An error trace data processing system

Abstract

The application discloses an error tracing data processing system and relates to the technical field of electric digital data processing. In the cross-border transmission process of multi-source fund data, the system constructs a full-link traceable collaborative data set through link logic state matching; the heterogeneous data in the collaborative data set is parsed and converted with the established data conversion rule library to obtain standardized adaptive data; in the cross-link transmission and multi-node circulation process of the standardized adaptive data, transmission errors of single data interaction links and / or composite transmission errors in the superposition state of multi-link data circulation are identified, and error root cause positioning is realized through error tracing data processing.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to an error tracing data processing system. Background Technology

[0002] Against the backdrop of deepening global economic integration, the scale of cross-border trade continues to expand. As a core support link in trade, the security of cross-border payments directly affects the smooth operation of international trade.

[0003] From a data processing perspective, in traditional cross-border data exchange models, each cross-border transaction must independently undergo multiple processes, including data format conversion, cross-border network transmission, and compliance audits in multiple countries. During data format conversion, the accuracy of matching financial data is difficult to guarantee due to factors such as inconsistent data standards among different institutions and conversion losses. The complex compliance audits and cross-border network transmissions not only further lengthen the data processing cycle but also increase the probability of data errors due to the cumbersome processes. Furthermore, error tracing data is scattered across various stages, making it difficult to quickly integrate and locate problems.

[0004] The aforementioned traditional cross-border data exchange models suffer from weak interaction infrastructure and inefficient error tracing, prompting innovative exploration of related technological solutions. Existing technologies have seen some improved solutions for fund tracing, attempting to optimize data processing and tracing efficiency. For example, Chinese invention patent CN117808601B discloses a fund tracing method and system based on big data. This solution includes: storing pre-processed data and constructing a user's fund flow graph through a fund flow graph database storage module; integrating multiple transaction records and calculating relevant data through a data processing module; calculating a risk volatility index and issuing alarm information through a real-time fund flow monitoring module; and receiving alarm information, executing fund tracing operations, and generating a fund tracing report through a fund tracing execution module.

[0005] Based on traditional cross-border data exchange scenarios and existing related technologies, current error tracing data processing is a decentralized and lagging step-by-step process, specifically divided into four stages: First, error triggering, where warnings are triggered when abnormalities such as format mismatch occur in cross-border transaction data transmission, verification, or processing; second, data collection, requiring manual or semi-automatic collection of scattered data from multiple parties; third, traceability analysis, where data is manually organized and compared to trace the flow trajectory; and fourth, results feedback, conveying conclusions and rectification suggestions through various means.

[0006] However, the core design logic of existing technologies has not focused on building a data collaboration framework, resulting in multiple related transaction data in cross-border scenarios being processed in isolation. Furthermore, the data processing module does not conduct timely lag analysis on the multi-dimensional data transmission links in cross-border scenarios, and is mainly limited to basic integration and analysis of existing transaction records. This not only makes it difficult to fully guarantee the accuracy of data matching, but also perpetuates the loss problem caused by format conversion to a certain extent.

[0007] Furthermore, in complex processes involving multiple parties and nodes in cross-border data exchange, the transmission status of data at each stage varies in time, and the data is stored in different institutions. If data from multiple stages cannot be analyzed in a timely manner, the difficulty in locating the root cause of errors will be further increased. These combined problems make it impossible for cross-border transaction data to be efficiently matched, which in turn affects the timeliness and completeness of error tracing data processing, ultimately making it difficult to improve the reliability of cross-border financial data exchange. Summary of the Invention

[0008] To address the technical problems in the prior art, embodiments of the present invention provide an error tracing data processing system. The technical solution is as follows:

[0009] The multi-entity data collaboration module is used to construct a fully traceable collaborative dataset through link logic state matching during the cross-border transmission of multi-source financial data. The cross-standard data adaptation module is used to parse and transform heterogeneous data in the collaborative dataset with the established data transformation rule base to obtain standardized adapted data. The full-chain error tracing module is used to identify transmission errors in a single data interaction link and / or compound transmission errors in the superimposed state of multiple data flow links during the cross-link transmission and multi-node flow of standardized adapted data, and to realize the root cause of errors through error tracing data processing.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0011] 1. This invention utilizes a multi-entity data collaboration module to construct a fully traceable collaborative dataset during the cross-border transmission of multi-source financial data. This design systematically establishes the multi-entity data collaboration framework required for order consolidation. A cross-standard data adaptation module calls a pre-defined data conversion rule library to parse and convert heterogeneous data within the collaborative dataset. This not only solves the legacy issues of format conversion losses in existing technologies but also ensures the accuracy of adapted data and enhances the reliability of financial data matching. A full-chain error tracing module focuses on standardized data cross-link and multi-node flow scenarios, accurately identifying single-stage transmission errors and compound errors from multiple stages. Specialized tracing processing enables root cause location of errors, overcoming the difficulty of error location caused by time-series differences among multiple objects and dispersed data storage. The collaborative operation of these three modules improves the timeliness and completeness of error tracing and enhances the reliability of cross-border financial data interaction.

[0012] 2. First, multi-source funding data is compared at the field level. The compliance logic status is quantified into binary identifiers and matched quantitatively, providing a precise basis for link matching and avoiding matching deviations caused by field confusion and ambiguous compliance. For dedicated line transmission links, the link protocol encoding is used to query the mapping rule set. Combined with weight coefficients, the transmission matching status value between the actual value of each dedicated line transmission field and the standard field value of the link, and the total number of dedicated line transmission fields, a coupled average algorithm is used to calculate the link field matching degree parameter. Matching is determined only when this parameter is not less than the corresponding preset value and all data sources are compliant, ensuring the accuracy of dedicated line transmission data matching and reducing interference from abnormal data in dedicated line transmission. In the construction of the collaborative dataset, effective data is screened through time-series consistency verification and interoperability calculation. Redundancy is removed before the dataset is formed, improving the reliability of the collaborative dataset. For proxy transit links, a multi-dimensional link field sequence is collected through a fixed sampling window. The mean and standard deviation offsets are calculated, and dual association criteria are set. A match is determined when both criteria are met and all data is compliant, accurately identifying valid correspondences in proxy links. A collaborative dataset is constructed by grouping data according to transit order and encapsulation format. Valid data is filtered based on the uniqueness of serial numbers and matching rate, ensuring the orderliness and integrity of proxy transit data. The overall process achieves accurate matching of two types of links and the construction of a high-quality collaborative dataset, laying the foundation for subsequent cross-standard adaptation and error tracing, and improving the reliability of cross-border financial data collaboration.

