An archive classification and archiving management method based on digital economy and data governance

Abstract

The application provides an archive classification and archiving management method based on digital economy and data governance, and relates to the technical field of computer data processing, wherein the method comprises: 101, establishing a standardized system; 102, constructing a unified data collection platform; 103, data auditing and returning processing; 104, data transfer and archiving; 105, multi-dimensional utilization of data. The application realizes the standardized integration of all business data, eliminates heterogeneous chaos, and radically solves the data island problem; the automatic collection verification + manual auditing + intelligent return closed loop ensures the integrity and availability and guarantees the quality of the data throughout the life cycle; secondly, through intensive storage and multi-dimensional analysis, the application supports transaction trend prediction, archiving monitoring and business decision-making, directly drives enterprise data governance and digital economic transformation, and empowers data value release.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a method for classifying and archiving archives based on digital economy and data governance. Background Technology

[0002] In the field of public resource transactions, transaction groups face challenges such as complex organizational structures, numerous categories of transaction resources, fragmented transaction systems, and overlapping regional businesses. This results in data being scattered across various business platforms, subsidiaries, and functional departments, creating data silos. This limits managers to a partial view of the data, hindering centralized management, comprehensive governance, and effective utilization. Furthermore, due to differences in transaction categories (such as procurement lots, equity products, and property leasing targets), the metadata item settings across different business systems are inconsistent, severely impeding cross-category data management and value extraction. In addition, existing business systems generally lack long-term retention mechanisms, leaving only paper or fragmented electronic documents after many years, while key structured data from the transaction process is often incomplete or lost, constituting significant losses to the company's data governance and digitalization process. Summary of the Invention

[0003] One of the objectives of this invention is to provide a method for classifying and archiving archives based on the digital economy and data governance, in order to solve the problems mentioned in the background art.

[0004] This invention provides a method for classifying and archiving archives based on digital economy and data governance, comprising:

[0005] 101. Establish a standardized system: Based on the categories of public resource transactions, business data, and organizational structure, formulate standards for business metadata, standards for the scope of business electronic archives, and standards for the relationship between business and organization;

[0006] 102. Construct a unified data acquisition platform: Based on the aforementioned standard, configure the hierarchical relationship of the organizational structure and business classification, as well as business metadata. Connect to upstream business systems through a dynamically configurable RESTful interface to achieve automated acquisition and preliminary verification of business data.

[0007] 103. Data Auditing and Return Processing: Manually audit verified data. Once the audit is passed, the data is marked as confirmed receipt. If the audit fails, the data is automatically returned to the upstream system via a message queue, and a return log is recorded.

[0008] 104. Data Transfer and Archiving: Directly transfer the confirmed received data to the archive system, generate a unique file number, and store it in a centralized manner according to the organizational structure and business classification;

[0009] 105. Multidimensional Data Utilization: Based on the tree-like organizational structure and business classification of the archive system, it provides multidimensional combined query, timeline analysis, data comparison analysis, and archiving work statistics functions.

[0010] Optionally, the business metadata standard includes:

[0011] Common metadata: unified fields across transaction categories;

[0012] Business characteristic metadata: exclusive fields for specific transaction categories.

[0013] Optionally, the construction of the unified data acquisition platform in step 102 includes:

[0014] Establish a tree-like organizational structure with the group as the root node, subsidiaries as second-level nodes, and specific business categories as third-level nodes;

[0015] Configure metadata name, type, and length for each service;

[0016] Generate RESTful interfaces based on business metadata and dynamically configure interface parameters, including the range of data to be collected, IP whitelist, and TOKEN verification method.

[0017] Dynamic integration with upstream business systems is achieved through the management of interface publishing, deactivation, and activation.

[0018] Optionally, the data auditing and return process described in step 103 includes:

[0019] Record the reasons for data that fails the audit and mark it as pending return.

[0020] Submit feedback to the business system via message queue. If the interface access fails, retry is delayed. After three failures, the process is suspended and the auditor is notified.

[0021] Once the return is successful, the business data is marked as returned.

