous mode, the customers in the store send the sales channels into foreign customers through international express packages after ordering, and the commodity package depends on the merchant; the FBA mode is that the warehouse is used for transporting the same stock package, the package of the package is not diversified, and the standard is uniform; if the corresponding score grades of the package environmental protection grades are 4,3,2 and 1;
obtaining the average transfer station number u in trade data, and setting the division of the score grades corresponding to the transfer station number as follows: u+b above, [ u, u+b ], [ u- (n-3) b, u ], [ u- (n-2) b, u- (n-3) b ]; b represents the number of transfer stations at the set interval. The more transfer stations, the greater the risk of express delivery in the transfer process, and the greater the possibility that the commodity is damaged after the buyer receives the commodity. If u=6 and b=3 is set, the level is as follows: 9 or more, [6,9], [3,6] and 3 or less.
Step S3 comprises the following analysis steps:
acquiring an average transportation period w1, a buyer forward evaluation proportion w2 and an average transfer station number w3 of the same type of commodity in the monitoring period in an FBM mode of operation; acquiring an average transportation period f1, a buyer forward evaluation proportion f2 and the number f3 of average transfer stations of the same type of commodity in the monitoring period under the FBA mode of operation;
the method comprises the steps of obtaining the recyclable material proportion b of express packages of the same type of commodity and the number q of the express packages, and calculating an environmental protection index z by using a formula, wherein z=b×q; when z=100% q, the output package environmental protection level is 1; when z belongs to [75 x q,100 x q ], the output package environmental protection level is 2, and the like, when z is less than 50% q, the output package environmental protection level is n; the proportion of the recyclable materials refers to the proportion of the recyclable materials in the express package, for example, the proportion of the recyclable materials in the carton is 100 percent, and the proportion of the recyclable materials in the plastic bag package is 75 percent, so that 100 percent is taken as an initial limit, and when the materials are completely recyclable, the materials are all environment-friendly;
acquiring a packaging environmental protection level w4 of the same type of commodity in a monitoring period in an FBM mode and an environmental protection index f4 in an FBA mode;
actual effective data { w1, w2, w3, w4} in the FBM mode and actual effective data { f1, f2, f3, f4} in the FBA mode are generated.
Step S4 comprises the following analysis steps:
determining a factor set and a corresponding weight vector;
the factor set is U, u= { p1, p2, p3, p4}; and sets the corresponding weight vector as a, a= {0.25,0.2,0.3,0.25};
set comment set V, v= {0,1,..n-1 }; the comment set represents the score grade of the evaluation index;
determining membership function C of each factor to comment set 0...n-1 (U),
C 0...n-1 (U)={C 0 (U),C 2 (U),...,C n-1 (U)}
C 0...n-1 (U)={C 0...n-1 (p1),C 0...n-1 (p2),C 0...n-1 (p3),C 0...n-1 (p4)}
Wherein C is n-1 (U) represents a membership function of the factor set U to the nth comment set; c (C) 0...n-1 (p 1) represents a factor p1, i.e. the membership function of the transport cycle to the 1 st to n th panel sets;
drawing an image of the membership function by utilizing Matlab;
establishing a fuzzy judgment matrix, and substituting actual effective data { w1, w2, w3, w4} in the FBM mode and actual effective data { f1, f2, f3, f4} in the FBA mode into corresponding membership functions respectively, wherein { w1, w2, w3, w4}, { f1, f2, f3, f4} and factor types in factor sets { p1, p2, p3, p4} are in one-to-one correspondence;
calculating a corresponding fuzzy judgment matrix;
r 1...n =[C 0 (pj),C 1 (pj),...,C n-1 (pj)]
p1={w1,f1};p2={w2,f2};p3={w3,f3};p4={w4,f4};
wherein Ri tableA fuzzy judgment matrix corresponding to the actual effective data in the ith operation mode is shown; r is (r) 1...n Representing the membership degree of four actual effective data to the comment set;
based on the fuzzy evaluation matrix, calculating the total evaluation value Bi of the actual effective data in the ith operation mode by using M (·, +) operator, wherein Bi=ARi, and carrying out normalization treatment on Bi; the M (·, +) operator refers to a weighted average fuzzy operator;
based on the total evaluation value and the maximum membership principle, outputting a corresponding evaluation result;
when the corresponding evaluation results in the two modes are the same, calculating a total score si, si= Σ (Bi x V) under the corresponding scheme based on the total evaluation value and the comment set; as the comment is a scoring, the total score of the two schemes is calculated, and the total score is more reasonable as an evaluation standard;
based on the total scores, judging that the scheme corresponding to the highest total score is the optimal operation mode of the monitoring type commodity, and performing early warning recommendation on the operation mode under the same type commodity. By the method, qualitative evaluation in the international trade transportation express is converted into quantitative evaluation, so that the most suitable operation mode of a type of commodity in the international trade transaction is effectively analyzed, and the high satisfaction degree of customers, the low trend of transportation cost, the minimization of transportation risk and the saving and environmental protection of packaging are realized.
