Cross-border trade data intelligent processing method and system based on intelligent port

Abstract

The invention relates to the technical field of data processing, and discloses a cross-border trade data intelligent processing method and system based on an intelligent port. The method comprises the steps of performing functional model hierarchical processing on cross-border trade business data, obtaining a functional module calling relation network, performing process template construction processing, obtaining a dynamic process assembly rule base, performing service burying point processing, obtaining a user behavior analysis data set, performing guide strategy construction processing, and obtaining a user behavior analysis result set; and obtaining a service guidance rule base, carrying out knowledge graph construction processing, obtaining a service knowledge inference rule set, and carrying out cross-border trade data processing to obtain a port service intelligent decision scheme. According to the method, intelligent processing of cross-border trade data is realized, a complete technical chain from data acquisition, behavior analysis to decision support is constructed, accurate business guidance and intelligent decision support are provided, and the problems of low business processing efficiency, inaccurate service guidance, weak decision support capability and the like in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for intelligent processing of cross-border trade data based on a smart port. Background Art

[0002] With the rapid development of cross-border trade business, the traditional port business processing methods can no longer meet the needs of modern trade. The current cross-border trade platform mainly adopts fixed business process templates, conducts business approval and processing manually, and relies on experience to arrange customs clearance links and logistics routes. In terms of data processing, the existing technology mainly performs simple data collection and analysis through preset rules, lacking a deep understanding of user behavior and business processes. In terms of service guidance, most of them adopt unified operating guidelines and fixed exception handling solutions, and cannot provide personalized service support based on the characteristics of different enterprises.

[0003] However, the existing technology has the following shortcomings: first, the business processing process lacks intelligent data analysis and decision-making support, resulting in low customs clearance efficiency and high logistics costs; second, due to the lack of in-depth analysis and understanding of user behavior, it is impossible to accurately identify the key nodes and potential risks in the business processing process, affecting the service quality; third, the existing service guidance method is too mechanical and unified, and it is impossible to provide accurate business guidance based on the specific circumstances of the enterprise; finally, the information is isolated between each business link, the value of data cannot be fully explored and utilized, and it is difficult to form a complete knowledge system to support business decision-making. Summary of the invention

[0004] The present application provides a method and system for intelligent processing of cross-border trade data based on a smart port, which is used to realize the intelligent processing of cross-border trade data, build a complete technical chain from data collection, behavior analysis to decision support, provide accurate business guidance and intelligent decision support, and solve the problems of low business processing efficiency, inaccurate service guidance, and weak decision support capabilities existing in the prior art.

[0005] In the first aspect, the present application provides a method for intelligent processing of cross-border trade data based on a smart port, and the method for intelligent processing of cross-border trade data based on a smart port includes: performing hierarchical processing of cross-border trade business data with a functional model to obtain a functional module call relationship network; the hierarchical processing of the functional model includes dividing the business data into an enterprise main function layer and a business function layer, and constructing a data interaction interface between functional modules; performing process template construction processing on the functional module call relationship network to obtain a dynamic process assembly rule library; the process template construction processing includes designing a business declaration process and an exception handling process system based on the function call relationship; performing service tracking processing on the dynamic process assembly rule library to obtain a user behavior analysis data set; the service tracking processing includes setting tracking points at key process nodes. location, collect user operation behavior data, business data change data and process execution status data; perform guidance strategy construction processing on the user behavior analysis data set to obtain a service guidance rule base; the guidance strategy construction processing includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans; the service guidance rule base is subjected to knowledge graph construction processing through a knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction processing includes extracting business entities, constructing relationship networks, discovering knowledge rules and establishing causal relationship chains; cross-border trade data processing is performed on the business knowledge reasoning rule set to obtain an intelligent decision-making plan for port business; the cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction.

[0006] In the second aspect, the present application provides a cross-border trade data intelligent processing system based on a smart port, and the cross-border trade data intelligent processing system based on a smart port includes: Functional modules are used to perform functional model hierarchical processing on cross-border trade business data to obtain a functional module call relationship network; the functional model hierarchical processing includes dividing the business data into an enterprise main function layer and a business function layer, and constructing a data interaction interface between the functional modules; A process module is used to construct a process template for the function module call relationship network to obtain a dynamic process assembly rule base; the process template construction process includes designing a business declaration process and an exception handling process system based on the function call relationship; A service tracking module is used to perform service tracking processing on the dynamic process assembly rule library to obtain a user behavior analysis data set; the service tracking processing includes setting tracking positions at key process nodes to collect user operation behavior data, business data change data and process execution status data; A service guidance module, which is used to perform guidance strategy construction processing on the user behavior analysis data set to obtain a service guidance rule base; the guidance strategy construction processing includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans; A graph module is used to construct a knowledge graph for the service guidance rule base through a knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction process includes extracting business entities, building a relationship network, discovering knowledge rules, and establishing a causal relationship chain; The decision-making module is used to process the cross-border trade data on the business knowledge reasoning rule set to obtain an intelligent decision-making plan for port business; the cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction.

[0007] In the technical solution provided by the present application, the cross-border trade business data is processed in a hierarchical manner through a functional model, the business data is divided into an enterprise main function layer and a business function layer, and a data interaction interface is constructed between functional modules, thereby realizing modular management and flexible configuration of business functions; by constructing a process template for the functional module call relationship network, a business declaration process and an exception handling process system are designed, and a complete dynamic process assembly rule library is established, thereby improving the standardization and automation level of business processes; by performing service tracking processing on the dynamic process assembly rule library, tracking locations are set at key process nodes, and user operation behavior data, business data change data, and process execution status data are collected. Based on the data, the comprehensive monitoring and analysis of the business execution process is realized; by constructing and processing the guidance strategy of the user behavior analysis data set, designing operation prompts, formulating exception handling suggestions and personalized guidance plans, the accuracy and efficiency of user operations are improved; through the knowledge extraction algorithm, the service guidance rule base is constructed and processed with the knowledge graph, business entities are extracted, relationship networks are constructed, knowledge rules are discovered, and causal relationship chains are established, forming a complete business knowledge system; through the cross-border trade intelligent analysis algorithm, the cross-border trade data is processed on the business knowledge reasoning rule set, and the customs clearance process optimization, logistics path planning, risk level assessment and customs clearance time prediction are completed, realizing the intelligent decision-making of port business. Through multi-level data processing and analysis, a complete technical chain from data collection, process monitoring to intelligent decision-making is constructed, which significantly improves the processing efficiency and service quality of cross-border trade business and reduces business risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of a method for intelligently processing cross-border trade data based on a smart port in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of an intelligent processing system for cross-border trade data based on a smart port in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method and system for intelligent processing of cross-border trade data based on smart ports. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the cross-border trade data intelligent processing method based on the smart port includes: Step S101: Performing functional model hierarchical processing on cross-border trade business data to obtain a functional module call relationship network; the functional model hierarchical processing includes dividing the business data into an enterprise main function layer and a business function layer, and constructing a data interaction interface between the functional modules; Step S102: Perform process template construction processing on the function module call relationship network to obtain a dynamic process assembly rule base; the process template construction processing includes designing a business declaration process and an exception handling process system based on the function call relationship; Step S103: Perform service tracking processing on the dynamic process assembly rule library to obtain a user behavior analysis data set; the service tracking processing includes setting tracking locations at key process nodes to collect user operation behavior data, business data change data, and process execution status data; Step S104: Perform guidance strategy construction processing on the user behavior analysis data set to obtain a service guidance rule base; the guidance strategy construction processing includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans; Step S105: Perform knowledge graph construction processing on the service guidance rule base through the knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction processing includes extracting business entities, building a relationship network, discovering knowledge rules and establishing a causal relationship chain; Step S106, cross-border trade data processing is performed on the business knowledge reasoning rule set to obtain an intelligent decision-making plan for port business; cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction.