[0022] Optionally, the intensive storage described in step 104 includes:

[0023] Link business data to a tree-like organizational structure and business category nodes;

[0024] Supports combined queries by file number, organizational level, business category, and time range;

[0025] It stores structured data, unstructured data entries, and processing logs, and supports layout file browsing.

[0026] Optionally, the multi-dimensional combined query in step 105 includes at least one of the following:

[0027] Transaction volume and value are categorized by organizational structure, business type, and geographical region.

[0028] Analyze trading trends using annual / quarterly / monthly timelines;

[0029] Comparative analysis of data based on year-on-year and month-on-month comparisons;

[0030] Archive progress is categorized by organization and business.

[0031] Optionally, the method further includes:

[0032] 106. Complex Custom Regret Prevention Assistance: When users define complex usage rules for the file system, the interpretability predicts the local rules within the complex usage rules that the user will regret in the future, and obtains the explanatory content corresponding to the local rules; based on the explanatory content, it determines multiple reasons why the user will regret the local rules in the future; and assists the user in understanding each reason as quickly as possible.

[0033] Optionally, the interpretability prediction of local rules in complex usage rules that indicate future user regret, and the acquisition of explanatory content corresponding to these local rules, includes:

[0034] Based on a pre-trained interpretability prediction model, the interpretability prediction model identifies local rules in complex usage rules that users will regret in the future, and determines the interpretability content corresponding to the local rules from the complete interpretability content output by the interpretability prediction model during the interpretability prediction process.

[0035] Optionally, the determination of multiple reasons why a user might regret a local rule in the future, based on explanatory content, includes:

[0036] Based on a preset reason extraction template, multiple reasons why users might regret certain rules in the future are extracted from explanatory content.

[0037] Optionally, the method to help users understand the reasons as quickly as possible includes:

[0038] For each reason, a preset understanding demonstration material and a corresponding preset demonstration rhythm control axis are matched for that reason;

[0039] For each set of understanding load overload points in the same order in the demonstration rhythm control axis matched by each cause, the rhythm of the demonstration rhythm control axis matched by each cause is compressed or slowed down so that the understanding load overload points in the same order reach the spatiotemporal misalignment condition.

[0040] Based on the final presentation rhythm control axes after compression or slowing down, the corresponding presentation materials are presented to the user in sync with the corresponding understanding of the presentation materials.

[0041] The present invention has achieved the following beneficial effects:

[0042] By establishing a standardized system (unifying business metadata, archiving scope, and organizational relationships), a configurable data acquisition platform is built to achieve automated collection and verification. Manual auditing and message queue return mechanisms are introduced to ensure data quality. A direct connection to the transfer archive system generates unique file numbers and stores them centrally, ultimately providing multi-dimensional analysis functions. This achieves standardized integration of all business data, eliminating heterogeneity and data silos. The automated collection and verification + manual auditing + intelligent return closed loop ensures integrity, availability, and data quality throughout its entire lifecycle. Furthermore, through centralized storage and multi-dimensional analysis (organizational / business / time / regional penetration), it supports transaction trend prediction, archive monitoring, and business decision-making, directly driving enterprise data governance and digital economy transformation, and empowering the release of data value.

[0043] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a schematic diagram of an archive classification and archiving management method based on digital economy and data governance in an embodiment of the present invention;

[0047] Figure 2 This is a structural diagram of the product management system in an embodiment of the present invention;

[0048] Figure 3 This is an example diagram of a product data acquisition platform in an embodiment of the present invention. Detailed Implementation

[0049] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0050] Figure 1 This application provides a flowchart of a method for classifying and archiving archives based on digital economy and data governance, as shown in the embodiments of this application. Figures 1 to 2 As shown, the method includes:

[0051] 101. Establish a standardized system: Based on the categories of public resource transactions, business data, and organizational structure, formulate standards for business metadata, standards for the scope of business electronic archives, and standards for the relationship between business and organization.

[0052] In this step, three core standards are established by analyzing public resource transaction categories, business data, and organizational structure:

[0053] Business metadata standards: Define common metadata (such as transaction time and amount) and business-specific metadata (such as procurement sections and equity products) to unify the data structure.

