Cross-border customs clearance declaration processing method and device and electronic commodity

Abstract

The invention discloses a cross-border customs clearance declaration processing method, which comprises the steps of obtaining to-be-declarated commodity information, and performing verification and fault-tolerant processing on the obtained to-be-declarated commodity information to obtain input data; on the basis of regular expression, with optimal commodity classification as a target, combining a secondary classification model in the input data and obtaining a commodity set, constructing a nonlinear programming optimization model, and solving through a numerical solution algorithm to obtain an initial commodity classification scheme; performing commodity classification constraint check on the initial commodity classification scheme to obtain an optimized commodity classification scheme; and smoothing the optimized commodity classification scheme to obtain a final commodity classification scheme so as to report the commodities to customs according to corresponding classifications. The invention further comprises a cross-border customs clearance declaration processing device and the electronic commodity, and through the setting, the efficiency of declarating the commodity to the customs can be improved.

Description

Technical Field

[0001] This application relates to the field of cross-border e-commerce logistics planning technology, and in particular to a cross-border customs declaration processing method, device, and electronic goods. Background Technology

[0002] In the current cross-border customs declaration field, the traditional commodity classification declaration model has many industry pain points, becoming a core issue restricting the efficiency and compliance of commodity declarations to customs. Under the existing model, commodity classification relies heavily on manual judgment, which is prone to inaccurate matching of commodities with corresponding categories due to subjective judgment bias. Problems such as ambiguous classification and misclassification occur frequently, which not only increases the customs review and return rate, but also easily triggers audit risks.

[0003] Meanwhile, the process of declaring goods to customs suffers from a lack of standardization in data collection and processing, inconsistent data formats from multiple sources, redundant and invalid information, and a lack of robust fault tolerance mechanisms. Consequently, data loss and transmission interruptions occur, leading to delays in the declaration process and hindering its ability to meet the real-time declaration needs of e-commerce bulk goods.

[0004] Furthermore, the commodity classification does not establish rigid constraints based on customs classification rules, nor does it set differentiated adjustment strategies for different commodity characteristics. The classification results are unstable, and frequent changes in commodity classification will cause confusion when declaring commodities to customs.

[0005] However, the existing scheme lacks a dedicated maintenance mode adaptation mechanism. In scenarios such as product registration updates and HS code adjustments, the classification strategy cannot be dynamically optimized, which can easily lead to unreasonable classification allocation. Furthermore, there is no closed-loop deviation correction system after declaration, making it impossible to adjust the classification scheme in a timely manner based on customs feedback. The overall process has a low level of intelligence and automation, high labor costs, and is difficult to meet the demand for efficient customs declaration under the rapid development of cross-border e-commerce. Summary of the Invention

[0006] In order to address the shortcomings of existing technologies, the purpose of this application is to provide a cross-border customs declaration processing method, device, and electronic goods that can improve the efficiency of customs declaration for goods.

[0007] To achieve the above objectives, this application adopts the following technical solution: A method for processing cross-border customs declarations includes: Obtain information on goods to be declared, perform verification and error handling on the obtained information to obtain input data; Based on regular expressions, with the goal of optimal product classification, a nonlinear programming optimization model is constructed by combining the quadratic classification model in the input data and the obtained product set. The model is then solved by a numerical solution algorithm to obtain the initial product classification scheme. The initial product classification scheme is checked for product classification constraints, and fuzzy classifications are processed until all products meet the safety constraints, resulting in an optimized product classification scheme. The optimized commodity classification scheme is smoothed by using the classification threshold of the commodity to be declared as a constraint, the difference of fuzzy classification is calibrated, and dynamic adaptation and adjustment are carried out in combination with HS code to obtain the final commodity classification scheme, so that the commodity can be reported to the customs according to the corresponding classification.

[0008] Furthermore, the specific process for obtaining information on the goods to be declared is as follows: Data is collected on the products to be declared and the merchants to which the products belong, to obtain the collected data on the categories of products to be declared; Based on the classification logic of the goods to be declared, the collected data is associated with goods and encapsulated with events to obtain encapsulated data. The encapsulated data is then uploaded to the preset central data platform through the goods reporting system of the goods to be declared. Real-time data messages pushed by the central data platform are received by message subscription, and the format of the real-time data messages is parsed to obtain parsed data. By associating the parsed data with the product and the context of the merchant to which it belongs, the information of the product to be declared is obtained.

[0009] Furthermore, the specific process of obtaining input data is as follows: For the core data in the declared commodity information, multi-source cross-validation of primary and secondary classification data is adopted, and a classification deviation threshold is set. When the deviation of the primary classification data exceeds the classification deviation threshold, the predicted value derived by the secondary classification model is used for replacement. If a single classification model parameter is missing or abnormal, extract the historical data of similar merchants for that product in the most recent period, combine it with the parameter mapping relationship of products of the same model, and generate a temporary classification model through transfer learning. When data transmission is interrupted, the optimized product classification scheme of the previous cycle is cached, and linear fine-tuning is performed based on the classification change trend of the past 4 cycles until the data is restored and the normal data processing flow is switched. The data processed above is then subjected to integrity verification to remove invalid and redundant data, resulting in the input data.

[0010] Furthermore, the specific process of obtaining the initial product classification scheme is as follows: Based on the quadratic classification model in the input data, the classification change rate function for each product is derived. The classification change rate function is the first derivative of the quadratic classification model with respect to classification. With the objective function of obtaining the optimal product classification, and with the constraints of equal classification change rates for all obtained products and the final classification meeting real-time reporting requirements, a nonlinear programming optimization model is constructed. Determine the set of equations corresponding to the nonlinear programming optimization model. The set of equations includes equations that ensure the rate of change of each commodity category is equal and equations that ensure the final category balance. The equation system is solved iteratively using linear discriminant analysis. An iterative convergence threshold is set, and the solution is stopped when the iterative result meets the iterative convergence threshold, thus obtaining the initial commodity classification scheme.

[0011] Furthermore, the specific process for obtaining the optimized product classification scheme is as follows: Determine the pre-classification options for each product. The pre-classification options include product classification boundaries and merchant classification boundaries. The classification boundary values ​​are based on the product's factory classification and historical data classification. Verify one by one whether the classification of each product in the initial product classification scheme is within its corresponding safety boundary range; If a product category exceeds the safety boundary, the product category is fixed at the corresponding category boundary value, and the category boundary value is deducted from the real-time declaration demand to obtain the remaining declaration. Using the remaining declarations as the new classification requirement, a new nonlinear programming optimization model is reconstructed and solved for the remaining acquired goods to obtain a new classification scheme. Repeat the above verification and solution steps until the classification of all products satisfies the safety boundary constraints, and obtain the optimized product classification scheme.

[0012] Furthermore, the specific process for obtaining the final product classification scheme is as follows: Based on the product model, acquisition period, and usage parameters, a preset classification threshold is set for each product, and the classification threshold is dynamically adjusted according to the product acquisition status. Calculate the difference between the classification of each product in the optimized product classification scheme and the actual classification in the previous period, and determine whether the difference exceeds the preset classification threshold. If the difference exceeds the classification threshold, the classification of the product in this cycle will be calibrated to the sum of the actual classification and the classification threshold of the previous cycle, and the classification difference after calibration will be calculated. The classification difference is evenly distributed to other products, and the classification of other products is fine-tuned in combination with the principle of equal classification change rate; The periodic fluctuation trend of real-time classification is statistically analyzed. When the real-time classification fluctuates in the same direction for three or more consecutive periods, the classification threshold is relaxed by a preset ratio. Based on the above constraints, calibration, and adaptation results, the final product classification scheme is obtained.

