Import commodity dynamic risk iterative judgment method and system based on incremental data stream fusion

Abstract

The application relates to the technical field of customs big data risk control, and particularly discloses an import commodity dynamic risk iterative judgment method and system based on incremental data stream fusion, which collects multi-source incremental data streams, compares mode fingerprint identification data sources, generates a candidate mapping based on a four-dimensional weighted mechanism, generates standard fusion data based on an effective mapping, retains field contribution traces, combines risk score distribution and inspection index monitoring to judge degradation, positions abnormal mappings by means of field aggregation contribution, sets a multi-layer verification mechanism to update the mapping, adds external event discrimination to avoid invalid iteration, simultaneously distinguishes continuous and discrete characteristics, realizes delayed risk label backtracking calibration by corresponding statistical tests, constructs a long-short cycle collaborative iteration loop, adapts to multi-source data evolution, opens the closed loop of judgment effect reverse optimization data fusion, completes deep calibration by using lagging inspection labels, considers real-time self-adaptive adjustment and long-term judgment accuracy, and adapts to various import commodity customs real-time risk control scenes.

Description

Technical Field

[0001] This invention relates to the field of customs big data risk control technology, and in particular to a method and system for dynamic risk iterative assessment of imported goods based on incremental data stream fusion. Background Technology

[0002] In the customs clearance and supervision of imported goods, customs needs to integrate various heterogeneous data sources such as e-commerce orders, payment records, logistics waybills, customs declarations, and overseas notifications. It then relies on machine learning models to output real-time risk scores for each shipment, supporting on-site inspections and interception of violations. Existing mainstream risk control systems employ a unidirectional pipeline architecture combining static ETL data fusion and periodic model retraining. This solution suffers from inherent architectural flaws, leading to multi-layered, interconnected technical problems under the constraints of continuously evolving data sources and long-term lags in audit risk labeling.

[0003] In existing technologies, system data fusion rules rely on manual pre-configuration. When fields and codes of various business platforms undergo iterative changes, the mapping can only be manually updated, resulting in an adaptation cycle of several days. Traditional data quality verification only detects surface-level indicators such as null values ​​and formatting, failing to correlate with core business judgment effects such as detection rate and false alarm rate. Implicit drift in field value ranges continuously lowers the model's recognition accuracy, and the root cause of the fault is difficult to locate quickly. Furthermore, existing iterative methods only adjust model parameters, failing to incorporate the data fusion layer into the optimization process. When judgment performance declines, repeated model training cannot address the underlying data distortion problem. Imported goods inspection violation labels have a lag of several tens of days; existing solutions only use them for monthly batch retraining, failing to utilize the delayed inspection results to correct mapping matching biases.

[0004] Therefore, there is an urgent need for a dynamic risk iterative assessment method and system for imported goods based on incremental data stream fusion to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion, comprising the following steps: The system acquires incremental data streams from multiple data sources, obtains the current pattern fingerprint of each data source based on the incremental data streams, and compares the current pattern fingerprint with the historical pattern fingerprint to detect whether there is a pattern change event. Maintain a data fusion mapping relationship library containing confidence attributes. When the pattern change event is detected, automatically infer and generate candidate mappings based on the pattern change event and the data fusion mapping relationship library, and assign initial confidence and a state to be verified to the candidate mappings. Based on the mapping relationship that is currently in effect, the incremental data streams from the multiple data sources are fused and converted into standard fused data under a unified target model; Real-time risk reasoning is performed based on the standard fusion data to obtain a risk score for each imported commodity, and contribution trace records are obtained based on the contribution value of each fusion field in the standard fusion data to the risk score. The effect index within the current time window is obtained based on the risk score, and the effect index is compared with a preset baseline. When the degradation of the effect index relative to the preset baseline is detected to exceed a preset threshold, the candidate anomaly map is located based on the contribution trace record, the confidence decay of the candidate anomaly map is triggered, and the updated candidate map is re-inferred. The updated candidate mapping is verified according to a preset security policy. Once the verification is successful, the updated candidate mapping is updated to an active status.

[0006] Furthermore, this invention also discloses a dynamic risk iterative assessment system for imported goods based on incremental data stream fusion, comprising: The first acquisition module is used to acquire incremental data streams from multiple data sources, acquire the current pattern fingerprint of each data source based on the incremental data streams, and compare the current pattern fingerprint with the historical pattern fingerprint to detect whether there is a pattern change event. The maintenance module is used to maintain a data fusion mapping relationship library containing confidence attributes. When the pattern change event is detected, it automatically infers and generates candidate mappings based on the pattern change event and the data fusion mapping relationship library, and assigns an initial confidence level and a state to be verified to the candidate mappings. The conversion module is used to convert incremental data streams from multiple data sources into standard fused data under a unified target model based on the mapping relationship that is currently in effect. The second acquisition module is used to perform real-time risk reasoning based on the standard fusion data, acquire the risk score of each imported commodity, and acquire contribution trace records based on the contribution value of each fusion field in the standard fusion data to the risk score. The generation module is used to obtain the performance index within the current time window based on the risk score, and compare the performance index with a preset baseline. When the degradation of the performance index relative to the preset baseline is detected to exceed a preset threshold, the module locates the candidate anomaly map based on the contribution trace record, triggers the confidence decay of the candidate anomaly map, and re-infers and generates an updated candidate map. The verification module is used to verify the updated candidate mapping according to a preset security policy, and after the verification is successful, the updated candidate mapping is updated to an active state.

[0007] The beneficial effects of this application are as follows: Firstly, this invention enables fully automated perception of data source patterns and four-dimensional quantitative mapping reasoning, completely eliminating the need for manual static mapping mechanisms. By comparing pattern fingerprints, it captures changes in field structure and value range in real time. It then integrates four-dimensional indicators—semantics, value range, historical experience, and industry rules—to weighted calculate mapping confidence. This reduces field change adaptation time from several days to hours, eliminating fusion distortion caused by manual delays at the source. Simultaneously, it establishes a reverse link between data fusion quality and downstream detection and false alarm rates. Relying on SHAP marginal contribution aggregation, it quantitatively locates abnormal mappings, enabling the identification of failed mappings within seconds without manual investigation, thus overcoming the difficulty of tracing faults in traditional systems.

[0008] Secondly, this invention breaks down the unidirectional pipeline barrier between fusion and judgment by using the judgment effect to drive the closed-loop optimization logic of the data fusion layer. It no longer relies solely on model retraining to optimize risk control performance. When business indicators such as PSI distribution shift or detection rate decline deteriorate, it directly drives the decay of mapping confidence and automatically generates updated mappings. This corrects judgment errors from the root cause of data input, avoids meaningless model retraining, significantly saves cluster computing power, and steadily improves long-term judgment accuracy.

[0009] Third, this invention fully adapts to the business constraints of lagging import inspection labels by constructing a long-term retrospective calibration system for delayed risk labels. Based on the historical mapping version to reproduce historical fusion characteristics, it distinguishes between continuous and discrete fields and uses adaptive statistical tests to quantify the contribution of fields. The mapping confidence is dynamically adjusted with real inspection labels, transforming idle and lagging business feedback into continuous optimization signals and gradually eliminating long-term mapping matching bias.