[0054] Standards for the scope of electronic business records: Clearly define the transaction process materials (such as contracts and tender documents) that need to be archived.

[0055] Business and Organizational Relationship Standards: Standardize the business affiliation between the group and its subsidiaries.

[0056] 102. Build a unified data acquisition platform: Based on the aforementioned standards, configure the hierarchical relationship of organizational structure and business classification, as well as business metadata. Connect to upstream business systems through dynamically configurable RESTful interfaces to achieve automated acquisition and preliminary verification of business data.

[0057] 103. Data Audit and Return Processing: Manually audit verified data. Once the audit is passed, the data is marked as confirmed receipt. If the audit fails, the data is automatically returned to the upstream system via a message queue, and a return log is recorded.

[0058] 104. Data Transfer and Archiving: Directly transfer the confirmed received data to the archive system, generate a unique file number, and store it in a centralized manner according to the organizational structure and business classification.

[0059] 105. Multidimensional Data Utilization: Based on the tree-like organizational structure and business classification of the archive system, it provides multidimensional combined query, timeline analysis, data comparison analysis, and archiving work statistics functions.

[0060] In summary, steps 101 to 105 establish a standardized system (unifying business metadata, archiving scope, and organizational relationships), construct a configurable data acquisition platform to achieve automated data collection and verification, introduce manual auditing and message queue return mechanisms to ensure data quality, directly connect to the transfer archive system to generate unique file numbers and centrally store them, and ultimately provide multi-dimensional analysis functions. This achieves standardized integration of all business data, eliminating heterogeneous chaos and eradicating data silos; the automated collection and verification + manual auditing + intelligent return closed loop ensures integrity and availability, guaranteeing data quality throughout its entire lifecycle; furthermore, through centralized storage and multi-dimensional analysis, it supports transaction trend prediction, archive monitoring, and business decision-making, directly driving enterprise data governance and digital economy transformation, and empowering the release of data value.

[0061] In some embodiments, the business metadata standard includes:

[0062] Common metadata: Unified fields across transaction categories. Common metadata defines common fields (such as transaction number, creation time, transaction amount, and participant name) across all transaction categories to ensure data consistency.

[0063] Business characteristic metadata: Dedicated fields for specific transaction categories. Business characteristic metadata: Customized fields for specific transaction categories (such as the bid section number for procurement, the product code for equity, and the target area for property leasing), which can be adapted to business differences through extensible configuration.

[0064] Overall, common fields are standardized to eliminate data ambiguity and reduce integration complexity.

[0065] In some embodiments, such as Figure 3 As shown, the construction of the unified data acquisition platform in step 102 includes:

[0066] Establish a tree-like organizational structure with the group as the root node, subsidiaries as secondary nodes, and specific business categories as tertiary nodes.

[0067] In this step, for example: root node = group headquarters, second-level node = subsidiary (such as branch office), third-level node = business category (such as procurement - engineering construction).

[0068] Configure metadata name, type, and length for each service.

[0069] In this step, for example, you can configure metadata attributes (such as type=character, length=50) for each third-level node service.

[0070] Generate RESTful interfaces based on business metadata and dynamically configure interface parameters, including the range of data to be collected, IP whitelist, and TOKEN verification method.

[0071] This step includes, for example, automatically creating a RESTful interface and configuring the collection scope (such as contract number and amount), IP whitelist (such as 192.168.1.0 / 24), and TOKEN verification (such as HMAC-SHA256).

[0072] Dynamic integration with upstream business systems is achieved through the management of interface publishing, deactivation, and activation.

[0073] In this step, the publish / deactivate / enable operations are synchronized to the upstream system (such as SAP, Yonyou) in real time.

[0074] Overall, the tree architecture supports the dynamic addition and deletion of subsidiaries, shortens the response time for business adjustments, reduces the development cycle of interface configuration, and improves integration efficiency.