[0013] Furthermore, after obtaining the final product classification scheme, the final product classification scheme is distributed to each product classification module, and the actual acquisition status and declaration parameters of each product are collected in real time. The actual acquired status and declared parameters are compared with the preset parameters in the final commodity classification scheme to determine whether there is any excessive acquisition deviation. If there is an excessive deviation in the acquisition, the final product classification scheme will be dynamically fine-tuned based on the degree of deviation, and a revised classification scheme will be generated and reissued. The deviation events and correction process are recorded synchronously to form a deviation handling log.

[0014] Furthermore, during the verification and error-tolerant processing of the information of the goods to be declared, the maintenance trigger status of each goods is monitored simultaneously. The maintenance trigger status includes manually issued maintenance instructions, the acquisition parameters reaching the preset maintenance threshold, or the cumulative acquisition time meeting the periodic maintenance requirements. When any product is detected to be in a maintenance-triggered state, the product to be maintained is marked as a non-priority acquisition product and its classification and allocation weight is reduced when constructing the nonlinear programming optimization model. After checking the safety boundary constraints of the initial product classification scheme, the classification allocation ratio of the products to be maintained is further verified to ensure that their classification does not exceed the preset upper limit of the maintenance period classification. During the smoothing phase, a stricter maximum classification threshold is adopted for the classification adjustment of the products to be maintained. At the same time, based on the classification affiliation of other acquired products and the safety margin, the classification assignment strategy is optimized to ensure the final classification is optimal and the product classification is stable. Once the goods have been properly classified and categorized, they will be reported to customs according to their corresponding classification.

[0015] To achieve the above objectives, this application adopts the following technical solution: A cross-border customs declaration processing device includes an acquisition module for acquiring information on goods to be declared; a processing module for processing the information on goods to be declared; and a declaration module for submitting the processed information on goods.

[0016] To achieve the above objectives, this application adopts the following technical solution: An electronic product includes a memory, a processor, and a computer program stored on the memory, wherein the processor is configured to retrieve the computer program to perform a cross-border customs declaration processing method.

[0017] The aforementioned cross-border customs declaration processing method ensures the integrity and accuracy of declaration data through multi-source data collection and verification, fault tolerance mechanisms, and regular expression preprocessing, resolving issues such as missing and anomalies, and providing a reliable foundation for commodity classification. Simultaneously, by combining a quadratic classification model with a nonlinear programming optimization model, and using iterative calculations with linear discriminant analysis, optimal commodity classification is achieved, significantly improving the accuracy of matching the declared commodities with HS codes. Furthermore, through multi-layer constraint checks, smoothing processing, and dynamic HS code adaptation, the method effectively solves the problem of fuzzy commodity classification, ensuring that the classification results of the declared commodities meet customs security and compliance requirements, thereby improving the efficiency of commodity declaration to customs. Attached Figure Description

[0018] Figure 1 This is a hardware structure block diagram of an electronic device according to an embodiment of this application.

[0019] Figure 2 This is a flowchart of a cross-border customs declaration processing method according to an embodiment of this application.

[0020] Figure 3 This is a structural block diagram of the cross-border customs declaration processing device according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in specific embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] It should be noted that the terms "first," "second," and similar terms used in this application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, "a" or "one," and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. "A plurality" or "several" indicates at least two. Unless otherwise stated, terms such as "front," "back," "left," "right," "lower," and / or "upper" are for illustrative purposes only and are not limited to a location or spatial orientation. Terms such as "comprising" or "including" indicate that the elements or objects preceding "comprising" encompass the elements or objects listed following "comprising" or "including" and their equivalents, and do not exclude other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0023] The singular forms “a,” “the,” and “the” used in this application specification and appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0024] The multimodal fusion-based flaw detection method provided in this embodiment can be executed in electronic device 100 or similar device. Figure 1 This is a hardware structure block diagram of an electronic device 100 that implements an embodiment of this application. For example... Figure 1 As shown, the electronic device 100 may include one or more ( Figure 1 (Only one is shown in the image) memory 12 and processor 11. The electronic device 100 is a control terminal for classifying information on goods to be declared. It is used to accurately classify goods to be declared to customs, thereby improving the efficiency of electronic goods declaration to customs.

[0025] The memory 12 stores program instructions, such as software programs and modules for application software, like the computer program for a train traction power supply control method in this embodiment. The processor 11 executes the program instructions stored in the memory 12. By retrieving the computer program stored in the memory 12, it can perform various functional applications and data processing, namely, classifying the goods to be declared to customs.

[0026] The processor 11 may include, but is not limited to, a microprocessor 11 (Microcontroller Unit, abbreviated as MCU) or a programmable gate array (FPGA).

[0027] Those skilled in the art will understand that Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device 100 described above. For example, the electronic device 100 may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0028] like Figure 2 As shown, this application also includes a cross-border customs declaration processing method, comprising: Obtain information on goods to be declared, perform verification and error handling on the obtained information, and obtain input data.

[0029] As one implementation method, the specific process of obtaining information on goods to be declared is as follows: Data is collected on the goods to be declared and the merchants to which they belong, resulting in collected data on the categories of goods to be declared. Based on the classification logic of the goods to be declared, the collected data is associated with goods and encapsulated into events to obtain encapsulated data. This encapsulated data is then uploaded to a preset central data platform through the goods reporting system for goods to be declared. Real-time data messages pushed by the central data platform are received via message subscription, and the format of the real-time data messages is parsed to obtain parsed data. The parsed data is associated with goods and the context of the merchant to which it belongs to obtain the information on goods to be declared.

[0030] As one implementation method, the specific process of obtaining input data is as follows: For core data in the declared product information, multi-source cross-validation of primary and secondary category data is employed. A classification deviation threshold is set; when the primary category data deviation exceeds the threshold, the predicted value derived from the secondary classification model is used for replacement. If a single classification model parameter is missing or abnormal, historical data of similar merchants for that product in the most recent period is extracted. Combined with the parameter mapping relationship of similar products, a temporary classification model is generated through transfer learning. When data transmission is interrupted, the optimized product classification scheme of the previous period is cached, and linear fine-tuning is performed based on the classification change trend of the past four periods until the data is restored and the normal data processing flow is switched to. The data processed in the above way is subjected to integrity verification, and invalid and redundant data is removed to obtain the input data.

[0031] Based on regular expressions, and aiming at optimal product classification, this paper constructs a nonlinear programming optimization model by combining a quadratic classification model from the input data and the obtained product set. The model is then solved using a numerical algorithm to obtain an initial product classification scheme. In this application, a regular expression is a set of text matching patterns composed of ordinary characters and special metacharacters according to fixed syntax rules; it is a standardized text processing tool commonly used in the computer field.

[0032] In this application, the quadratic classification model is the core foundational model for accurate classification of goods using HS codes. It is used to accurately fit the nonlinear mapping relationship between goods attributes and classification results. Furthermore, the classification change rate function can be obtained through differentiation, providing crucial mathematical basis for the subsequent construction and solution of the nonlinear programming optimization model. This model uses a quadratic polynomial as its core structure, balancing the accuracy of nonlinear fitting with the convenience of mathematical solution. A standardized construction process and online iterative training strategy are designed to meet the real-time and compliance requirements of cross-border customs declarations. The specific construction steps of the quadratic classification model are as follows: The secondary classification model, as the basic predictive model for commodity classification in cross-border customs declarations, is constructed around four core principles: "accurate fitting, easy differentiation, online updating capability, and adaptation to the declaration process." This forms a standardized and practical construction system, the specific process of which is as follows: First, it is clear that the core positioning and goal of the quadratic classification model is to accurately fit the first... Items to be declared as commodity classification variables Category matching degree The model must be able to perform nonlinear operations while ensuring model differentiability, providing constraint support for subsequent nonlinear programming optimization models, and adapting to the real-time requirements of edge computing deployment, while supporting single-item customization and dynamic updates.