As shown in the examples:
if the analysis operation scheme evaluation index data is classified as follows:
the actual valid data in the two modes of operation are as follows:
{w1,w2,w3,w4}={8,95%,4,2};
{f1,f2,f3,f4}={12,74%,7,3};
then the membership function of the transportation cycle p1 to "0" is calculated:
membership function of transport period p1 to "1":
membership function of transport period p1 to "2":
membership function of transport period p1 to "3":
calculating membership functions of the buyer forward proportion p2, the number of transfer stations p3 and the packaging environmental protection level p4 to '0', '1', '2' and '3' by analogy;
substituting the actual effective data { w1, w2, w3, w4} = {8, 95%,4,2}, calculating membership degrees of the comment sets of "0", "1", "2" and "3"; r1= [0,0,0.143,1]; similarly, calculating r2, r3 and r4; r1, R2, R3 and R4 form a fuzzy judgment matrix R1;
let b1=aRi=[0.25,0.2,0.3,0.25]/>Ri, and performing normalization treatment to obtain B1= [0.3178,0.3256,0.2417,0.2019 ]]The method comprises the steps of carrying out a first treatment on the surface of the At this time, according to the maximum membership rule, it is known that B1 belongs to comment 1; the evaluation result is known according to the result calculated by the B2, and the larger the comment value is, the better the evaluation result is;
if the comment of B1 is calculated to be the same as the comment of B2,
then si= Σ (bi×v) =0.3178×0+0.3256×1+0.2417×2+0.2019×3= 1.4147 is calculated and a comparison analysis is performed according to a specific total score.
The international trade information data digital supervision system based on big data comprises an international trade data acquisition module, an evaluation index extraction module, a grading module, an actual effective data analysis module and an evaluation result analysis module;
the international trade data acquisition module is used for acquiring trade data and operation modes recorded in the international trade database for selling different types of commodities by merchants;
the evaluation index extraction module is used for extracting an evaluation index corresponding to the commodity based on the trade data; the evaluation indexes comprise a transportation period, a buyer forward evaluation proportion, the number of transfer stations and a packaging environmental protection level;
the grade classification module is used for setting the score grade of the evaluation index;
the actual effective data analysis module is used for analyzing and confirming the actual effective data of the evaluation index under different operation data;
the evaluation result analysis module is used for analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
The grading module comprises a grading quantity setting unit and a grading analysis unit;
the grade number setting unit is used for setting the grade number of the score based on the evaluation indexes corresponding to the commodities of the same type;
the grading analysis unit is used for analyzing grading of the transportation period, grading of the forward evaluation proportion of the buyers and grading corresponding to the packaging environmental protection grade based on the number of the score grades.
The actual effective data analysis module comprises an average value analysis unit and an environmental protection index calculation unit;
the average value calculation unit is used for obtaining the average transportation period, the forward evaluation proportion of buyers and the average number of transfer stations of the same type of commodities in the monitoring period in the FBM mode of the operation mode; and the average transportation period, the forward evaluation proportion of buyers and the average number of transfer stations in the FBA mode of operation;
the environment protection index calculating unit is used for obtaining the proportion of recyclable materials in the express packages of the same type of commodities and the number of the recyclable packages used for express in a monitoring period, calculating the environment protection index and outputting the corresponding package environment protection level according to a set rule.