[0075] In some embodiments, the data auditing and rollback process in step 103 includes:

[0076] For data that fails the audit, record the reason for return and mark it as pending return.

[0077] In this step, dedicated personnel verify the compliance of the data that has passed the initial verification (such as the completeness of contract terms), and mark the data as confirmed and received.

[0078] Feedback is submitted to the business system via message queue. If the interface access fails, a retry is delayed. After three failures, the process is suspended and the auditors are notified.

[0079] In this step, the reason for the problem data is recorded (e.g., missing amount), and a pending return status is marked. A message queue (such as RabbitMQ) automatically pushes the return information (including data ID and reason) to the business system. Automatic retry is performed when the interface fails (e.g., at 5-minute intervals), and the system is suspended and an auditor is notified via email after three consecutive failures.

[0080] Once the return is successful, the business data is marked as returned.

[0081] In this step, the status is updated to "returned successfully" and the log records the operator and timestamp.

[0082] Overall, manual auditing can intercept issues missed by automatic verification, while message queues ensure a high success rate for data return and improve the timeliness of data correction.

[0083] In some embodiments, the intensive storage described in step 104 includes:

[0084] Link business data to a tree-like organizational structure and business category nodes.

[0085] In this step, business data is bound to tree nodes (such as branch office - procurement - bid section A).

[0086] It supports combined queries by file number, organizational level, business category, and time range.

[0087] It stores structured data, unstructured data entries, and processing logs, and supports layout file browsing.

[0088] In this step, structured data (database storage) + unstructured data (distributed file system) + processing logs (such as reception time, auditor ID); layout files (PDF / OFD) support online rendering and viewing.

[0089] Overall, tree-like associations enable full data query responses, save on hybrid storage hardware costs, and improve the efficiency of unstructured data retrieval.

[0090] In some embodiments, the multi-dimensional combined query in step 105 includes at least one of the following:

[0091] The number and value of transactions are statistically analyzed by organizational structure, business category, and geographical region.

[0092] This step, for example, involves: categorizing transactions by subsidiaries, procurement categories, and the distribution of buyers and sellers according to Guangdong Province's statistics on transaction volume, amount, and parties.

[0093] Analyze trading trends using annual / quarterly / monthly timelines.

[0094] Compare and analyze data by year-on-year / month-on-month comparison.

[0095] Archive progress is categorized by organization and business.

[0096] The system provides multi-dimensional data retrieval, statistical analysis, and visualization capabilities. Firstly, users can precisely retrieve specific business data archives, browse detailed business data, and view data collection and processing logs. Through data analysis of data types, timelines, and comparisons (month-on-month / year-on-year, etc.), it provides data support for enterprise business decisions. Users can also analyze work performance using work data, providing data support for work supervision, management, and performance evaluation. These data processing and analysis capabilities provide essential and effective support for enterprise digital economy and data governance, empowering enterprise management.

[0097] In some embodiments, the method further includes:

[0098] 106. Complex custom anti-regret assistant:

[0099] 201. When a user defines complex usage rules for the profile system, interpretability predicts which local rules within the complex usage rules the user might regret in the future, and obtains the interpretive content corresponding to these local rules. Step 201 specifically includes:

[0100] Based on a pre-trained interpretability prediction model, the interpretability prediction model identifies local rules in complex usage rules that users will regret in the future, and determines the interpretability content corresponding to the local rules from the complete interpretability content output by the interpretability prediction model during the interpretability prediction process.

[0101] In this step, a pre-trained interpretability prediction model is used to analyze user-defined complex usage rules and predict local rules that might lead to future user regret. During the prediction process, the model outputs complete explanatory content, detailing why these local rules might cause regret. The pre-training process of the interpretability prediction model involves using historical data to teach the model to identify local rules that users might regret in a complex rule system. Specifically, the training input includes example data containing multiple complex rule sets, as well as records of users regretting certain rules in actual use. By analyzing this data, the model can learn which features or patterns in the rules are associated with the user's future regret. For interpretability, transparent machine learning techniques such as decision trees and rule base systems are used for training. After training, the model can not only predict local rules that might lead to regret but also generate detailed explanations explaining why these rules might cause regret.