[0033] Based on the compliance requirements of cross-border customs declaration, a multi-dimensional feature system is built. Feature sources cover core product attributes and auxiliary declaration attributes. Core attributes include merchant-defined product names, materials, functions, specifications, brands, and models, while auxiliary attributes include declaration entity information, trade methods, country of origin, and regulatory document requirements. Historical declaration features of the product are also incorporated. The collected features undergo standardized preprocessing, including data type conversion, unit standardization, and synonym replacement. Outliers are removed using the 3σ criterion, and missing values ​​are filled using linear interpolation or Kalman filtering algorithms. Categorical features are encoded, and numerical features are normalized, ultimately forming a structured feature vector. This vector is then clustered according to product category and regulatory level, laying the foundation for building single-item-level models.

[0034] The mathematical structure of the model is determined to be a univariate quadratic polynomial. This structure balances the accuracy of nonlinear fitting with the convenience of mathematical solution. The core expression is: ; in, For product category matching, For categorical variables, For constant terms, For linear terms, The coefficients are quadratic terms, and the first derivative of the quadratic classification model with respect to the categorical variable is explicitly given. and This is a classification rate of change function, which is directly used to construct the constraints of the subsequent optimization model.

[0035] Next, the input and output dimensions are customized. The input is a standardized feature vector of a single product, and the output is a single-dimensional classification matching degree (value range 0-100, the higher the value, the stronger the matching degree). An independent set of ternary parameters is assigned to each product. This ensures the model's adaptability to individual products. Simultaneously, it enhances the model's compatibility with the overall application process, ensuring that the classification change rate function can be directly used as a constraint condition for the nonlinear programming optimization model, that the output results can be quickly integrated into the classification constraint checking process, and that the model's computational complexity is adapted to edge computing node deployment, with reserved interfaces for handling data anomaly scenarios.

[0036] Finally, solidify the basic framework of the model, including mathematical expressions, input and output dimensions, feature system, and parameter storage format. Formulate standardized parameter storage and retrieval rules, store all parameters on the cloud platform and be responsible for updating them, and cache the parameters of the current batch of declared products locally on edge computing nodes to ensure that the model can still be called normally when data transmission is interrupted.

[0037] As one implementation method, the specific process for obtaining the optimized product classification scheme is as follows: First, determine the pre-classification options for each product. These options include product classification boundaries and merchant classification boundaries. The classification boundary values ​​are determined based on the product's factory classification and historical data. Second, verify that the classification of each product in the initial product classification scheme is within its corresponding safety boundary range. If a product classification exceeds the safety boundary, fix the product's classification at the corresponding classification boundary value and deduct this boundary value from the real-time reporting demand to obtain the remaining reporting. Third, using the remaining reporting as the new classification demand, reconstruct and solve the nonlinear programming optimization model for the remaining acquired products to obtain a new classification scheme. Repeat the above verification and solution steps until the classification of all products satisfies the safety boundary constraints, thus obtaining the optimized product classification scheme.

[0038] In this application, the nonlinear programming optimization model serves as the core model for optimal commodity classification decision-making. Its construction process deeply integrates with the needs of cross-border customs declaration, transforming the business objective of "optimal commodity classification" into a solvable mathematical problem. The specific process is as follows: Before construction, preprocessing and data preparation are required. This involves text cleaning of the input data using regular expressions to remove interfering characters and marketing modifiers from product names and to standardize customs terminology; extracting the core expression and classification change rate function of the secondary classification model as the core data foundation for model construction; and addressing real-time declaration requirements. Product category safety boundary ( , Quantification and normalization are performed to unify data units and value ranges, avoiding solution errors caused by differences in dimensions.

[0039] The model construction goals and principles are clearly defined. The core objective is to "maximize the matching degree of all commodity categories while meeting the compliance constraints of customs declaration and real-time requirements." It follows four principles: compliance first, optimal matching, real-time performance, and dynamic adaptation, to ensure that the model solution not only complies with customs supervision rules but also adapts to the dynamic scenarios of cross-border declarations.

[0040] Define a precise mathematical objective function, optimizing by minimizing the sum of the category matching degrees of all goods to be declared. Combining this with the quadratic classification model expression, the objective function is: .in, This represents the total category matching score for all products. This represents the current set of valid commodities awaiting declaration. The quadratic structure of the objective function ensures the accuracy of the nonlinear fitting and the feasibility of the solution.

[0041] Then, a multi-dimensional constraint system is constructed, including equality constraints and inequality constraints. The core of the equality constraint is "all effective commodity categories have the same rate of change," that is ( Based on the theory of "equal marginal cost," it ensures optimal classification, and "full-volume commodity classification meets real-time reporting requirements." This is used to ensure that the classification results are consistent with the declaration requirements; the inequality constraint ensures that the commodity classification is within the compliance and safety boundaries. The boundary values ​​are determined by a combination of product manufacturing parameters, historical declaration data, and customs rulings; at the same time, an extended constraint interface is reserved to adapt to special scenarios such as product maintenance.

[0042] The core equations of the model are derived, transforming the objective function and constraints into a set of mathematical equations that can be directly solved. Equations ( (The number of valid goods) and the quantity to be solved are Product category variables Equilibrium value of one category change rate The system of equations is in the form of: In the above system of equations, Let n be a constant term, where n is the constant term. N, m N.

[0043] Based on this set of equations, the overall mathematical framework of the model is solidified, clearly defining the inputs (parameters of the quadratic classification model, real-time reporting requirements, and safety boundaries), the quantities to be solved, the output (initial classification scheme), the solution algorithm (linear discriminant analysis), and the convergence criteria, thus forming a standardized computation process. Simultaneously, model deployment and adaptation are completed, solidifying the mathematical framework and solution algorithm into runnable programs on edge computing nodes, optimizing code execution efficiency, and ensuring that the solution is completed within a 1-5 second algorithm call cycle. An interface is established to connect with subsequent reporting processes, achieving seamless integration of model output with classification constraint checks and HS encoding mapping, ensuring rapid conversion of mathematical solutions into actual reporting classification results.

[0044] Specifically, the final product classification scheme is obtained as follows: Based on the product model, acquisition period, and usage parameters, a preset classification threshold is set for each product. This threshold is dynamically adjusted according to the product's acquisition status. The difference between the classification of each product in the optimized product classification scheme and the actual classification in the previous period is calculated, and it is determined whether the difference exceeds the preset classification threshold. If the difference exceeds the threshold, the product's current classification is calibrated to the sum of the actual classification in the previous period and the classification threshold, and the calibrated classification difference is calculated. The classification difference is evenly distributed to other products, and the classification of other products is fine-tuned based on the principle of equal classification change rate. The periodic fluctuation trend of real-time classification is statistically analyzed. When the real-time classification fluctuates in the same direction for three or more consecutive periods, the classification threshold is relaxed by a preset proportion. Based on the above constraints, calibration, and adaptation adjustment results, the final product classification scheme is obtained.