[0010] Fourth, this invention adds a dual protection mechanism of external event discrimination and multi-layer gray-scale security verification. It automatically distinguishes between external global indicator deviations such as policies, epidemics, and trade fluctuations and intrinsic drift in mapping, suppressing invalid iterations. The new mapping goes through four layers of security processes in sequence: shadow copy verification, small-volume gray-scale monitoring, full-volume continuous monitoring, and automatic rollback. Only a very small proportion of business traffic participates in the verification, completely avoiding the risk of end-to-end judgment failure caused by the launch of the new mapping, and meeting the high stability and compliance requirements of customs supervision. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a method flow proposed in an embodiment of this application.

[0012] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] The following detailed description of a dynamic risk iterative assessment method for imported goods based on incremental data stream fusion, using specific embodiments, is provided in this invention.

[0014] This invention is deployed on an Apache Flink distributed real-time stream processing cluster and is suitable for real-time risk assessment of customs clearance for cross-border e-commerce and general trade imported goods. It can achieve adaptive fusion of multi-source incremental data streams, self-monitoring of risk assessment results, accurate location of anomalies, and self-healing iteration.

[0015] like Figure 1 As shown, a method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion includes the following steps: S1: Obtain incremental data streams from multiple data sources, obtain the current pattern fingerprint of each data source based on the incremental data streams, and compare the current pattern fingerprint with the historical pattern fingerprint to detect whether there is a pattern change event. Regarding the acquisition of incremental data streams from multiple data sources, the system collects four core heterogeneous incremental data streams in real time and in parallel under the risk control scenario for imported goods. These include: incremental data streams from cross-border e-commerce orders, payment institutions, logistics companies' waybills, and customs declaration systems. All data streams are continuously connected to the Apache Flink real-time computing cluster in a streaming sharding manner. Each data item carries a unique identifier of the data source, a collection timestamp, and original field key-value pairs, achieving incremental data access within seconds, with no backlog and no loss, perfectly matching the business timing requirements for real-time customs clearance analysis of imported goods.

[0016] The current pattern fingerprint generation for each data source involves extracting three core meta-features from the full incremental sampling data within a pre-defined 5-minute sliding statistical window for each independent data source: field presence rate, basic data type of the field, and quantile distribution of the field value range. These three statistical meta-features are then hashed and serialized according to fixed rules to generate a unique, comparable, and storable current pattern fingerprint for the data source. Each data source generates its own fingerprint independently, and the fingerprint data is synchronously refreshed and persistently stored in a local fingerprint snapshot library when the window is updated.

[0017] Fingerprint comparison and pattern change event detection are performed by retrieving historical stable pattern fingerprints of the corresponding data source stored in the fingerprint snapshot database. Accurate field-by-field comparison is achieved from two dimensions: hash encoding consistency and field statistical feature deviation. In this embodiment, the window existence rate of the original product code field `product_id` in the cross-border e-commerce data source is reduced to zero, and a new `sku_code` (Stock Keeping Unit code, the minimum inventory code field for e-commerce products) is added, resulting in a significant shift in the field's quantiles. The value range of the numeric status code in the logistics enterprise data source is completely replaced with the alphanumeric status code value range. Both types of changes exceed the data noise tolerance range, and the system determines that a valid pattern change event is generated and stored in the asynchronous event scheduling queue, providing a triggering basis for subsequent candidate mapping inference.

[0018] It can automatically detect changes in data source structure, accurately identify all data source evolution scenarios including field addition, field deletion, and value domain encoding iteration, filter out accidental triggers caused by momentary dirty data, improve the accuracy of pattern change recognition, eliminate the need for manual maintenance of field whitelists, and achieve unattended automated data monitoring.

[0019] S2: Maintain a data fusion mapping relationship library containing confidence attributes. When the pattern change event is detected, automatically infer and generate candidate mappings based on the pattern change event and the data fusion mapping relationship library, and assign initial confidence and a state to be verified to the candidate mappings. Among them, the confidence mapping library is continuously maintained. The system backend continuously maintains the data fusion mapping relationship library in an online structured manner. Each mapping entry in the library is bound to an iteratively updatable confidence attribute. It fully stores field mapping relationships, field conversion functions, floating-point confidence values, running status (active / pending verification / degrading / invalid), activation timestamp, and previous verification snapshot records. It supports real-time read and write, version backtracking, and status updates, providing core data support for dynamic mapping inference and confidence iteration.

[0020] Candidate mappings are automatically generated through reasoning. After the system detects a pattern change event, it automatically locates the source field that has changed, traverses all standard target fusion fields in the mapping relationship library, and combines field semantics, value range features, historical change patterns, and industry rules for multi-dimensional matching to automatically reason for the optimal field mapping relationship and generate exclusive candidate mappings.

[0021] Candidate mapping status and confidence level assignment: Newly generated candidate mappings are uniformly configured with an initial confidence level and marked as pending verification by default, prohibiting direct access to online production traffic and mitigating the risk of data distortion caused by unverified mappings from the source. In this embodiment, the sku_code matches optimally with the standard product code field commodity_code, generating candidate mappings with an initial confidence level of 0.78. Logistics letter status codes are simultaneously used to generate candidate mappings with an initial confidence level of 0.75, both marked as pending verification and entered into the database.

[0022] This step can completely replace the traditional manual configuration of mapping rules, enabling fully automatic generation of mapping relationships after field changes; through the unverified state isolation mechanism, it balances the flexibility of system iteration with the stability of online business, and prevents new mapping defects from polluting production data.

[0023] S21, obtain the first semantic feature vector according to the name of the source field where the pattern change occurs, obtain the second semantic feature vector according to the name of each target field in the data fusion mapping relation library, and calculate the semantic similarity between the first semantic feature vector and each of the second semantic feature vectors; Obtain the name of the source field where the pattern change occurs, input it into a pre-trained BERT model in the customs domain, and output a 768-dimensional first semantic feature vector. Traverse all standard target fields in the mapping library to generate corresponding second semantic feature vectors. Calculate the semantic fit between the two sets of vectors using cosine similarity to obtain the semantic similarity. In this embodiment, the semantic similarity between sku_code and commodity_code is calculated to be 0.82.

[0024] S22, obtain the source field value range set according to the value range distribution of the source field that has undergone pattern change in the current time window, obtain the historical value range set of each target field according to the value range distribution of each target field in historical data, and calculate the value range overlap between the source field value range set and each of the historical value range sets; Extract all duplicate values ​​from the source field within the current 5-minute stable window and construct a source value domain set. Retrieve all historical values ​​of the target field over the past 6 months, remove duplicates, and construct a target value range set. The overlap of the ranges is calculated using the Jaccard formula: ; in, Indicates the overlap of value ranges. For the set of discrete value ranges of the source field, Let be the set of historical values ​​of the target field, ∩ be the intersection of the sets, ∪ be the union of the sets, and |·| be the cardinality (number) of the elements in the set.