[0102] Here's a simple example: Suppose a user sets a rule in their file system: all files containing a specific keyword will be automatically deleted after 30 days. An interpretability prediction model, after analysis, predicts that the user might regret this rule in the future because they have previously mistakenly deleted important files due to similar automatic deletion rules. The model's explanatory output is: Based on the user's historical behavior data, automatic deletion after 30 days may lead to the loss of important files, and the user has expressed regret over similar rules in the past.

[0103] 202. Based on the explanatory content, identify multiple reasons why users might regret certain local rules in the future. Step 202 specifically includes:

[0104] Based on a preset reason extraction template, multiple reasons why users might regret certain rules in the future are extracted from explanatory content.

[0105] In this step, based on a preset reason extraction template, specific reasons why users might regret their actions in the future are extracted from the explanatory content output by the interpretability prediction model. These reasons can be attributed to rules, user habits, or other relevant factors, and the reason extraction template can be set in advance as needed.

[0106] Continuing with the simplified example above: The explanatory content is: Based on user historical behavior data, automatic deletion after 30 days may result in the loss of important files, and users have expressed regret over similar rules. Using a preset reason extraction template, the specific reasons are extracted: users have accidentally deleted important files due to automatic deletion rules in the past, and the 30-day time period may not be sufficient for users to check their files in time.

[0107] 203. Assist users in quickly understanding each reason. Step 203 specifically includes:

[0108] 301. For each reason, match the reason with preset understanding demonstration materials and the corresponding preset demonstration rhythm control axis.

[0109] In this step, the understanding demonstration material refers to the material that enables users to understand a single reason through continuous material demonstrations, and its corresponding demonstration rhythm control axis is the time axis that controls the demonstration rhythm of the material content.

[0110] Continuing with the simplified example above: For reason 1 (the user accidentally deleted important files due to the automatic deletion rules in the past), the demo material is: an animation showing that after the user set automatic deletion, important files were deleted, causing other related files to also become unusable. The corresponding demo pacing is: the animation duration is 30 seconds, with two overload points (the moment the file is deleted T1=10 seconds, and the moment other related files become unusable T2=25 seconds).

[0111] For reason 2 (a 30-day timeframe may not be sufficient for users to check files in a timely manner), the demo material is: a timeline animation showing files piling up over 30 days, which users fail to check in time, and eventually the files are deleted.

[0112] The corresponding demonstration rhythm control axis: animation duration 40 seconds, with two understanding overload points set (file accumulation peak T1=15 seconds, file deletion T2=35 seconds).

[0113] 302. For each set of understanding load overload points in the same order in the demonstration rhythm control axis matched by each cause, the rhythm of the demonstration rhythm control axis matched by each cause is compressed or slowed down so that the understanding load overload points in the same order reach the spatiotemporal misalignment condition.

[0114] In this step, each presentation rhythm control axis pre-sets the rhythm points at which the presentation content will cause the user's cognitive overload. The cognitive overload points in each presentation rhythm control axis in the same order refer to the first cognitive overload point of each axis, the second cognitive overload point of each axis, the third cognitive overload point of each axis, and so on.

[0115] The spatiotemporal misalignment conditions include:

[0116] Condition 1: The understanding load overload points in the overload point cluster are spatiotemporally misaligned in descending order of their respective understanding aid effects, wherein the misalignment interval between any two adjacent spatiotemporally misaligned understanding load overload points exceeds a preset first interval threshold; the understanding load overload point in the overload point cluster is the understanding load overload point whose sum of understanding aid effects is closest to the preset effect threshold and whose number is less than the preset number among the understanding load overload points of the same order.