[0012] It is understandable that the execution subject of the present application can be a cross-border trade data intelligent processing system based on a smart port, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Specifically, the cross-border trade business data is processed in a hierarchical manner using a functional model. By analyzing and classifying data such as enterprise basic information, qualification certificates, and business declarations, the data is divided into the enterprise main function layer and the business function layer. The enterprise main function layer includes functions such as enterprise basic information management, qualification management, and authorization management, and the business function layer includes functions such as customs clearance services, inspection services, manifest services, transit services, and logistics services. By designing interface rules and data exchange formats, a data interaction interface between functional modules is established to achieve data sharing and business collaboration between different functional modules. For example, the basic information of an import and export enterprise includes data such as enterprise registration information, business scope, and credit rating. These data are classified and sorted and stored in the enterprise main function layer; the enterprise's customs declaration, packing list, bill of lading, and other business data are stored in the business function layer. When constructing a process template for the functional module call relationship network, the corresponding business declaration process and exception handling process system are designed according to the characteristics and requirements of different business types. Taking the customs clearance of goods as an example, the declaration process includes filling out the customs declaration form, submitting documents, paying taxes and fees, etc.; the exception handling process includes supplementing documents, modifying declarations, applying for reconsideration, etc. These processes are flexibly configured through dynamic assembly rules to form a complete business processing chain.

[0014] In the service tracking point processing link, by setting tracking points at key nodes of the business process, user operation behavior data, business data change data and process execution status data are collected. For example, tracking points are set in the customs declaration form filling link to record the time, number of modifications, error rate and other data when users fill in each field; tracking points are set in the document review link to record the review time, reason for return, modification content and other data; tracking points are set in the inspection link to record the inspection method, inspection results, abnormal conditions and other data. After sorting and analyzing these data, a user behavior analysis data set is formed. Based on the user behavior analysis data set, guidance strategy construction and processing are carried out. By analyzing user operation habits and business handling characteristics, targeted operation prompts, abnormal handling suggestions and personalized guidance plans are designed. For example, for users who are handling business for the first time, detailed form filling guidance and document requirements are provided; for users who often make specific errors, targeted error warnings and modification suggestions are provided; for users with specific business characteristics, the most suitable business handling path and processing method are recommended.

[0015] The knowledge extraction algorithm is used to construct the knowledge graph of the service guidance rule base, extract business entities, build relationship networks, discover knowledge rules, and establish causal relationship chains. Taking cargo declaration as an example, business entities include enterprises, commodities, and means of transport; relationship networks include trade relations between enterprises and commodities, and loading relations between commodities and means of transport; knowledge rules include declaration requirements, inspection standards, and tax rate regulations for various commodities; and causal relationship chains include logical relationships such as document errors leading to order returns and commodity attributes affecting inspection methods. This knowledge is organized and applied in the form of inference rules. Finally, the business knowledge inference rule set is used to process cross-border trade data, optimize customs clearance processes, plan logistics routes, assess risk levels, and predict customs clearance timeliness. For example, during the customs clearance process of a container cargo, by analyzing historical data and current status, it is identified that the declaration and inspection links are the main time bottlenecks, and the customs clearance process is optimized, the inspection batches are adjusted, and the operation time is reasonably arranged; by analyzing data such as ports, warehouses, and transportation routes, the optimal logistics route is planned to reduce transportation costs; by analyzing factors such as corporate credit, product attributes, and document quality, the business risk level is assessed and the inspection intensity is determined; by analyzing data such as the time consumption of each link, personnel allocation, and equipment status, the overall customs clearance time is predicted, and corresponding arrangements are made in advance.

[0016] It should be noted that the cross-border trade data intelligent processing system based on smart ports in this application can also serve as a smart enterprise service engine, using artificial intelligence technology to comprehensively sort out the various functions of the platform's business applications, learn customs, border inspection, transportation, aviation, railways and other cross-border trade business knowledge, reshape the platform's business processing procedures, and use service embedding technology to embed services into various business processing links on the platform. Provide platform users with full-business cycle services such as intelligent business judgment, process recommendation, and business processing guidance to help users efficiently manage cross-border trade customs clearance declaration business processes. By real-time monitoring of user business processing dynamics and using data visualization tools to explore users' potential needs and behavior patterns, the platform can continuously optimize the user experience. Taking enterprises as units, it gathers basic information, trade information, customs clearance information and logistics information of enterprises, and provides enterprises with customized collection, enterprise statistical query, industry trend, logistics status query, risk warning, preferential subsidy query and other data services. In accordance with the principle of "enterprise self-authorization, real foreign trade data, and single window certification", based on the real foreign trade data gathered by the platform, it automatically and intelligently provides a variety of data services, builds an enterprise service system that integrates data integrity verification, authorization data completion, data collaborative confirmation, and data service use, helps the development of enterprises' foreign trade business, supports direct connection of enterprise systems, improves system interoperability, and meets the personalized and diversified needs of enterprises.

[0017] For example: When an import and export company declares a batch of medical devices, it first performs layered processing on the company's qualification certificate, business scope, product attributes and other data to confirm that the company has the qualifications to import medical devices and that the products belong to special regulatory categories. Based on these characteristics, the corresponding business processes are dynamically assembled, including special commodity declaration processes, quality and safety approval processes, special inspection processes, etc. In the course of business handling, it is collected through buried points that users frequently make mistakes when filling in the medical device registration certificate number, and the description of the relevant product specifications and models is inaccurate, and targeted reporting guidance and error reminders are generated based on this. Based on the regulatory rules and risk characteristics related to medical devices in the knowledge graph, it is assessed that the risk level of this batch of goods is medium, and inspection is required. The customs clearance time is predicted to be 3 working days, and the optimal inspection time and cargo transportation route are planned accordingly.