[0013] 3. Full-Chain Error Root Cause Localization: Differentiated tracing logic is constructed for different error types to achieve accurate and efficient localization. For single-link node transmission errors, the error feature library is first retrieved, and transmission data fragments containing structured data sets are extracted. After differential quantization encoding and normalization, a feature matrix is ​​generated. This matrix is ​​compared with a standard template to filter out deviation data items. Then, the corresponding node processing log is traced to output a report. This process relies on feature matching to accurately pinpoint single-node problems, improving the accuracy of single error localization. For multi-link superimposed composite errors, standardized adaptation data is used as an index to trace the logs of all nodes in the entire chain. Key information such as time series and rule calls are extracted to form a trajectory dataset. By calculating the field deviation of adjacent nodes, the first node exceeding the threshold is filtered out. The root cause is located by combining historical records and deviation transmission paths, achieving full-chain penetrating tracing of composite errors. For scenarios where two types of errors coexist, the errors are first classified and labeled, and an error association index is established to determine whether a single error node belongs to the scope of the composite error chain. If it does, it is verified whether it is the initial cause; otherwise, it is traced independently. Finally, the analysis results are summarized and a report is output. This design enables precise differentiation of error relationships, avoids interference between the two types of errors, ensures the accuracy of single error tracing, and ensures the integrity of complex error root cause tracing, thus comprehensively improving the systematicness and reliability of full-link error root cause localization. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the structure of an error tracing data processing system provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0017] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] The specific implementation methods of this invention are as follows:

[0020] Example 1

[0021] This invention provides an error tracing data processing system, such as... Figure 1 The diagram shown illustrates the structure of an error tracing data processing system, which may include the following modules:

[0022] The multi-entity data collaboration module is used to construct a fully traceable collaborative dataset through link logic state matching during the cross-border transmission of multi-source financial data. The cross-standard data adaptation module is used to parse and transform heterogeneous data in the collaborative dataset with the established data transformation rule base to obtain standardized adapted data. The full-chain error tracing module is used to identify transmission errors in a single data interaction link and / or compound transmission errors in the superimposed state of multiple data flow links during the cross-link transmission and multi-node flow of standardized adapted data, and to realize the root cause of errors through error tracing data processing.

[0023] The link logic state matching process includes the following steps: Based on the cross-border fund transaction business rules, which cover the core requirements such as preset cross-border fund transaction standard fields and field format specifications, the pre-processed (data cleaning, redundant data removal, etc.) multi-source fund data is compared one by one with the preset cross-border fund transaction standard fields to obtain the field mapping status of the multi-source fund data. That is, the matching relationship between each field of the multi-source fund data and the standard fields and the corresponding compliance logic status of the fund transaction, including the compliance logic status marked as 1 and the non-compliance logic status marked as 0. Multi-source fund data usually includes data reported or transmitted by different entities such as transaction flow of third-party payment institutions, order data of cross-border e-commerce platforms, and filing data of foreign exchange management departments. The one-by-one comparison is as follows: Assuming that the cross-border fund transaction standard fields include core items such as the country code of the payer and the currency code of the transaction, the country code of the payment data of the company in country A is checked to see if it is a standard 3-digit code and whether the account type matches the enterprise classification identifier.

[0024] The matching metric is based on binary identifiers. Specifically, the link protocol code corresponding to the current leased line transmission task is obtained. This code serves as the unique query key. The field mapping rule set, which matches the transmission fields corresponding to the current link protocol code, is retrieved from the preset link protocol-transmission field mapping rule table. This table includes core content such as a list of required fields, field data type requirements, and field value verification standards. The preset link protocol-transmission field mapping rule table covers the encoding identifiers of mainstream cross-border leased line transmission protocols (such as the ISO 20022 financial message protocol and the cross-border UnionPay leased line transmission protocol), the list of required transmission fields for each protocol, and the field data type specifications, providing a unified reference standard for matching leased line data of different protocol types. The field matching degree of the preprocessed leased line transmission data is calculated based on the field mapping rule set to obtain the link field matching degree parameters. Leased line transmission data typically represents multi-source fund data transmitted by cross-border transaction participants through dedicated communication links.

[0025] If the link field matching degree parameter is not less than the preset link field matching degree parameter, and the compliance logic status identifier of the leased line transmission data reported by each leased line data source is 1, then it is determined that the link logic status matches and a collaborative dataset is constructed. Otherwise, it is determined that the link logic status does not match, triggering a leased line transmission anomaly warning. The warning content can be that the current leased line transmission data field matching degree does not meet the standard, there is a field missing / mismatch problem, and the transmission data and link protocol configuration should be checked to enable staff to quickly locate the problem. The preset link field matching degree parameter is represented by the sum and average of the historical link field matching degree parameters in the historical link field matching process.