[0117] In this condition, each comprehension overload point is pre-set with a comprehension assistance effect. This effect refers to the effect that, upon reaching the comprehension overload point, if the user understands the content presented at that moment, it will lead to their understanding of the reason. Spatiotemporal misalignment refers to the fact that different comprehension overload points are positioned separately on their respective presentation rhythm control axes. The misalignment interval refers to the time interval between the positions of two comprehension overload points on their respective presentation rhythm control axes. The preset first interval threshold represents a relatively large interval, such as 20 seconds. The preset effect threshold represents a general, sequential arrival of multiple comprehension overload points, allowing the user to psychologically understand the local rules and the potential for future regret through sequential presentation of the content. The preset number represents a relatively small number of comprehension overload points. In condition one, the understanding load overload points whose sum of understanding aid effects is closest to the preset effect threshold and whose number is less than the preset number are included in the overload point cluster. The understanding load overload points are spatiotemporally misaligned in order of their respective understanding aid effects from largest to smallest. There is a large time interval between each pair of adjacent spatiotemporally misaligned understanding load overload points, which allows users enough time to understand the content of the material demonstration when these understanding load overload points arrive, thereby achieving the effect of psychologically understanding that the local rules will cause regret in the future.

[0118] Continuing with the simplified example above: Adjust the understanding overload points for each group in the same order (Group T1: 10 seconds for cause 1, 15 seconds for cause 2; Group T2: 25 seconds for cause 1, 35 seconds for cause 2). Assume the understanding assistance effect is preset to T1=8 (high) and T2=5 (medium) for cause 1, T1=6 (medium) and T2=7 (high) for cause 2, with an effect threshold of 15 and a number threshold of 3.

[0119] T1 group overload point cluster: Select the point with the effect and the closest 15 and the number less than 3. Only select the T1 of reason 1 (effect 8), and then the T1 of reason 2 (effect 6). Adjust the rhythm to T1 of reason 1 = 10 seconds, T1 of reason 2 = 30 seconds, and the interval is 20 seconds (greater than the first interval threshold of 20 seconds).

[0120] T2 group overload cluster: Select T2 of reason 2 (effect 7), followed by T2 of reason 1 (effect 5), and adjust the rhythm to T2 of reason 2 = 35 seconds, T2 of reason 1 = 55 seconds, with an interval of 20 seconds.

[0121] Condition 2: The load overload points outside the overload point cluster start from the interval of the last spatiotemporally misaligned load overload point in the overload point cluster, and are spatiotemporally misaligned in order of their respective understanding assistance effects from large to small. The misalignment interval between any two adjacent spatiotemporally misaligned load overload points does not exceed the preset second interval threshold.

[0122] In this condition, the preset second interval threshold can be 25 seconds. The preset second interval threshold represents a smaller interval, such as 8 seconds. After the user understands the content of the demonstration of load overload points within the overload point cluster, they will psychologically understand that the local rules will lead to future regrets. Subsequent load overload points can be quickly reached sequentially, and the user will not consciously pay attention to them. This improves the efficiency of assisting the user in understanding various causes.

[0123] Continuing with the simplified example above: Points outside the overloaded point cluster (none) require no further adjustment. 303. Based on the final demonstration rhythm control axes after rhythm compression or slowing, simultaneously demonstrate the corresponding materials to the user according to their understanding of the demonstration materials.

[0124] Different presentation rhythm control axes are synchronized to control the corresponding presentation materials, improving the efficiency of helping users understand the various reasons.

[0125] Continuing with the simplified example above:

[0126] Final rhythm: Reason 1 animation (T1=10 seconds, T2=55 seconds), Reason 2 animation (T1=30 seconds, T2=35 seconds).

[0127] Two animations are played simultaneously, and the user sees them in sequence: file deletion (10 seconds), file accumulation (30 seconds), file deletion (35 seconds), and other related files becoming unusable (55 seconds), with staggered timings to avoid information overlap.

[0128] By identifying potential regret rules through interpretability prediction models, extracting specific reasons, and utilizing demonstration materials and pacing control, the system helps users quickly understand them. This not only provides decision support when setting complex rules but also enhances user experience and system trust through an intuitive and efficient understanding process, ultimately achieving better rule design and usage results.