[0045] The initial product classification scheme is checked for product classification constraints, and fuzzy classifications are processed until all products meet the safety constraints, resulting in an optimized product classification scheme.

[0046] As one implementation method, the specific process of obtaining the initial commodity classification scheme is as follows: Based on the quadratic classification model in the input data, the classification change rate function for each commodity is derived. The classification change rate function is the first derivative of the quadratic classification model with respect to classification. Taking the optimal commodity classification as the objective function, and with the constraints of equal classification change rates for all acquired commodities and the final classification meeting real-time reporting requirements, a nonlinear programming optimization model is constructed. The system of equations corresponding to the nonlinear programming optimization model is determined, including equations for equal classification change rates for each commodity and equations for final classification balance. The system of equations is iteratively solved using linear discriminant analysis. An iterative convergence threshold is set. When the iterative result meets the iterative convergence threshold, the solution is stopped, and the initial commodity classification scheme is obtained.

[0047] Specifically, after obtaining the final product classification scheme, it is distributed to each product classification module, and the actual acquisition status and declared parameters of each product are collected in real time. The actual acquisition status and declared parameters are compared with the preset parameters in the final product classification scheme to determine if there are any acquisition deviations exceeding the limit. If acquisition deviations exceed the limit, the final product classification scheme is dynamically fine-tuned based on the degree of deviation, a revised classification scheme is generated, and it is re-distributed. Deviation events and the correction process are recorded synchronously to form a deviation handling log.

[0048] The optimized commodity classification scheme is smoothed by using the classification threshold of the commodity to be declared as a constraint, the difference of fuzzy classification is calibrated, and dynamic adaptation and adjustment are carried out in combination with HS code to obtain the final commodity classification scheme, so that the commodity can be reported to the customs according to the corresponding classification.

[0049] As one implementation method, during the verification and fault-tolerant processing of the information of goods to be declared, the maintenance trigger status of each goods is monitored simultaneously. Maintenance trigger status includes manually issued maintenance instructions, acquisition parameters reaching a preset maintenance threshold, or accumulated acquisition time meeting periodic maintenance requirements. When any goods are detected to be in a maintenance trigger status, during the construction of the nonlinear programming optimization model, the goods to be maintained are marked as non-priority acquisition goods, and their classification allocation weight is reduced. After checking the safety boundary constraints of the initial goods classification scheme, the classification allocation ratio of the goods to be maintained is further verified to ensure that its classification does not exceed the preset maintenance period classification upper limit. In the smoothing stage, a stricter maximum classification threshold is adopted for the classification adjustment of the goods to be maintained. Simultaneously, based on the classification affiliation of other acquired goods and the safety boundary margin, the classification acceptance allocation strategy is optimized to ensure optimal final classification and stable goods classification. After the goods to be maintained are classified and properly classified, the goods are reported to customs according to their corresponding classification.

[0050] In this application, comprehensive compliance data collection is conducted based on the goods to be declared and the declaring entity, constructing a complete and traceable information system for the goods to be declared, providing a comprehensive and reliable data foundation for subsequent classification and processing. The specific execution process is as follows: Comprehensive Compliance Data Collection: Multi-source data collection is conducted based on the goods to be declared and their respective declaring entities to obtain all the necessary data for classifying the goods. Data sources cover four methods: API integration with cross-border e-commerce platforms, data import from enterprise ERP systems, synchronization with customs broker systems, and manual compliance data entry. The collected fields are divided into two aspects: first, core product attribute fields, including merchant-defined product name, product description, material, function and use, specifications, brand, and model; second, declaration auxiliary fields, including declaring entity information, trade method, transportation method, country of destination, country of origin, country of origin information, and regulatory document requirements. The collection process strictly adheres to customs data standards to ensure the authenticity, completeness, and compliance of the collected data.

[0051] Commodity Association and Event Encapsulation: Based on the customs commodity declaration classification logic, the collected data is associated with unique commodity identifiers and encapsulated as compliance events to generate standardized encapsulated data. First, a unique commodity identifier is assigned to each commodity to be declared, with the encoding format "Declarant Entity Number - Commodity Category - Commodity SKU Number", for example: "KM01-09-0056". Collected data items for the same commodity and with the same millisecond-level timestamp are associated and bound to ensure a one-to-one correspondence between data and commodity. Then, event encapsulation is completed using a JSON structured format. The encapsulated data includes a 16-bit unique data gene tag (encoding rule: "Module Identifier - Data Type - Timestamp - Random Sequence", where the module identifier is 2 bits, the data type is 2 bits, the timestamp is 8 bits, and the random sequence is 4 hexadecimal characters), a unique commodity identifier, a collection timestamp (format YYYY-MM-DDHH:MM:SS.sss), a data type identifier, valid data (including the name, value, unit, and compliance description of each collected parameter), and a CRC32 checksum, ensuring the uniqueness, integrity, traceability, and immutability of the encapsulated data throughout the process.

[0052] Standardized data transmission and centralized management: Encapsulated data is uploaded to the pre-defined central customs declaration data platform via the standardized interface of the cross-border declaration management system. The transmission mode is publish-subscribe, with communication parameters set as a 3-second timeout and 3 retransmissions. Data compression uses the LZ77 algorithm (compression ratio ≥3:1) to effectively reduce network bandwidth usage. The data upload process employs a dual security system: a TLS 1.3 encrypted channel and dynamic token identity verification. The dynamic token is updated every 10 minutes to prevent theft or tampering during data transmission. After receiving the encapsulated data, the central data platform completes data storage, format standardization conversion, and removal of invalid and redundant data, forming a unified real-time declaration data resource pool.

[0053] Real-time data subscription and format parsing: Real-time data messages are received from the central data platform via message subscription, using a QoS level 2 message transmission protocol to ensure messages are delivered only once, avoiding the risk of duplicate reporting. Edge computing nodes pre-subscribe to reporting topics to ensure accurate reception of target commodity data; standardized format parsing is performed immediately upon receiving the data.

[0054] Specifically, the parsing process strictly follows a four-step verification rule: The first step is to verify the CRC32 checksum. If the verification fails, the message is discarded and an exception is logged. Only if the verification passes can the subsequent process proceed.

[0055] The second step is to extract data gene tags, product unique identifiers, data types, and collection timestamps from the message header.

[0056] The third step is to extract core product information from the valid data, complete data type conversion, unit format standardization, and replacement of synonyms between industry slang and customs standard terms.

[0057] The fourth step is to perform abnormal data filtering, using classification criteria to remove abnormal values, using linear interpolation to fill in continuously missing single-period data, using Kalman filtering algorithm to dynamically estimate data missing for more than 3 periods, and using the threshold of the customs import and export commodity declaration standard to determine parameters without a clear rated range, and finally generating standardized analytical data.

[0058] Commodity and Declaration Entity Context Association: The parsed data is used to establish a contextual association between the commodity and its corresponding declaration entity, ultimately generating complete information on the commodity to be declared. The association rules employ a triple verification mechanism: timestamp alignment, commodity identifier matching, and declaration entity dimension supplementation. Timestamp alignment uses a ±300ms deviation tolerance mechanism to associate commodity attribute data, declaration entity qualification data, and trade scenario data within the same time window. Commodity identifier matching supports fuzzy matching with a matching threshold ≥90%, and is compatible with common formatting issues such as case sensitivity and redundant spaces in commodity numbers. Declaration entity dimension supplementation links the associated commodity data with static compliance data (including commodity registration information, historical declaration records, HS code classification history, customs supervision requirements, and trade agreement-related rules) stored on the central data platform, endowing the parsed data with complete declaration scenario background information. This ultimately forms complete information on the commodity to be declared, encompassing core commodity attribute data, declaration compliance auxiliary data, and trade scenario-related data, providing comprehensive and reliable data support for subsequent verification and optimization.

