Big data-based import and export decision analysis method

Abstract

The invention discloses an import and export decision analysis method based on big data, and particularly relates to the technical field of import and export trade, and the method comprises the steps: 1, determining the feature content of a to-be-analyzed import and export decision, and carrying out the marking and dividing of different feature contents in the import and export decision; 2, collecting historical import and export data of different feature contents, and determining key import and export data based on the historical import and export data; 3, integrating key import and export data of each commodity type in different import and export regions in different time periods, setting an import risk index and an export risk index, and analyzing and adjusting an import and export decision according to the import risk index and the export risk index; according to the method, the import and export risk index threshold values are set, and import and export risk commodity types in different regions in different time periods are determined, so that the import and export strategy is adjusted, the change trend of the risk along with time is analyzed, the future risk level can be predicted, regions with relatively low risks or relatively high risks are found out, and the import and export layout is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of import and export trade, and specifically relates to an import and export decision analysis method based on big data. Background Art

[0002] In recent years, on the basis of vigorously developing import and export trade, due to the wide variety of import and export goods information, import and export decisions urgently need effective methods to analyze and judge import and export goods. Therefore, more and more attention is paid to the control of import and export goods in order to adapt to social development.

[0003] Risks are inevitable in international import and export trade. However, traditional risk identification methods are often based on experience and intuition, lacking objectivity and accuracy. In addition, even if risks are identified, how to quantify them and make decisions based on the quantified results is also a difficult problem. The analysis of import and export decisions requires comprehensive consideration of many factors, involving multiple commodity types, multiple import and export regions, and complex market dynamics. How to effectively and accurately judge the potential risks of import and export decisions through these complex factors is a major challenge facing decision makers. Summary of the invention

[0004] The purpose of the present invention is to provide an import and export decision analysis method based on big data, which is used to solve the technical problem of how to accurately judge potential risks by analyzing import and export decisions in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An import and export decision analysis method based on big data, including:

[0007] Step 1: Determine the characteristic content of the import and export decision to be analyzed, and mark and divide the different characteristic contents in the import and export decision;

[0008] Step 2: Collect historical import and export data with different characteristics, and determine key import and export data based on the historical import and export data;

[0009] Step 3: Summarize the key import and export data of each commodity type in different import and export regions in different time periods, set import risk indicators and export risk indicators, and use the import risk indicators and export risk indicators to analyze and adjust import and export decisions.

[0010] Furthermore, the characteristic contents of the import and export decisions to be analyzed are determined by:

[0011] Divide the time period into time segments with \(T\) as the time period and \(r\) as the time length. Collect the historical import and export decisions within each time segment. Based on the characteristic content of the import and export decision to be analyzed, expand the data collection objects. That is, according to the commodity types in the import and export decision to be analyzed, number and mark different commodity types respectively. Denote the obtained set of commodity type numbers as \(\{1,\cdots,i,\cdots,x\}\), where \(x\) represents the total number of import and export commodity types, and \(i\) represents the \(i\)-th import and export commodity type;

[0012] Determine the other import and export regions of this commodity type in the historical import and export decisions. According to the import and export regions in the import and export decision to be analyzed, determine the other commodity types imported and exported in this import and export region in the historical import and export decisions. Number and mark different import and export regions respectively. Denote the obtained set of import and export region numbers as \(\{1,\cdots,j,\cdots,y\}\), where \(y\) represents the total number of import and export regions, and \(j\) represents the \(j\)-th import and export region;

[0013] The characteristic content of the import and export decision to be analyzed includes: commodity type, time period, and import and export region.

[0014] Furthermore, collect the historical import and export data of different characteristic contents, and determine the key import and export data based on the historical import and export data. The specific method is as follows:

[0015] Collect the historical import and export data of different commodity types in different import and export regions in different time segments. The historical import and export data includes: commodity type cost, transportation loss, commodity type revenue, trade time, production volume and export volume of the commodity type in the import region, and sales volume and import volume of the commodity type in the export region. Based on the set of commodity type numbers, the set of import and export region numbers, and the historical import and export data, determine the key import and export data of each commodity type in different time segments and different import and export regions. The key import and export data includes: estimated transportation loss, estimated return value, estimated return period, supply index in the import region, and demand index in the export region;