[0026] Link field matching degree parameter: The link weight coefficient of each leased line transmission field is multiplied by the transmission matching status value to reflect the matching contribution of fields with different levels of importance. All multiplication results are then summed to obtain a weighted matching status sum. This weighted matching status sum is then divided by the total number of leased line transmission fields corresponding to the preprocessed leased line transmission data to obtain the coupling matching degree result, i.e., the link field matching degree parameter. Its range is a closed interval from 0 to 1; the closer it is to 1, the higher the overall matching degree of the leased line transmission. The link weight coefficient is set differently based on the core importance of the corresponding leased line transmission field in cross-border financial transactions. For example... For example, core compliance fields such as payer qualification verification code and payee country code are weighted at 0.2, basic business fields such as transaction amount and currency code are weighted at 0.1, and non-mandatory fields such as remarks are weighted at 0.05. In actual cross-border fund transaction scenarios, these fields can be fine-tuned according to the foreign exchange regulatory requirements of different countries and the version updates of the dedicated line transmission protocol. The transmission matching status value adopts a binary identifier. It is recorded as 1 when the current dedicated line link field completely matches the preset standard field, and as 0 when there are missing fields. The preset standard fields are uniformly set by the industry standards for cross-border fund transactions and the requirements of the dedicated line transmission protocol.

[0027] The collaborative dataset construction process is as follows: Leased line transmission data that are determined to match the link logical state are grouped and categorized according to a dual-key field hierarchy: the link protocol code of each data entry is extracted as the primary grouping criterion, and data with the same protocol type are grouped into the same major category; within the same major protocol category, the data transmission object is used as the secondary grouping criterion, further subdivided into smaller subgroups; ultimately forming a leased line transmission data group set with the same transmission subject and protocol type; the data transmission object may include terminal equipment, data storage nodes, subnet gateways, etc. Subsequently, the link sending timestamp and receiving timestamp of each leased line transmission data entry within the leased line transmission data group set with the same transmission subject and protocol type are obtained. The difference between the receiving timestamp and the sending timestamp is recorded as the single data transmission delay. The maximum and minimum values ​​of all transmission delays are filtered, and the difference between them is the maximum transmission delay difference. Then, the average value of all transmission delays is calculated, and the absolute value of the deviation between each data transmission delay and the average value is taken as the mean deviation; the absolute value of the difference between the maximum transmission delay difference and the mean deviation is recorded as the link timing consistency deviation.

[0028] The system summarizes leased line transmission data whose link timing consistency deviation is less than the corresponding preset allowable deviation, along with the link field matching integrity rate and leased line data compliance rate. It then calculates the arithmetic mean of the link field matching integrity rate and the leased line data compliance rate to obtain the leased line link interoperability parameter. The preset allowable deviation is represented by the summation and averaging of historical link timing consistency deviations during historical leased line transmission data transmission. If the obtained leased line link interoperability parameter is less than the preset leased line interoperability parameter, the corresponding leased line transmission data group is marked as pending link verification. The preset leased line interoperability parameter is represented by the summation and averaging of historical leased line interoperability parameters during historical leased line transmission data transmission. Otherwise, the leased line transmission data group corresponding to the group set is determined as a valid interoperable group, and all valid interoperable groups are summarized. After removing redundant information, the collaborative dataset of the leased line transmission links is obtained.

[0029] Specifically, the link field matching completeness rate is obtained by dividing the number of fields matching the preset cross-border fund transaction standard fields in the data transmitted through the dedicated line by the total number of preset cross-border fund transaction standard fields. The higher the value, the more comprehensive the field matching. Dedicated line data compliance rate: the compliance rate is obtained by dividing the number of data records that meet the cross-border regulatory requirements in the data group set of the dedicated line transmission data by the total number of data records in the data group set. The closer the value is to 1, the higher the proportion of compliant data and the more reliable the data quality.

[0030] When a transmission error is detected in a single link data interaction node, the specific implementation process for error root cause localization is as follows: A preset link node transmission error feature library is retrieved, and the full transmission messages during the time period of the node error are captured simultaneously using an error node localization algorithm. After filtering out invalid and redundant data, the transmission data fragments of the corresponding link interaction node are obtained. The transmission data fragments contain a structured data set of link-related transmission data items, specifically covering the protocol encoding of the corresponding link, the link's sending and receiving timestamps, transmission matching status values, etc. The preset link node transmission error feature library consists of structured information such as error cases, error feature tags, node fault types, and root cause solutions for historical cross-border fund transaction links.

[0031] The link-related transmission data items in the transmitted data segments are compared one by one with the preset transmission data items (including protocol encoding specifications, field format standards, data integrity requirements, timing synchronization parameters, etc.), and a list of discrepancies and non-discrepancies is compiled. Discrepancies indicate that the link-related transmission data items do not match the preset transmission data items in terms of actual value, format specifications, integrity, or timing parameters; non-discrepancies indicate that both fully meet the preset requirements in the above dimensions. Then, the discrepancies and non-discrepancies are quantified and encoded using 0-1 (discrepancies are denoted as 1, non-discrepancies as 0), arranged according to the preset cross-border fund transaction standard field order. For example, if the preset cross-border fund transaction standard field order is [payer qualification verification code, payee country code, transaction currency code, transaction amount format, and remarks information integrity], and after comparison, only the payee country code and transaction amount format differ, while the other fields match, the encoding result is [0,1,0,1,0]. The encoding results of multiple data points are arranged row by row to generate a link transmission error feature data matrix.

[0032] The process involves obtaining single-row vectors from the link transmission error feature data matrix, then acquiring the corresponding standard feature template vectors from the link node transmission error feature library. The cosine similarity algorithm is used to calculate the cosine of the angle between the single-row vector and the standard feature template vector, which is the similarity value. Data items with similarity values ​​lower than the corresponding similarity threshold are designated as link transmission deviation data items. Data items with similarity values ​​not lower than the corresponding similarity threshold indicate that their error features match the standard template and can be classified as known types of transmission deviations, requiring no manual review. The standard feature data template covers the feature codes of standard data items corresponding to historical transmission error types. The similarity threshold is a preset error judgment benchmark, calibrated based on historical error recognition accuracy, typically set to 0.8. In practical applications, it can be fine-tuned according to specific error types. Based on the link transmission deviation data items, the transmission data processing logs of the corresponding link interaction nodes are traced, including the entire process of data reception, parsing, and forwarding. Finally, a link transmission error root cause localization report is obtained, containing details of deviation data items, abnormal log fragments, root cause analysis conclusions, and rectification suggestions.

