Big data-based CPTPP membership agricultural food product trade barrier prediction and analysis system

Abstract

The invention provides a big data-based CPTPP commercial country agricultural food product trade barrier prediction and analysis system. The system comprises a data acquisition module, a data processing module, a prediction model construction module, a non-tax barrier analysis module, a risk assessment and coping suggestion module, a visual display module and the like. By integrating an international trade database, tax and non-tax barrier data, policies and regulations and other multi-dimensional information and combining a machine learning algorithm, the system can predict agricultural food product trade barriers among CPTPP adult countries in the future. According to the system, algorithm models such as a random forest and a support vector machine are adopted, the probability of occurrence of trade barriers is analyzed based on historical data and real-time policy changes, and a prediction result is displayed through a visual means. The system also provides risk assessment and corresponding coping suggestions to help users to optimize export strategies and reduce trade risks.

Description

Technical Field

[0001] The present invention relates to the field of chemical workshop protection technology, and specifically to a prediction and analysis system for agricultural and food product trade barriers of CPTPP member countries based on big data. Background Art

[0002] The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is one of the important regional economic and trade agreements in the world, covering many countries in the Asia-Pacific region, including Japan, Canada, Australia, New Zealand, Mexico, Vietnam, Singapore, etc. The agreement aims to promote trade liberalization among member countries, especially in the field of agricultural and food products, by eliminating tariff barriers, simplifying trade rules and strengthening market access. Since most CPTPP member countries have rich agricultural resources and food manufacturing industries, trade in agricultural and food products is one of the focuses of economic cooperation among member countries. However, in addition to tariffs, the circulation of agricultural and food products in international trade is often restricted by a series of non-tariff barriers, such as technical barriers to trade (TBT), sanitary and phytosanitary measures (SPS), food safety standards and differences in domestic regulations. These non-tariff barriers pose a significant challenge to the free flow of agricultural and food products.

[0003] Although the CPTPP agreement has made significant progress in eliminating tariffs, there are still a large number of non-tariff barriers among member countries, especially in terms of technical standards for agricultural and food products, animal and plant quarantine requirements, and food safety certification. For example, some member countries have extremely strict supervision on pesticide residues and genetically modified crops, while other countries have relatively loose standards. This difference has hindered the free trade of agricultural and food products to a certain extent. In addition, differences in animal and plant quarantine measures among different countries have also created uncertainty in the export of agricultural and food products, especially livestock products, aquatic products, fresh fruits and vegetables, etc., which require strict quarantine. Non-tariff barriers not only increase the compliance costs of exporters, but may also cause products to be stranded or denied entry, which has a greater economic impact on traders and producers.

[0004] As global supply chains become more complex and international market competition intensifies, companies and governments are in urgent need of more accurate tools to predict and analyze the changing trends of trade barriers in order to formulate corresponding countermeasures. Traditional trade barrier analysis mainly relies on expert experience or simple statistics of historical data, which cannot process large amounts of multi-dimensional trade data and policy change information in real time, and does not respond to dynamic changes in non-tariff barriers in a timely manner, making it difficult to cope with the complex international trade environment.

[0005] In recent years, the rapid development of big data and artificial intelligence technologies has provided new possibilities for the prediction and analysis of trade barriers. Through large-scale data collection, processing and analysis, combined with machine learning algorithms, it is possible to dig out hidden patterns and laws from a large amount of historical data and real-time data, identify trade barriers that may appear in the future and predict their impact on agricultural and food product trade. Big data technology can not only cover tariffs and non-tariff barriers, but also analyze the potential impact of various external factors (such as policy changes, market dynamics, adjustments to international regulations and standards, etc.) on agricultural and food product trade. This data-driven predictive analysis provides more scientific decision-making support for enterprises, governments and decision makers, helping them to respond to potential trade barriers in advance, optimize trade policies and export strategies, and reduce losses and risks.

