Trade whole industry chain data intelligent matching and resource docking method

Abstract

The invention provides a trade whole industry chain data intelligent matching and resource docking method, and relates to the technical field of international trade data analysis and geographic information systems.The method comprises the steps that customs declaration data, logistics tracking data, enterprise supply chain data and market public opinion data are collected in real time through a standardized interface; constructing a multi-source heterogeneous data fusion system of block chain evidence storage; dynamically modeling a whole industry chain relation network based on the knowledge graph, and quantifying the supply chain dependency, the trade flow direction and the policy influence weight; calculating an industrial chain correlation index ICRI and a resource matching priority coefficient RMPC, optimizing a logistics path in combination with a geographic space analysis algorithm, and avoiding a policy restriction area in real time; and rendering a matching result through a three-dimensional visualization platform, and iteratively updating model parameters based on user interaction feedback. According to the method, the limitation of a traditional data island is broken through, dynamic quantitative analysis and accurate resource matching are realized, and the trade data integration efficiency is remarkably improved through intelligent path planning and interactive decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of international trade data analysis and geographic information systems, and in particular to a method for intelligent data matching and resource docking for the entire trade industry chain. Background Art

[0002] The field of international trade data analysis has long faced multiple technical challenges, directly impacting decision-making efficiency and accuracy. Existing systems generally suffer from insufficient data integration capabilities. Customs declaration data, logistics tracking information, enterprise supply chain data, and market sentiment data are scattered across different platforms, with significant format differences and a lack of unified interfaces, leading to severe data silos. The real-time collection and cleaning of heterogeneous multi-source data is inefficient, and data standardization is time-consuming. Ensuring cross-platform data consistency is difficult, and reliable mechanisms for verifying data authenticity are impossible, creating hidden dangers for subsequent analysis.

[0003] Traditional analytical methods, based on data processing, are limited to a static perspective and a single dimension, failing to dynamically capture the complex connections between trading entities. Key parameters such as supply chain dependencies and policy influence weights lack real-time updating mechanisms. The cumulative effect of policy barriers on geographical distance remains unquantified. Regional trade activity assessments and risk forecasts lag behind actual changes in the trade environment, making it difficult to support rapid decision-making.

[0004] Furthermore, resource matching and route optimization lack scientific quantitative models. Existing technologies are unable to accurately assess the topological influence of industry chain nodes and resource matching priorities through dynamic parameters. Core indicators such as trade complementarity weights and resource demand urgency rely on manual empirical estimation. This results in generated docking solutions that struggle to balance multiple constraints such as transportation costs, timeliness, and policy compliance. Consequently, actual implementation results deviate significantly from theoretical expectations.

[0005] A deeper challenge lies in weak geospatial analysis and dynamic avoidance capabilities. Logistics routing algorithms lack deep integration with real-time policy databases and lack intelligent detection mechanisms for geofences such as trade embargoes and technical barriers. Optimized routing results may violate policy restrictions or increase compliance risks. Furthermore, the 3D visualization system lacks interactivity, making it difficult to intuitively visualize the spatiotemporal evolution of complex trade relationships. This prevents users from quickly locating key information and participating in plan adjustments.

[0006] Finally, existing systems generally lack adaptive and iterative capabilities. Analytical models and matching logic are rigid, making it impossible to optimize parameters in real time based on user interaction feedback. The lack of incremental learning mechanisms makes it difficult for the system to adapt to market fluctuations and personalized needs. Consequently, analytical accuracy and decision support effectiveness continue to decline over the long term. Summary of the Invention

[0007] In order to solve the technical problems in the existing technology such as the difficulty in integrating multi-source heterogeneous data, the lack of dynamic quantitative models for industrial chain analysis, the insufficient accuracy of resource matching and path planning, the weak ability to dynamically avoid geographic fences, and the lack of a system adaptive iterative mechanism, the present invention provides a method for intelligent matching of data and resource docking for the entire trade industry chain.