[0059] As one implementation method, the information on goods to be declared includes three input items: real-time declaration requirements: compliance declaration requirements, declaration timeliness requirements, and total batch classification processing requirements for goods to be declared in the current batch; the latest secondary classification model for goods: the latest HS code classification prediction model parameters received and retrieved from the cloud platform. The model expression can accurately fit the matching relationship between goods attributes and HS codes. The model parameters are updated online using recursive least squares and a sliding window. The data window length is fixed at 72 hours, and parameter identification is performed once every hour. New data replaces old data according to the first-in-first-out principle; the model validation index is set to a goodness of fit R² ≥ 0.95. When R² < 0.9, parameter retraining is triggered to ensure the prediction accuracy of the classification model; and the set of goods to be declared: the set of valid goods that need to be classified and processed in the current declaration batch received from the cloud platform.

[0060] After completing the verification and error-tolerant processing of the product information to be declared, standardized input data is generated. This application conducts multi-dimensional verification and full-scenario error-tolerant processing based on the product information to be declared, resolving issues such as abnormal, missing, and biased declaration data, and generating standardized input data that can be directly used for classification modeling. The specific execution process is as follows: Based on the core data (core product attributes, declaration compliance parameters, and classification model parameters) in the information of the goods to be declared, a multi-source cross-validation mechanism of primary and secondary classification data is adopted. A classification deviation threshold is preset. When the deviation of the primary classification data exceeds the classification deviation threshold, the predicted value derived by the secondary classification model is used to replace the data. If the classification model parameters of a single product are missing or abnormal, the historical declaration data of similar products in the most recent period and the parameter mapping relationship of the same model of product are extracted. A temporary classification model is generated through transfer learning to ensure that the classification process is not interrupted. When data transmission is interrupted, the optimized product classification scheme of the previous period is automatically cached, and linear fine-tuning is performed based on the product classification change trend of the past four periods to ensure the continuous execution of the declaration process until the data is restored and the normal data processing process is switched. The complete data after the above processing is subjected to integrity verification, and invalid and redundant data and invalid fields that do not comply with customs declaration specifications are removed, and finally standardized input data is generated.

[0061] Then, an initial commodity classification scheme is generated by constructing and solving a nonlinear programming optimization model. Based on the preprocessing results of regular expressions, this application focuses on optimizing commodity classification and minimizing compliance risks. Combining the quadratic classification model in the input data and the set of commodities to be declared, a nonlinear programming optimization model for commodity classification is constructed. Iterative solutions are obtained through numerical algorithms to generate an initial commodity HS code classification scheme. The specific execution process is as follows: Classification matching degree change rate function calculation: Based on the quadratic classification model in the input data, the classification matching degree change rate function of each product is derived. This function is the first derivative of the quadratic classification model with respect to product classification. It can accurately represent the matching degree change trend of the product under the current attribute features and corresponding to different HS codes, providing the core calculation basis for optimization modeling.

[0062] Construction of a Nonlinear Programming Optimization Model: A nonlinear programming optimization model is constructed with the core objective functions of maximizing commodity classification matching degree and minimizing declaration compliance risk, and the core constraints of customs classification rule compliance, consistency between commodity attributes and HS codes, and completeness of declaration elements. Specifically, the objective function is to maximize the comprehensive HS code matching degree of all commodities to be declared. Constraint 1 requires that all commodity classification results must comply with the General Rules for the Classification of Import and Export Commodities of Customs and the requirements of subheading notes; Constraint 2 requires that the classification result of each commodity must be within the valid applicable range of the corresponding HS code and meet the matching requirements of core attributes such as commodity material, function, and specifications.

[0063] The classification optimization equation system is determined as follows: The necessary and sufficient condition for satisfying the above optimization problem is that the rate of change of classification matching degree of all commodities to be declared that meet the constraints is in an equilibrium state. Combining the total number of declarations and compliance constraints, a core equation system is formed. The equation system includes two core parts: the equilibrium equation of the classification matching degree of each commodity and the equilibrium equation of the classification compliance of all commodities, which provides a clear mathematical basis for numerical solution.

[0064] Iterative solution and initial scheme generation: The above equation system is solved iteratively using linear discriminant analysis. An iterative convergence threshold is preset. When the iterative result meets the convergence threshold, the solution is stopped, and the result is extracted to generate an initial commodity classification scheme. The specific iterative process is as follows: Firstly, the optimized product classification scheme from the previous cycle is used as the initial value for each product category to be declared, significantly shortening the convergence time. For scenarios without historical data, such as first-time applications or new product registrations, initial values ​​are allocated according to the matching weights of the product's core attributes and HS codes to ensure the initial values ​​are within compliance constraints. The equation system is transformed into residual form, constructing a Jacobian matrix that conforms to the classification solution rules, significantly improving computational efficiency and solution accuracy. The residual vector corresponding to the current iteration value is calculated, and the linear equation system is solved using the LU decomposition method to obtain the incremental values ​​of each variable to be solved. These are then superimposed to generate new iteration values. The incremental step size can be dynamically adjusted according to the convergence situation to avoid iteration oscillations. A convergence threshold is set to a relative error of ≤1 / 10,000 between two adjacent iterations to balance computational accuracy and solution efficiency. When the iteration results meet the convergence requirements, it is determined that the iteration has converged, the calculation is stopped, the HS code classification results for each product are extracted, and an initial product classification scheme is generated. If convergence is not achieved after 10 iterations, a linear approximation fallback strategy is used to simplify the quadratic classification model into a linear form and then re-solve it, ensuring that an effective and compliant classification scheme is always output.

[0065] The optimization logic in this application is based on the principle of "compliance first, matching best" in customs commodity classification. The mathematical derivation is completed through the Lagrange multiplier method to ensure that the HS code matching degree of all commodities to be declared is maximized under the premise of meeting the customs declaration compliance constraints, thus ensuring the scientificity and compliance of the classification results from a mathematical perspective.

[0066] Then, compliance checks are performed on the commodity classification to generate an optimized commodity classification scheme. This application conducts a full-dimensional customs classification rule constraint check based on the initial commodity classification scheme, handling ambiguous classifications and non-compliant classification items until all commodity classifications meet customs declaration security and compliance constraints, generating an optimized commodity classification scheme. The specific execution process is as follows: Compliance and security boundaries are defined: the pre-classification compliance options for each product are clarified, the minimum and maximum compliance boundaries for product classification are determined, and the classification boundary values ​​are comprehensively determined based on the official annotations of the "Import and Export Tariff", the product's factory attribute description, historical compliance declaration data, and customs classification rulings. The applicable HS code compliance range for each product is clarified, and invalid codes that do not comply with the classification rules are excluded.

[0067] Compliance constraint verification: Verify the classification result of each item in the initial commodity classification scheme one by one to see if it is within its corresponding compliance and security boundary, and whether it complies with the general rules of customs classification, material priority, function priority and other hard classification rules, and at the same time verify the completeness of the declaration elements corresponding to the classification results.

[0068] Handling of non-compliant items exceeding limits: If a product classification result exceeds the compliance and safety boundary or does not comply with the hard classification rules, the product classification result will be fixed to the corresponding compliance boundary value, and the product will be removed from the equal matching degree optimization group to ensure that non-compliant items do not affect the overall classification optimization results.