[0006] However, there is a lack of forecasting and analysis tools specifically targeting trade barriers for agricultural and food products among CPTPP member countries. Existing research or tools focus on the analysis of trade barriers for a single country or a single product, lacking systematic, comprehensive and dynamic forecasting functions. With the increase in the scale of trade data and the increase in policy complexity, there is an urgent need for a systematic tool that can integrate big data technology, machine learning algorithms and trade barrier analysis to help relevant companies and government departments predict and respond to trade barriers for agricultural and food products among CPTPP member countries.

[0007] Based on the above background, the present invention proposes a prediction and analysis system for agricultural and food product trade barriers among CPTPP member countries based on big data. Summary of the invention

[0008] The purpose of the present invention is to address the shortcomings of the prior art and provide a CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data to solve the problems raised in the background technology.

[0009] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data, comprising:

[0010] Data collection module: used to obtain multi-dimensional data related to agricultural and food product trade among CPTPP member countries from international trade databases, tariff and non-tariff barrier databases, policy and regulation databases, CPTPP policy documents and market dynamics information. The data include but are not limited to tariff data, technical trade barrier data, legal and regulatory data, product quarantine standard data, and import and export data of agricultural and food products;

[0011] Data processing and cleaning module: used to clean, preprocess, format and standardize the collected raw data, including deleting duplicate data, filling missing values ​​and filtering irrelevant information to ensure data consistency and accuracy;

[0012] Prediction model building module: used to build a prediction model based on the collected historical data and current trade data through machine learning algorithms to predict future agricultural and food product trade barriers between CPTPP member countries. The model adopts algorithms such as support vector machine (SVM), decision tree, random forest or deep learning;

[0013] Non-tariff barrier analysis module: used to identify and predict possible non-tariff barriers in the future by analyzing historical technical trade barriers, animal and plant quarantine measures, and differential data of laws and regulations;

[0014] Risk assessment and response suggestion module: used to conduct risk assessment on possible trade barriers in the future based on the results of the forecast model, and provide response measures and strategic suggestions for trade barriers;

[0015] Visualization display module: used to display the forecast results and related analysis data in the form of charts, trend charts, etc. through visualization technology, so that users can understand and use them intuitively.

[0016] As a preferred technical solution of the present invention, the prediction model building module predicts trade barriers based on the following algorithm formula:

[0017] P(B|X)=f(W T X+b)

[0018] Among them, P(B|X) is the probability of trade barriers occurring given the input feature X, W is the model weight, b is the bias, and f is the activation function (such as Logistic or ReLU function). The model determines the parameters W and b by training historical data and predicts future barrier trends.

[0019] As a preferred technical solution of the present invention, the data acquisition module obtains agricultural and food product trade data and related policy and regulatory information from different data sources in real time through crawler technology and API interface, and stores it in a distributed database to support the storage and processing of large-scale data.

[0020] As a preferred technical solution of the present invention, the data processing and cleaning module uses natural language processing (NLP) technology to process text data (such as policy and regulatory documents), specifically including text segmentation, topic extraction, named entity recognition and other operations.

[0021] As a preferred technical solution of the present invention, the prediction model building module uses a random forest algorithm for prediction. The formula of the random forest algorithm is:

[0022]

[0023] Among them, T i(x) represents the prediction result of the i-th decision tree, N is the number of decision trees, f(x) is the final prediction result, and the accuracy of prediction is improved by integrating multiple decision trees.

[0024] As a preferred technical solution of the present invention, the non-tariff barrier analysis module identifies high-risk barrier categories by clustering the historical data of technical barriers to trade (TBT) and sanitary and phytosanitary measures (SPS) of each CPTPP member country, and clusters them using the following formula:

[0025]

[0026] Where d(i,j) represents the policy difference distance between two countries i and j, x ik and x jk They represent the k-th barrier characteristics respectively, and the Euclidean distance is used to measure the differences between policies.