[0016] Furthermore, estimate the transportation loss. If the import and export decision is preset to import or export \(M\) quantity of commodity type \(A\) from region \(C\) using transportation method \(B\) within \(S\) time segments, and collect the historical import and export data of importing or exporting \(m\) quantity of commodity type \(A\) from region \(C\) using the same transportation method, and the transportation loss in the historical import and export data is \(n1\), then the estimated transportation loss of the import and export decision is equal to \(v1\) represents the transportation loss weight coefficient

[0017] Furthermore, for the expected return value, if the import and export decision is preset within the S time period, and transportation method B is used to import or export M quantities of commodity type A from region C, historical import and export data of importing or exporting m quantities of commodity type A from region C using the same transportation method is collected. If the unit commodity type revenue in the historical import and export data is n2, then the expected return value of the import and export decision is equal to v2 represents the commodity type revenue weight coefficient;

[0018] Furthermore, for the expected return period, if the import and export decision is preset within the S time period, and transportation method B is used to import or export M quantities of commodity type A from region C, historical import and export data of importing or exporting m quantities of commodity type A from region C using the same transportation method is collected. If the trade time in the historical import and export data is n3, then the expected return period of the import and export decision is equal to v3 represents the trade time weight coefficient;

[0019] Furthermore, the import region supply index specifically includes:

[0020] Based on the historical import and export data, through time series analysis and using the trend extrapolation method, the estimated values of the production volume and export volume of the commodity type in the import region at the current moment are obtained, the deviation degree between the estimated value and the actual value is calculated, and the formula represents the import region supply index, where F 1 represents the import region supply index, j represents the jth region, i represents the ith commodity type, t represents the tth time period, w1 represents the production volume weight coefficient, w2 represents the export volume weight coefficient, Sc t (i, j) represents the true value of the production volume of commodity type i in region j within the t time period, Sc′ t (i, j) represents the estimated value of the production volume of commodity type i in region j within the t time period, Ck t (i, j) represents the true value of the export volume of commodity type i in region j within the t time period, Ck t ′(i, j) represents the estimated value of the export volume of commodity type i in region j within the t time period.

[0021] Furthermore, the export region demand index, the specific content includes:

[0022] Based on the historical import and export data, through time series analysis and using the trend extrapolation method, the estimated values of the sales volume and import volume of the commodity type in the export region at the current moment are obtained, the deviation degree between the estimated value and the actual value is calculated, and the formula represents the export region demand index, where F 2Indicates the demand indicator for the export region, j represents the jth region, i represents the ith commodity type, t represents the tth time period, w3 represents the sales volume weight coefficient, w4 represents the import volume weight coefficient, Xc t (i, j) represents the true value of the sales volume of commodity type i in region j during the tth time period, Xc′ t (i, j) represents the estimated value of the sales volume of commodity type i in region j during the tth time period, Jk t (i, j) represents the true value of the import volume of commodity type i in region j during the tth time period, Jk t ′(i, j) represents the estimated value of the import volume of commodity type i in region j during the tth time period.

[0023] Furthermore, by integrating the key import and export data of various commodity types in different time periods and different import and export regions, an import risk indicator is set. The specific method is as follows:

[0024] Using the formula represents the import risk indicator, where represents the import risk indicator of commodity type i in region j during the tth time period, represents the estimated transportation loss of importing commodity type i from region j during the tth time period, represents the total import amount of commodity type i from region j during the tth time period, represents the cost of the imported commodity type of commodity type i in region j during the tth time period, represents the estimated return value of importing commodity type i from region j during the tth time period, represents the estimated return period of importing commodity type i from region j during the tth time period, represents the supply indicator of the import region for importing commodity type i from region j during the tth time period.

[0025] Furthermore, by integrating the key import and export data of various commodity types in different time periods and different import and export regions, an export risk indicator is set. The specific method is as follows:

[0026] Using the formula represents the export risk indicator, where represents the export risk indicator of commodity type i in region j during the tth time period, represents the estimated transportation loss of exporting commodity type i from region j during the tth time period, represents the total export amount of commodity type i from region j during the tth time period, represents the cost of the exported commodity type of commodity type i in region j during the tth time period, represents the estimated return value of exporting commodity type i from region j during the tth time period, It represents the expected return period for exporting commodity type i from region j during time period t. It represents the export regional demand index for exporting commodity type i from region j during time period t.