The evaluation result analysis module comprises a factor set determination unit, a weight vector determination unit, a comment set setting unit, a membership function analysis unit, a fuzzy judgment matrix establishment unit and a total evaluation value analysis unit;
the factor set determining unit is used for determining a factor set;
the weight vector determining unit is used for determining weight vectors of the corresponding factor sets;
the comment set setting unit is used for setting a comment set corresponding to the factor set;
the membership function analysis unit is used for analyzing membership functions of the corresponding comment sets of all factors and calculating corresponding membership;
the fuzzy judgment matrix establishing unit is used for establishing a fuzzy judgment matrix based on membership;
the total evaluation value analysis unit is used for analyzing the total evaluation value and the total score, outputting a scheme corresponding to the highest total score as an optimal operation mode of the monitoring type commodity, and performing early warning recommendation on the operation mode under the same type commodity.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The international trade information data digital supervision method based on big data is characterized by comprising the following analysis steps:
step S1: acquiring trade data and operation modes of selling different types of commodities by a merchant recorded in an international trade database, wherein the trade data comprises transportation data and sales feedback data; the operation modes include an FBA mode and an FBM mode; the transportation data comprise the transportation period, the number of transfer stations and the environmental protection level of express packages of the commodities in the transportation process; the sales feedback data refers to evaluation data of commodities by buyers after the international trade transaction is completed;
step S2: based on the trade data in the step S1, extracting the evaluation index corresponding to the commodity, and establishing the grading of the commodity operation mode evaluation index data;
step S3: analyzing and confirming actual effective data corresponding to the evaluation index in the step S2 under different operation data;
step S4: based on the actual effective data in the step S3, analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
2. The method for digitally supervising international trade information data based on big data according to claim 1, wherein: the step S2 includes the following analysis steps:
extracting j-th evaluation indexes pj, pj= { p1, p2, p3, p4}, corresponding to the same type of commodity, in trade data; p1 is a transportation period, p2 is a buyer forward evaluation proportion, p3 is the number of transfer stations, and p4 is a packaging environmental protection level; setting the number of score grades of the evaluation index as n; the buyer forward evaluation proportion is obtained from the sales feedback data;
obtaining the maximum value max [ Tp1] and the minimum value min [ Tp1] of the transportation period in trade data; calculating a period difference value as T0, wherein T0=max [ Tp1] -min [ Tp1], and dividing the corresponding fraction level of the output transportation period into: more than "T0/n" of min+ (n-1), [ min+ (n-2), "T0/n", min+ (n-1), "T0/n", [ min [ Tp1] + "T0/n", [ min+2 "+" T0/n ", [ min [ Tp1] +" T0/n "]; wherein "T0/n" represents rounding T0/n;
the buyer forward evaluation proportion refers to the proportion of the buyer to forward evaluate the commodity after confirming the receiving commodity, and the forward evaluation refers to default good evaluation under the premise of not generating bad evaluation or carrying out returning and changing due to the commodity transportation and packaging problem; setting the classification of the score grades corresponding to the buyer forward evaluation proportion as follows: below "100%/n", [ "100%/n", "2" 100%/n ", ], -, [ (n-2)," 100%/n "," (n-1), "100%/n", -, above;
the classification of the corresponding score grades of the environmental protection level of the package is set as follows: n, n-1, n-2, 1;
obtaining the average transfer station number u in trade data, and setting the division of the score grades corresponding to the transfer station number as follows: u+b above, [ u, u+b ], [ u- (n-3) b, u ], [ u- (n-2) b, u- (n-3) b ]; b represents the number of transfer stations at the set interval.
3. The digital supervision method for international trade information data based on big data according to claim 2, wherein: the step S3 includes the following analysis steps:
acquiring an average transportation period w1, a buyer forward evaluation proportion w2 and an average transfer station number w3 of the same type of commodity in the monitoring period in an FBM mode of operation; acquiring an average transportation period f1, a buyer forward evaluation proportion f2 and the number f3 of average transfer stations of the same type of commodity in the monitoring period under the FBA mode of operation;
the method comprises the steps of obtaining the recyclable material proportion b of express packages of the same type of commodity and the number q of the express packages, and calculating an environmental protection index z by using a formula, wherein z=b×q; when z=100% q, the output package environmental protection level is 1; when z belongs to [75 x q,100 x q ], the output package environmental protection level is 2, and the like, when z is less than 50% q, the output package environmental protection level is n;
acquiring a packaging environmental protection level w4 of the same type of commodity in a monitoring period in an FBM mode and an environmental protection index f4 in an FBA mode;
actual effective data { w1, w2, w3, w4} in the FBM mode and actual effective data { f1, f2, f3, f4} in the FBA mode are generated.