[0027] As a preferred technical solution of the present invention, the risk assessment and response suggestion module scores the severity of the predicted barriers using the risk assessment formula:

[0028] R=P(B|X)×C

[0029] Among them, R is the risk assessment value, P(B|X) is the probability of trade barriers occurring, and C is the cost of the impact of barriers on agricultural and food product trade. The system provides different response suggestions based on the risk value, including adjusting export plans, strengthening policy responses and other measures.

[0030] As a preferred technical solution of the present invention, the visualization display module uses dynamic visualization tools to generate interactive charts, trend charts and geographic distribution maps to display the history, current status and future trends of agricultural and food product trade barriers of each CPTPP member country.

[0031] As a preferred technical solution of the present invention, the system also includes a data update module for regularly or in real time updating trade data and policy changes among member countries to ensure the accuracy and timeliness of the prediction model. The data update cycle is set by the user or dynamically adjusted by the system according to data changes.

[0032] As a preferred technical solution of the present invention, the system further includes a user feedback learning module, and the user can submit actual trade barrier information through the feedback interface provided by the system. The system uses this information to adjust and optimize the prediction model to improve the accuracy of future predictions.

[0033] Compared with the prior art, the beneficial effect of the present invention is that the present invention can accurately predict the agricultural and food product trade barriers that may appear in the future between CPTPP member countries through big data technology and machine learning algorithms. The system integrates multiple data sources, including trade data, tariffs and non-tariff barriers, policies and regulations, and market dynamics, combines historical data for model training, and uses support vector machines, random forests and other algorithms for prediction. This technical means significantly improves the accuracy and timeliness of trade barrier predictions, helps enterprises and government departments respond to changes in trade policies in a timely manner, optimizes export strategies for agricultural and food products, and reduces compliance risks and trade costs.

[0034] In addition, the system also has visualization and risk assessment functions, which can present the forecast results in an intuitive way and provide corresponding response suggestions. This not only improves users' understanding of the dynamics of trade barriers, but also provides a scientific basis for corporate market expansion and government policy formulation. Through real-time data updates and user feedback, the system can continuously optimize the model to make the forecast more accurate and timely, and has broad application value and prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a structural block diagram of the CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data proposed in the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The following is combined with Figure 1 With reference to the accompanying drawings and a number of embodiments, specific embodiments of the present invention are described in detail.

[0038] Example 1: Data collection and processing

[0039] The embodiment of the present invention provides a data collection and processing method for a CPTPP member country agricultural and food product trade barrier prediction and analysis system based on big data. The system obtains data from multiple data sources through a data collection module, including but not limited to international trade databases (such as UN Comtrade, WTO databases), tariff and non-tariff barrier databases (such as the United Nations Technical Barriers to Trade Database), CPTPP policy documents, and regulations and policy databases of each member country.

[0040] Specifically, the system uses crawler technology and API interfaces to regularly obtain the latest agricultural and food product import and export data, tariff policies, technical barriers to trade (TBT), animal and plant health and quarantine measures (SPS) and other information from these data sources. By regularly and automatically updating data, the timeliness of the data processed by the system is ensured.

[0041] After receiving the data, the data processing and cleaning module cleans and standardizes the data. This includes deleting duplicate data, filling missing values, and format conversion (such as unifying the date format in different data sources into a standard format). For text data (such as regulatory and policy documents issued by member states), the system uses natural language processing (NLP) technology, including word segmentation, word frequency analysis, named entity recognition (NER), etc., to extract key information related to trade barriers, such as specific tariff adjustments, SPS / TBT standard changes, etc.

[0042] In this embodiment, the main algorithms used by the system include:

[0043] NLP model: used to extract specific barrier keywords in policy documents, such as "pesticide residue limits", "animal and plant quarantine requirements", etc.

[0044] Time series processing algorithms: used to process and analyze time series data on trade volumes and policy changes to provide input data for forecasting models.