[0008] The technical solutions provided by the present invention are as follows:

[0009] The present invention provides a method for intelligent data matching and resource docking for the entire trade industry chain, comprising:

[0010] S1. Multi-source heterogeneous data collection and integration: Customs declaration data, logistics tracking data, enterprise supply chain data, and market sentiment data are collected in real time through standardized interfaces, and the data is cleaned, classified, and stored on the blockchain.

[0011] S2. Dynamic Modeling of the Industrial Chain: Build a full industrial chain relationship network based on knowledge graph technology, define commodities, enterprises, ports, and policies as core entity nodes, and extract multi-dimensional relationships between entities, including supply chain dependencies, trade flows, and policy influence weights;

[0012] S3. Intelligent matching parameter calculation: Calculate the industrial chain correlation index (ICRI), which combines trade complementarity weights, trade density, and trade distance, and calculate the resource matching priority coefficient (RMPC), which is based on resource demand urgency, economic benefit weights, and competition intensity;

[0013] S4. Dynamic resource matching and optimization: Generate resource matching solutions based on ICRI and RMPC, optimize logistics routes using geospatial analysis algorithms, and render matching results in real time through a 3D visualization platform;

[0014] S5: Real-time feedback and iterative updates: Based on user interaction feedback data, the relationship network and matching logic are dynamically adjusted through incremental learning algorithms.

[0015] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0016] (1) In this invention, customs, logistics, supply chain, and public opinion data are integrated through standardized interfaces, combined with blockchain evidence storage technology to build a unified data processing pipeline, solving the problem of difficulty in integrating multi-source heterogeneous data. The data cleaning engine is based on rules and machine learning algorithms, automatically filtering outliers and missing data, and the spatiotemporal four-dimensional index supports multi-dimensional retrieval, significantly improving data consistency. Blockchain technology ensures that data cannot be tampered with, achieves full-link traceability, provides a high-reliability data foundation for subsequent analysis, and breaks the limitations of traditional data silos.

[0017] (2) In this invention, a dynamic modeling and parameter calculation system based on knowledge graphs (such as ICRI and RMPC) is used to quantify the topological influence and resource matching value of industry chain nodes in real time. Node weights are updated through a dynamic PageRank algorithm, and policy barrier coefficients are superimposed to accurately capture the dynamic changes in trade relations. The entropy weight method is combined to assign parameter weights to achieve scientific resource matching priority assessment. This technology breaks through the limitations of traditional static analysis and generates solutions that balance cost, timeliness, and policy constraints, significantly improving the accuracy of supply chain decision-making.

[0018] (3) In this invention, geospatial analysis algorithms are deeply integrated with a real-time policy database. Logistics routes are optimized using the Dijkstra algorithm, and geofences are dynamically avoided using the ray method to ensure the compliance and efficiency of transportation plans. A three-dimensional visualization platform integrates the Unity3D engine and gesture interaction technology to render heat maps, flow diagrams, and path planning results in real time, supporting multi-dimensional data drilling and perspective adjustment. This technology transforms complex trade data into an intuitive visualization interface, lowering the user's operational threshold. At the same time, it dynamically optimizes model parameters through an incremental learning mechanism, achieving closed-loop iteration and adaptive decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a method for intelligent data matching and resource docking for the entire trade industry chain provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the multi-source heterogeneous data collection and fusion process of a method for intelligent matching of data and resource docking across the entire trade industry chain provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the industrial chain dynamic modeling process for a method for intelligent data matching and resource docking of the entire trade industry chain provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of a logistics path optimization process for a method for intelligent data matching and resource docking across the entire trade industry chain provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of the incremental learning algorithm flow for a method of intelligent matching of data and resource docking across the entire trade industry chain provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0026] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0027] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0028] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0029] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0030] Reference Manual Figure 1 , which shows a flow chart of a method for intelligent matching of data and resource docking of the entire trade industry chain provided by an embodiment of the present invention.

[0031] The embodiment of the present invention provides a method for intelligent data matching and resource docking of the entire trade industry chain. The processing flow may include the following steps:

[0032] S1. Multi-source heterogeneous data collection and integration: Through standardized interfaces, customs declaration data, logistics tracking data, enterprise supply chain data and market sentiment data are collected in real time, and the data are cleaned, classified and stored on the blockchain.