4. The method for digitally supervising the international trade information data based on big data according to claim 3, wherein: the step S4 includes the following analysis steps:
determining a factor set and a corresponding weight vector;
the factor set is U, u= { p1, p2, p3, p4}; and sets the corresponding weight vector as a, a= {0.25,0.2,0.3,0.25};
set comment set V, v= {0,1,..n-1 }; the comment set represents the score grade of the evaluation index;
determining membership functions C0.. N-1 (U) for the panel sets for each factor,
C0...n-1(U)={C0(U),C2(U),...,Cn-1(U)}
C0...n-1(U)={C0...n-1(p1),C0...n-1(p2),C0...n-1(p3),C0...n-1(p4)}
wherein Cn-1 (U) represents a membership function of the factor set U to the nth comment set; c0. n-1 (p 1) represents the factor p1, i.e. the membership function of the transport cycle to the 1 st to n th panel sets;
drawing an image of the membership function by utilizing Matlab;
establishing a fuzzy judgment matrix, and substituting actual effective data { w1, w2, w3, w4} in the FBM mode and actual effective data { f1, f2, f3, f4} in the FBA mode into corresponding membership functions respectively, wherein { w1, w2, w3, w4}, { f1, f2, f3, f4} and factor types in factor sets { p1, p2, p3, p4} are in one-to-one correspondence;
calculating a corresponding fuzzy judgment matrix;
r1...n=[C0(pj),C1(pj),...,Cn-1(pj)]
p1={w1,f1};p2={w2,f2};p3={w3,f3};p4={w4,f4};
ri represents a fuzzy judgment matrix corresponding to actual effective data in an ith operation mode; r1. n represents the membership of four actual effective data to a panel;
based on the fuzzy evaluation matrix, calculating the total evaluation value Bi of the actual effective data in the ith operation mode by using M (·, +) operator, wherein Bi=ARi, and carrying out normalization treatment on Bi; the M (·, +) operator refers to a weighted average fuzzy operator;
based on the total evaluation value and the maximum membership principle, outputting a corresponding evaluation result;
when the corresponding evaluation results in the two modes are the same, calculating a total score si, si= Σ (Bi x V) under the corresponding scheme based on the total evaluation value and the comment set;
based on the total scores, judging that the scheme corresponding to the highest total score is the optimal operation mode of the monitoring type commodity, and performing early warning recommendation on the operation mode under the same type commodity.
5. A big data-based international trade information data digital supervision system applying the big data-based international trade information data digital supervision method according to any one of claims 1 to 4, characterized by comprising an international trade data acquisition module, an evaluation index extraction module, a grading module, an actual effective data analysis module, and an evaluation result analysis module;
the international trade data acquisition module is used for acquiring trade data and operation modes recorded in the international trade database for selling different types of commodities by merchants;
the evaluation index extraction module is used for extracting an evaluation index corresponding to the commodity based on trade data; the evaluation indexes comprise a transportation period, a buyer forward evaluation proportion, the number of transfer stations and a package environmental protection level;
the grading module is used for setting the score grade of the evaluation index;
the actual effective data analysis module is used for analyzing and confirming actual effective data of the evaluation index under different operation data;
the evaluation result analysis module is used for analyzing the digital evaluation result of the corresponding type of commodity; and recommending and early warning the operation mode of the analyzed type commodity according to the digital evaluation result.
6. The big data based digital monitoring system for international trade information data according to claim 5, wherein: the grading module comprises a grading quantity setting unit and a grading analysis unit;
the grade number setting unit is used for setting the grade number of the score based on the evaluation indexes corresponding to the commodities of the same type;
the grading analysis unit is used for analyzing grading of the transportation period, grading of the forward evaluation proportion of the buyers and grading corresponding to the packaging environment-friendly grade based on the number of the score grades.
7. The big data based digital supervision system for international trade information data according to claim 6, wherein: the actual effective data analysis module comprises an average value analysis unit and an environmental protection index calculation unit;
the average value calculation unit is used for obtaining the average transportation period, the forward evaluation proportion of buyers and the average number of transfer stations of the same type of commodities in the operation mode FBM in the monitoring period; and the average transportation period, the forward evaluation proportion of buyers and the average number of transfer stations in the FBA mode of operation;
the environment protection index calculating unit is used for obtaining the proportion of recyclable materials in the express packages of the same type of commodities and the number of the recyclable packages used for express in a monitoring period, calculating the environment protection index and outputting corresponding package environment protection levels according to a set rule.
8. The big data based digital regulatory system of international trade information data according to claim 7, wherein: the evaluation result analysis module comprises a factor set determination unit, a weight vector determination unit, a comment set setting unit, a membership function analysis unit, a fuzzy evaluation matrix establishment unit and a total evaluation value analysis unit;
the factor set determining unit is used for determining a factor set;
the weight vector determining unit is used for determining weight vectors of the corresponding factor sets;
the comment set setting unit is used for setting a comment set corresponding to the factor set;
the membership function analysis unit is used for analyzing membership functions of the corresponding comment sets of all factors and calculating corresponding membership;
the fuzzy judgment matrix establishing unit is used for establishing a fuzzy judgment matrix based on membership;
the total evaluation value analysis unit is used for analyzing the total evaluation value and the total score, outputting a scheme corresponding to the highest total score as an optimal operation mode of the monitoring type commodity, and performing early warning recommendation on the operation mode under the same type commodity.
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