[0033] When a complex transmission error is identified under the superposition of data flow across multiple link nodes, the specific implementation process for error root cause localization is as follows: Using the standardized adaptation data corresponding to the complex transmission error as an index, trace the transmission data processing logs of each node in the full-link flow graph to obtain the link flow timestamp, transmission rule call identifier, and link data verification status code, as the full-link transmission data trajectory dataset; according to the node flow order in the full-link transmission data trajectory dataset, i.e., the sequential flow path from the cross-border payment initiating node, transit interaction node to the terminal receiving node, obtain the common source transmission fields between adjacent link nodes in the full-link transmission data trajectory dataset, i.e., the related fields from the upstream node output and the downstream node reception; using the upstream node output field value as a benchmark, calculate the deviation value between the downstream node reception field value and the benchmark value, i.e., the ratio of the absolute value of the difference between the downstream reception value and the upstream output value to the upstream output value; filter out those with deviation values ​​exceeding the benchmark. The transmission node corresponding to the preset value is the initial trigger node for composite transmission errors. The preset value is represented by the sum and average of historical deviation values ​​in the historical composite transmission error identification process. The upstream output value here is usually not 0 because it corresponds to the core business fields of cross-border fund transactions, such as currency codes and account verification codes. These fields have clear valid values ​​in compliant cross-border payment data and are not allowed to be empty or zero. If the upstream output value is 0, it is invalid data and will be removed in the early data cleaning and compliance verification stages, and cannot enter the subsequent analysis process of the entire link flow. The historical transmission data processing records, transmission rule configuration change logs and link transmission anomaly alarm data of each transmission node are retrieved. Combined with the transmission path of the deviation value between multiple link nodes, the corresponding link transmission error root cause location report is output. Its content can be details of the node where the deviation first appears, deviation transmission path map and transmission rule configuration anomaly point.

[0034] The end-to-end transmission data trajectory dataset is a structured data collection that covers the entire process of error-related data flow through each node in the end-to-end transmission chain. It can completely reconstruct the data transmission path and the processing status of each node. The end-to-end flow map includes the unique code of each link data interaction node, the transmission flow order relationship between nodes, the data transmission interface protocol, the transmission processing rule version corresponding to each node, and the link transmission field mapping relationship. It is set by the technical operation and maintenance team based on the dedicated line transmission network topology, business flow logic, and transmission protocol specifications and is updated regularly.

[0035] When a transmission error at a single link data interaction node is detected, coexisting with a composite transmission error in the superposition of data flow across multiple link nodes, the specific implementation process for error root cause localization is as follows: classify and label the link node transmission errors and composite transmission errors. For example, label a single link node error as the first error code and a composite transmission error as the second error code; obtain the transmission data segments corresponding to the above-mentioned link node transmission errors and the full-link transmission data trajectory dataset corresponding to the composite transmission errors, and establish a link error association index using the unique identifier of the transmission data corresponding to the two types of errors.

[0036] Based on the end-to-end flow map, the unique identifier of the transmission data corresponding to the transmission error of the link node is first matched by the error association index to locate the link interaction node to which the error belongs. Then, the end-to-end flow node list (including node unique code, node hierarchical relationship, etc.) corresponding to the uploaded composite transmission error is obtained. The located link interaction node is compared with the end-to-end flow node list to determine whether it belongs to the end-to-end flow node range of the composite transmission error. If so, the transmission deviation data item (such as field format abnormality) corresponding to the link node transmission error is obtained and used as the initial deviation candidate feature of the composite transmission error. By comparing the transmission time sequence, field association relationship and node influence range of the deviation feature, the preset personnel verify whether the link node transmission error is the initial cause of the composite transmission error. If not, it is determined that the two types of errors have no direct link association. The independent tracing logic of the single link node transmission error and the composite transmission error is followed respectively. The root cause localization process of the two types of errors is started simultaneously to clarify that the two are independent link transmission error relationships.

[0037] If the verification confirms that the link node transmission error is the initial cause of a composite transmission error, the node interaction logs corresponding to the two types of errors need to be summarized (including the send and receive logs of the node with the initial error and the forwarding / parsing logs of the entire link nodes with the composite error, etc.). The focus should be on integrating the trajectory data of the initial deviation characteristics spreading from a single node to the entire link, such as nodes whose deviation field format has been tampered with. If the verification result indicates that it is not the initial cause, or if the two types of errors were previously determined to exist independently, then the transmission formation trajectory of each link should be traced separately: for single-link node transmission errors, the focus should be on the transmission rule configuration, data processing flow, and hardware operating status of the node to determine its core root cause and the local link range affected; for composite transmission errors, the analysis should continue throughout the entire link deviation propagation to identify key abnormal nodes and the overall affected link range. Finally, the above tracing analysis results (including root cause details, impact range, and abnormal data fragments for both types of errors) and the link error correlation determination conclusions (initial cause / non-initial cause) should be summarized to generate and output a corresponding full-link transmission error root cause location report. The report should clearly indicate the error correlation attributes, the priority of each error root cause, and targeted rectification suggestions.