[0027] Furthermore, analyze and adjust the import and export decisions based on the import risk index and the export risk index, set the threshold of the import or export risk index for commodity types, and on the premise of the same time period and the same region, determine the risk indexes of different commodity types. Record the commodity types with the import risk index greater than or equal to the import risk index threshold as import risk commodity types, reduce the import volume of import risk commodity types until the import risk index of the import risk commodity types is less than the import risk index threshold, so as to realize the adjustment of the import risk commodity types in the import and export decisions. Record the commodity types with the export risk index greater than or equal to the export risk index threshold as export risk commodity types. Based on the above judgment of import and export risk commodity types, by reducing the import volume of export risk commodity types until the import risk index of the export risk commodity types is less than the import risk index threshold, or finding suppliers and markets for alternative commodity types, realize the adjustment of the export risk commodity types in the import and export decisions;

[0028] On the premise of the same region and the same commodity type, determine the risk indexes for different time periods. Record the time periods with the import risk index greater than or equal to the import risk index threshold as import risk time periods, and record the time periods with the export risk index greater than or equal to the export risk index threshold as export risk time periods. By reducing or suspending the import of commodity types during the import risk time periods and the export risk time periods, realize the adjustment of the import and export risk time periods in the import and export decisions;

[0029] On the premise of the same commodity type and the same time period, determine the risk indexes for different regions. Record the regions with the import risk index greater than or equal to the import risk index threshold as import risk regions, and record the regions with the export risk index greater than or equal to the export risk index threshold as export risk regions. By finding alternative import and export regions, realize the adjustment of the import and export risk regions in the import and export decisions.

[0030] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0031] 1. Through the detailed data analysis based on the commodity type number set and the import and export region number set, the present invention can more accurately determine the key import and export data of each commodity type in different time periods and different import and export regions. By evaluating the transportation loss, return value, and return period in the import and export decisions, it provides intuitive information about cost, revenue, and time efficiency for decision-makers, which helps to accurately adjust the import and export decisions;

[0032] 2. By comprehensively considering historical import and export data, setting supply indicators for import regions and demand indicators for export regions, the present invention evaluates the stability of commodity supply and demand in import and export regions, which helps import and export decision-makers identify potential market fluctuations and risks, and thus take measures in advance to adjust potential risks in import and export decisions.

[0033] 3. By comprehensively considering multiple factors such as commodity type, time period, import and export regions, and key import and export data, the present invention sets import risk indicators and export risk indicators. By comparing risk indicators with their corresponding thresholds, it automatically screens out commodity types, time periods, and regions with risks, enabling decision-makers to quickly identify and respond to potential risks. This method not only improves the scientific nature of import and export decisions but also enhances the risk prevention and control ability of import and export decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 Shows a flowchart of a method for import and export decision-making analysis based on big data;

[0036] Figure 2 Shows a flowchart of a method for analyzing import and export decisions based on commodity type, time period, and import and export regions;

[0037] Figure 3 Shows a flowchart of a method for setting import and export risk indicators by comprehensively considering key import and export data. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0039] As Figure 1 、 Figure 2 、 Figure 3 shown, a method for import and export decision-making analysis based on big data specifically includes the following steps:

[0040] Step 1: Determine the characteristic content of the import and export decision to be analyzed, and mark and divide different characteristic contents in the import and export decision.

[0041] Determine the import and export decisions to be analyzed, and extract the characteristic content containing the information of import and export commodity types. The characteristic content includes: commodity type, time period, and import and export regions. Divide the time period with T as the time cycle and r as the time length. Collect the historical import and export decisions within each time period. According to the commodity type and import and export regions included in the import and export decisions to be analyzed, expand the data collection objects, that is, according to the commodity type in the import and export decisions to be analyzed, determine the other import and export regions of this commodity type in the historical import and export decisions, and according to the import and export regions in the import and export decisions to be analyzed, determine the other commodity types imported and exported in this import and export region in the historical import and export decisions.

