CN116151933B - International trade information data digital supervision system and method based on big data - Google Patents

International trade information data digital supervision system and method based on big data Download PDF

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CN116151933B
CN116151933B CN202310408962.XA CN202310408962A CN116151933B CN 116151933 B CN116151933 B CN 116151933B CN 202310408962 A CN202310408962 A CN 202310408962A CN 116151933 B CN116151933 B CN 116151933B
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CN116151933A (en
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谭凯达
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Shenzhen Ganen Network Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products

Abstract

The application 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

International trade information data digital supervision system and method based on big data
Technical Field
The application 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 application 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 application 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: greater than (min [ Tp1] + (n-1), "T0/n ], (min [ Tp1] + (n-2)," T0/n ], "min [ Tp1] + (n-1)," T0/n ], "min [ Tp1] + (n-3)," T0/n ], "min [ Tp1] + (n-2)," T0/n ], "min [ Tp1] +" T0/n "," min [ Tp1] +2 "," T0/n "," less than min [ Tp1] + "T0/n", "wherein" T0/n "represents the whole of T0/n, and the transport period is extracted as an evaluation index because the transport period reflects the transport efficiency, the shorter the transport period, the higher the transport efficiency;
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: less than "100%/n", [ "100%/n", "2" 100%/n ", (2" 100%/n "," 3 "100%/n", "((n-2)" 100%/n "," (n-1) "" 100%/n "," "greater than (n-1)" "100%/n"; 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, and the loss of a customer group is easy to cause;
the classification of the corresponding score grades of the environmental protection level of the package is set as follows: m, m-1, m-2,..1, m=n; 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: greater than u+b, [ u, u+b ], [ u-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%. Q, 100%. 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 m;
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 of each factor to comment setsDegree function C 0...n-1 (Ux)，
C 0...n-1 (Ux)＝{C 0 (Ux),C 1 (Ux),...,C n-1 (Ux)}
Wherein C is n-1 (Ux) represents a membership function of a factor Ux in the factor set U to the nth comment set, ux represents the xth factor in the factor set U; ux ε U; 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 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 an M (·, +) operator,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 optimal 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 analysis unit is used for acquiring 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 optimal operation mode under the same type commodity.
Compared with the prior art, the application has the following beneficial effects: according to the application, based on analysis of recorded data generated by cross-border electronic commerce in international trade data, optimization and rationalization of commodity operation modes are analyzed by extracting evaluation indexes from four directions of transportation cost, user stability, transportation risk, saving and environmental protection, 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, and the problems that some merchants are difficult to select in the operation modes of international trade, and the problems that the cost is too high, the user is unstable and the like are solved 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 application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. 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 application.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the present application 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 application 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: greater than (min [ Tp1] + (n-1), "T0/n ], (min [ Tp1] + (n-2)," T0/n ], "min [ Tp1] + (n-1)," T0/n ], "min [ Tp1] + (n-3)," T0/n ], "min [ Tp1] + (n-2)," T0/n ], "min [ Tp1] +" T0/n "," min [ Tp1] +2 "," T0/n "," less than min [ Tp1] + "T0/n", "wherein" T0/n "represents the whole of T0/n, and the transport period is extracted as an evaluation index because the transport period reflects the transport efficiency, the shorter the transport period, the higher the transport efficiency;
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: less than 9, [9,15], (15-21 ], greater than 21, in days;
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: less than "100%/n", [ "100%/n", "2" 100%/n ", (2" 100%/n "," 3 "100%/n", "((n-2)" 100%/n "," (n-1) "" 100%/n "," "greater than (n-1)" "100%/n"; the forward evaluation proportion of the buyers is analyzed because the feedback of the buyers to the commodities can effectively judge the operation mode of the commodities, when a certain operation mode of a certain commodity is not proper, the buying experience brought to the buyers is also not good, and the loss of the client group is easy to be caused, for example, the forward evaluation proportion of the buyers is divided into the corresponding score grades of less than 25 percent, [25 percent, 50 percent ], (50 percent, 75 percent ], > 75 percent;
the classification of the corresponding score grades of the environmental protection level of the package is set as follows: m, m-1, m-2,..1, m=n; 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: greater than u+b, [ u, u+b ], [ u-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: greater than 9, [6,9], [3, 6), and less than 3.
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%. Q, 100%. 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 m; 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 (Ux)，
C 0...n-1 (Ux)＝{C 0 (Ux),C 1 (Ux),...,C n-1 (Ux)}
Wherein C is n-1 (Ux) represents a membership function of a factor Ux in the factor set U to the nth comment set, ux represents the xth factor in the factor set U; ux ε U; 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 Representing the membership degree of four actual effective data to the comment set;
based on the fuzzy judgment matrix, calculating actual effective data in the ith operation mode by using M (·, +) operatorIs a total evaluation value Bi of the (c),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 optimal 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:
score of Period/day of transport Buyer forward evaluation ratio Number of transfer stations/day Packaging environmental protection grade
3 Less than 9 More than 75% 3 1
2 [9,15] (50％,75％] [3,6) 2
1 (15,21] [25,50％] [6,9] 3
0 Greater than 21 Less than 25% Greater than 9 4
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;
assume thatAnd normalized 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 analysis unit is used for acquiring 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 optimal 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 application, and the present application 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 application 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 application should be included in the protection scope of the present application.

Claims (7)

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;
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: greater than (min [ Tp1] + (n-1), "T0/n ], (min [ Tp1] + (n-2)," T0/n ], "min [ Tp1] + (n-1)," T0/n ], "min [ Tp1] + (n-3)," T0/n ], "min [ Tp1] + (n-2)," T0/n ], "min [ Tp1] +" T0/n ], "min [ Tp1] +2" T0/n ], "less than min [ Tp1] +" T0/n "," T0/n "means 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: less than "100%/n", [ "100%/n", "2" 100%/n ", (2" 100%/n "," 3 "100%/n", "((n-2)" 100%/n "," (n-1) "" 100%/n "," "greater than (n-1)" "100%/n";
the classification of the corresponding score grades of the environmental protection level of the package is set as follows: m, m-1, m-2,..1, m=n;
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: greater than u+b, [ u, u+b ], [ u-b, u), [ u- (n-2) b, u- (n-3) b); b represents the number of transfer stations at intervals;
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 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%. Q, 100%. 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 m;
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.
3. The digital supervision method for international trade information data based on big data according to claim 2, 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 function C of each factor to comment set 0...n-1 (Ux)，
C 0...n-1 (Ux)＝{C 0 (Ux),C 1 (Ux),...,C n-1 (Ux)}
Wherein C is n-1 (Ux) represents the membership function of the factor Ux in the factor set U to the nth comment set; ux represents the x-th factor in factor set U, ux ε U;
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 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 an M (·, +) operator,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 optimal operation mode under the same type commodity.
4. 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 3, 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.
5. The big data based digital monitoring system for international trade information data according to claim 4, 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.
6. The big data based digital monitoring system for international trade information data according to claim 5, wherein: the actual effective data analysis module comprises an average value analysis unit and an environmental protection index calculation unit;
the average value analysis unit is used for acquiring an average transportation period, a buyer forward evaluation proportion and an average transfer station number of the same type of commodities in the operation mode FBM in a 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.
7. The big data based digital supervision system for international trade information data according to claim 6, 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 optimal operation mode under the same type commodity.
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