[0025] S23, obtain multiple historical pattern change events with similar types to the current pattern change event from the historical pattern change record library, and statistically analyze the mapping scheme finally adopted in the multiple historical pattern change events to obtain the historical migration similarity. The historical pattern change record database is searched, and historical events with the same change type as the current field are filtered. The probability of similar changes ultimately mapping to each target field is calculated as the historical migration similarity. In this embodiment, 85% of similar product code changes are mapped to the standard product field.

[0026] S24, perform rule matching on the source field where the pattern change occurs based on a predefined industry knowledge rule base to obtain the rule hit rate; Match the data against the customs' predefined industry knowledge rule base to determine if the current source field matches the official standardized mapping rules. If there is a complete match, the rule hit rate is calculated. =1.0, 0 if no match is found. This example matches the e-commerce product code mapping specific rules. =1.0.

[0027] S25, a weighted fusion is performed based on the semantic similarity, the value range overlap, the historical migration similarity, and the rule hit rate to generate a mapping confidence score between the source field that has undergone pattern change and each target field; By using a fixed weighted fusion formula, the four-dimensional indicators are normalized and fused to obtain the matching confidence score of each target field. The field corresponding to the highest score is selected as the optimal mapping object. The specific formula for the weighted fusion confidence score is as follows: ; In the formula, Indicates the confidence score. For semantic similarity, For the overlap of the value range, For historical migration similarity, For rule hit rate, This is the semantic similarity weight coefficient. This is the overlap coefficient of the value range. This represents the weighting coefficient for historical migration similarity. The rule hit rate weight coefficient is substituted into the weight. =0.3、 =0.3、 =0.2、 =0.2, calculated as follows: =0.3×0.82+0.3×0.60+0.2×0.85+0.2×1.0=0.816.

[0028] In the candidate mapping generation and state assignment process, the commodity_code with the highest confidence score is selected as the mapping target, and a candidate mapping of sku_code-commodity_code is generated. An initial confidence score of 0.78 is assigned, and the state to be verified is marked and stored in the database. The same reasoning process is performed on the logistics letter status code to generate logistics candidate mapping with an initial confidence score of 0.75 and a state to be verified.

[0029] S26, the target field with the highest mapping confidence score is determined as the mapping target of the source field where the pattern change occurs, a candidate mapping is generated, and an initial confidence score is assigned to the candidate mapping.

[0030] This step abandons the crude approach of traditional single field name matching and single value domain matching. Instead, it adopts a four-dimensional weighted fusion mechanism based on semantic understanding, real data overlap, historical experience, and official rules to achieve intelligent, quantitative, and standardized field mapping. This completely solves the problems of strong subjectivity, error-prone single-dimensional matching, and lagging adaptation in manual configuration mapping, resulting in a significant improvement in mapping accuracy, automation, and adaptability.

[0031] S3: Based on the mapping relationship that is currently in effect, merge the incremental data streams from the multiple data sources and convert them into standard fused data under a unified target model.

[0032] Among them, the effective mapping screening and data fusion module strictly filters data according to the mapping status, only retrieves the stable mapping relationships that are in effect in the mapping library to participate in real-time calculation, and all mappings that are pending verification, degrading, or in failure status are physically isolated and do not participate in online data stream fusion.

[0033] The standardization and fusion transformation of multi-source data, based on the effective mapping rules after screening, completes the alignment of field names, conversion of data types, normalization of numerical units, null value repair, and unified encoding processing for four types of heterogeneous incremental data streams line by line. The original incremental data with heterogeneous structure, inconsistent encoding, and messy format from multiple sources is uniformly converted into standard fusion data of a unified target model that can be directly called by the risk assessment model.

[0034] In this embodiment, the old version of the logistics data source mapping is only compatible with traditional numeric status codes and cannot be compatible with the new version of letter status codes. This results in a systematic shift in the value range distribution of the fused logistics status field, and the standard fused data has feature distortion, providing a traceable underlying cause for the degradation of subsequent risk assessment results.

[0035] It can achieve complete decoupling between iterative updates and production operations, while supporting dynamic iterative optimization of mapping, ensuring uninterrupted and fault-free operation of real-time risk control business 24 / 7, and stable and controllable data fusion output results.

[0036] S4: Perform real-time risk reasoning based on the standard fusion data to obtain a risk score for each imported commodity, and obtain contribution trace records based on the contribution value of each fusion field in the standard fusion data to the risk score.

[0037] The system inputs standardized and integrated data into the online CATBOOST import commodity risk assessment model on a per-ticket basis. Taking the single import declaration commodity as the smallest independent reasoning unit, the model outputs a continuous risk score in the range of 0-1. The higher the score, the higher the corresponding import commodity's violation risk level.

[0038] Based on the contribution value of each fusion field in the standard fusion data to the risk score, a contribution trace record is obtained. After each product risk reasoning is completed, the TreeExplainer type SHAP model interpreter is called synchronously to quantify the marginal contribution value of each fusion field to the current product risk score. The contribution weight of all fields is fully recorded, and the product unique identifier, reasoning timestamp, name of each fusion field, and corresponding marginal contribution value are fully and persistently stored to form a traceable and reproducible contribution trace record, providing fine-grained data support for subsequent anomaly mapping and location, and effect degradation tracing.

[0039] This allows for the establishment of a reverse tracing link between the data fusion layer and the risk assessment layer, overcoming the shortcomings of the traditional two-layer architecture which is completely disconnected and unable to locate the root cause of the fault, and enabling precise tracing of the problem to specific fusion fields and mapping relationships.

[0040] S5: Obtain the performance index within the current time window based on the risk score, and compare the performance index with a preset baseline. When the degradation of the performance index relative to the preset baseline is detected to exceed a preset threshold, locate the candidate anomaly map based on the contribution trace record, trigger the confidence decay of the candidate anomaly map, and re-infer and generate an updated candidate map.

[0041] S51: Obtain the current risk score distribution based on the risk scores of all imported goods within the current sliding time window, obtain the baseline risk score distribution based on the risk scores of all imported goods within the baseline time window, and calculate the risk score distribution offset based on the current and baseline distributions. Using 24 hours as the current sliding statistical window and 72 hours as the historical stable baseline window, the 0-1 risk score interval output by the model is equally divided into 10 statistical intervals, and the sample proportion of each interval in the current window is calculated. Percentage of samples in each interval of the baseline window The degree of data drift is quantified using the PSI distribution offset calculation formula: ; in, This represents the risk score distribution offset, used to quantify the difference in distribution between the current and baseline risk scores, with a value ≥ 0; This represents the percentage of product samples in the i-th rating interval within the current 24-hour sliding window. The percentage of product samples in the i-th scoring interval within the baseline 72-hour stable window; n is the total number of scoring intervals, and in this embodiment, it is fixed at 10.