[0069] Updated reporting requirements: Subtract the processing volume of product categories that have been fixed to the compliance boundary from the real-time reporting requirements to obtain the remaining reporting requirements to be processed, and clarify the set of remaining products to be reported.

[0070] Iterative re-solution: Taking the remaining pending declaration requirements as the new classification processing target, the complete optimization solution process is re-executed based on the set of remaining pending declaration goods: Calculate the rate of change function of classification matching degree based on the latest classification model parameters of the remaining goods; reconstruct the nonlinear programming optimization model and update the objective function and constraints; reconstruct the classification equation system based on the matching degree balance principle; use linear discriminant analysis to complete the iterative solution and generate a new classification scheme based on the remaining declaration requirements, ensuring that the scheme satisfies both the remaining declaration processing requirements and achieves optimal classification matching of the remaining goods.

[0071] Iterative verification and optimal solution generation: Repeat the entire process of compliance constraint checks, over-limit handling, declaration requirement updates, and iterative re-solution until all commodity classification results meet the customs declaration compliance and security boundary constraints, with no non-compliant items and no fuzzy classification items, and finally generate the optimal commodity HS code classification scheme.

[0072] The generated classification scheme is smoothed and dynamically adapted to obtain the final commodity classification scheme. In this application, the optimized commodity classification scheme undergoes smoothing processing. This is achieved through classification threshold constraints, fuzzy classification difference calibration, dynamic adaptation to declaration trends, and adjustments based on the dynamic update rules of customs HS codes. This results in a final commodity classification scheme that balances compliance stability and matching accuracy. The specific execution process is as follows: Differentiated classification thresholds are preset: Based on the product category type, registration period, core attribute stability, historical declaration compliance record, and customs supervision risk level, differentiated classification thresholds are preset for each product. Specifically, the basic threshold for high-risk products (such as medical devices, food, and chemicals) is set at 2% per cycle, and the basic threshold for ordinary consumer goods is set at 5% per cycle. For each additional year of product registration, the threshold decreases by 0.5%. For products with declaration return records or classification disputes in the past 6 months, the threshold is further reduced by 1%, strictly preventing customs audit risks caused by frequent and significant changes in product classification. The classification thresholds can be dynamically adjusted according to the product declaration status. When the product's core parameters or customs supervision requirements change for three consecutive cycles, the threshold is immediately lowered; when the product's registration information is updated and its compliance status returns to normal, it gradually rises back to the initial threshold on a cycle basis.

[0073] Classification Difference Verification and Calibration: Calculate the absolute difference between the classification result of each product in the optimized product classification scheme and the actual declared classification in the previous period. Compare the absolute difference with the preset classification threshold to determine if it exceeds compliance limits. If the difference exceeds the classification threshold, complete the compliance calibration of the product's classification in this period according to the direction of classification adjustment: when the positive change between the optimal scheme classification and the declared classification in the previous period exceeds the threshold, the calibration value is the sum of the actual classification in the previous period and the threshold; when the negative change exceeds the threshold, the calibration value is the difference between the actual classification in the previous period and the threshold. After calibration, calculate the classification difference of the product, summarize the classification differences of all products exceeding the limit, and form the total difference to be allocated.

[0074] Scientific allocation and fine-tuning of the difference: The total difference to be allocated is distributed to other non-excessive goods using a matching degree weighting method. The allocation process prioritizes goods with low regulatory risk, high matching degree, and sufficient category redundancy. The allocation ratio and category adjustment amount for each non-excessive goods are calculated using the category matching degree of the non-excessive goods as the weight, ensuring that the allocation process takes into account the core objective of optimal category matching. After allocation, the classification results of non-excessive goods are fine-tuned based on the principle of balanced category matching degree. If the matching degree difference between goods exceeds a preset threshold, further small adjustments are made until the category matching degree of all non-excessive goods tends to be balanced, and the adjusted classification results do not exceed their respective compliance and safety boundaries, avoiding new non-compliance issues caused by allocation.

[0075] Declaration Trend Adaptation and Threshold Dynamic Adjustment: The system tracks the cyclical fluctuations in real-time declaration demands. If, for three consecutive cycles, the total declared commodity volume and commodity category structure show consistent month-on-month changes, with a single month-on-month change ≥1%, it is considered continuous fluctuation. In this case, the classification threshold is relaxed by a preset percentage of 10%-20%, with an upper limit constraint: the maximum relaxation for high-risk commodities cannot exceed 3%, and for ordinary commodities, it cannot exceed 6%, strictly preventing excessive relaxation from causing compliance risks. When declaration demands remain stable for two consecutive cycles or the fluctuation direction reverses, a threshold recovery mechanism is activated, gradually reverting to the initial threshold periodically. Based on the above-mentioned threshold preset and dynamic adjustment, classification difference calibration and scientific allocation, and fluctuation trend adaptation constraints throughout the entire process, combined with the dynamic update rules of the Customs HS code, an adaptation adjustment is completed, ultimately generating a final commodity HS code classification scheme that balances the stability of commodity classification compliance and matching accuracy.

[0076] The classification scheme is then distributed and declaration deviations are dynamically corrected. In this application, the final commodity classification scheme is distributed to each declaration execution module to complete the customs declaration submission. Based on the declaration execution results and customs feedback, classification deviations are corrected in real time, forming a closed-loop management of declaration execution. The specific execution process is as follows: Standardized Classification Scheme Distribution: The HS code classification results of each commodity in the final commodity classification scheme are converted into standardized declaration data according to customs declaration requirements. Required fields on the declaration form are automatically filled, including standardized commodity name, 8 / 10-digit complete HS code, specifications, brand, and declaration elements, generating XML / JSON format declaration data conforming to the China International Trade Single Window interface specifications. Different instruction distribution methods are adopted based on different declarants and trade methods. Instructions are distributed to the corresponding declaration execution modules (enterprise declaration end, customs broker system, e-commerce platform declaration module) via a dedicated cross-border declaration communication protocol. Communication parameters are set to a 1-second data frame timeout and 3 retransmission attempts. Before distribution, the declarant's qualifications and online status are verified. Declaration instructions are only issued to entities with valid qualifications and no declaration restrictions. After distribution, an instruction confirmation frame is received. If no confirmation is received, a retry is triggered. If three retries fail, an alarm log is recorded.

[0077] Real-time collection of declaration status and feedback data: At a frequency consistent with the algorithm execution cycle (1-5 seconds / time), the execution status of each declared commodity, the declaration parameters and review results fed back by customs are collected in real time. The collected data includes: declaration form status (pending review, approved, returned for modification, inspection notice), HS code classification review results, compliance feedback of declaration elements, reasons for customs return, and verification results of regulatory documents, etc. After standardized preprocessing, the collected data is uploaded to the edge computing node through a dedicated communication protocol to ensure the consistency of the timing of data collection and instruction issuance.

[0078] Declaration Deviation Calculation and Exceeding Standard Judgment: The actual declaration feedback results collected are compared with the preset compliance parameters in the final commodity classification scheme. Differentiated deviation calculation methods are used according to parameter type: relative deviation calculation is used for declaration classification and declaration elements, while absolute deviation calculation is used for regulatory compliance requirements and customs review rules. Based on customs declaration compliance standards, tiered deviation exceeding thresholds are set: minor deviations are issues that do not affect classification compliance, such as incomplete declaration elements or non-standard commodity name formats; moderate deviations are issues requiring supplementary explanations, such as questionable HS code matching or disputes over the application of classification rules; severe deviations are issues leading to declaration rejection due to incorrect HS code classification, missing regulatory documents, or non-compliance with customs classification rules.