[0042] Number and mark different commodity types respectively. For example, mark the mobile phone type commodity as commodity type No. 1, and mark the earphone type commodity as commodity type No. 2. Thus, determine the numbers of all import and export commodity types, and record the obtained set of commodity type numbers as {1,..., i,..., x}, where x represents the total number of import and export commodity types;

[0043] Number and mark different import and export regions respectively. For example, in the import and export decision, if 1m share of commodity type No. 8 is imported from region A, mark region A as region No. 6, and if 2m share of commodity type No. 9 is exported in region B, mark region B as region No. 4. Thus, determine the numbers of all import and export regions, and record the obtained set of import and export region numbers as {1,..., j,..., y}, where y represents the total number of import and export regions.

[0044] Step 2: Collect the historical import and export data of different characteristic contents, and determine the key import and export data based on the historical import and export data.

[0045] Collect the historical import and export data of different commodity types in different import and export regions in different time periods. The historical import and export data includes: commodity type cost, transportation loss, commodity type revenue, trade time, production volume and export volume of commodity types in the import region, and sales volume and import volume of commodity types in the export region. Based on the set of commodity type numbers, the set of import and export region numbers, and the historical import and export data, determine the key import and export data of each commodity type in different time periods and different import and export regions. The key import and export data includes: estimated transportation loss, estimated return value, estimated return period, supply index in the import region, and demand index in the export region;

[0046] Among them, the commodity type cost in the import region refers to the procurement cost of the commodity type, and the commodity type cost in the export region refers to the sum of the procurement cost or production cost and transportation cost of the commodity type;

[0047] Expected transportation loss. If the import and export decision is preset within the S time period, and it is to import or export M quantity of commodity type A from region C using transportation method B, and historical import and export data of importing or exporting m quantity of commodity type A from region C using the same transportation method is collected, and the transportation loss in the historical import and export data is n1, then the expected transportation loss of the import and export decision is equal to v1 represents the transportation loss weight coefficient

[0048] Expected return value. If the import and export decision is preset within the S time period, and it is to import or export M quantity of commodity type A from region C using transportation method B, and historical import and export data of importing or exporting m quantity of commodity type A from region C using the same transportation method is collected, and the unit commodity type revenue in the historical import and export data is n2, then the expected return value of the import and export decision is equal to v2 represents the commodity type revenue weight coefficient;

[0049] Expected return period. If the import and export decision is preset within the S time period, and it is to import or export M quantity of commodity type A from region C using transportation method B, and historical import and export data of importing or exporting m quantity of commodity type A from region C using the same transportation method is collected, and the trading time in the historical import and export data is n3, then the expected return value of the import and export decision is equal to v3 represents the trading time weight coefficient;

[0050] Import region supply index, which is determined by calculating the supply stability of the production volume and export volume of the commodity type in the import region. The larger the import region supply index, the greater the volatility of the supply situation of the commodity type in the import region;

[0051] Furthermore, based on the historical import and export data, through time series analysis and using the trend extrapolation method, the estimated values of the production volume and export volume of the commodity type in the import region at the current moment are obtained. The import region supply index is obtained by comparing the deviation degree between the estimated values and the actual values of the production volume and export volume of the commodity type in the import region at the current moment. The specific calculation formula of the import region supply index is as follows:

[0052]

[0053] Among them, F 1 represents the import region supply index, j represents the jth region, i represents the ith commodity type, t represents the tth time period, w1 represents the production volume weight coefficient, w2 represents the export volume weight coefficient, Sc t (i, j) represents the true value of the production volume of commodity type i in region j within the t time period, Sc′ t (i, j) represents the estimated value of the production volume of commodity type i in region j within the t time period, Ck t(i, j) represents the true value of the export volume of commodity type i in region j during the t time period, Ck t ′(i, j) represents the estimated value of the export volume of commodity type i in region j during the t time period.

[0054] The export region demand indicator is determined by calculating the demand stability of the sales volume and import volume of commodity types in the export region. The larger the export region demand indicator, the greater the volatility of the market demand in the export region of the commodity type;

[0055] Furthermore, based on historical import and export data, through time series analysis and using the trend extrapolation method, the estimated values of the sales volume and import volume of commodity types in the export region at the current moment are obtained. The export region demand indicator is obtained by comparing the deviation degree between the estimated values and the actual values of the sales volume and import volume of commodity types in the export region at the current moment. The specific calculation formula of the export region demand indicator is as follows:

[0056]

[0057] Among them, F 2 represents the export region demand indicator, j represents the jth region, i represents the ith commodity type, t represents the tth time period, w3 represents the sales volume weight coefficient, w4 represents the import volume weight coefficient, Xc t (i, j) represents the true value of the sales volume of commodity type i in region j during the t time period, Xc′ t (i, j) represents the estimated value of the sales volume of commodity type i in region j during the t time period, Jk t (i, j) represents the true value of the import volume of commodity type i in region j during the t time period, Jk t ′(i, j) represents the estimated value of the import volume of commodity type i in region j during the t time period.