[0038] In Implementation Example 1, a fully traceable collaborative dataset is constructed by matching the logical state of the data link. Combined with dual verification of cross-border business rules and protocol adaptation, accurate collaboration of multi-source financial data is achieved, ensuring the compliance and integrity of cross-border data transmission. The data conversion rule base is used to complete the standardized adaptation of heterogeneous data, improving the accuracy of cross-standard data interaction and adapting to the needs of diverse cross-border financial transaction scenarios. Accurate identification of single-stage and complex errors is achieved. Through feature matching, full-link trajectory tracing, and error correlation analysis, the root cause of errors is accurately located, and targeted rectification suggestions are output. Simultaneously, the combination of anomaly warning mechanisms and full-link traceability capabilities strengthens the risk control capabilities of cross-border financial data transmission, ensuring the security and stability of order consolidation-related financial data processing, and providing strong technical support for the smooth operation of cross-border financial transactions.

[0039] Example 1 describes a solution for pre-processing data in dedicated line transmission links. Specifically, it requires logical state matching and determination of multi-source dedicated line transmission data, and details the specific process of corresponding matching quantification. However, in actual cross-border fund transaction data transmission scenarios, in addition to dedicated line transmission links, there may also be application scenarios involving proxy relay transmission links. The transmission characteristics of proxy relay links differ from those of dedicated line transmission, and the original matching quantification process for dedicated line transmission links cannot meet the needs of this scenario. Therefore, to improve the data pre-processing capabilities under different transmission link scenarios and enhance the system's adaptability to diverse transmission links, a supplementary example, Example 2, corresponding to Example 1, is added, which describes the matching quantification process for pre-processing data in proxy relay links.

[0040] Example 2

[0041] The specific process of matching and quantification includes: targeted collection of data transmitted through the relay link using a preset data sampling window and sampling frequency. The preset data sampling window can be flexibly configured according to the transmission rate, data throughput, and real-time requirements of the proxy relay link. The sampling frequency must match the peak period of the link data transmission to ensure that the collected data fully covers the key states of the link transmission. The preset data sampling window and sampling frequency add a unique identifier to each transaction and associate it with a cross-window event chain, thus avoiding link breakage and data incompleteness issues. Through this collection method, a link field sequence containing multi-dimensional core fields can be obtained, typically presented in a key-value dictionary format. Specifically, it covers the link transmission time field sequence, the relay node identifier field sequence, and the data encapsulation format field sequence, forming a complete multi-dimensional link field sequence dataset.

[0042] Based on the multi-dimensional link field sequence obtained above, further quantitative calculations of field features are performed: for the link transmission time field sequence, the mean of transmission time data per unit time (i.e., field mean) is calculated to reflect the overall level of link transmission time, and the standard deviation of transmission time data is calculated to characterize the degree of fluctuation in transmission time; for the transit node identification field sequence, the mean and standard deviation of the field are calculated after the standardized encoding conversion of the identification information, so as to achieve accurate extraction of the core statistical features of each sequence of the multi-dimensional link field sequence, providing data support for the construction of subsequent link association criteria.

[0043] The system retrieves a pre-defined reference dataset for link fields in proxy relay scenarios. This dataset is based on a large amount of historical, normally transmitted proxy relay link data, and is calibrated in accordance with industry standards and business requirements. It includes reference mean values ​​for link fields that correspond one-to-one with each field sequence (such as reference mean value for link transmission time, reference mean value for relay node identifier encoding, and reference mean value for data encapsulation format encoding) and reference standard deviations for link fields (such as reference standard deviation for transmission time fluctuation, reference standard deviation for node identifier distribution, and reference standard deviation for encapsulation format type distribution), providing a unified benchmark for offset determination.

[0044] The actual field mean values ​​of each field sequence obtained above are subtracted from the preset reference mean values ​​of the corresponding link fields to obtain the link mean offset for each field dimension. This offset directly reflects the degree of deviation of the actual field mean from the benchmark mean. A positive offset indicates that the actual mean is higher than the benchmark, while a negative offset indicates that it is lower than the benchmark. On the other hand, the standard deviation offset is calculated simultaneously. The actual field standard deviation of each field sequence is subtracted from the preset reference standard deviation of the corresponding link field to obtain the link standard deviation offset, which characterizes the difference between the fluctuation level of the actual field data and the benchmark fluctuation level. By simultaneously calculating these two types of offsets, the deviation of the multi-dimensional link field sequence from the normal transmission benchmark state can be comprehensively quantified from two core dimensions: mean level and fluctuation amplitude.

[0045] The first association criterion is that the link mean offset is less than a preset link mean offset threshold, and the second association criterion is that the link standard deviation offset is less than a preset link standard deviation offset threshold. Both the preset link mean offset threshold and the preset link standard deviation offset threshold are set comprehensively based on the cross-border transit transmission quality standards and business fault tolerance requirements of the proxy transit link. The first association criterion and the second association criterion are used as the data association criterion set for the proxy transit link. Only when both of the above two association criteria are satisfied can the link be determined as a valid correspondence. On this basis, the link logical state matching judgment is further completed by combining the data compliance verification results: if the compliance logical state identifier of each proxy transit link data is 1 at this time, it is determined that the link logical state matches and a collaborative dataset is constructed; otherwise, it is determined that the link logical state does not match and a proxy transit link anomaly prompt is issued. The anomaly prompt can be specifically: some data in the current proxy transit link transmission data has a compliance logical state identifier of 0, which is suspected to be a problem of data tampering or non-standard format of the transit node. Please check the running status of the transit node and the integrity of the transmitted data.

[0046] The collaborative dataset construction process involves hierarchically classifying compliant and link-matched proxy relay transmission data based on two grouping dimensions: the processing order of relay nodes and the data transmission encapsulation format. The processing order of relay nodes is determined according to the actual data flow path of the proxy relay link, i.e., the hierarchy is divided according to the encoding order of the relay nodes the data passes through (e.g., node A → node B → node C). The data transmission encapsulation format is classified according to a preset encapsulation format classification standard (e.g., XML format, JSON format, fixed-length message format, etc.). This results in a set of proxy relay transmission data groups with the same transmission subject and encapsulation format, ensuring that data within the same group has a consistent flow trajectory and data format foundation.