[0018] In the embodiment of the present application, by performing layered processing on the functional model of cross-border trade business data, the business data is divided into the enterprise main function layer and the business function layer, and a data interaction interface between functional modules is constructed, thereby realizing modular management and flexible configuration of business functions; by performing process template construction processing on the functional module call relationship network, a business declaration process and an exception handling process system are designed, and a complete dynamic process assembly rule library is established, thereby improving the standardization and automation level of business processes; by performing service tracking processing on the dynamic process assembly rule library, tracking locations are set at key process nodes, and user operation behavior data, business data change data, and process execution status data are collected, It realizes comprehensive monitoring and analysis of the business execution process; through the construction and processing of guidance strategies for user behavior analysis data sets, designing operation prompts, formulating exception handling suggestions and personalized guidance plans, the accuracy and efficiency of user operations are improved; through the knowledge extraction algorithm, the service guidance rule base is constructed and processed with knowledge graphs, business entities are extracted, relationship networks are constructed, knowledge rules are discovered, and causal relationship chains are established, forming a complete business knowledge system; through the cross-border trade intelligent analysis algorithm, the cross-border trade data is processed on the business knowledge reasoning rule set, customs clearance process optimization, logistics path planning, risk level assessment and customs clearance time prediction are completed, and intelligent decision-making of port business is realized. Through multi-level data processing and analysis, a complete technical chain from data collection, process monitoring to intelligent decision-making is constructed, which significantly improves the processing efficiency and service quality of cross-border trade business and reduces business risks.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Classify the cross-border trade business data by enterprise entity to obtain the enterprise basic information set, and construct the enterprise entity function layer on the enterprise basic information set to obtain the enterprise entity function layer; (2) Perform filing qualification processing on the enterprise main function layer to obtain the enterprise qualification certificate set, and perform authorization processing on the enterprise qualification certificate set to obtain the enterprise function authorization table; (3) Classify the cross-border trade business data by business function to obtain a business function element set, and construct a business function layer on the business function element set to obtain a business function layer; (4) Perform customs clearance service function configuration processing on the business function layer to obtain a customs clearance business process set, and perform inspection declaration, manifest, transit and logistics service function configuration processing on the customs clearance business process set to obtain a business service function set; (5) Design and process the data interaction interface of the enterprise main function layer and the business function layer to obtain the function interaction interface table, and configure the interface rules of the function interaction interface table to obtain the data interaction interface; (6) Through module association analysis, the call relationship of the data interaction interface is constructed and processed to obtain the function module call relationship network.

[0020] Specifically, the original data is cleaned and standardized, including basic data such as enterprise registration information, organization code, legal person information, business scope, registered capital, etc., and data in different formats are uniformly converted into a standard format through data standardization. For the enterprise name, string standardization is used to convert full-width characters into half-width characters, and remove redundant spaces and special characters; the registered capital amount is uniformly converted into units of 10,000 yuan; and the date type data is uniformly converted into the "YYYY-MM-DD" format. After data cleaning and standardization, the basic information set of the enterprise is obtained. Subsequently, the enterprise main function layer is constructed for the basic information set of the enterprise, which mainly includes information classification and sorting, function permission configuration and data association processing. Information classification and sorting classifies the basic information of the enterprise according to different dimensions, such as basic information, qualification information, credit information, etc.; function permission configuration sets corresponding access and operation permissions for different types of information; data association processing establishes the association relationship between various types of enterprise information to form a complete enterprise main function layer.

[0021] When processing the qualification of the enterprise's main function layer, first extract the information of various qualification certificates of the enterprise, including import and export rights, customs declaration registration certificates, food business licenses, etc., verify the validity, manage the timeliness and classify and store these qualification certificates, and generate a set of enterprise qualification certificates. Then, the enterprise qualification certificate set is authorized, and the corresponding business operation permissions are set according to different qualification types, the business scope and operation permissions that the enterprise can handle on the platform are determined, and the enterprise function authorization table is generated. When classifying the cross-border trade business data, the main thing is to classify various business data according to the function type, including customs declaration business data, inspection business data, logistics business data, etc., extract the key elements and characteristic parameters of various businesses, and form a set of business function elements. Then, the business function element set is processed by the business function layer, the relationship between business functions is established, and the business processing rules and flow logic are set to form a complete business function layer.

[0022] When configuring the customs clearance service function of the business function layer, set up corresponding function modules according to the characteristics of customs clearance business, including customs declaration form entry, document review, inspection application, tax calculation and other functions, and formulate corresponding business processing rules and operation procedures to generate a customs clearance business process set. Then configure the customs clearance business process set for inspection, manifest, transshipment and logistics service functions, configure corresponding function modules and processing rules for different types of business, realize the collaborative processing of various types of business, and form a business service function set. Based on the enterprise main function layer and the business function layer, design and process the data interaction interface, design the data exchange rules and interface parameters between the two function layers, define the data transmission format and interaction mode, and generate the function interaction interface table. Configure the interface rules of the function interaction interface table, set data access rights, interface call rules and data synchronization mechanism, and form a standardized data interaction interface.

[0023] Finally, the calling relationship of the data interaction interface is constructed and processed through module association analysis, the dependency relationship and calling sequence between the functional modules are analyzed, a complete function calling link is established, and a functional module calling relationship network is formed.

[0024] For example, when registering on the platform, an import and export trading company submitted information such as the company's business license, foreign trade operator registration form, and customs declaration unit registration certificate. First, these original data are standardized, and the company name "ABC Trading Co., Ltd." is standardized as "ABC Trading Co., Ltd.", and the registered capital of "5 million yuan" is standardized as "500", etc. Then the standardized data is classified and sorted, and the business license information is classified into the basic information category, and the foreign trade operator registration is classified into the qualification information category. Based on the qualification certificate provided by the enterprise, the corresponding business permissions are set, such as general trade import and export permissions, customs declaration registration permissions, etc. Then the goods data declared by the enterprise is functionally classified, and the customs declaration form data, inspection and quarantine certificates, transportation documents, etc. are classified according to the business type, and key elements such as commodity code, origin, packaging specifications, etc. are extracted. On this basis, the customs clearance service function is configured, such as the automatic verification function of the customs declaration form data, setting the matching rules between the commodity code and the declaration elements, and the tariff commodity classification rules. The association between enterprise subject information and business data is achieved through interface design, such as the association rules between enterprise qualifications and the types of goods that can be declared, the association rules between enterprise credit rating and inspection and release methods, etc. Ultimately, a complete function call network is formed, enabling enterprises to efficiently handle various cross-border trade businesses on the platform according to their own qualifications and permissions.

[0025] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Analyze and process the business declaration process of the function module call relationship network to obtain a declaration process element set, and design the business declaration process of the declaration process element set to obtain a business declaration process; (2) Identify and process the abnormal types of the business declaration process to obtain a list of abnormal situations, and construct an abnormal handling process system for the abnormal situation list to obtain an abnormal handling process system; (3) Integrate the business application process and exception handling process system into process rules to obtain a process assembly specification table, and dynamically configure the rules of the process assembly specification table to obtain a candidate process set; (4) The candidate process set is subjected to rule assembly processing through process rule mapping to obtain a dynamic process assembly rule base.