[0079] Tiered Fine-tuning and Scheme Reissue: If the declaration deviation exceeds the threshold, a tiered fine-tuning strategy is adopted based on the degree of deviation. The fine-tuning priority follows the principle of "classification compliance first, high-risk commodities first correction": minor deviations only require partial correction of the declaration data of the current commodity, without adjusting the classification results; moderate deviations require review of the classification matching degree of the current commodity, fine-tuning of the classification scheme, supplementing and improving the declaration elements, and adjusting the declaration data of related commodities in a coordinated manner to ensure that the total declaration volume and compliance remain unchanged; severe deviations immediately suspend the declaration process for the commodity, re-verify the commodity classification results, correct the HS code classification scheme, and if the compliance requirements are still not met after correction, a high-priority alarm is triggered, and manual review is pushed out. At the same time, the classification anomaly data is recorded for model optimization. After generating the corrected classification scheme and declaration data, it is resubmitted to the customs declaration system according to the original issuance process to ensure that the declaration deviation converges quickly and improve the declaration pass rate.

[0080] Deviation Event Recording and Closed-Loop Optimization: Every deviation event and correction process is recorded synchronously, generating a standardized deviation handling log. The log includes fixed fields such as event number, occurrence time, involved product information, deviation parameters, deviation level, correction strategy, correction result, and compliance verification status. Log data is archived daily on the local edge computing node (retained for 90 days) and simultaneously synchronized daily to the cloud platform. This provides data support for subsequent optimization of classification model parameters, updates to the classification rule base, and optimization of the declaration process, achieving closed-loop optimization of deviation handling and the classification model.

[0081] During the commodity classification process before customs declaration of the aforementioned goods, a commodity declaration maintenance mode can be entered for synchronous adaptation. In this application, the maintenance trigger status related to commodity declaration is monitored synchronously throughout the entire process, and the classification strategy is dynamically adjusted to adapt to special scenarios such as commodity registration maintenance, HS code updates, and enterprise qualification maintenance, ensuring the continuity of the declaration process and the compliance of classification results during maintenance. The specific execution process is as follows: Real-time monitoring of maintenance trigger status: During the verification and fault-tolerant processing of information of goods to be declared, the maintenance trigger status of each goods is monitored synchronously at a frequency consistent with the algorithm execution cycle (1-5 seconds / time) to ensure the real-time response of maintenance status. Maintenance triggering states are divided into three core scenarios, with clearly defined trigger thresholds and judgment rules: First, manual maintenance instructions are issued through the cloud platform or the local operation interface of edge computing nodes, including the maintenance type (update of commodity registration information, adjustment of HS code classification, maintenance of enterprise qualifications), planned maintenance duration, and maintenance priority. After issuance, the maintenance mode is triggered immediately after administrator permission verification. Second, the over-limit triggering of declaration compliance parameters involves real-time monitoring of changes in core commodity declaration parameters, customs supervision requirements, and classification rules. When the cumulative thresholds for commodity registration information expiration, HS code update repeal, changes in supervision requirements, and declaration returns are reached, and the situation does not return to normal for two consecutive cycles, the maintenance mode is automatically triggered. Third, periodic maintenance triggering involves setting tiered maintenance cycles according to commodity registration management specifications and customs supervision requirements. When the time thresholds for annual review of registration information, review of classification rules, and update of qualification documents are reached, a maintenance warning is triggered, and the maintenance mode is automatically triggered when the maintenance deadline is reached.

[0082] Dynamic adjustment of classification weights and constraints: When any product is detected to be in a maintenance-triggered state, during the construction of the nonlinear programming optimization model, the product to be maintained is automatically marked as a non-priority declaration product, and its classification allocation weight is reduced to 50% of the normal weight, reducing the proportion of its classification adjustment and reserving compliance margin for maintenance operations; at the same time, a "maintenance period classification restriction constraint" is added to the model constraints, which clarifies that the classification allocation of the product to be maintained must not affect the declaration compliance and maintenance operation execution.

[0083] Special verification of classification compliance during the maintenance period: After completing the compliance and security boundary constraint check on the initial commodity classification scheme, a further special verification is conducted on the classification allocation ratio of commodities to be maintained to ensure that their classification does not exceed the preset classification upper limit during the maintenance period. The classification upper limit during the maintenance period is dynamically set according to the maintenance type, based on customs supervision requirements, commodity filing specifications, and historical declaration data: During routine updates and maintenance of commodity information, the upper limit is 70% of the normal compliant classification boundary; during changes in commodity filing information and adjustments to HS code classification, the upper limit is 60%; during scenarios affecting the qualifications of the declarant, such as enterprise qualification maintenance and regulatory certificate updates, the upper limit is 50%, to avoid declaration risks caused by non-compliant commodity classification during the maintenance period. If the verification finds that the classification exceeds the upper limit, it is immediately downgraded to the upper limit value, and the excess classification is distributed to other non-maintained commodities according to the "matching degree weighting method".

[0084] Emergency Adaptation for Sudden Reporting Needs: When there is a sudden surge in real-time reporting needs, and the classification of non-maintained products has reached 90% of its compliance limit, making it impossible to handle additional reporting needs, an emergency classification adjustment strategy is activated. The upper limit of the classification of products to be maintained is temporarily relaxed (80% of the normal compliance limit during routine maintenance, 70% during medium maintenance, and no relaxation in high-risk maintenance scenarios). The relaxation period only lasts until the next algorithm cycle. At the same time, the backup reporting channel and the registered product library are automatically activated. If there are no available backup resources, the upper limit of the classification of products to be maintained will continue to be relaxed until the reporting needs are met. The maximum relaxation period will not exceed 5 minutes. During this period, a high-priority alarm will be pushed to the administrator every 30 seconds to ensure that maintenance operations and reporting compliance are not seriously affected.

[0085] Smoothing and Category Acceptance Optimization: During the smoothing phase, stricter classification thresholds are applied to the classification adjustments of goods requiring maintenance. These thresholds are refined according to maintenance type: 80% of the normal threshold for routine maintenance, 65% for medium-risk maintenance, and 50% for high-risk maintenance. By reducing the magnitude of classification changes, fluctuations in the declared status of goods during maintenance are minimized, thus reducing compliance risks. Simultaneously, based on the real-time classification affiliation and compliance margin of other declared goods, the classification acceptance allocation strategy is optimized. Goods with classification affiliations below the system average and classification redundancy ≥20% are prioritized for acceptance of the reduced classifications of goods requiring maintenance. Allocation follows the principle of "the lower the classification affiliation and the higher the classification redundancy, the higher the acceptance ratio," ensuring optimal classification across all goods and stability of the declaration system.

[0086] Smooth Recovery After Maintenance: After the maintenance process for a product is completed, two recovery conditions must be met before the maintenance mode can be lifted: first, a manually issued "maintenance complete" confirmation instruction must be received; second, the product's core declaration parameters and compliance status must be within the normal range for three consecutive algorithm cycles, with no new declaration anomalies recorded. After recovery is confirmed, the optimized product classification scheme is regenerated according to the core process, and a gradient recovery strategy is adopted to gradually restore its classification proportion. In the first cycle, it is restored to 30% of the normal weight, and then increased by 20% in each subsequent cycle, until it is fully restored to the normal weight after four cycles. This achieves a smooth switch between maintenance mode and normal declaration mode, avoiding the impact of a sudden increase in classification on the product declaration system and customs compliance.