[0047] Obtain the multi-source relay link serial numbers within each proxy relay data packet set. Calculate the uniqueness matching rate using a serial number uniqueness verification algorithm (such as hash deduplication and duplicate identifier statistics). This parameter characterizes the consistency of multi-source data serial numbers within the same packet. Uniqueness matching rate = number of unique serial numbers / total number of serial numbers within the packet × 100%. Simultaneously summarize two types of core consistency parameters: first, the proxy relay verification consistency rate, which is the percentage of data within the packet that passes the proxy node verification rules (such as data signature verification and transmission integrity verification); second, the link encapsulation format consistency rate, which is the percentage of data within the packet whose actual encapsulation format matches the preset encapsulation format standard.

[0048] The three parameters mentioned above—uniqueness matching rate, proxy relay verification consistency rate, and link encapsulation format consistency rate—are normalized (mapping each parameter value to the [0,1] interval) to eliminate the dimensional differences between different parameter dimensions. Then, the arithmetic mean is calculated through an equal weight allocation method to finally obtain the proxy link compatibility parameter, which comprehensively reflects the collaborative adaptation level of data within the group.

[0049] The specific logic for the equal weight allocation is as follows: based on the principle of equal importance of the three parameters in the data collaboration and adaptation of the proxy transit link, the weight value of each parameter is uniformly set to 1 / 3. This weight setting needs to be verified and calibrated through multiple rounds of historical data: first, select historical valid proxy transit transmission data packets under different business scenarios (such as cross-border e-commerce small-amount high-frequency transactions, cross-border settlement of bulk commodities, etc.), and calculate the interoperability parameters using equal weights (1 / 3, 1 / 3, 1 / 3) and differentiated weights (such as 0.4, 0.3, 0.3); then, conduct correlation analysis between the calculation results under different weight schemes and the actual data collaboration application effect (such as the subsequent cross-standard adaptation success rate, error tracing accuracy), and verify that the equal weight allocation scheme can more evenly take into account the data uniqueness, verification compliance and format uniformity in most proxy transit link scenarios, and can most realistically reflect the comprehensive collaborative adaptation level of the grouped data. Therefore, it is determined as the default weight setting. Meanwhile, to adapt to the needs of special business scenarios, a dynamic weight adjustment interface is reserved. Technical operation and maintenance personnel can fine-tune the weight of a single parameter in the range of [0.2, 0.5] according to the priority requirements of specific businesses (such as increasing the weight of the consistency rate of the proxy transfer verification in high security scenarios), and the sum of the weights of the three parameters always remains at 1.

[0050] The system retrieves a preset proxy link compatibility threshold (this threshold is calibrated based on the business data collaboration accuracy requirements of historical valid collaborative data groups). If the proxy link compatibility parameter of a certain group is less than this threshold, the transmitted data group is marked as a group to be verified through relay, and a manual review or secondary data verification process needs to be initiated subsequently. If the proxy link compatibility parameter is not less than the preset threshold, the group is determined to be a valid compatibility group. The data of all valid compatibility groups is aggregated, and after data purification processing using a redundancy information removal algorithm (such as removing duplicate data, invalid redundant fields, duplicate verification records, etc.), the collaborative dataset of the proxy relay link is finally formed.

[0051] In Example 2, by collecting multi-dimensional field sequences and quantifying features, combined with two-dimensional offset analysis of mean and standard deviation, a comprehensive and accurate characterization of the transmission status of the proxy relay link is achieved, providing reliable data support for link matching judgment. Based on a rigorous judgment logic using dual-association criteria, both link transmission status and data compliance are considered, improving the accuracy of link logical status matching and reducing false positives and false negatives. In the construction of the collaborative dataset, dual-dimensional grouping ensures data homogeneity and format consistency, while multi-parameter weighted calculation and dynamic weight adjustment mechanisms balance the balance of data collaboration and adaptation with scenario flexibility, ultimately outputting a high-quality collaborative dataset. The overall solution improves the standardization and reliability of the pre-processing of proxy relay link data, ensuring the safe and efficient processing of cross-border fund transaction proxy relay link data.

[0052] Based on the completion of the collaborative dataset construction in Examples 1 and 2, in order to achieve unified subsequent processing of the two types of link collaborative data, it is necessary to obtain standardized adaptation data through a unified cross-standard data adaptation process. The specific acquisition method is as follows:

[0053] Based on the preset data transformation rules in the data transformation rule base, the heterogeneous data of leased lines / agents in the collaborative dataset is parsed and normalized to obtain field difference statistics. The field difference statistics represent the number of business fields that do not meet the adaptation requirements after parsing and transformation (the total number of core business and auxiliary information fields explicitly required in the preset standard business field list for cross-border fund transactions; for example, if the standard business field list includes 15 fields such as payer code, payee country, and transaction amount, then the number of business fields is 15), and the proportion of the total number of business fields that need to be adapted in the cross-border settlement scenario. If the obtained field difference statistics are not greater than the preset values ​​in the data transformation rule base, the data transformation process will be considered successful. If the field difference statistics are met, the data is deemed acceptable, and the acceptable heterogeneous data is summarized and recorded as standardized adapted data for error root cause localization. Otherwise, the corresponding heterogeneous data is marked as abnormal, and the corresponding rule verification process is triggered: the system automatically generates an abnormal data verification work order, which includes the source link of the abnormal data (dedicated line / proxy relay), data identifier, list of non-compliant fields, and difference details; the work order is pushed to both technical operation and maintenance and business review positions, the operation and maintenance personnel check the adaptability of the data conversion rules, and the business personnel review the rationality of the field adaptation standards; the verification and review results are fed back to the data reporting entity for manual intervention and correction, and the cross-standard data adaptation process is re-executed after correction.