[0026] Specifically, the core elements of business declaration are extracted, including cargo information (commodity code, quantity, value), enterprise information (importers and exporters, consignees and consignees), transportation information (transportation mode, entry and exit ports), etc. The dependency relationship between the elements is identified through correlation analysis, such as the correspondence between commodity codes and declaration elements, the correlation between transportation modes and declaration documents, etc., so as to form a declaration process element set. When designing the business declaration process for the declaration process element set, the order of the declaration links is designed according to the logical relationship of the elements, such as filling in the basic information first, then filling in the commodity information, and finally completing the upload of the attached documents, thereby forming a complete business declaration process.

[0027] The main purpose of identifying and processing abnormal types in the business declaration process is to analyze various abnormal situations that appear in historical data, such as incomplete documents, incorrect commodity classification, incorrect filling of declaration elements, etc., to form a list of abnormal situations. Then, the abnormal situation list is processed by the construction of the abnormal processing process system, and corresponding processing processes are designed for different types of abnormalities, such as document correction process, commodity reclassification process, declaration element modification process, etc., to build a complete abnormal processing process system.

[0028] After completing the above process design, the business declaration process and exception handling process system are integrated with process rules, the rules of each process are integrated, the association rules between processes are established, and a process assembly specification table is formed. The process assembly specification table is dynamically configured with rules, and various process rules are flexibly combined according to the needs of different business scenarios to generate a candidate process set. Finally, the candidate process set is assembled through process rule mapping, and various rules are assembled according to business logic to form a dynamically adjustable process assembly rule library.

[0029] For example, for a batch of medical devices that need to be imported, the first elements that need to be filled in during the business declaration process include: medical device registration certificate number, product specifications and models, inspection and quarantine code and other special information, plus general enterprise information, goods information and other basic elements. These elements are analyzed through association to form a declaration element set. In accordance with the special requirements for the import of medical devices, a special declaration process has been designed, requiring the submission of special documents such as medical device registration certificates to be added on the basis of regular declaration information.

[0030] In actual operation, if the medical device registration certificate is about to expire, the exception handling process will be triggered. Exception handling includes specific operation steps such as notifying the enterprise to update the registration certificate, suspending the declaration process, setting automatic warnings, etc. Through rule integration, these special requirements are integrated with the regular process to form a complete medical device import business declaration process, and the processing rules can be dynamically adjusted according to different models of medical devices.

[0031] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Perform key node analysis on the dynamic process assembly rule base to obtain a business key node table, and perform point location setting on the business key node table to obtain a key node point distribution map; (2) Collect and process page access records of key node buried point distribution maps to obtain user operation behavior data, and annotate the user operation behavior data with operation trajectories to obtain an operation trajectory feature set; (3) Performing statistical processing on the function usage data of the operation trajectory feature set to obtain a node operation frequency table, and performing business data change monitoring processing on the node operation frequency table to obtain business data change data; (4) Collect and process the process execution status of business data change data to obtain process execution status data, and perform key node monitoring on the process execution status data to obtain a node status sequence table; (5) Perform abnormal point identification processing on the node state sequence table to obtain an abnormal event feature set, and perform process jam warning analysis processing on the abnormal event feature set to obtain a process warning indicator table; (6) The user operation behavior data, business data change data and process execution status data are integrated and processed through the time series feature analysis algorithm to obtain the user behavior analysis data set.

[0032] Specifically, when analyzing and processing the key nodes of the dynamic process assembly rule base, it is necessary to identify the important operation points, state transition points and data change points in the business process. Key nodes include declaration document submission nodes, document review nodes, inspection operation nodes, release approval nodes, etc. Each node is marked with attribute information such as node type, node function, previous node and subsequent node to form a business key node table. The business key node table is processed by setting the location of the buried point, setting the data collection point at each key node, determining the collected data items, collection frequency and data format, and generating a key node buried point distribution map. When collecting and processing page access records, record the user's access time, stay time, number of clicks and other information at each business node. The user operation behavior data is converted to a unified format and cleaned to remove invalid access records and abnormal data. The user operation behavior data is processed by operation trajectory annotation, recording the user's operation sequence, operation timing, operation results and other information between different nodes to form an operation trajectory feature set.

[0033] Perform statistical processing on the function usage data of the operation trajectory feature set, and calculate indicators such as the access frequency, average processing time, and operation success rate of each node. The node operation frequency table reflects the usage and efficiency of each business node. Perform business data change monitoring processing on the node operation frequency table, track and record changes in business data of each node, including data addition, deletion, modification, and query operations, data value changes, data state conversion, etc., and generate business data change data. In the process execution status collection and processing link, focus on collecting the task execution status of each node, including task start time, execution time, completion status and other information to form process execution status data. Perform key node monitoring processing on the process execution status data, analyze the execution efficiency, task backlog, abnormal interruption, etc. of each node, and generate a node status sequence table.

[0034] The node status sequence table is processed for abnormal point identification. By setting threshold rules, nodes with abnormal execution are identified, such as execution time timeout, repeated execution, inconsistent data, etc., to generate abnormal event feature sets. The abnormal event feature set is processed for process jam warning analysis. According to the type, frequency and impact of abnormal events, different levels of warning indicators are set to form a process warning indicator table. Finally, the user operation behavior data, business data change data and process execution status data are integrated and processed through the time series feature analysis algorithm. The time series feature analysis algorithm processes data through the following steps: first, time series alignment is performed to unify the timestamps of different data sources to the same time scale; then, time series features are extracted, including trend features, period features, mutation features, etc.; finally, feature fusion is performed to generate a complete user behavior analysis data set.

[0035] For example, in cross-border trade business, a food import company completed 10 batches of goods declarations in one day. Through point collection, it was found that in the commodity code filling link, each batch was modified 3 times on average, with an interval of 5 minutes between each modification, and the filling accuracy rate was 70%. Data change monitoring showed that the frequency of modification of commodity specifications, weight and other factors was high, mainly occurring within 30 minutes after the first filling. Node status monitoring found that the average processing time of food inspection and quarantine was 4 hours, which was more than 1 times the normal processing time (2 hours). The abnormal identification results showed that 3 of the company's 10 recent batches of declarations were interrupted due to incomplete document information, and the health certificate needed to be submitted additionally. Based on these data, targeted early warning indicators were generated: a yellow warning was issued when the number of document filling and modification exceeded 5 times, and a red warning was issued when the food inspection time exceeded 3 hours. These data and early warning mechanisms help companies identify potential risks in advance and optimize business processing procedures.