[0045] Example 2: Construction and application of prediction model

[0046] In this embodiment, the prediction model building module in the system uses the random forest algorithm to analyze and model historical data. The features used include the import and export volume of agricultural and food products, changes in tariff policies, historical data on non-tariff barriers of each member country (such as technical trade barriers, animal and plant quarantine measures), market dynamics, etc.

[0047] The model training process is based on the following features: Tariff data of various countries (such as changes in tariff rates under the CPTPP framework) Implementation of historical non-tariff barriers Historical import and export volumes of agricultural and food products SPS / TBT policy adjustment records of various countries Through these data, the random forest model uses multiple decision trees to make predictions, and the formula is:

[0048]

[0049] Among them, f(x) represents the final prediction result, N represents the number of decision trees, and T i (x) is the output of the i-th decision tree. The model inputs current and predicted policy changes and trade data, outputs the probability of possible trade barriers in the future, and evaluates its impact on agricultural and food product trade.

[0050] For example, it is predicted that a member country may strengthen quarantine measures due to SPS / TBT requirements, resulting in increased import restrictions on specific agricultural and food products.

[0051] Example 3: Non-tariff barrier analysis and risk assessment

[0052] This embodiment provides specific implementation methods of the non-tariff barrier analysis module and the risk assessment and response suggestion module. The non-tariff barrier analysis module identifies the changing trend of each member country's barrier measures on specific agricultural and food products by analyzing the historical data of technical barriers to trade (TBT) and plant and animal quarantine measures (SPS) between member countries. The module uses a cluster analysis algorithm to classify these historical data and identify the types of barriers with high risks.

[0053] This module uses the following clustering algorithm formula:

[0054]

[0055] Among them, d(i,j) represents the policy difference between two countries i and j, x ik and x jk They represent the k-th barrier characteristics. By calculating the policy difference, we can analyze which countries are more likely to introduce new policies that have a negative impact on agricultural and food products in the future.

[0056] On this basis, the risk assessment and response recommendation module assesses the risk of trade barriers to agricultural and food product trade based on the predicted probability and potential impact of trade barriers. The specific assessment formula is:

[0057] R=P(B|X)×C

[0058] Among them, R is the risk value, P(B|X) is the probability of trade barriers occurring, and C is the impact cost of barriers. Through this formula, the system can output the trade barrier risk rating of each country and provide corresponding response suggestions for enterprises and governments, such as adjusting export country targets, strengthening compliance measures with specific countries, etc.

[0059] For example, if a country’s non-tariff barriers impose significant restrictions on beef exports, the system will recommend that relevant companies strengthen research on that country’s agricultural and food product quarantine standards, or seek other potential markets.

[0060] Example 4: Visual display and user feedback

[0061] In this embodiment, the system displays the prediction results to the user in a graphical manner through a visualization module. Users can view the trade data, policy change trends, and predicted probability of trade barriers of agricultural and food products of each CPTPP member country through the system. The visualization display forms include interactive trend charts, radar charts, geographic distribution charts, etc., so that users can intuitively understand the potential impact of policy changes of each member country on agricultural and food product trade.

[0062] In addition, the system also provides a user feedback learning module, through which users can submit information on actual trade barriers. The system will integrate user feedback data into the model for further learning and optimization to improve the accuracy of future forecasts.

[0063] For example, if a country suddenly introduces new import quarantine requirements, users can upload this policy change to the system, and the system will use this information to further update the prediction model to ensure that the prediction results are more timely and accurate.

[0064] Example 5: Dynamic data update and model optimization

[0065] In this embodiment, the system is also equipped with a data update module for regularly or in real time updating the agricultural and food product trade data and policy and regulatory information in the system. The data update module automatically obtains the latest data from the international trade database and the policy document library of each country through the API interface, and dynamically updates the prediction model in combination with machine learning technology. The user can set the data update cycle, and the system can also automatically adjust the update cycle according to the frequency of data changes.

