AI-based multi-source data fusion processing credential center management platform and method

Abstract

This application relates to the field of voucher management technology, and discloses a voucher center management platform and method based on AI-driven multi-source data fusion processing. The platform includes: a data fusion processing module for fusing and processing multi-source user appeal data to construct a unified voucher element library; an intelligent report generation module for automatically retrieving and populating data from the voucher element library based on templates to generate an initial appeal report; an intelligent auditing module for automatically auditing the report by combining rule comparison and AI analysis; and a feedback optimization module for analyzing the root causes of defects based on the audit results and driving adaptive optimization of the data association logic of the voucher element library and / or the report template logic. This method corresponds to the platform. Using this application, unified governance and efficient utilization of multi-source voucher data can be achieved, and high-quality compliance reports can be generated automatically and intelligently, solving the problems of data silos, low efficiency, and unstable quality in traditional manual processing modes.

Description

Technical Field

[0001] This application relates to the field of voucher management technology, specifically a voucher center management platform and method based on AI-based multi-source data fusion processing. Background Technology

[0002] In telecommunications, finance, and other sectors, processing user complaints and generating standardized complaint reports is a critical and demanding task. Currently, relevant business data is typically scattered across multiple independent and heterogeneous business systems (such as CRM and customer service systems), creating data silos. When compiling reports, personnel must manually search, filter, and piece together various original business vouchers and data from these multiple independent and heterogeneous systems—a time-consuming, labor-intensive, and error-prone process. Furthermore, the format and quality of these reports heavily rely on individual experience, lacking standardized criteria, resulting in inconsistent report quality. Compliance audits also primarily depend on manual, item-by-item checks, which is inefficient and difficult to guarantee accuracy.

[0003] Therefore, existing technologies present a core problem: how to efficiently and automatically integrate and govern the credential data used as the basis for reporting from dispersed, multi-source business systems, and intelligently generate high-quality, compliant, and standardized reports, while simultaneously achieving automated review and continuous optimization of report quality. Summary of the Invention

[0004] Therefore, it is necessary to provide an AI-based multi-source data fusion processing voucher center management platform and method that can solve the problems of low efficiency, unstable quality and difficulty in review under the manual processing mode, in order to address the above-mentioned technical issues.

[0005] On the one hand, this application provides a certificate center management platform based on AI-driven multi-source data fusion processing, the platform comprising: The data fusion and processing module is configured to access multiple heterogeneous business systems, collect user complaint-related data scattered across these systems, and clean and standardize the collected data to build a unified credential element library. The intelligent report generation module is configured to access a pre-set standardized report template library, respond to report generation requests, retrieve data elements from the voucher element library and fill them into the corresponding positions of the selected report template to generate an initial appeal report; The intelligent review module is configured to automatically audit the initial appeal report. The automated audit includes comparing the report content with a pre-set business rule base to output error indicators, and performing AI analysis on the report text to identify logical contradictions and potential risk points. The feedback optimization module is configured to analyze the root causes of report defects based on the automated audit results output by the intelligent audit module, and drive the data association logic of the voucher element library and / or the template logic of the standardized report template library to perform adaptive optimization.

[0006] In one embodiment, the data fusion processing module includes: The entity merging unit is configured to identify and extract core business entities from different business data through a recognition model pre-trained based on domain knowledge, and semantically merge heterogeneous representations pointing to the same real object to form standardized entities; The relationship building unit is configured to establish relationships between standardized entities after merging based on business rules and data co-occurrence analysis, and to assign a quantitative relationship confidence level to each relationship in order to form the voucher element library.

[0007] In one embodiment, the association confidence level is calculated through the following process: Three aspects of evidence were obtained, including the semantic relevance of entity pairs calculated based on a pre-trained language model. The frequency of co-occurrence of the entity pairs across systems in historical complaint data. And relationship existence scoring based on a predefined business rule base. ; The three pieces of evidence obtained are weighted and fused, and then normalized using the Sigmoid function to obtain the association confidence level. : in, , and This is a dynamic adjustment coefficient used to balance different evidence weights.

[0008] In one embodiment, the intelligent report generation module includes a template adaptation unit, which is configured to evaluate the fill support of the currently available data set for the selected report template when retrieving data elements from the voucher element library; if the support is lower than a preset threshold, the template is dynamically adjusted to generate a derived template adapted to the current data situation by combining preset template components to complete the generation of the initial appeal report.