[0036] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Performing behavior pattern feature extraction processing on the user behavior analysis data set to obtain a user behavior pattern feature table, and performing operation prompt rule generation processing on the user behavior pattern feature table to obtain an operation prompt rule set; (2) Performing intelligent prompt configuration processing on the operation prompt rule set to obtain a reporting guidance rule table, and performing error reminder rule configuration processing on the reporting guidance rule table to obtain an error reminder solution; (3) Performing business suggestion generation processing on the error reminder scheme to obtain a business suggestion set, and performing exception handling suggestion compilation processing on the business suggestion set to obtain an exception handling suggestion; (4) Perform optimal path analysis on the exception handling suggestions to obtain a recommended process list, and configure the operation steps on the recommended process list to obtain operation step prompts; (5) Performing relevant business association analysis on the operation step prompts to obtain a personalized guidance plan, and performing guidance rule aggregation processing on the personalized guidance plan to obtain a collection of guidance rules; (6) The guidance rule collection is integrated through rule mapping to obtain a service guidance rule base.

[0037] Specifically, the operation characteristics of users in the process of business handling are analyzed, including operation sequence characteristics, time characteristics, error characteristics, etc. The operation sequence characteristics reflect the user's operation habits in different business links, the time characteristics include the processing time and waiting time of each link, and the error characteristics record the common error types and error frequencies. These characteristics form different behavior pattern categories through cluster analysis and are recorded in the user behavior pattern characteristic table. The user behavior pattern characteristic table is processed for operation prompt rules generation, and corresponding operation prompt strategies are designed for different behavior patterns, such as adding detailed descriptions to the links with frequent errors and providing quick operation guidance for the links that take a long time, and finally forming an operation prompt rule set. In the intelligent prompt configuration link, the operation prompt rules are converted into specific filling guidance rules, including field filling specifications, document submission requirements, data format descriptions, etc., to generate a filling guidance rule table. When configuring the error reminder rules for the filling guidance rule table, set the judgment conditions and reminder methods for various errors, such as providing correct examples for commodity coding errors and displaying standard units for weight unit errors, to form an error reminder plan.

[0038] Generate business suggestions for the error reminder solution, provide specific solutions and improvement suggestions based on the error type and business scenario, and form a business suggestion set. Then compile exception handling suggestions for the business suggestion set, provide processing steps and precautions for different exception situations, and generate exception handling suggestions. Perform optimal path analysis based on the exception handling suggestions, find the operation path with the highest processing efficiency and the lowest error rate through historical data analysis, and generate a process recommendation list. Configure the operation steps for the process recommendation list, convert the recommended path into detailed operation instructions, including specific operation requirements and precautions for each link, and form operation step prompts.

[0039] Conduct relevant business association analysis on the operation step prompts, consider the association between different business types, such as the association requirements between import declaration and inspection and quarantine application, and generate personalized guidance plans for different enterprise characteristics. Perform guidance rule aggregation processing on the personalized guidance plan, classify and organize various guidance rules according to business types and scenarios, and form a guidance rule collection. Finally, through rule mapping processing, establish a corresponding relationship between the rules in the guidance rule collection and specific business scenarios to form a complete service guidance rule library.

[0040] For example, an import company has been mainly engaged in the import of cold chain food recently. By analyzing the declaration data of the company in the past three months, it was found that in the filling stage, the correct rate of commodity coding was 85%, but the correct rate of filling in relevant information of cold chain requirements (such as temperature control, storage conditions, etc.) was only 60%; in the document submission stage, the submission completeness rate of food safety related certificates was 90%, but the submission completeness rate of cold chain monitoring records was only 75%. Based on these data, targeted operation prompt rules were constructed: on the commodity information filling page, a filling prompt for special requirements of cold chain commodities was added; in the document upload stage, a reminder that cold chain monitoring records must be filled in was set. When the user fills in the temperature control requirements, the intelligent prompt is configured with a standard format example: "Storage temperature: -18℃±2℃". If the user enters an irregular format (such as directly entering "-18 degrees"), an error reminder and modification suggestions will be provided immediately. When a cold chain monitoring record is missing, the business suggestion automatically prompts for additional requirements and provides a standard template. For the complete cold chain food declaration process, the optimal operation path is recommended: first complete the basic information declaration → fill in the special requirements for the cold chain → submit the food safety certificate → upload the cold chain monitoring record → submit the inspection application. In the specific operation, provide personalized document requirements and operation guidelines according to the company's cold chain food categories (such as seafood, dairy products, etc.). These rules are finally integrated into the service guidance rule library to form a complete cold chain food import business guidance plan.

[0041] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Using the knowledge extraction algorithm, the service guidance rule base is processed to extract business entities to obtain an entity feature set, and the entity feature set is annotated with business entity attributes to obtain a business entity dictionary; (2) Perform relationship network construction on the business entity dictionary to obtain an initial relationship graph, and perform relationship strength calculation on the initial relationship graph to obtain a relationship network; (3) Perform knowledge rule extraction on the relationship network to obtain a rule candidate set, and perform rule verification on the rule candidate set to obtain knowledge rules; (4) Constructing a causal chain for the knowledge rules to obtain a causal relationship table, and performing causal strength evaluation on the causal relationship table to obtain a causal relationship chain; (5) Using the rule induction algorithm, the knowledge rules and causal relationship chains are processed to generate inference rules to obtain a basic inference rule set, and the basic inference rule set is tested for completeness to obtain a rule verification report; (6) Perform rule supplement processing on the rule verification report to obtain a business knowledge reasoning rule set.

[0042] Specifically, named entity recognition technology is used to identify key entities in business texts, including enterprise entities (such as importers and exporters, consignees and consignees), commodity entities (such as commodity names, specifications and models), document entities (such as customs declarations, bills of lading), etc. Feature extraction is performed on the identified entities, including basic attributes (such as entity name, coding), business attributes (such as business type, processing authority), attribute value constraints (such as data format, value range), etc., to form an entity feature set. Then, the entity feature set is annotated with business entity attributes, and the complete attribute information is annotated for each entity, and the corresponding relationship between attributes and values ​​is established to generate a business entity dictionary. When constructing a relationship network for the business entity dictionary, it is necessary to analyze various types of associations between entities, such as the transaction relationship between enterprises and commodities, the attribution relationship between commodities and documents, and the derivative relationship between documents. The initial relationship map is established through relationship extraction technology, and the entity associations in the map are quantitatively evaluated to calculate the association strength between entities. The association strength calculation takes into account factors such as business frequency, data consistency, and time series correlation, and uses the following formula: in: Representing Entities and The strength of the association between Indicates the frequency of business interactions. Indicates the degree of data consistency. Indicates the degree of timing correlation, , , is the weight coefficient.

[0043] Extract knowledge rules from the constructed relationship network and identify the business rules contained in the network, such as the filling rules of declaration elements, the timing rules of document submission, the logical rules of data verification, etc., to form a rule candidate set. Verify the rule candidate set, check the correctness, completeness and consistency of the rules, and screen out effective knowledge rules. The scoring of rule verification adopts the following formula: in: Representation Rules r The verification score, represents the accuracy of the rule, Indicates the completeness of the rules, Indicates rule consistency, , , is the weight of each indicator.