[0054] The preset field difference statistics are represented by the summation and averaging of historical field difference statistics during the historical cross-standard data adaptation process. Before calculating the mean of the historical field difference statistics, the quartile method is used according to the abnormal data removal operation rules to eliminate the influence of extreme values ​​caused by system data transmission errors. Similarly, other preset values ​​obtained based on the historical data mean in this embodiment of the invention, including but not limited to the preset reference standard deviation of the corresponding link field, must be subjected to abnormal data removal to ensure the rationality and stability of the preset value setting. Heterogeneous data in dedicated lines / agent relays refers to non-uniform format data from different transmission links in the collaborative dataset. Specifically, it includes data that conforms to SWIFT, CHIPS and other protocol specifications, such as transaction flow data of third-party payment institutions and order data of cross-border e-commerce platforms in dedicated line transmission scenarios, as well as cross-border fund transaction related data with different encapsulation formats (XML / JSON, etc.) that are transferred through multiple nodes in agent relay scenarios. Its core characteristics are that there are differences in field definitions, format specifications and data dimensions, which cannot be directly used for subsequent unified processing.

[0055] In this embodiment, precise statistical values ​​of field differences are obtained through explicit field validation logic, and judgment criteria are set in conjunction with historical averages, thereby improving the objectivity and reliability of suitability determination. Qualified data is aggregated to generate standardized adaptation data, providing a unified and high-quality data foundation for subsequent error root cause localization. The closed-loop validation process for abnormal data (dual-position verification, source correction, and re-adaptation) ensures the integrity and accuracy of data adaptation, reduces the impact of data anomalies on subsequent business operations, and overall improves the standardization and reliability of cross-border fund data processing.

[0056] In summary, this invention constructs an error tracing system. By adapting a collaborative dataset construction scheme to both dedicated lines and proxy relay links, and combining it with a unified cross-standard data adaptation process, heterogeneous data standardization is achieved. Relying on a full-chain error tracing mechanism, various transmission errors are accurately located. This ensures data compliance and transmission security, providing reliable technical support for cross-border financial transactions.

[0057] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0058] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0059] In various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0060] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0061] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An error tracing data processing system, characterized in that, The system includes: The multi-entity data collaboration module is used to construct a fully traceable collaborative dataset through link logic status matching during the cross-border transmission of multi-source financial data. The cross-standard data adaptation module is used to parse and transform heterogeneous data in the collaborative dataset with the established data transformation rule base to obtain standardized adapted data; The full-chain error tracing module is used to identify transmission errors in a single data interaction link and / or compound transmission errors in the superposition of multiple data flow states during the cross-link transmission and multi-node flow of standardized and adapted data, and to locate the root cause of errors through error tracing data processing. The process of constructing the collaborative dataset is as follows: The leased line transmission data that is determined to match the link logical state is grouped and classified according to the link protocol encoding of the leased line transmission and the data transmission object to obtain a leased line transmission data group set with the same transmission subject and protocol type. Obtain the timestamps of each transmission link of the corresponding dedicated line transmission data within the dedicated line transmission data packet set, calculate the maximum transmission delay difference and mean deviation of the corresponding transmission link timestamp, obtain the link timing consistency deviation, synchronously summarize the dedicated line transmission data whose link timing consistency deviation is less than the corresponding preset allowable deviation, as well as the link field matching completeness rate and dedicated line data compliance rate of the dedicated line transmission data, and calculate the arithmetic mean to obtain the dedicated line link interoperability parameter. If the obtained leased link interoperability parameter is less than the preset leased link interoperability parameter, then mark the corresponding leased line transmission data group as the group to be verified by the link. Otherwise, the leased line transmission data group corresponding to the leased line transmission data group set is determined as a valid inter-match group, and all valid inter-match groups are summarized. After removing redundant information, the collaborative dataset of the leased line transmission link is obtained.

2. The error tracing data processing system as described in claim 1, characterized in that, The link logical state matching process includes the following steps: Perform field-level comparison on the preprocessed multi-source funding data to obtain the field mapping status of the multi-source funding data; The compliance logic status of fund transactions in each data source in the field mapping status is quantified into binary identifiers, with compliance logic status identified as 1 and non-compliance logic status identified as 0. The matching metric is quantified by combining binary identifiers.

3. The error tracing data processing system as described in claim 2, characterized in that, When performing preprocessing of leased line transmission link data, and when it is necessary to perform logical state matching and determination on multi-source leased line transmission data, the specific process of matching quantification includes: Obtain the link protocol code corresponding to the current leased line transmission task, and use it as the unique query key to retrieve the field mapping rule set from the preset link protocol-transmission field mapping rule table; Based on the field mapping rule set, the preprocessed leased line transmission data is subjected to field matching degree calculation to obtain the link field matching degree parameter; The link field matching degree parameter is obtained by combining the link weight coefficient corresponding to the leased line transmission field, the transmission matching status value between the actual value of each leased line transmission field and the standard value of the link field, and the total number of leased line transmission fields, and then processing it with a coupled average algorithm. If the link field matching degree parameter is not less than the preset link field matching degree parameter, and the compliance logic status identifier of the leased line transmission data reported by each leased line data source is 1, then it is determined that the link logic status matches and a collaborative dataset is constructed; otherwise, it is determined that the link logic status does not match and a leased line transmission anomaly warning is triggered.