[0033] like Figure 2 As shown, in a possible implementation, this is achieved by the following steps:

[0034] S101. Obtain customs declaration and tax bill data in real time through the International Trade Single Window API and store them in a distributed database by HS code. For example, data for men's suits with the HS code "6204" is stored separately and indexed to support fast retrieval.

[0035] S102. Integrate API interfaces of international logistics companies such as DHL and FedEx to analyze cargo transportation trajectory data in real time, including origin, destination, transportation time and real-time coordinates. It should be noted that invalid coordinates (such as data with longitude and latitude outside the geographic range) must be filtered during data cleaning, and the data format must be verified using regular expressions;

[0036] S103. Enterprise supply chain data evidence storage, specifically including:

[0037] S1031. Data is divided into independent blocks, each of which contains a timestamp, hash value, and digital signature.

[0038] S1032. Use a practical Byzantine fault-tolerant algorithm to verify data authenticity, with the number of verification nodes ≥ 4;

[0039] S1033. Tamper detection is achieved through hash chain consistency comparison, with a detection accuracy of no less than 99.99%.

[0040] S104. Build a targeted crawler based on the Scrapy framework to collect text information from global trade portals and extract keywords (such as "tariff adjustment" and "supply chain disruption") using NLP technology. It is understandable that a stop word list must be defined in advance to filter out irrelevant words.

[0041] S2. Dynamic modeling of the industrial chain: Build a full industrial chain relationship network based on knowledge graph technology, define commodities, enterprises, ports and policies as core entity nodes, and extract multi-dimensional correlation relationships between entities, including supply chain dependencies, trade flows and policy impact weights.

[0042] Specifically, if Figure 3 As shown, the quantification of the policy barrier coefficient in S2 includes:

[0043] S201. Use the BERT model to parse policy documents, extract restrictive clauses, and perform multi-label classification.

[0044] S202. Classification labels include economic sanctions, technical barriers, and environmental restrictions, with weights assigned to each category through an expert scoring method;

[0045] S203. The policy barrier coefficient is the weighted sum of the weights of each category, with the weight range being 0.5 to 1.0.

[0046] The update logic of the dynamic PageRank algorithm in S2 is:

[0047] S204. The initial node weight is determined by trade flow, and the calculation formula is the product of cargo value and transaction frequency;

[0048] S205. After superimposing the policy impact coefficient every hour, the node influence ranking is iteratively calculated with a damping factor of 0.85.

[0049] To achieve dynamic modeling of the industrial chain, in one possible implementation, a knowledge graph is first constructed, which includes entity recognition: using a bidirectional LSTM-CRF model to identify core entities, such as mapping commodities to HS codes (such as "8542" corresponds to integrated circuits), mapping enterprises to unified credit codes, and mapping ports to UNLOCODE; relationship extraction: based on the attention mechanism model, supply chain dependencies are identified (such as "Enterprise A supplies commodity B to Port C"), trade flows (such as "the average monthly throughput of Port D") and policy impact weights (such as "the restriction coefficient of environmental protection policies on commodity E").

[0050] Then, dynamic weight updates are performed. The initial node weight is calculated based on trade flow using the formula: cargo value (10,000 yuan) × transaction frequency (times / month). The node influence ranking is updated every hour using the dynamic PageRank algorithm. The damping factor is set to 0.85, and the policy impact coefficient is superimposed.

[0051] S3. Intelligent matching parameter calculation: Calculate the industrial chain correlation index ICRI, which combines trade complementarity weight, trade density and trade distance, and calculate the resource matching priority coefficient RMPC, which is based on the urgency of resource demand, economic benefit weight and competition intensity.

[0052] In a possible implementation manner, the S3 further includes:

[0053] The trade complementarity weight CT is calculated according to the following formula i :

[0054]

[0055] Among them, EX ij is the export volume of the i-th commodity to the j-th country, obtained in real time through the International Trade Single Window API, IM ij is the import volume of the i-th category of goods from the j-th country, analyzed through the General Administration of Customs database, EX i and IM i is the global total export and import volume of commodity i, calculated by aggregating the import and export data of all countries.