[0044] Construct a causal chain based on knowledge rules, analyze the causal relationship in business scenarios, such as audit rejection caused by missing documents, inspection methods affected by product attributes, etc., to form a causal relationship table. Perform strength assessment on each causal chain in the causal relationship table, calculate the degree of causal impact, and establish a complete causal relationship chain. Integrate knowledge rules and causal relationship chains through rule induction algorithms to form a basic reasoning rule set, perform completeness tests on the rule set, and generate a rule verification report. Finally, based on the results of the verification report, make necessary supplements and improvements to the rules to form the final business knowledge reasoning rule set.

[0045] For example, in the import business of hazardous chemicals, the core entities identified through entity extraction include: hazardous chemicals (commodity entity), dangerous goods transport documents (document entity), hazardous goods storage enterprises (enterprise entity), etc. Each entity is annotated with specific attributes, such as dangerous chemicals are annotated with UN number, hazard level, storage requirements and other attributes. In the construction of the relationship network, it is found that the correlation strength between hazardous chemicals and transport documents is 0.85 (calculated based on business frequency 0.9, data consistency 0.8, and time series correlation 0.85), indicating that the two are highly correlated. Through rule extraction, it is found that when the hazardous goods level is Class A, a special transport license must be provided; transport documents must be submitted before goods are declared; storage enterprises must have the corresponding level of qualifications and other rules. These rules have been verified and scored (accuracy 0.95, completeness 0.9, consistency 0.85, weights 0.4, 0.3, 0.3 respectively), and the final verification score is 0.903, which is confirmed as a valid rule. In the causal analysis, a causal chain of "dangerous goods level → transportation requirements → document type → declaration sequence" was established, and ultimately a complete set of knowledge reasoning rules for hazardous chemicals import business was formed.

[0046] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Analyze the customs clearance business process based on the business knowledge reasoning rule set to obtain a set of process bottleneck points, and optimize the customs clearance process based on the set of process bottleneck points to obtain candidate solutions for the customs clearance process; (2) Perform process node configuration processing on candidate solutions for customs clearance process to obtain a customs clearance business flow diagram, and perform path deviation analysis on the customs clearance business flow diagram to obtain a customs clearance process benchmark path; (3) Perform logistics resource allocation processing on the customs clearance process benchmark path to obtain a logistics resource distribution map, and perform logistics path planning processing on the logistics resource distribution map to obtain a logistics path planning solution; (4) Performing a transportation timeliness evaluation on the logistics route planning scheme to obtain a transportation timeliness evaluation table, and performing logistics node optimization on the transportation timeliness evaluation table to obtain a logistics distribution route map; (5) Identify the risk factors of the logistics distribution route map to obtain a risk factor list, and then conduct a risk level assessment on the risk factor list to obtain a risk level assessment report; (6) Extract the customs clearance timeliness factors from the risk level assessment report to obtain a timeliness influencing factor table, and perform customs clearance timeliness prediction processing on the timeliness influencing factor table to obtain customs clearance timeliness prediction results; (7) Integrate the customs clearance process benchmark path, logistics distribution path map and customs clearance time prediction results to obtain a set of decision candidate solutions, and conduct feasibility evaluation on the decision candidate solution set to obtain a solution evaluation report; (8) Perform intelligent decision-making processing on the scheme evaluation report to obtain an intelligent decision-making scheme for port operations.

[0047] Specifically, identify the key business nodes in the customs clearance process, including declaration, inspection, taxation, release and other links, and find out the bottleneck points that cause customs clearance delays or inefficiency through timeliness analysis and resource occupancy analysis. The bottleneck point determination is based on indicators such as node processing time, backlog quantity, resource utilization rate, etc., to form a set of process bottleneck points. The customs clearance process is optimized for the bottleneck point set, and improvement plans are designed for different types of bottlenecks, such as adjusting the processing order, optimizing resource allocation, simplifying the operation process, etc., to generate multiple customs clearance process candidate plans. When configuring the process nodes of the customs clearance process candidate plans, specific processing resources and operation rules are allocated to each business node to form a complete business flow route and generate a customs clearance business flow diagram. Perform path deviation analysis on the customs clearance business flow diagram, compare the actual execution effects of different plans with the expected goals, and select the path with the smallest deviation as the customs clearance process benchmark path.

[0048] According to the customs clearance process benchmark path, logistics resources are allocated, and the distribution of various logistics resources, including transportation tools, storage facilities, loading and unloading equipment, etc., is considered to generate a logistics resource distribution map. The logistics resource distribution map is processed for path planning, and the optimal logistics distribution route is designed by comprehensively considering factors such as transportation distance, cost, and timeliness, and a logistics path planning scheme is formed. The logistics path planning scheme is evaluated for transportation timeliness, and the time consumption of each transportation link and possible delay factors are analyzed to generate a transportation timeliness evaluation table. Based on the evaluation results, the logistics nodes are optimized, and the resource allocation and operation sequence of key nodes are adjusted to obtain an optimized logistics distribution path map.

[0049] In terms of risk management, risk factors are identified on the logistics distribution route map, including cargo safety risk, transportation delay risk, customs clearance blockage risk, etc., to form a risk factor list. By analyzing the probability of risk occurrence and the degree of impact, the risk factors are graded and a risk grade assessment report is generated. Based on the risk grade assessment report, the key factors affecting customs clearance timeliness are extracted to form a timeliness influencing factor table, and the overall customs clearance timeliness is predicted through historical data analysis and trend prediction to obtain the customs clearance timeliness prediction result.

[0050] The customs clearance process benchmark path, logistics distribution path map and customs clearance time prediction results are integrated to form multiple decision candidate solutions. The feasibility of these solutions is evaluated, and the solution evaluation report is generated by considering factors such as implementation difficulty, resource investment, and expected results. Finally, the optimal solution is selected based on the evaluation report to form the final intelligent decision-making solution for port business.

[0051] For example, in the customs clearance business of cold chain aquatic products of an import enterprise, the process analysis found that the inspection link was the main bottleneck, with an average waiting time of 4 hours, which was much higher than other links. The optimization plan includes: adding dedicated inspection channels, adjusting the distribution of inspection time periods, and making appointments for inspection. Through node configuration and path analysis, the benchmark path of "appointment inspection + dedicated channel" was determined, reducing the inspection waiting time to less than 2 hours. In the logistics link, considering the temperature control requirements of aquatic products, the logistics path of "wharf-cold storage-inspection site-enterprise warehouse" was planned, and refrigerated transportation vehicles and temperature control equipment were configured. The timeliness assessment showed that the complete logistics distribution took 8 hours, including 2 hours for loading and unloading, 4 hours for transportation, and 2 hours for inspection. The risk identification results showed that the main risks included: temperature control interruption risk (high risk), transportation delay risk (medium risk), and inspection backlog risk (medium risk). The timeliness prediction showed that after considering various risk factors, 90% of the goods could be cleared within 24 hours. The final decision-making plan includes specific measures such as selecting an appointment inspection time, using designated cold storage, equipping spare transport vehicles, and setting up full-process temperature control monitoring. The overall feasibility score of the plan reached 85 points (out of 100 points), and it is expected that the total customs clearance time can be controlled within 20 hours.