[0042] S52: Obtain the current detection rate and false alarm rate based on the current window inspection results, and obtain the baseline detection rate and false alarm rate based on historical stable phase data.

[0043] Retrieving the current 24-hour customs on-site inspection and audit data, the current detection rate is 3.1% and the current false alarm rate is 18.7%. Retrieving the historical 72-hour stable operation data, the baseline detection rate is 5.2% and the baseline false alarm rate is 12.3%.

[0044] The detailed calculation method for the decrease in the detection rate is as follows: ; S53: When the following conditions are met: If the PSI exceeds a preset distribution offset threshold, and the decrease in detection rate / increase in false alarm rate exceeds a preset business threshold, the analysis effectiveness indicator can be determined to have degraded beyond the standard. In this embodiment, the PSI exceeds the limit and the detection rate drops significantly, fully meeting the degradation judgment conditions. The system confirms the deterioration of the analysis effectiveness and triggers the subsequent anomaly mapping location and iteration process.

[0045] This step replaces the single-indicator judgment with a dual judgment mechanism that combines statistical distribution drift and business audit indicators, accurately distinguishing between normal business fluctuations and systemic judgment failures, reducing the probability of system erroneous iterations, and ensuring the accuracy and rationality of iteration triggers.

[0046] S54: Obtain the marginal contribution value of each fusion field to each imported commodity based on the contribution trace record; The full contribution trace records stored in the time series database are retrieved. Based on the SHAP model attribution mechanism, the marginal contribution value corresponding to each standardized fusion field in each product inference record is extracted. This marginal contribution value directly represents the influence weight of a single field on the risk score of a single product.

[0047] S55: Obtain a sample set of imported goods that have recently been judged as incorrectly assessed; Error-based assessment samples are defined as imported goods whose risk level predicted by the model is inconsistent with the actual customs inspection results. A set of error samples is constructed by filtering all error-based assessment data from the past 24 hours. This serves as the core analysis dataset for anomaly localization.

[0048] S56: Calculate field contribution statistics and map aggregated contribution values; Based on the field marginal contribution values ​​of the error sample set, aggregate the data by mapping relationship and calculate the aggregate contribution value of a single mapping. The formula is as follows: ; in, This represents the aggregate contribution value of the k-th data fusion mapping relationship. The larger the value, the more erroneous judgments are caused by the mapping, and the higher the probability of anomalies. This represents a recent set of erroneous assessments of imported goods. This represents the set of all standardized fusion fields bound to the k-th mapping relationship; This represents the marginal contribution value of the j-th fusion field in the s-th erroneous sample.

[0049] S57: The mapping with the highest aggregate contribution value is identified as a candidate anomaly mapping.

[0050] The system iterates through the aggregated contribution values ​​of all effective mappings and compares their values. In this embodiment, the old mapping corresponding to the logistics status field has the highest aggregated contribution value globally, and the system accurately marks it as a candidate abnormal mapping.

[0051] To distinguish between the analytical degradation caused by system mapping failure and normal indicator fluctuations caused by changes in the external policy environment, and to avoid ineffective self-healing iterations, save system computing power, and improve iteration accuracy, this embodiment also includes: S571, Obtain external event signals, wherein the external event signals include at least one of policy change information, regulatory adjustment information, sudden trade event information, and epidemic information; The system routinely connects to the General Administration of Customs' policy announcement API, the international trade event message queue, and the import and export regulatory law update database, collecting four types of external event signals in real time: customs policy change information, import and export regulatory law adjustment information, sudden international trade events, and cross-border trade epidemic control information. All external events are uniformly stored in the database to form an external event signal pool.

[0052] S572, based on the timestamp of the external event signal and the time window of the effect index degradation, determine whether there is an external event signal that is temporally related to the effect index degradation; Upon detecting a significant degradation in risk control effectiveness indicators, the system immediately initiates an external event correlation search. It compares the release timestamps of external events with the time window of the effectiveness indicator degradation to determine if there is any temporal overlap or causal relationship between the two. In this embodiment, the release time of the new regulations on the prohibition and restriction of imported cosmetic ingredients is found to highly overlap with the time window of the effectiveness degradation assessment, indicating a temporally related external event. However, no matching external event signal is found for the indicator degradation caused by changes in logistics codes.

[0053] S573, When there are time-related external events: it is determined that the current indicator degradation is a normal business fluctuation caused by changes in external environment policies, and not a failure caused by system mapping failure. The system actively suppresses the confidence decay of the abnormal mapping of the corresponding category and re-inference iterative process. At the same time, it generates operation and maintenance prompt information for the fluctuation caused by external policy changes for manual filing reference. When there are no external events with temporal correlation: the current metric degradation is determined to be a real fault caused by system-endogenous mapping drift or data adaptation failure. The iteration process is not suppressed, and the entire self-healing operation of anomaly mapping location, confidence decay, re-inference, and safe online deployment is performed normally.

[0054] In this embodiment, invalid iterations are suppressed only for cosmetic categories. If the logistics field mapping is abnormal and there is no external event association, self-healing iteration is performed normally, thus achieving precise differentiation processing.

[0055] The technical effects of this step are: effectively distinguishing between external environmental fluctuations and system-endogenous faults, eliminating invalid iterations under normal scenarios such as policy changes and trade fluctuations, avoiding frequent and meaningless system mapping updates, reducing computing power consumption, improving system operational stability, and completely solving the defects of traditional adaptive iteration that lack a discrimination mechanism and are prone to error iteration.

[0056] S58: For the candidate anomaly mapping that has been located, the system automatically performs confidence decay, reducing the original high confidence of 0.95 to 0.55, and triggers a new round of field mapping inference to generate updated candidate mappings that are adapted to the new version of logistics coding rules, and enters the subsequent security verification process.

[0057] This step enables precise source tracing from the degradation of assessment results to the mapping of specific anomalies, completely solving the industry pain point that traditional technologies cannot locate faults in the data fusion layer. Based on model attribution and statistical aggregation calculation, it achieves fully automated, second-level anomaly location without the need for manual investigation, and significantly improves iteration efficiency.

[0058] S6: Verify the updated candidate mapping according to the preset security policy. After successful verification, update the updated candidate mapping to the active state.

[0059] After the confidence decay of the abnormal mapping and the re-inference of the new mapping are completed, the system performs offline, gray-scale and full-scale multi-stage verification of the updated candidate mapping according to the preset multi-layer security verification strategy. After the verification is passed, the candidate mapping status is updated from pending verification to effective, and the end-to-end adaptive iterative optimization is completed.

[0060] S61, Shadow Verification Stage: The updated candidate mapping is fused and transformed on a copy of the production data in a parallel environment to obtain the transformation result of the updated candidate mapping. The transformation result is compared with the transformation result of the currently effective mapping to calculate the difference rate. When the difference rate is lower than a preset difference threshold, the traffic grayscale verification stage is entered. In an independent offline parallel computing environment, the entire online real-time production data stream is completely replicated as a data copy, perfectly replicating the online data format, temporal characteristics, and sampling distribution without data distortion or scene bias. The updated candidate mappings, generated after confidence decay and re-inference, are then compared with the currently stable and effective online mappings. Data fusion and transformation operations are performed in parallel on the same replica dataset, outputting two sets of standard fused data transformation results.