[0129] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for classifying and archiving archives based on digital economy and data governance, characterized in that, include:

101. Establish a standardized system: Based on the categories of public resource transactions, business data, and organizational structure, formulate standards for business metadata, standards for the scope of business electronic archives, and standards for the relationship between business and organization; 102. Construct a unified data acquisition platform: Based on the aforementioned standard, configure the hierarchical relationship of the organizational structure and business classification, as well as business metadata. Connect to upstream business systems through a dynamically configurable RESTful interface to achieve automated acquisition and preliminary verification of business data.

103. Data Auditing and Return Processing: Manually audit verified data. Once the audit is passed, the data is marked as confirmed receipt. If the audit fails, the data is automatically returned to the upstream system via a message queue, and a return log is recorded.

104. Data Transfer and Archiving: Directly transfer the confirmed received data to the archive system, generate a unique file number, and store it in a centralized manner according to the organizational structure and business classification; 105. Multidimensional Data Utilization: Based on the tree-like organizational structure and business classification of the archive system, it provides multidimensional combined query, timeline analysis, data comparison analysis and archiving work statistics functions; 106. Complex Custom Regret Prevention Assistance: When users define complex usage rules for the profile system, the interpretability predicts which local rules the user might regret in the future and obtains the explanatory content corresponding to the local rules; based on the explanatory content, it identifies multiple reasons why the user might regret the local rules in the future; and assists the user in understanding each reason as quickly as possible. The interpretability predicts local rules in complex usage rules that indicate future user regret, and obtains the explanatory content corresponding to these local rules, including: Based on a pre-trained interpretability prediction model, the interpretability prediction model can identify local rules in complex usage rules that users will regret in the future, and determine the interpretability content corresponding to the local rules from the complete interpretability content output by the interpretability prediction model during the interpretability prediction process. Based on explanatory content, the determination of multiple reasons why a user might regret a local rule in the future includes: Based on a preset reason extraction template, multiple reasons why users might regret certain rules in the future are extracted from explanatory content; The assistance provided to users in quickly understanding each reason includes: For each reason, a preset understanding demonstration material and a corresponding preset demonstration rhythm control axis are matched for that reason; For each set of understanding load overload points in the same order in the demonstration rhythm control axis matched by each cause, the rhythm of the demonstration rhythm control axis matched by each cause is compressed or slowed down so that the understanding load overload points in the same order reach the spatiotemporal misalignment condition. Based on the final presentation rhythm control axes after compression or slowing down, the corresponding presentation materials are presented to the user in sync with the corresponding understanding of the presentation materials.

2. The method as described in claim 1, characterized in that, The business metadata standards include: Common metadata: unified fields across transaction categories; Business characteristic metadata: Dedicated fields for each transaction category.

3. The method as described in claim 1, characterized in that, The construction of the unified data acquisition platform mentioned in step 102 includes: Establish a tree-like organizational structure with the group as the root node, subsidiaries as second-level nodes, and specific business categories as third-level nodes; Configure metadata name, type, and length for each service; Generate RESTful interfaces based on business metadata and dynamically configure interface parameters, including the range of data to be collected, IP whitelist, and TOKEN verification method. Dynamic integration with upstream business systems is achieved through the management of interface publishing, deactivation, and activation.

4. The method as described in claim 1, characterized in that, The data auditing and return process described in step 103 includes: Record the reasons for data that fails the audit and mark it as pending return. Submit feedback to the business system via message queue. If the interface access fails, retry is delayed. After three failures, the process is suspended and the auditor is notified. Once the return is successful, the business data is marked as returned.

5. The method as described in claim 1, characterized in that, The intensive storage mentioned in step 104 includes: Link business data to a tree-like organizational structure and business category nodes; Supports combined queries by file number, organizational level, business category, and time range; It stores structured data, unstructured data entries, and processing logs, and supports layout file browsing.

6. The method as described in claim 1, characterized in that, The multi-dimensional combined query in step 105 includes at least one of the following: Transaction volume and value are categorized by organizational structure, business type, and geographical region. Analyze trading trends using annual / quarterly / monthly timelines; Comparative analysis of data based on year-on-year and month-on-month comparisons; Archive progress is categorized by organization and business.

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