[0052] The above describes the cross-border trade data intelligent processing method based on the smart port in the embodiment of the present application. The following describes the cross-border trade data intelligent processing system based on the smart port in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the cross-border trade data intelligent processing system based on the smart port includes: Functional module 201 is used to perform functional model hierarchical processing on cross-border trade business data to obtain a functional module call relationship network; the functional model hierarchical processing includes dividing the business data into an enterprise main function layer and a business function layer, and constructing a data interaction interface between functional modules; The process module 202 is used to construct a process template for the function module call relationship network to obtain a dynamic process assembly rule base; the process template construction process includes designing a business declaration process and an exception handling process system based on the function call relationship; The service tracking module 203 is used to perform service tracking processing on the dynamic process assembly rule library to obtain a user behavior analysis data set; the service tracking processing includes setting tracking positions at key process nodes to collect user operation behavior data, business data change data and process execution status data; The service guidance module 204 is used to perform guidance strategy construction processing on the user behavior analysis data set to obtain a service guidance rule base; the guidance strategy construction processing includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans; A graph module 205 is used to construct a knowledge graph for the service guidance rule base through a knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction process includes extracting business entities, building a relationship network, discovering knowledge rules, and establishing a causal relationship chain; The decision module 206 is used to process the cross-border trade data on the business knowledge reasoning rule set to obtain an intelligent decision-making plan for port business; the cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction.

[0053] Through the collaborative cooperation of the above-mentioned components, the business data of cross-border trade is divided into the enterprise main function layer and the business function layer through the hierarchical processing of the functional model of the cross-border trade business data, and the data interaction interface between the functional modules is constructed to realize the modular management and flexible configuration of the business functions; by constructing the process template for the function module call relationship network, the business declaration process and the exception handling process system are designed, and a complete dynamic process assembly rule library is established, which improves the standardization and automation level of the business process; by processing the service point of the dynamic process assembly rule library, the point position is set at the key process node, and the user operation behavior data, business data change data and process execution status are collected. The system uses dynamic data to realize comprehensive monitoring and analysis of the business execution process; through the construction and processing of guidance strategies for user behavior analysis data sets, design of operation prompts, formulation of exception handling suggestions and personalized guidance plans, the accuracy and efficiency of user operations are improved; through the knowledge extraction algorithm, the service guidance rule base is processed through knowledge graph construction, business entities are extracted, relationship networks are constructed, knowledge rules are discovered, and causal relationship chains are established, forming a complete business knowledge system; through the cross-border trade intelligent analysis algorithm, the cross-border trade data of the business knowledge reasoning rule set is processed, and the customs clearance process optimization, logistics path planning, risk level assessment and customs clearance time prediction are completed, realizing intelligent decision-making of port business. Through multi-level data processing and analysis, a complete technical chain from data collection, process monitoring to intelligent decision-making is constructed, which significantly improves the processing efficiency and service quality of cross-border trade business and reduces business risks.

[0054] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent processing of cross-border trade data based on smart ports, characterized in that: The cross-border trade data intelligent processing method based on the smart port includes: Performing hierarchical processing of the cross-border trade business data using a functional model to obtain a functional module call relationship network; the hierarchical processing of the functional model includes dividing the business data into an enterprise main function layer and a business function layer, and constructing a data interaction interface between the functional modules; Performing process template construction processing on the function module call relationship network to obtain a dynamic process assembly rule base; the process template construction processing includes designing a business declaration process and an exception handling process system based on the function call relationship; Performing service tracking processing on the dynamic process assembly rule library to obtain a user behavior analysis data set; the service tracking processing includes setting tracking locations at key process nodes to collect user operation behavior data, business data change data and process execution status data; Performing guidance strategy construction processing on the user behavior analysis data set to obtain a service guidance rule base; the guidance strategy construction processing includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans; The service guidance rule base is subjected to a knowledge graph construction process by a knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction process includes extracting business entities, constructing a relationship network, discovering knowledge rules, and establishing a causal relationship chain; The business knowledge reasoning rule set is subjected to cross-border trade data processing to obtain an intelligent decision-making plan for port business; the cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction.

2. According to claim 1, the intelligent processing method for cross-border trade data based on smart ports is characterized in that: The cross-border trade business data is subjected to hierarchical processing of the functional model to obtain a functional module calling relationship network; The functional model layered processing includes dividing the business data into the enterprise main function layer and the business function layer, and constructing the data interaction interface between the functional modules, including: Performing enterprise subject classification processing on cross-border trade business data to obtain an enterprise basic information set, and performing enterprise subject function layer construction processing on the enterprise basic information set to obtain an enterprise subject function layer; Performing filing qualification processing on the enterprise main function layer to obtain an enterprise qualification certificate set, and performing authorization processing on the enterprise qualification certificate set to obtain an enterprise function authorization table; Performing business function classification processing on the cross-border trade business data to obtain a business function element set, and performing business function layer construction processing on the business function element set to obtain a business function layer; Performing customs clearance service function configuration processing on the business function layer to obtain a customs clearance business process set, and performing inspection declaration, manifest, transit and logistics service function configuration processing on the customs clearance business process set to obtain a business service function set; Performing data interaction interface design processing on the enterprise main function layer and the business function layer to obtain a function interaction interface table, and performing interface rule configuration processing on the function interaction interface table to obtain a data interaction interface; The data interaction interface is processed by performing call relationship construction through module association analysis to obtain a function module call relationship network.

3. The cross-border trade data intelligent processing method based on smart ports according to claim 1 is characterized in that: The process template construction process is performed on the function module call relationship network to obtain a dynamic process assembly rule library; The process template construction process includes designing a business declaration process and an exception handling process system based on a function call relationship, including: Performing business declaration process analysis on the function module call relationship network to obtain a declaration process element set, and performing business declaration process design on the declaration process element set to obtain a business declaration process; Performing an abnormal type identification process on the business declaration process to obtain an abnormal situation list, and constructing an abnormality handling process system on the abnormal situation list to obtain an abnormality handling process system; Performing process rule integration processing on the business declaration process and exception handling process system to obtain a process assembly specification table, and performing rule dynamic configuration processing on the process assembly specification table to obtain a candidate process set; The candidate process set is subjected to rule assembly processing through process rule mapping to obtain a dynamic process assembly rule base.