[0066] The workflow of this module is: regularly obtain the latest trade data and policy information from the data source → clean the data through the data processing module → update the prediction model with the latest data → apply the updated model to new prediction tasks.

[0067] The data update module in this embodiment ensures that the system responds promptly to policy changes of CPTPP member countries and can provide users with the latest trade barrier forecasts and decision support.

[0068] Conclusion: Through the above embodiments, the CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data of the present invention can realize intelligent prediction and analysis of the whole process from data collection, data processing, prediction model construction, non-tariff barrier analysis, risk assessment and visualization display, ensuring that users can accurately grasp the international trade dynamics, reasonably avoid potential risks, and thus optimize international trade strategies.

[0069] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data is characterized by: include: Data collection module: used to obtain multi-dimensional data related to agricultural and food product trade among CPTPP member countries from international trade databases, tariff and non-tariff barrier databases, policy and regulation databases, CPTPP policy documents and market dynamics information. The data include but are not limited to tariff data, technical trade barrier data, legal and regulatory data, product quarantine standard data, and import and export data of agricultural and food products; Data processing and cleaning module: used to clean, preprocess, format and standardize the collected raw data, including deleting duplicate data, filling missing values ​​and filtering irrelevant information; Prediction model building module: used to build a prediction model based on the collected historical data and current trade data through machine learning algorithms to predict future agricultural and food product trade barriers between CPTPP member countries. The model adopts support vector machines, decision trees, random forests or deep learning algorithms; Non-tariff barrier analysis module: used to identify and predict possible non-tariff barriers in the future by analyzing historical technical trade barriers, animal and plant quarantine measures, and differential data of laws and regulations; Risk assessment and response suggestion module: used to conduct risk assessment on emerging trade barriers based on the results of the forecast model, and provide response measures and strategic suggestions for trade barriers; Visualization display module: used to display the prediction results in the form of charts and trend charts through visualization technology.

2. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The prediction model building module predicts trade barriers based on the following algorithm formula: P(B|X)=f(W T X+b) Among them, P(B|X) is the probability of trade barriers occurring given the input feature X, W is the model weight, b is the bias, and f is the activation function.

3. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The data collection module acquires agricultural and food product trade data and relevant policy and regulatory information from different data sources in real time through crawler technology and API interface, and stores them in a distributed database.

4. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 or 3, characterized in that: The data processing and cleaning module processes text data using natural language processing technology, specifically including text segmentation, topic extraction, and named entity recognition operations.

5. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The prediction model building module uses the random forest algorithm for prediction. The formula of the random forest algorithm is: Among them, T i (x) represents the prediction result of the i-th decision tree, N is the number of decision trees, and f(x) is the final prediction result.

6. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The non-tariff barrier analysis module identifies high-risk barrier categories by clustering the historical data of technical trade barriers and animal and plant health quarantine measures of each CPTPP member country, and clusters them using the following formula: Where d(i,j) represents the policy difference distance between two countries i and j, x ik and x jk They represent the k-th barrier features respectively.

7. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The risk assessment and response suggestion module scores the severity of the predicted barriers using the risk assessment formula: R=P(B|X)×C Among them, R is the risk assessment value, P(B|X) is the probability of trade barriers occurring, and C is the cost of the impact of barriers on agricultural and food product trade.

8. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The visualization display module uses dynamic visualization tools to generate interactive charts, trend charts and geographic distribution maps to display the history, current status and future trends of agricultural and food product trade barriers of each CPTPP member country.

9. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The system also includes a data update module for regularly or in real time updating trade data and policy changes among member countries.

10. The CPTPP member countries' agricultural and food product trade barrier prediction and analysis system based on big data according to claim 1 is characterized in that: The system further includes a user feedback learning module, where users can submit actual trade barrier information through the feedback interface provided by the system, and the system uses this information to adjust and optimize the prediction model.

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