[0061] The two sets of fused output data are compared vote by vote and field by field. The difference rate between the old and new mapping data is calculated globally. The difference rate is calculated as: number of samples with inconsistent fields in the old and new mappings / total number of samples. This fully satisfies the conditions for passing shadow verification and automatically enters the traffic gray-scale verification stage.

[0062] The core function of this stage is to verify the data compatibility of the newly mapped data in advance without touching the actual online business, and to intercept basic defects such as field mismatch and conversion anomalies in advance, so as to achieve zero business risk in advance verification.

[0063] S62, Traffic Gray-Scale Verification Stage: The updated candidate mapping is only effective for a preset proportion of traffic subset in real-time traffic. The risk score distribution offset, detection rate and false alarm rate corresponding to the traffic subset are continuously monitored. If the risk score distribution offset does not exceed the preset distribution offset threshold and the detection rate and false alarm rate do not degrade beyond the preset business threshold within the preset verification time, the verification is deemed successful. The system uses a gray-scale traffic scheduling service to accurately distribute online real-time business traffic. Only a 3% preset proportion of traffic subset is allocated to use the updated candidate mapping for data fusion and risk assessment, while the remaining 97% of main traffic continues to use the original effective mapping, ensuring the complete stability of core business.

[0064] Strictly adhere to the preset 12-hour verification duration for continuous monitoring of core performance indicators corresponding to gray-scale traffic, including Risk Score Distribution Offset (PSI), product detection rate, and risk assessment false alarm rate. During monitoring, continuously reuse the calculation and judgment rules for these indicators, and compare them in real-time with preset thresholds. The grayscale window PSI stabilized and dropped to 0.12, which is below the preset distribution offset threshold of 0.2, indicating no data distribution drift issue. For example, the gray-scale detection rate recovered to 4.9%, which is less than the preset business threshold of 20% compared to the baseline indicator fluctuation. The false alarm rate did not rise abnormally, and there was no excessive degradation. The indicators were stable and anomalies-free throughout the day, and the system determined that the gray-scale verification was passed and could enter the full-scale effective phase.

[0065] S63, Full Activation Phase: The updated candidate mapping that has passed verification is updated to an active state, and the confidence value of the updated candidate mapping is increased to a high confidence threshold. After gray-scale verification is completed and all indicators are qualified, the system automatically completes the mapping status iteration: the updated candidate mapping is officially changed from the original pending verification status to the effective status, completely replacing the old abnormal mapping, and all online real-time business traffic is switched to the new mapping rules for data fusion.

[0066] For example, the confidence level can be upgraded simultaneously, increasing the initial confidence level of the new mapping from 0.81 to a high confidence threshold of 0.9, marking it as a stable and reliable mapping, and persistently updating it to the data fusion mapping relationship library, thus completing the formal iteration of the mapping version.

[0067] S64, Fast Rollback Phase: During the preset continuous monitoring period after the traffic grayscale verification phase or the updated candidate mapping has been updated to the active state, if the risk score distribution offset exceeds the preset secondary alarm threshold, or the detection rate and the false alarm rate degrade beyond the preset secondary alarm threshold, the updated candidate mapping or the active mapping will be automatically rolled back to the previous stable version.

[0068] After the mapping is fully deployed, the system automatically initiates a 72-hour preset continuous monitoring period, continuously monitoring PSI distribution offset indicators, detection rate, and false alarm rate in real time, using the original alarm thresholds as the secondary alarm judgment criteria. If indicators exceed the limits or degrade, or data drifts abnormally during the monitoring period, the system will trigger a rapid rollback mechanism, automatically canceling the new mapping's effectiveness and rolling back to the previous stable mapping version, preventing abnormal mappings from polluting online business for a long time.

[0069] During the 72-hour monitoring period in this embodiment, all risk control indicators remained stable without exceeding the limit, and the automatic rollback logic was not triggered. The new version of the mapping operated stably for a long time.

[0070] This step completely solves the business risk issues in the dynamic mapping iteration process by building a four-layer security protection closed loop of offline shadow verification, small-volume gray-scale verification, full deployment, and continuous monitoring and rollback. It enables unattended adaptive iteration of mapping, while ensuring the stability and accuracy of customs import risk control business and meeting the regulatory compliance requirements of zero major failures.

[0071] To adapt to the unique business scenario of delayed arrival of inspection labels for imported goods, compensate for the judgment bias in short-cycle label-less iterations, and achieve long-cycle deep and accurate calibration, this embodiment also includes: S71, when the risk label of the imported goods arrives late, obtain the corresponding product identifier and historical fusion timestamp based on the risk label; Customs inspection of imported goods inherently has a lag; after goods complete customs clearance, inspection, and violation determination, risk labels may be delayed for several days before being entered into the warehouse in batches. In this embodiment, the delayed risk labels for a batch of non-compliant imported cosmetics that were declared 45 days ago are pushed to the system's time-series storage repository in batches. The system captures the delayed label data in real time, accurately extracts the unique identifier of the product bound to each label, and the historical fusion timestamp corresponding to the product's historical fusion processing, providing accurate time and sample positioning for tracing historical data.

[0072] S72, based on the historical fusion timestamp, trace back the mapping version used by the imported goods corresponding to the goods identifier during historical fusion, and obtain the historical fusion features of the imported goods during historical fusion based on the mapping version; The system relies on a mapping version snapshot mechanism to trace back the historical mapping version used for this batch of products at the time of historical fusion processing, based on precise historical fusion timestamps, and accurately retrieve the complete set of data fusion mapping rules that were in effect at that time. Based on the historical mapping version, the system reproduces the complete historical fusion feature data of this batch of products, ensuring that the traced data is completely consistent with the historical real judgment data, without deviation or distortion.

[0073] S73, calculate the significance of the distribution difference of each fusion field between positive label samples and negative label samples based on the risk label and the historical fusion features, and use the measure of the significance of the distribution difference as the discriminative contribution of each fusion field; Using the delay risk label as the basis for dividing positive and negative samples, the historical fusion features obtained from backtracking are divided into: a set of positive label samples corresponding to non-compliant products and a set of negative label samples corresponding to compliant products. Statistical tests are used to quantify the significance of the distribution differences of each fusion field between positive and negative samples, and the measure of significance is defined as the discriminant contribution of the field. The discriminant contribution directly characterizes the field's ability to distinguish product risks; the higher the contribution, the higher the risk control value of the field.

[0074] To employ suitable statistical testing models for both continuous and discrete fusion fields, and to achieve accurate quantification of the field's contribution, which is the core calculation basis for long-term deep calibration, this embodiment also includes: S731, the historical fusion features are divided into a positive label sample set and a negative label sample set according to the risk label; Based on the historical fusion characteristics and delayed risk labels obtained through backtracking, the full backtracking samples are divided into two independent sets: a set of positively labeled samples carrying violation risk labels (violation samples) and a set of negatively labeled samples carrying compliance risk labels (compliance samples). The sample division is entirely based on real customs audit results, without any human labeling errors, providing a real and reliable experimental sample set for subsequent statistical verification.