[0058] Step 3: Synthesize the key import and export data of each commodity type in different time periods and different import and export regions, set the import risk indicator and the export risk indicator, and analyze and adjust the import and export decisions based on the import risk indicator and the export risk indicator.

[0059] Furthermore, the specific calculation formula of the import risk indicator of a commodity type in different import regions is as follows:

[0060]

[0061] Among them, represents the import risk indicator of commodity type i in region j during the t time period, represents the estimated transportation loss of importing commodity type i from region j during the t time period, represents the total import amount of commodity type i imported from region j during the t time period, Denote the import commodity type cost of commodity type i in region j during period t. Denote the expected return value of importing commodity type i from region j during period t. Denote the expected return period of importing commodity type i from region j during period t. Denote the import region supply index of importing commodity type i from region j during period t;

[0062] Furthermore, the calculation formula of the export risk index of commodity types in different export regions is specifically as follows:

[0063]

[0064] Among them, Denote the export risk index of commodity type i in region j during period t, Denote the expected transportation loss of exporting commodity type i from region j during period t, Denote the total export amount of commodity type i from region j during period t, Denote the export commodity type cost of commodity type i in region j during period t, Denote the expected return value of exporting commodity type i from region j during period t, Denote the expected return period of exporting commodity type i from region j during period t, Denote the export region demand index of exporting commodity type i from region j during period t.

[0065] Through the calculation formula of the import and export risk index, calculate the import and export risk indexes of importing and exporting different commodity types from different regions in different periods in the import and export decision-making, obtain the import risk index set and the export risk index set in the import and export decision-making, set the threshold of the import or export risk index of the commodity type according to the historical import and export data, and conduct a classified discussion on the import risk index set and the export risk index set;

[0066] On the premise of the same time period and the same region, determine the risk indicators for different commodity types. Denote the commodity types with import risk indicators greater than or equal to the import risk indicator threshold as import risk commodity types, and denote the commodity types with export risk indicators greater than or equal to the export risk indicator threshold as export risk commodity types. Determine the import risk commodity types and export risk commodity types existing in the import and export decisions in different time periods and different regions. Reduce the import volume of import risk commodity types until the import risk indicators of the import risk commodity types are less than the import risk indicator threshold, so as to realize the adjustment of the import risk commodity types in the import and export decisions. By reducing the import volume of export risk commodity types until the import risk indicators of the export risk commodity types are less than the import risk indicator threshold, or by finding suppliers and markets for alternative commodity types, realize the adjustment of the export risk commodity types in the import and export decisions;

[0067] On the premise of the same region and the same commodity type, determine the risk indicators for different time periods. Denote the time periods with import risk indicators greater than or equal to the import risk indicator threshold as import risk time periods, and denote the time periods with export risk indicators greater than or equal to the export risk indicator threshold as export risk time periods. Determine the import risk time periods and export risk time periods existing in the import and export of different commodity types in different regions in the import and export decisions, and thus analyze the changing trend of risks over time. By reducing or suspending the import of commodity types within the import risk time periods and export risk time periods, realize the adjustment of the import and export risk time periods in the import and export decisions;

[0068] On the premise of the same commodity type and the same time period, determine the risk indicators for different regions. Denote the regions with import risk indicators greater than or equal to the import risk indicator threshold as import risk regions, and denote the regions with export risk indicators greater than or equal to the export risk indicator threshold as export risk regions. Determine the import risk regions and export risk regions existing in the import and export of different commodity types in different time periods in the import and export decisions, and thus find the regions with lower or higher risks in order to optimize the import and export layout. By finding alternative import and export regions, realize the adjustment of the import and export risk regions in the import and export decisions;

[0069] Through classifying and discussing the import risk indicator set and the export risk indicator set, screen and predict and evaluate the implementation effect of the import and export decisions, and thus screen out the effective strategies and the strategies that need to be improved in the import and export decisions.