[0056] Then, the Gaussian kernel function is used to estimate the kernel density of regional trade data, and the trade density TD is obtained. i The bandwidth parameter is automatically optimized by the Silverman criterion; the geographical distance is calculated by the Haversine formula, and the policy barrier coefficient is superimposed to obtain the trade distance DT i ;

[0057] Final synthetic industry chain correlation index ICRI:

[0058]

[0059] Among them, α and β are the normalized distribution of the coefficient of variation of trade complementarity weight, trade density, trade distance and total number of nodes through the entropy weight method, and the CT i Calculation of import and export volume of commodities, TD i In order to fit the probability density of regional trade data distribution based on kernel density estimation algorithm, DT i It is calculated by superimposing the policy barrier coefficient by geographical distance, and NS is the total number of supply chain network nodes.

[0060] Furthermore, the calculation formula of the resource matching priority coefficient RMPC is:

[0061]

[0062] Among them, UR is the short-term demand gap predicted based on historical supply and demand volatility and exponential smoothing method, with a smoothing coefficient of 0.3; ER is the expected return for the next three months output after training historical market data through the ARIMA model; CR is the bidding frequency and premium rate in the statistical enterprise bidding database; TF is the time decay factor, calculated as TF = e -λt , where λ = 0.05, t is the time interval in days; MF is the TF-IDF algorithm used to extract market sentiment text keywords and calculate the correlation score.

[0063] S4. Dynamic resource matching and optimization: Generate resource docking plans based on ICRI and RMPC, optimize logistics routes using geospatial analysis algorithms, and render matching results in real time through a 3D visualization platform.

[0064] Specifically, if Figure 4 As shown in Figure 1, the specific steps of logistics path optimization include:

[0065] Path weight calculation:

[0066] S401. Transportation costs consist of fuel and tariffs. Fuel is calculated based on real-time oil prices and mileage, while tariffs are matched to tax rates using HS codes.

[0067] Transportation cost = fuel cost (real-time fuel price × mileage) + tariff (HS code matching tax rate × cargo value)

[0068] S402. Timeliness is composed of transportation time and customs clearance time. The customs clearance time is matched with the historical average value through the policy database;

[0069] Timeliness = transportation time (mileage / average speed) + customs clearance time (policy database matches historical average)

[0070] S403, the total weight is the normalized weighted sum of transportation cost and timeliness, with weight ratios of 0.6 and 0.4 respectively;

[0071] Total weight = 0.6 × normalized cost + 0.4 × normalized timeliness

[0072] Path planning and avoidance:

[0073] S404, using Dijkstra algorithm to solve the shortest path with the total weight as the edge weight;

[0074] S405: Match the geo-fence coordinates in the policy database in real time. The geo-fence coordinates are polygonal areas. If the paths intersect, re-planning is performed.

[0075] The real-time circumvention strategies for geo-fencing include:

[0076] S4051. Map the policy restriction area into a polygon in the geographic coordinate system;

[0077] S4052. Detecting the intersection of the path segment and the polygon by using the ray method;

[0078] S4053. If they intersect, call the Dijkstra algorithm to replan the path and give priority to the alternative path with the lowest total weight.

[0079] In one possible implementation, to achieve three-dimensional visual interaction, the Unity3D engine is used to render global trade flow heat maps and logistics routes in real time, supporting dynamic display of millions of data points; the MediaPipe framework is integrated to parse gesture operations (such as pinch-to-zoom, slide and drag), and the operation instructions are mapped to viewing parameters (longitude, latitude, zoom level) in real time.

[0080] S5: Real-time feedback and iterative updates: Based on user interaction feedback data, the relationship network and matching logic are dynamically adjusted through incremental learning algorithms.