4. The cross-border trade data intelligent processing method based on smart ports according to claim 1 is characterized in that: The dynamic process assembly rule base is subjected to service tracking processing to obtain a user behavior analysis data set; the service tracking processing includes setting tracking locations at key process nodes, collecting user operation behavior data, business data change data and process execution status data, including: Performing key node analysis processing on the dynamic process assembly rule base to obtain a business key node table, and performing point location setting processing on the business key node table to obtain a key node point distribution map; Performing page access record collection and processing on the key node buried point distribution map to obtain user operation behavior data, and performing operation trajectory annotation processing on the user operation behavior data to obtain an operation trajectory feature set; Performing function usage data statistical processing on the operation trajectory feature set to obtain a node operation frequency table, and performing business data change monitoring processing on the node operation frequency table to obtain business data change data; Performing process execution status collection processing on the business data change data to obtain process execution status data, and performing key node monitoring processing on the process execution status data to obtain a node status sequence table; Performing abnormal point identification processing on the node state sequence table to obtain an abnormal event feature set, and performing process jam warning analysis processing on the abnormal event feature set to obtain a process warning indicator table; The user operation behavior data, business data change data and process execution status data are integrated and processed through a time series feature analysis algorithm to obtain a user behavior analysis data set.

5. The cross-border trade data intelligent processing method based on smart ports according to claim 1 is characterized in that: The user behavior analysis data set is processed for guiding strategy construction to obtain a service guiding rule base; The guidance strategy construction process includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans, including: Performing behavior pattern feature extraction processing on the user behavior analysis data set to obtain a user behavior pattern feature table, and performing operation prompt rule generation processing on the user behavior pattern feature table to obtain an operation prompt rule set; Performing intelligent prompt configuration processing on the operation prompt rule set to obtain a reporting guidance rule table, and performing error reminder rule configuration processing on the reporting guidance rule table to obtain an error reminder solution; Performing a business suggestion generation process on the error reminder scheme to obtain a business suggestion set, and performing an exception handling suggestion compilation process on the business suggestion set to obtain an exception handling suggestion; Performing optimal path analysis on the exception handling suggestion to obtain a process recommendation list, and performing operation step configuration on the process recommendation list to obtain operation step prompts; Performing relevant business association analysis on the operation step prompt to obtain a personalized guidance plan, and performing guidance rule aggregation processing on the personalized guidance plan to obtain a guidance rule collection; The guidance rule collection is subjected to rule integration processing through rule mapping processing to obtain a service guidance rule library.

6. The cross-border trade data intelligent processing method based on smart ports according to claim 1 is characterized in that: The service guidance rule base is subjected to a knowledge graph construction process by a knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction process includes extracting business entities, constructing a relationship network, discovering knowledge rules, and establishing a causal relationship chain, including: Performing business entity extraction processing on the service guidance rule base through a knowledge extraction algorithm to obtain an entity feature set, and performing business entity attribute annotation processing on the entity feature set to obtain a business entity dictionary; Performing a relationship network construction process on the business entity dictionary to obtain an initial relationship graph, and performing an inter-entity association strength calculation process on the initial relationship graph to obtain a relationship network; Performing knowledge rule extraction processing on the relationship network to obtain a rule candidate set, and performing rule verification processing on the rule candidate set to obtain knowledge rules; Performing causal chain construction processing on the knowledge rules to obtain a causal relationship table, and performing causal strength evaluation processing on the causal relationship table to obtain a causal relationship chain; Performing inference rule generation processing on the knowledge rules and causal relationship chain through a rule induction algorithm to obtain a basic inference rule set, and performing rule completeness verification processing on the basic inference rule set to obtain a rule verification report; The rule verification report is subjected to rule supplementation processing to obtain a business knowledge reasoning rule set.

7. The cross-border trade data intelligent processing method based on smart ports according to claim 1 is characterized in that: The cross-border trade data is processed on the business knowledge reasoning rule set to obtain an intelligent decision-making solution for port business; The cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction, including: Performing customs clearance business process analysis on the business knowledge reasoning rule set to obtain a set of process bottleneck points, and performing customs clearance process optimization processing on the set of process bottleneck points to obtain candidate solutions for the customs clearance process; Performing process node configuration processing on the candidate solutions for the customs clearance process to obtain a customs clearance business flow diagram, and performing path deviation analysis processing on the customs clearance business flow diagram to obtain a customs clearance process reference path; Performing logistics resource allocation processing on the customs clearance process reference path to obtain a logistics resource distribution map, and performing logistics path planning processing on the logistics resource distribution map to obtain a logistics path planning solution; Performing a transportation timeliness evaluation process on the logistics path planning scheme to obtain a transportation timeliness evaluation table, and performing a logistics node optimization process on the transportation timeliness evaluation table to obtain a logistics distribution path diagram; Performing risk factor identification processing on the logistics distribution route map to obtain a risk factor list, and performing risk level assessment processing on the risk factor list to obtain a risk level assessment report; Extracting customs clearance timeliness elements from the risk level assessment report to obtain a timeliness influencing factor table, and performing customs clearance timeliness prediction processing on the timeliness influencing factor table to obtain a customs clearance timeliness prediction result; Performing scheme integration processing on the customs clearance process benchmark path, logistics distribution path map and customs clearance time prediction results to obtain a decision candidate scheme set, and performing scheme feasibility evaluation processing on the decision candidate scheme set to obtain a scheme evaluation report; The scheme evaluation report is subjected to intelligent decision-making generation processing to obtain an intelligent decision-making scheme for port business.

8. A cross-border trade data intelligent processing system based on a smart port, used to implement the cross-border trade data intelligent processing method based on a smart port as described in any one of claims 1 to 7, characterized in that: The cross-border trade data intelligent processing system based on the smart port includes: Functional modules are used to perform functional model hierarchical processing on cross-border trade business data to obtain a functional module call relationship network; the functional model hierarchical processing includes dividing the business data into an enterprise main function layer and a business function layer, and constructing a data interaction interface between the functional modules; A process module is used to construct a process template for the function module call relationship network to obtain a dynamic process assembly rule base; the process template construction process includes designing a business declaration process and an exception handling process system based on the function call relationship; A service tracking module is used to perform service tracking processing on the dynamic process assembly rule library to obtain a user behavior analysis data set; the service tracking processing includes setting tracking positions at key process nodes to collect user operation behavior data, business data change data and process execution status data; A service guidance module, which is used to perform guidance strategy construction processing on the user behavior analysis data set to obtain a service guidance rule base; the guidance strategy construction processing includes designing operation prompts based on user behavior patterns, formulating exception handling suggestions and personalized guidance plans; A graph module is used to construct a knowledge graph for the service guidance rule base through a knowledge extraction algorithm to obtain a business knowledge reasoning rule set; the knowledge graph construction process includes extracting business entities, building a relationship network, discovering knowledge rules, and establishing a causal relationship chain; The decision-making module is used to process the cross-border trade data on the business knowledge reasoning rule set to obtain an intelligent decision-making plan for port business; the cross-border trade data processing includes customs clearance process optimization, logistics route planning, risk level assessment and customs clearance time prediction.

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