[0070] As mentioned above, it is only the preferred specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and all should be covered within the protection scope of the present invention.

[0071] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A method for import and export decision-making analysis based on big data, characterized in that, Including: Step 1: Determine the characteristic content of the import and export decision to be analyzed, and mark and classify different characteristic contents in the import and export decision; Step 2: Collect historical import and export data of different characteristic contents, and determine the key import and export data based on the historical import and export data; Step 3: Synthesize the key import and export data of each commodity type in different time periods and different import and export regions, set import risk indicators and export risk indicators, and analyze and adjust the import and export decision by the import risk indicators and export risk indicators.

2. The method for analyzing import and export decisions based on big data according to claim 1, wherein To determine the characteristic content of the import and export decision to be analyzed, the specific method is as follows: Taking T as the time period and r as the time length to divide time periods, collect historical import and export decisions within each time period. Based on the characteristic content of the import and export decision to be analyzed, expand the data collection objects, that is, according to the commodity types in the import and export decision to be analyzed, number and mark different commodity types respectively, and denote the obtained set of commodity type numbers as {1,..., i,..., x}, where x represents the total number of import and export commodity types, and i represents the i-th import and export commodity type; Determine other import and export regions of this commodity type in the historical import and export decision. According to the import and export regions in the import and export decision to be analyzed, determine other commodity types imported and exported in this import and export region in the historical import and export decision, number and mark different import and export regions respectively, and denote the obtained set of import and export region numbers as {1,..., j,..., y}, where y represents the total number of import and export regions, and j represents the j-th import and export region; The characteristic content of the import and export decision to be analyzed includes: commodity type, time period, and import and export region.

3. A method for import and export decision-making analysis based on big data according to claim 1, characterized in that, To collect historical import and export data of different characteristic contents and determine the key import and export data based on the historical import and export data, the specific method is as follows: Collect historical import and export data of different commodity types in different import and export regions in different time periods. Based on the set of commodity type numbers, the set of import and export region numbers, and the historical import and export data, determine the key import and export data of each commodity type in different time periods and different import and export regions; The historical import and export data includes: commodity type cost, transportation loss, commodity type revenue, trade time, production volume and export volume of the commodity type in the import region, and sales volume and import volume of the commodity type in the export region; The key import and export data includes: estimated transportation loss, estimated return value, estimated return period, import region supply index, and export region demand index.

4. The method for import and export decision-making analysis based on big data according to claim 3, wherein, The estimated transportation loss, estimated return value, and estimated return period, the specific content includes: Expected transportation loss. If the import and export decision is preset within the S time period, and the transportation method B is used to import or export M quantity of commodity type A from region C, and historical import and export data of importing or exporting m quantity of commodity type A from region C using the same transportation method is collected, and the transportation loss in the historical import and export data is n1, then the expected transportation loss of the import and export decision is equal to v1 represents the transportation loss weight coefficient; The expected return value. If the import and export decision is preset within the S time period, and the transportation method B is used to import or export M quantities of commodity type A from region C, and historical import and export data of importing or exporting m quantities of commodity type A from region C using the same transportation method is collected, and the unit commodity type revenue in the historical import and export data is n2, then the expected return value of the import and export decision is equal to v2 represents the revenue weight coefficient of the commodity type; The expected return period. If the import and export decision is preset within the S time period, and when using transportation method B to import or export M quantity of commodity type A from region C, historical import and export data of importing or exporting m quantity of commodity type A from region C using the same transportation method is collected, and the trading time in the historical import and export data is n3, then the expected return period of the import and export decision is equal to v3 represents the trading time weight coefficient.

5. The method for import and export decision-making analysis based on big data according to claim 4, wherein, The import region supply index, the specific content includes: Based on historical import and export data, through time series analysis and using the trend extrapolation method, the estimated values of the production volume and export volume of commodity types in the import region at the current moment are obtained, the deviation degree between the estimated value and the actual value is calculated, and the formula is used represents the supply index of the import region, where F 1 represents the supply index of the import region, j represents the j-th region, i represents the i-th commodity type, t represents the t-th time period, w1 represents the production volume weight coefficient, w2 represents the export volume weight coefficient, Sc t (i, j) represents the true value of the production volume of commodity type i in region j during the t time period, Sc′ t (i, j) represents the estimated value of the production volume of commodity type i in region j during the t time period, Ck t (i, j) represents the true value of the export volume of commodity type i in region j during the t time period, Ck t ′(i, j) represents the estimated value of the export volume of commodity type i in region j during the t time period.