4. The error tracing data processing system as described in claim 2, characterized in that, When performing preprocessing of data in a proxy relay link, and when logical state matching and determination are required for multi-source proxy relay transmission data, the specific process of matching quantification includes: Data transmission data of the relay link is collected with a preset data sampling window and sampling frequency to obtain a multi-dimensional link field sequence including the link transmission time field sequence, the relay node identification field sequence and the data encapsulation format field sequence. The mean and standard deviation of each sequence in the multi-dimensional link field sequence are calculated. The mean of each field sequence is compared with the preset reference mean of the link field in the proxy relay link scenario, and the absolute value is taken to obtain the link mean offset. At the same time, the standard deviation of each field sequence is compared with the preset reference standard deviation of the link field in the proxy relay link scenario, and the absolute value is taken to obtain the link standard deviation offset. The first association criterion is that the link mean offset is less than the preset link mean offset threshold, and the second association criterion is that the link standard deviation offset is less than the preset link standard deviation offset threshold. The first association criterion and the second association criterion are used as the set of association criteria for proxy relay link data. If and only if the above two association criteria are satisfied at the same time, it is determined that the link has a valid correspondence. If the compliance logic status identifier of each proxy relay link data is 1 at this time, it is determined that the link logic status matches and the collaborative dataset construction process is started. Otherwise, it is determined that the link logic status does not match and the proxy relay link is prompted with an abnormality.

5. The error tracing data processing system as described in claim 1, characterized in that, The process of constructing the collaborative dataset is as follows: The proxy relay transmission data that is determined to match the link logical state is grouped and classified according to the processing order of the relay node and the data transmission encapsulation format to obtain a proxy relay transmission data group set with the same transmission subject and the same encapsulation format; The uniqueness matching rate of the serial number of the multi-source relay link within each agent relay transmission data packet set is obtained. The relay transmission data with a uniqueness matching rate greater than the corresponding preset matching rate are summarized synchronously, as well as the agent relay verification consistency rate and link encapsulation format consistency rate of the relay transmission data. After normalization processing, the arithmetic mean is calculated to obtain the agent link interoperability parameter. If the proxy link compatibility parameter is less than the preset proxy link compatibility threshold, then the corresponding data transmission group is marked as a group to be relayed and verified. Otherwise, the transmission data groups corresponding to the proxy relay transmission data group set are determined as valid matching groups, and all valid matching groups are summarized. After removing redundant information, the collaborative dataset of the proxy relay link is obtained.

6. The error tracing data processing system as described in claim 5, characterized in that, The standardized adaptation data is obtained in the following way: Based on the preset data transformation rules in the data transformation rule library, the heterogeneous data of leased / agent transit in the collaborative dataset is parsed and format normalized to obtain statistical values ​​of field differences. The field difference statistics represent the number of business fields that do not meet the adaptation requirements after parsing and conversion, and the proportion of the total number of business fields that need to be adapted in the cross-border settlement scenario. The obtained field difference statistics are compared with the preset field difference statistics in the data transformation rule base to obtain the cross-standard suitability results. If the cross-standard fit test result is qualified, the corresponding heterogeneous data will be summarized and recorded as standardized fit data, and the root cause of the error will be located through data linkage analysis. Otherwise, the corresponding heterogeneous data will be marked as abnormal, and the corresponding rule verification process will be triggered.

7. The error tracing data processing system as described in claim 1, characterized in that, When a transmission error is detected in a single link data interaction node, the specific implementation process for error root cause localization is as follows: Retrieve a preset link node transmission error feature library and synchronously extract the transmission data fragments of the link interaction nodes corresponding to the transmission errors. The transmission data fragments contain a structured data set of link-related transmission data items. The link-related transmission data items in the transmission data segment are subjected to differential quantization encoding and normalization processing with the preset transmission data items to generate a link transmission error feature data matrix. This matrix is ​​then compared with the standard feature data template in the link node transmission error feature library to filter out the link transmission deviation data items. Based on the link transmission deviation data item, trace the transmission data processing log of the corresponding link interaction node and output the corresponding link transmission error root cause location report.

8. The error tracing data processing system as described in claim 1, characterized in that, When a complex transmission error is identified in a state of overlapping data flow across multiple link nodes, the specific implementation process for error root cause localization is as follows: Using the standardized adaptation data corresponding to composite transmission errors as an index, we trace the transmission data processing logs of each node in the full-link flow graph, extract the link flow timestamps, transmission rule call identifiers and link data verification status codes to form a full-link transmission data trajectory dataset. Calculate the deviation value of the transmission field between adjacent link nodes in the whole link transmission data trajectory dataset, and filter out the transmission nodes whose deviation value exceeds the corresponding preset value for the first time; Retrieve historical transmission data processing records, transmission rule configuration change logs, and link transmission anomaly alarm data from each transmission node. Combine the deviation values ​​with the transmission path between multiple link nodes to output the corresponding link transmission error root cause location report.

9. The error tracing data processing system as described in claim 1, characterized in that, When a transmission error at a single link data interaction node is detected, coexisting with a composite transmission error in the superposition of data flow across multiple link nodes, the specific implementation process for error root cause localization is as follows: Classify and label link node transmission errors and composite transmission errors, extract the transmission data segments corresponding to link node transmission errors and the full-link transmission data trajectory datasets corresponding to composite transmission errors, and establish a link error association index using the unique identifier of the transmission data corresponding to the two types of errors. Based on the end-to-end flow map, locate the link interaction node to which the transmission error belongs, and determine whether the node belongs to the end-to-end flow node range corresponding to the composite transmission error: If so, the transmission deviation data item corresponding to the link node transmission error is taken as the initial deviation candidate feature of the composite transmission error, and substituted into the whole link deviation transmission path analysis of the composite transmission error to verify whether the link node transmission error is the initial cause of the composite transmission error. If not, then the independent tracing logic for single link node transmission errors and composite transmission errors will be used to simultaneously locate the root cause of both types of errors. If the link node transmission error is verified to be the initial cause of a composite transmission error, then the node interaction logs corresponding to the two types of errors are summarized. If it is verified that the error is not the initial cause or that the two types of errors exist independently, then the link transmission formation trajectory of the two types of errors is traced separately to determine the root cause of their respective core transmission errors and the scope of their impact on the link. Summarize the above tracing analysis results and the conclusions on the correlation between link errors, and output the corresponding root cause localization report for the entire link transmission error.

Images