[0075] S732, this invention strictly classifies the fused fields into two categories: continuous fields and discrete fields. For different field data characteristics, a differentiated testing method is adopted to avoid the problems of poor adaptability and distorted results of a single testing model. For continuous fusion fields: the Mann-Whitney U nonparametric statistical test is used.

[0076] Applicable scenarios: continuous numerical fields such as declared unit price, declared quantity, and product weight; The specific calculation principle is that it does not require assuming that the data follows a normal distribution, but adapts to the non-normal, long-tailed, and skewed distribution characteristics of customs risk control data. It quantifies the difference in the distribution of two groups of samples through the rank-sum test and outputs the test p-value.

[0077] For discrete fusion fields: use the chi-square test of contingency tables.

[0078] Applicable scenarios: Discrete enumeration type fusion fields such as logistics status codes, country of origin codes, and product category codes; The calculation principle is as follows: by constructing an attribute-label contingency table, the deviation between the theoretical frequency and the actual frequency is statistically analyzed, the correlation between the field value and the product risk label is quantified, and the test p-value is output.

[0079] S733, compare the statistical test p-value with a preset significance level. When the statistical test p-value is lower than the preset significance level, determine that the discriminative contribution of the fusion field is higher than the preset discriminative contribution threshold. The p-values ​​output by the two test models are compared with the preset significance level α=0.05 to complete the field discrimination ability rating: When p < 0.05: This proves that the distribution of the fusion field is significantly different between positive and negative samples, the field has effective risk discrimination ability, the discrimination contribution is higher than the preset threshold, the corresponding mapping rule is effective and there is no need for significant downgrading; When p≥0.05: This proves that the fusion field has no significant distribution difference between positive and negative samples, the field cannot effectively distinguish between compliant and non-compliant products, the discrimination contribution is lower than the preset threshold, the corresponding mapping rule is invalid, the confidence level needs to be lowered and iterated again.

[0080] S734, when the statistical test p-value is higher than or equal to the preset significance level, it is determined that the discriminative contribution of the fusion field is lower than the preset discriminative contribution threshold.

[0081] For continuous fields (declared unit price): Mann-Whitney U test, p=0.001<0.05, the contribution is qualified, and the mapping is valid; Discrete field (country of origin code): Chi-square test, p=0.002<0.05, the contribution is qualified, and the mapping is valid; Discrete fields (logistics status codes): Chi-square test, p=0.47≥0.05, indicating that the contribution is insufficient and the mapping is degraded. The system automatically lowers the confidence of the mapping corresponding to this field and triggers re-inference iteration.

[0082] This step enables differentiated and precise inspections for fields of different data types, fully adapting to the real distribution characteristics of customs import data and solving the problems of poor applicability and inaccurate judgment of traditional single inspection models. By relying on the real audit labels to quantify the risk control value of fields, the adjustment of mapping confidence is based on statistical evidence rather than empirical threshold adjustment, which greatly improves the scientificity and accuracy of long-term calibration.

[0083] S74, compare the discriminant contribution of each fusion field with a preset discriminant contribution threshold. When the discriminant contribution of a certain fusion field is lower than the preset discriminant contribution threshold, lower the confidence value of the mapping relationship corresponding to that fusion field. The system compares the discriminative contribution of each fusion field with the system's preset discriminative contribution threshold. If the discriminative contribution of a certain fusion field is lower than the preset threshold, it proves that the current mapping rule of that field is invalid and the risk differentiation ability is degraded. The system automatically lowers the confidence value of the mapping relationship corresponding to that field, marks it as a degraded state, and cancels the high-confidence qualification.

[0084] S75, based on the mapping relationship after the confidence level is lowered, a re-inference of the mapping relationship is triggered to generate an updated candidate mapping, and the updated candidate mapping is entered into the step of verification according to the preset security policy.

[0085] After the confidence level is lowered, the system automatically triggers a new round of four-dimensional weighted mapping inference, generates updated candidate mappings that are adapted to the characteristics of the current data source and have effective risk discrimination capabilities, and automatically pushes the newly generated candidate mappings to the corresponding four-stage security verification process to complete the closed loop of the entire process of verification, deployment, and iteration.

[0086] This step can be adapted to the industry characteristics of delayed arrival of risk labels for imported goods. It uses real inspection labels to correct the statistical bias of short-term label-less iterations, and achieves two-way optimization of short-term data drift adaptation and long-term real result calibration, which greatly improves the long-term stability and judgment accuracy of dynamic mapping.

[0087] To differentiate between minute-level rapid self-healing short-cycle loops and day-level deep calibration long-cycle loops, and to set dual-loop linkage triggering rules to achieve a balance between efficiency and accuracy, this embodiment also includes: S81, the iterative process consisting of monitoring the effect indicators, confidence decay and re-inference of the candidate anomaly mapping, and verification according to the preset security strategy is defined as a short-cycle iterative loop. The response cycle of the short-cycle iterative loop is from minutes to hours, which is used to achieve rapid adaptive adjustment of the mapping relationship without the need for real risk labels. This invention defines the entire adaptive iterative process of performance indicator monitoring, anomaly mapping and localization, confidence decay, mapping re-inference, and multi-stage security verification as a short-cycle iterative loop.

[0088] The response cycle ranges from minutes to hours. It does not rely on real risk labels throughout the process. It achieves rapid self-healing by mapping through data distribution, risk control indicators, and model attribution traces. It is specifically designed to quickly adapt to sudden scenarios such as changes in data source fields, short-term data drift, and coding rule iterations.

[0089] S82, the iterative process consisting of the delayed tag backtracking calibration step is defined as a long-cycle iterative loop, the response period of the long-cycle iterative loop is from days to weeks, and it is used to perform in-depth calibration of the confidence of the mapping relationship after the real risk tag arrives. This invention defines the iterative process of retrospective calibration, statistical testing, contribution quantification, and confidence correction based on delay risk labels as a long-cycle iterative loop.

[0090] Operational characteristics: The response cycle is from days to weeks, relying entirely on real audit risk labels. It is used to correct statistical biases in short-cycle unlabeled iterations, and to perform in-depth and precise calibration of mapping matching capabilities and field discrimination capabilities, thus solving the problem of long-term cumulative errors in short-cycle iterations.

[0091] S83, count the number of times the short-cycle iterative loop adjusts the confidence level of the same mapping relationship within a preset time window. When the number of confidence adjustments exceeds a preset threshold, trigger the long-cycle iterative loop to perform deep calibration on the mapping relationship. The system uses a 7-day short-cycle iteration statistical window to continuously count the number of short-cycle confidence adjustments for the same mapping. When the number of short-cycle adjustments for a single mapping exceeds the threshold of 3 within 7 days, the system determines that the mapping is frequently self-healing, the short-cycle iteration is unstable, and there is a continuous deviation, and automatically forces the long-cycle deep calibration process.