6. The method for import and export decision-making analysis based on big data according to claim 1, wherein, The export region demand index, the specific content includes: Based on historical import and export data, through time series analysis and using the trend extrapolation method, the estimated values of the sales volume and import volume of commodity types in the export region at the current moment are obtained, the deviation degree between the estimated values and the actual values is calculated, and the formula is used represents the demand index of the export region, where F 2 represents the demand index of the export region, j represents the jth region, i represents the ith commodity type, t represents the tth time period, w3 represents the sales volume weight coefficient, w4 represents the import volume weight coefficient, Xc t (i, j) represents the true value of the sales volume of commodity type i in region j during the t time period, Xc′ t (i, j) represents the estimated value of the sales volume of commodity type i in region j during the t time period, Jk t (i, j) represents the true value of the import volume of commodity type i in region j during the t time period, Jk t ′(i, j) represents the estimated value of the import volume of commodity type i in region j during the t time period.

7. A method for import and export decision-making analysis based on big data according to claim 1, characterized in that, To synthesize the key import and export data of each commodity type in different time periods and different import and export regions and set import risk indicators, the specific method is as follows: Using the formula represents the import risk indicator, where represents the import risk indicator of commodity type i in region j during the t time period, represents the estimated transportation loss of importing commodity type i from region j during the t time period, represents the total import amount of commodity type i imported from region j during the t time period, represents the cost of the imported commodity type of commodity type i in region j during the t time period, represents the estimated return value of importing commodity type i from region j during the t time period, represents the estimated return period of importing commodity type i from region j during the t time period, represents the import region supply indicator of importing commodity type i from region j during the t time period.

8. The import and export decision analysis method based on big data according to claim 1, wherein, To synthesize the key import and export data of each commodity type in different time periods and different import and export regions and set export risk indicators, the specific method is as follows: Using the formula represents the export risk indicator, where represents the export risk indicator of commodity type i in region j during the t time period, represents the estimated transportation loss of exporting commodity type i from region j during the t time period, represents the total export amount of commodity type i exported from region j during the t time period, represents the cost of the export commodity type of commodity type i in region j during the t time period, represents the estimated return value of exporting commodity type i from region j during the t time period, represents the estimated return period of exporting commodity type i from region j during the t time period, represents the export region demand indicator of exporting commodity type i from region j during the t time period.

9. The method for import and export decision-making analysis based on big data according to claim 1, characterized in that, To analyze and adjust the import and export decision by the import risk indicators and export risk indicators, the specific method is as follows: Set the risk indicator thresholds for imported or exported commodity types. On the premise of the same time period and the same region, determine the risk indicators for different commodity types. Mark the commodity types with import risk indicators greater than or equal to the import risk indicator threshold as import risk commodity types, and reduce the import volume of import risk commodity types until the import risk indicators of import risk commodity types are less than the import risk indicator threshold, so as to realize the adjustment of import risk commodity types in import and export decisions. Mark the commodity types with export risk indicators greater than or equal to the export risk indicator threshold as export risk commodity types. Based on the above judgments of import and export risk commodity types, by reducing the import volume of export risk commodity types until the import risk indicators of export risk commodity types are less than the import risk indicator threshold, or by finding suppliers and markets for alternative commodity types, realize the adjustment of export risk commodity types in import and export decisions; On the premise of the same region and the same commodity type, determine the risk indicators for different time periods. Mark the time periods with import risk indicators greater than or equal to the import risk indicator threshold as import risk time periods, and mark the time periods with export risk indicators greater than or equal to the export risk indicator threshold as export risk time periods. By reducing or suspending the import of commodity types within import risk time periods and export risk time periods, realize the adjustment of import and export risk time periods in import and export decisions; On the premise of the same commodity type and the same time period, determine the risk indicators for different regions. Mark the regions with import risk indicators greater than or equal to the import risk indicator threshold as import risk regions, and mark the regions with export risk indicators greater than or equal to the export risk indicator threshold as export risk regions. By finding alternative import and export regions, realize the adjustment of import and export risk regions in import and export decisions.

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