[0087] like Figure 3 As shown in the figure, this application embodiment provides a cross-border customs declaration processing device 200, including an acquisition module 21, a processing module 22, and a declaration module 23. The acquisition module 21 is used to acquire information on goods to be declared. The processing module 22 is used to process the classification of goods to be declared. The declaration module 23 is used to report the classified goods to customs.

[0088] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage commodity such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a solid-state disk (SSD), etc.

[0089] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for processing cross-border customs declarations, characterized in that, include: Obtain information on goods to be declared, perform verification and error handling on the obtained information to obtain input data; Based on regular expressions, with the goal of optimal product classification, a nonlinear programming optimization model is constructed by combining the quadratic classification model in the input data and the obtained product set. The model is then solved by a numerical solution algorithm to obtain the initial product classification scheme. The initial product classification scheme is checked for product classification constraints, and fuzzy classifications are processed until all products meet the safety constraints, resulting in an optimized product classification scheme. The optimized commodity classification scheme is smoothed by using the classification threshold of the commodity to be declared as a constraint, the difference of fuzzy classification is calibrated, and dynamic adaptation and adjustment are carried out in combination with HS code to obtain the final commodity classification scheme, so that the commodity can be reported to the customs according to the corresponding classification.

2. The cross-border customs declaration processing method according to claim 1, characterized in that, The specific process for obtaining the information of the goods to be declared is as follows: Data is collected on the products to be declared and the merchants to which the products belong, to obtain the collected data on the categories of products to be declared; Based on the classification logic of the goods to be declared, the collected data is associated with goods and encapsulated with events to obtain encapsulated data. The encapsulated data is then uploaded to the preset central data platform through the goods reporting system of the goods to be declared. Real-time data messages pushed by the central data platform are received by message subscription, and the format of the real-time data messages is parsed to obtain parsed data. By associating the parsed data with the product and the context of the merchant to which it belongs, the information of the product to be declared is obtained.

3. The cross-border customs declaration processing method according to claim 2, characterized in that, The specific process of obtaining the input data is as follows: For the core data in the declared commodity information, multi-source cross-validation of primary and secondary classification data is adopted, and a classification deviation threshold is set. When the deviation of the primary classification data exceeds the classification deviation threshold, the predicted value derived by the secondary classification model is used for replacement. If a single classification model parameter is missing or abnormal, extract the historical data of similar merchants for that product in the most recent period, combine it with the parameter mapping relationship of products of the same model, and generate a temporary classification model through transfer learning. When data transmission is interrupted, the optimized product classification scheme of the previous cycle is cached, and linear fine-tuning is performed based on the classification change trend of the past 4 cycles until the data is restored and the normal data processing flow is switched. The data processed above is then subjected to integrity verification to remove invalid and redundant data, resulting in the input data.

4. The cross-border customs declaration processing method according to claim 3, characterized in that, The specific process of obtaining the initial product classification scheme is as follows: Based on the quadratic classification model in the input data, the classification change rate function for each product is derived. The classification change rate function is the first derivative of the quadratic classification model with respect to classification. With the objective function of obtaining the optimal product classification, and with the constraints of equal classification change rates for all obtained products and the final classification meeting real-time reporting requirements, a nonlinear programming optimization model is constructed. Determine the set of equations corresponding to the nonlinear programming optimization model. The set of equations includes equations that ensure the rate of change of each commodity category is equal and equations that ensure the final category balance. The equation system is solved iteratively using linear discriminant analysis. An iterative convergence threshold is set, and the solution is stopped when the iterative result meets the iterative convergence threshold, thus obtaining the initial commodity classification scheme.

5. The cross-border customs declaration processing method according to claim 4, characterized in that, The specific process of obtaining the optimized product classification scheme is as follows: Determine the pre-classification options for each product. The pre-classification options include product classification boundaries and merchant classification boundaries. The classification boundary values ​​are based on the product's factory classification and historical data classification. Verify one by one whether the classification of each product in the initial product classification scheme is within its corresponding safety boundary range; If a product category exceeds the safety boundary, the product category is fixed at the corresponding category boundary value, and the category boundary value is deducted from the real-time declaration demand to obtain the remaining declaration. Using the remaining declarations as the new classification requirement, a new nonlinear programming optimization model is reconstructed and solved for the remaining acquired goods to obtain a new classification scheme. Repeat the above verification and solution steps until the classification of all products satisfies the safety boundary constraints, and obtain the optimized product classification scheme.

6. The cross-border customs declaration processing method according to claim 5, characterized in that, The specific process for obtaining the final product classification scheme is as follows: Based on the product model, acquisition period, and usage parameters, a preset classification threshold is set for each product, and the classification threshold is dynamically adjusted according to the product acquisition status. Calculate the difference between the classification of each product in the optimized product classification scheme and the actual classification in the previous period, and determine whether the difference exceeds the preset classification threshold. If the difference exceeds the classification threshold, the classification of the product in this cycle will be calibrated to the sum of the actual classification and the classification threshold of the previous cycle, and the classification difference after calibration will be calculated. The classification difference is evenly distributed to other products, and the classification of other products is fine-tuned in combination with the principle of equal classification change rate; The periodic fluctuation trend of real-time classification is statistically analyzed. When the real-time classification fluctuates in the same direction for three or more consecutive periods, the classification threshold is relaxed by a preset ratio. Based on the above constraints, calibration, and adaptation results, the final product classification scheme is obtained.

7. The cross-border customs declaration processing method according to claim 6, characterized in that, After obtaining the final product classification scheme, the final product classification scheme is distributed to each product classification module, and the actual acquisition status and declaration parameters of each product are collected in real time. The actual acquired status and declared parameters are compared with the preset parameters in the final commodity classification scheme to determine whether there is any excessive acquisition deviation. If there is an excessive deviation in the acquisition, the final product classification scheme will be dynamically fine-tuned based on the degree of deviation, and a revised classification scheme will be generated and reissued. The deviation events and correction process are recorded synchronously to form a deviation handling log.

8. The cross-border customs declaration processing method according to claim 7, characterized in that, During the verification and error handling of the declared product information, the maintenance trigger status of each product is monitored simultaneously. The maintenance trigger status includes manually issued maintenance instructions, the acquisition parameters reaching the preset maintenance threshold, or the cumulative acquisition time meeting the periodic maintenance requirements. When any product is detected to be in a maintenance-triggered state, the product to be maintained is marked as a non-priority acquisition product and its classification and allocation weight is reduced when constructing the nonlinear programming optimization model. After checking the safety boundary constraints of the initial product classification scheme, the classification allocation ratio of the products to be maintained is further verified to ensure that their classification does not exceed the preset upper limit of the maintenance period classification. During the smoothing phase, a stricter maximum classification threshold is adopted for the classification adjustment of the products to be maintained. At the same time, based on the classification affiliation and safety margin of other acquired products, the classification assignment strategy is optimized to ensure optimal final classification and stable product classification. Once the goods have been properly classified and categorized, they will be reported to customs according to their corresponding classification.

9. A cross-border customs declaration processing device, characterized in that, include: The acquisition module is used to obtain information about the goods to be declared; The processing module is used to process the categories of goods to be declared; The declaration module is used to report the classified goods to customs.

10. An electronic product, comprising: A memory, a processor, and a computer program stored on the memory, the processor being configured to retrieve the computer program to execute the cross-border customs declaration processing method according to any one of claims 1 to 8.

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