[0092] S84, the depth calibration is: obtaining the discriminant contribution of the mapping relationship according to the delayed label backtracking calibration step, and adjusting the confidence value of the mapping relationship according to the discriminant contribution.

[0093] After triggering the forced deep calibration, the system retrieves all delay risk label samples for the mapping over the past three months, requantifies the contribution of the fields using statistical testing methods, corrects the mapping confidence based on real business results, and re-infers the optimal mapping relationship to eliminate the biased error of short-cycle unlabeled iterations.

[0094] In this embodiment, the logistics status mapping is adjusted 4 times in a short period within a 7-day window. If the threshold of 3 times is exceeded, the system will automatically force the initiation of long-term statistical verification and deep calibration to correct the mapping confidence and generate a new version of the optimal mapping, thus completing the dual-loop closed-loop collaborative optimization.

[0095] This step constructs a two-layer iterative architecture that ensures rapid response speed through short-term self-healing and accurate judgment through long-term precision calibration. It solves the problem of sudden data drift in a short period of time and corrects iterative errors in a long period of time. The two complement each other in time and are logically linked, which completely solves the dilemma of traditional risk control model iteration that is either slow to respond or lacks accuracy.

[0096] This invention also discloses a dynamic risk iterative assessment system for imported goods based on incremental data stream fusion, comprising: The first acquisition module is used to acquire incremental data streams from multiple data sources, acquire the current pattern fingerprint of each data source based on the incremental data streams, and compare the current pattern fingerprint with the historical pattern fingerprint to detect whether there is a pattern change event. The maintenance module is used to maintain a data fusion mapping relationship library containing confidence attributes. When the pattern change event is detected, it automatically infers and generates candidate mappings based on the pattern change event and the data fusion mapping relationship library, and assigns an initial confidence level and a state to be verified to the candidate mappings. The conversion module is used to convert the incremental data streams from the multiple data sources into standard fused data under a unified target model based on the mapping relationship that is currently in effect. The second acquisition module is used to perform real-time risk reasoning based on the standard fusion data, acquire the risk score of each imported commodity, and acquire contribution trace records based on the contribution value of each fusion field in the standard fusion data to the risk score. The generation module is used to obtain the performance index within the current time window based on the risk score, and compare the performance index with a preset baseline. When the degradation of the performance index relative to the preset baseline is detected to exceed a preset threshold, the module locates the candidate anomaly map based on the contribution trace record, triggers the confidence decay of the candidate anomaly map, and re-infers and generates an updated candidate map. The verification module is used to verify the updated candidate mapping according to a preset security policy, and after the verification is successful, the updated candidate mapping is updated to an active state.

[0097] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0098] The above description is merely a preferred embodiment of the present invention and does not limit the scope of this application. Any equivalent results or equivalent process transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion, characterized in that, Includes the following steps: The system acquires incremental data streams from multiple data sources, obtains the current pattern fingerprint of each data source based on the incremental data streams, and compares the current pattern fingerprint with the historical pattern fingerprint to detect whether there is a pattern change event. Maintain a data fusion mapping relationship library containing confidence attributes. When the pattern change event is detected, automatically infer and generate candidate mappings based on the pattern change event and the data fusion mapping relationship library, and assign initial confidence and a state to be verified to the candidate mappings. Based on the mapping relationship that is currently in effect, the incremental data streams from the multiple data sources are fused and converted into standard fused data under a unified target model; Real-time risk reasoning is performed based on the standard fusion data to obtain a risk score for each imported commodity, and contribution trace records are obtained based on the contribution value of each fusion field in the standard fusion data to the risk score. The effect index within the current time window is obtained based on the risk score, and the effect index is compared with a preset baseline. When the degradation of the effect index relative to the preset baseline is detected to exceed a preset threshold, the candidate anomaly map is located based on the contribution trace record, the confidence decay of the candidate anomaly map is triggered, and the updated candidate map is re-inferred. The updated candidate mapping is verified according to a preset security policy. Once the verification is successful, the updated candidate mapping is updated to an active status.

2. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, The step of obtaining the performance index within the current time window based on the risk score and comparing the performance index with a preset baseline includes: The current risk score distribution is obtained based on the risk scores of all imported goods within the current sliding time window. The baseline risk score distribution is obtained based on the risk scores of all imported goods within the baseline time window. The risk score distribution offset is calculated based on the current risk score distribution and the baseline risk score distribution. The current detection rate and current false alarm rate are obtained based on the inspection results of the inspected goods within the current sliding time window, and the baseline detection rate and baseline false alarm rate are obtained based on the detection rate and false alarm rate during the historical stable operation phase. The risk score distribution offset is compared with a preset distribution offset threshold, the current detection rate is compared with the baseline detection rate, and the current false alarm rate is compared with the baseline false alarm rate. When the risk score distribution offset exceeds the preset distribution offset threshold, and the decrease in the current detection rate relative to the baseline detection rate exceeds the preset business threshold, or the increase in the current false alarm rate relative to the baseline false alarm rate exceeds the preset business threshold, it is determined that the performance indicator has degraded beyond the preset threshold relative to the preset baseline.

3. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, The step of locating candidate anomaly maps based on the contribution trace records includes: The marginal contribution value of each fusion field to the risk score of each imported commodity is obtained based on the contribution trace record. Obtain a sample set of imported goods that have recently been identified as erroneous assessments, including imported goods whose risk levels predicted by the model are inconsistent with the actual inspection results; The contribution statistic of each fusion field in the sample set is calculated based on the marginal contribution value. The contribution statistic is the average or sum of the marginal contribution values ​​of each fusion field in the sample set. The contribution value statistics are grouped and aggregated according to the mapping relationship to which each fusion field belongs, to obtain the aggregated contribution value for each mapping relationship; The one or more mapping relationships with the highest aggregation contribution value are identified as the candidate anomaly mappings.

4. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, The step of verifying the updated candidate mapping according to a preset security policy includes: Shadow verification phase: The updated candidate mapping is fused and transformed on a copy of the production data in a parallel environment to obtain the transformation result of the updated candidate mapping. The transformation result is compared with the transformation result of the currently effective mapping to calculate the difference rate. When the difference rate is lower than a preset difference threshold, the traffic grayscale verification phase is entered. Traffic gray-scale verification phase: The updated candidate mapping is only applied to a preset proportion of traffic subset in real-time traffic. The risk score distribution offset, detection rate and false alarm rate corresponding to the traffic subset are continuously monitored. If the risk score distribution offset does not exceed the preset distribution offset threshold and the detection rate and false alarm rate do not degrade beyond the preset business threshold within the preset verification time, the verification is deemed successful. Full activation phase: The updated candidate mappings that have passed verification are updated to the active state, and the confidence value of the updated candidate mappings is increased to the high confidence threshold; Fast rollback phase: During the preset continuous monitoring period after the traffic grayscale verification phase or the updated candidate mapping has been updated to the active state, if the risk score distribution offset is found to exceed the preset secondary alarm threshold, or the detection rate and the false alarm rate degrade beyond the preset secondary alarm threshold, the updated candidate mapping or the active mapping will be automatically rolled back to the previous stable version.

5. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, It also includes a delayed tag backtracking calibration step: When the risk label of imported goods arrives late, the corresponding product identifier and historical fusion timestamp are obtained based on the risk label; Based on the historical fusion timestamp, the mapping version used by the imported goods corresponding to the product identifier during historical fusion is traced back, and the historical fusion characteristics of the imported goods during historical fusion are obtained based on the mapping version. The significance of the distribution difference of each fusion field between positive and negative label samples is calculated based on the risk label and the historical fusion features, and the measure of the significance of the distribution difference is used as the discriminative contribution of each fusion field. The discriminant contribution of each fusion field is compared with a preset discriminant contribution threshold. When the discriminant contribution of a certain fusion field is lower than the preset discriminant contribution threshold, the confidence value of the mapping relationship corresponding to that fusion field is lowered. Based on the mapping relationship after the confidence level is lowered, a new reasoning of the mapping relationship is triggered to generate an updated candidate mapping, and the updated candidate mapping is then entered into the step of verification according to the preset security policy.

6. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, After detecting that the degradation of the performance indicator relative to the preset baseline exceeds the preset threshold, and before triggering the confidence decay of the candidate anomaly mapping, the method further includes an external event discrimination step: Acquire external event signals, which include at least one of policy change information, regulatory adjustment information, sudden trade event information, and epidemic information; Based on the timestamp of the external event signal and the time window of the performance indicator degradation, determine whether there is an external event signal that is temporally related to the performance indicator degradation; When an external event signal that is temporally related to the degradation of the effect metric exists, the confidence decay of the candidate anomaly mapping and the triggering of re-inference are suppressed, and a prompt message indicating that changes in the external environment have caused effect fluctuations is generated. When there is no external event signal that is temporally related to the degradation of the effect metric, the confidence decay and re-inference triggering of the candidate anomaly mapping continue.

7. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 5, characterized in that, The step of calculating the significance of the distribution difference of each fusion field between positive-label samples and negative-label samples based on the risk label and the historical fusion features includes: Based on the risk labels, the historical fusion features are divided into a positive label sample set and a negative label sample set; For continuous fusion features, nonparametric statistical tests are used to calculate the significance of the distribution difference of the fusion feature between the positive label sample set and the negative label sample set, and the statistical test p-value is obtained. For discrete fusion features, the chi-square test of contingency tables is used to calculate the significance of the distribution difference of the fusion feature between the positive label sample set and the negative label sample set, and to obtain the statistical test p value; The statistical test p-value is compared with a preset significance level. When the statistical test p-value is lower than the preset significance level, it is determined that the discriminative contribution of the fusion field is higher than the preset discriminative contribution threshold. When the statistical test p-value is higher than or equal to the preset significance level, the discriminative contribution of the fusion field is determined to be lower than the preset discriminative contribution threshold.

8. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, It also includes the coordinated steps of short-cycle iterative loops and long-cycle iterative loops: The iterative process consisting of monitoring the performance indicators, confidence decay and re-inference of the candidate anomaly mapping, and verification according to the preset security strategy is defined as a short-cycle iterative loop. The response cycle of the short-cycle iterative loop is from minutes to hours, which is used to achieve rapid adaptive adjustment of the mapping relationship without the need for real risk labels. The iterative process consisting of the delayed tag backtracking calibration steps is defined as a long-cycle iterative loop. The response period of the long-cycle iterative loop is from days to weeks, and it is used to perform in-depth calibration of the confidence of the mapping relationship after the real risk tag arrives. The number of confidence adjustments made by the short-cycle iterative loop to the same mapping relationship within a preset time window is counted. When the number of confidence adjustments exceeds a preset threshold, the long-cycle iterative loop is triggered to perform a deep calibration of the mapping relationship. The depth calibration is as follows: the discriminant contribution of the mapping relationship is obtained according to the delayed label backtracking calibration step, and the confidence value of the mapping relationship is adjusted according to the discriminant contribution.

9. The method for dynamic risk iterative assessment of imported goods based on incremental data stream fusion according to claim 1, characterized in that, The step of automatically inferring and generating candidate mappings based on the pattern change events and the data fusion mapping relationship library includes: A first semantic feature vector is obtained based on the name of the source field where the pattern change occurs, and a second semantic feature vector is obtained based on the name of each target field in the data fusion mapping relation library. The semantic similarity between the first semantic feature vector and each of the second semantic feature vectors is calculated. Based on the value range distribution of the source field that has undergone pattern change within the current time window, obtain the source field value range set; based on the value range distribution of each target field in historical data, obtain the historical value range set of each target field; and calculate the value range overlap between the source field value range set and each of the historical value range sets. Based on the historical pattern change record library, obtain multiple historical pattern change events that are similar in type to the current pattern change event, and statistically analyze the mapping schemes ultimately adopted in the multiple historical pattern change events to obtain the historical migration similarity. The source fields where the pattern changes occur are matched with rules based on a predefined industry knowledge rule base to obtain the rule hit rate; The semantic similarity, the value range overlap, the historical migration similarity, and the rule hit rate are weighted and fused to generate a mapping confidence score between the source field that has undergone pattern change and each target field. The target field with the highest mapping confidence score is determined as the mapping target of the source field where the pattern change occurs, a candidate mapping is generated, and an initial confidence score is assigned to the candidate mapping.

10. A dynamic risk iterative assessment system for imported goods based on incremental data stream fusion, characterized in that, include: The first acquisition module is used to acquire incremental data streams from multiple data sources, acquire the current pattern fingerprint of each data source based on the incremental data streams, and compare the current pattern fingerprint with the historical pattern fingerprint to detect whether there is a pattern change event. The maintenance module is used to maintain a data fusion mapping relationship library containing confidence attributes. When the pattern change event is detected, it automatically infers and generates candidate mappings based on the pattern change event and the data fusion mapping relationship library, and assigns an initial confidence level and a state to be verified to the candidate mappings. The conversion module is used to convert the incremental data streams from the multiple data sources into standard fused data under a unified target model based on the mapping relationship that is currently in effect. The second acquisition module is used to perform real-time risk reasoning based on the standard fusion data, acquire the risk score of each imported commodity, and acquire contribution trace records based on the contribution value of each fusion field in the standard fusion data to the risk score. The generation module is used to obtain the performance index within the current time window based on the risk score, and compare the performance index with a preset baseline. When the degradation of the performance index relative to the preset baseline is detected to exceed a preset threshold, the module locates the candidate anomaly map based on the contribution trace record, triggers the confidence decay of the candidate anomaly map, and re-infers and generates an updated candidate map. The verification module is used to verify the updated candidate mapping according to a preset security policy, and after the verification is successful, the updated candidate mapping is updated to an active state.

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