[0081] like Figure 5 As shown, the incremental learning algorithm is an online random forest model, and its update logic includes:

[0082] S501, the feature vector includes the number of solution modifications, parameter adjustment range (such as RMPC threshold adjustment) and feedback score (1 to 5 points) in the user adjustment record;

[0083] S502. The target variable is the actual benefit indicator of the matching solution, including profit margin and on-time delivery rate; the feature vector = [number of adjustments, parameter modification range, feedback score]

[0084] Target variable = actual performance indicator (profit margin, on-time delivery rate)

[0085] S503 , the classification threshold is updated through incremental training every 10 minutes, and the classification threshold is dynamically optimized through the ROC curve.

[0086] The updated model parameters are synchronized to the knowledge graph and matching logic in real time, such as adjusting the α and β weight distribution in the ICRI formula, or optimizing the TF decay rate in the RMPC calculation.

[0087] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0088] (1) In this invention, customs, logistics, supply chain, and public opinion data are integrated through standardized interfaces, combined with blockchain evidence storage technology to build a unified data processing pipeline, solving the problem of difficulty in integrating multi-source heterogeneous data. The data cleaning engine is based on rules and machine learning algorithms, automatically filtering outliers and missing data, and the spatiotemporal four-dimensional index supports multi-dimensional retrieval, significantly improving data consistency. Blockchain technology ensures that data cannot be tampered with, achieves full-link traceability, provides a high-reliability data foundation for subsequent analysis, and breaks the limitations of traditional data silos.

[0089] (2) In this invention, a dynamic modeling and parameter calculation system based on knowledge graphs (such as ICRI and RMPC) is used to quantify the topological influence and resource matching value of industry chain nodes in real time. Node weights are updated through a dynamic PageRank algorithm, and policy barrier coefficients are superimposed to accurately capture the dynamic changes in trade relations. The entropy weight method is combined to assign parameter weights to achieve scientific resource matching priority assessment. This technology breaks through the limitations of traditional static analysis and generates solutions that balance cost, timeliness, and policy constraints, significantly improving the accuracy of supply chain decision-making.

[0090] (3) In this invention, geospatial analysis algorithms are deeply integrated with a real-time policy database. Logistics routes are optimized using the Dijkstra algorithm, and geofences are dynamically avoided using the ray method to ensure the compliance and efficiency of transportation plans. A three-dimensional visualization platform integrates the Unity3D engine and gesture interaction technology to render heat maps, flow diagrams, and path planning results in real time, supporting multi-dimensional data drilling and perspective adjustment. This technology transforms complex trade data into an intuitive visualization interface, lowering the user's operational threshold. At the same time, it dynamically optimizes model parameters through an incremental learning mechanism, achieving closed-loop iteration and adaptive decision support.

[0091] The above content is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0092] There are a few points to note:

[0093] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0094] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" on or "under" the other element or intervening elements may be present.

[0095] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0096] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intelligent data matching and resource docking of the entire trade industry chain, characterized by: include: S1. Multi-source heterogeneous data collection and integration: Customs declaration data, logistics tracking data, enterprise supply chain data, and market sentiment data are collected in real time through standardized interfaces, and the data is cleaned, classified, and stored on the blockchain. S2. Dynamic Modeling of the Industrial Chain: Build a full industrial chain relationship network based on knowledge graph technology, define commodities, enterprises, ports, and policies as core entity nodes, and extract multi-dimensional relationships between entities, including supply chain dependencies, trade flows, and policy influence weights; S3. Intelligent matching parameter calculation: Calculate the industrial chain correlation index (ICRI), which combines trade complementarity weights, trade density, and trade distance, and calculate the resource matching priority coefficient (RMPC), which is based on resource demand urgency, economic benefit weights, and competition intensity; S4. Dynamic resource matching and optimization: Generate resource matching solutions based on ICRI and RMPC, optimize logistics routes using geospatial analysis algorithms, and render matching results in real time through a 3D visualization platform; S5: Real-time feedback and iterative updates: Based on user interaction feedback data, the relationship network and matching logic are dynamically adjusted through incremental learning algorithms.

2. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 1 is characterized in that: Said S3 further comprises: The calculation formula of the industrial chain correlation index ICRI is: Among them, α and β are normalized and distributed by the coefficient of variation of trade complementarity weight, trade density, trade distance and total number of nodes through the entropy weight method, and the CT i Calculation of import and export volume of commodities, TD i Based on the kernel density estimation algorithm, the probability density of regional trade data distribution is fitted. i Calculated by superimposing the policy barrier coefficient on geographical distance, NS is the total number of supply chain network nodes.

3. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 2 is characterized in that: The trade complementarity weights further include: The trade complementarity weight CT i The calculation formula is: Among them, EX ij is the export volume of the i-th commodity to the j-th country, obtained in real time through the International Trade Single Window API, IM ij is the import volume of the i-th category of goods from the j-th country, analyzed through the General Administration of Customs database, EX i and IM i is the global total export and import volume of commodity i, calculated by aggregating the import and export data of all countries.

4. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 1 is characterized in that: Said S3 includes: The calculation formula of the resource matching priority coefficient RMPC is: Among them, UR predicts short-term demand gaps based on historical supply and demand volatility and exponential smoothing, with a smoothing coefficient of 0.3; ER outputs expected returns for the next three months after training historical market data using the ARIMA model; CR calculates the bidding frequency and premium rate in the corporate bidding database; TF is the time decay factor, calculated as TF=e -λt , where λ = 0.05, t is the time interval in days; MF extracts market sentiment text keywords through the TF-IDF algorithm and calculates the relevance score.

5. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 2 is characterized in that: The quantification of the policy barrier coefficient in S2 includes: S201. Use the BERT model to parse policy documents, extract restrictive clauses, and perform multi-label classification. S202. Classification labels include economic sanctions, technical barriers, and environmental restrictions, with weights assigned to each category through an expert scoring method; S203. The policy barrier coefficient is the weighted sum of the weights of each category, with the weight range being 0.5 to 1.

0.

6. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 4 is characterized in that: The specific steps of logistics path optimization in S4 include: Path weight calculation: S401. Transportation costs consist of fuel and tariffs. Fuel is calculated based on real-time oil prices and mileage, while tariffs are matched to tax rates using HS codes. S402. Timeliness is composed of transportation time and customs clearance time. The customs clearance time is matched with the historical average value through the policy database; S403, the total weight is the normalized weighted sum of transportation cost and timeliness, with weight ratios of 0.6 and 0.4 respectively; Path planning and avoidance: S404, using Dijkstra algorithm to solve the shortest path with the total weight as the edge weight; S405: Match the geo-fence coordinates in the policy database in real time. The geo-fence coordinates are polygonal areas. If the paths intersect, re-planning is performed.

7. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 1 is characterized in that: The incremental learning algorithm in S5 is an online random forest model, and its update logic includes: S501, the feature vector includes the number of solution modifications, parameter adjustment range and feedback score in the user adjustment record; S502. The target variable is the actual benefit index of the matching solution, including profit margin and on-time delivery rate; S503 , the classification threshold is updated through incremental training every 10 minutes, and the classification threshold is dynamically optimized through the ROC curve.

8. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 1 is characterized in that: The specific implementation of blockchain evidence storage in S1 includes: S1031. Data is divided into independent blocks, each of which contains a timestamp, hash value, and digital signature. S1032. Use a practical Byzantine fault-tolerant algorithm to verify data authenticity, with the number of verification nodes ≥ 4; S1033. Tamper detection is achieved through hash chain consistency comparison, with a detection accuracy of no less than 99.99%.

9. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 2 is characterized in that: The update logic of the dynamic PageRank algorithm in S2 is: S204. The initial node weight is determined by trade flow, and the calculation formula is the product of cargo value and transaction frequency; S205. After superimposing the policy impact coefficient every hour, the node influence ranking is iteratively calculated with a damping factor of 0.

85.

10. The method for intelligent data matching and resource docking of the entire trade industry chain according to claim 6 is characterized in that: The real-time circumvention strategies for geo-fencing include: S4051. Map the policy restriction area into a polygon in the geographic coordinate system; S4052. Detecting the intersection of the path segment and the polygon by using the ray method; S4053. If they intersect, call the Dijkstra algorithm to replan the path and give priority to the alternative path with the lowest total weight.

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