[0009] In one embodiment, the fill support is calculated through the following process: Calculate the data completeness score: ,in The total number of key data elements required by the selected report template; For the first Preset weights for key data elements; It is an indicator function, and when the element The value is 1 if the request is successfully made, and 0 otherwise. Penalty for computational logic uncertainty: ,in This represents a set of template logic judgments where the execution path cannot be determined due to missing key data. Indicates the logical path under the condition that data has been retrieved. conditional entropy, This is a penalty factor for logical uncertainty. Calculate the difference between the data completeness score and the logical uncertainty penalty: in, This is the final calculated fill support.

[0010] In one embodiment, the intelligent review module includes an AI risk analysis unit, which is configured to perform end-to-end semantic understanding of the initial appeal report using a deep learning model trained on historical appeal reports, quantify the completeness of the evidence chain and the logical consistency of the arguments in the report, and output a logical risk score and risk paragraph location.

[0011] In one embodiment, the logical risk score is calculated through the following process: For each user claim and its supporting evidence in the report, a pre-trained semantic encoding model is used to map its text content into a high-dimensional semantic vector, denoted as the i-th. The semantic vector of each claim is The semantic vector of the corresponding evidence is ; Calculate the semantic consistency measure between the claim and the evidence: ,in Represents the cosine similarity function; Calculate the deviation measure between the semantics of the paragraph containing the claim and the overall semantics of the entire text: ,in Indicates based on the first The semantic probability distribution calculated from the text of the paragraph containing each claim. This represents the semantic probability distribution calculated based on the entire appeal report document. Represents the KL divergence function; Based on all user claims and their supporting evidence, the final logical risk score is calculated using the following formula. : in, This indicates the number of supporting pieces of evidence for each user claim identified in the report. This is a temperature coefficient used to adjust the intensity of the impact of local deviations on the overall risk.

[0012] In one embodiment, the feedback optimization module includes a pattern analysis unit and an optimization instruction unit: The pattern analysis unit is configured to perform statistical analysis on the batch audit results output by the intelligent audit module to identify frequently occurring defect patterns. The optimization instruction unit is configured to trace the defect pattern to entity relationships in the data fusion processing module with a confidence level lower than a preset low threshold, and / or map it to report templates in the intelligent report generation module whose template adaptation average is lower than a preset statistical threshold under a preset appeal scenario, and generate corresponding data association optimization instructions or template logic adjustment instructions.

[0013] In one embodiment, the feedback optimization module further includes a closed-loop control unit; the closed-loop control unit is configured to execute the following process: Confidence optimization sub-process: When the optimization instruction unit generates the data association optimization instruction, the closed-loop control unit instructs the relationship construction unit of the data fusion processing module to recalculate and improve the association confidence of the target entity relationship based on the new evidence or the revised business rules involved in the defect pattern. In the template reconstruction subprocess, when the optimization instruction unit generates the template logic adjustment instruction, the closed-loop control unit instructs the template adaptation unit of the intelligent report generation module to modify the element combination logic or condition judgment path of the target report template based on historical instances of successfully generating high-quality reports in the preset appeal scenario, so as to improve its template adaptability in the scenario. Risk mitigation verification sub-process: After the confidence optimization sub-process or template reconstruction sub-process is executed, the closed-loop control unit monitors the newly generated appeal reports involving the optimized objects; obtains the logical risk score through the intelligent review module and compares it with the average logical risk score of similar reports before optimization; if the score does not drop to the expected level, a new round of defect pattern analysis and optimization instruction generation is triggered.

[0014] On the other hand, this application provides a voucher center management method based on AI-based multi-source data fusion processing, applied to the voucher center management platform based on AI-based multi-source data fusion processing as described above. The method includes the following steps: The system connects to multiple heterogeneous business systems, collects user complaint-related data scattered across these systems, cleans and standardizes the collected data, and constructs a unified credential element library. Based on a pre-built standardized report template library, in response to a report generation request, data elements are retrieved from the voucher element library and filled into the corresponding positions of the selected report template to generate an initial appeal report; The initial appeal report is automatically audited. The automatic audit includes comparing the report content with a pre-set business rule base to output error indicators, and performing AI analysis on the report text to identify logical contradictions and potential risk points. Based on the results of automated audits, the root causes of report defects are analyzed, and the data association logic of the voucher element library and / or the template logic of the standardized report template library are adaptively optimized.

[0015] The aforementioned AI-based multi-source data fusion processing voucher center management platform and method, by constructing a closed-loop technology chain integrating data fusion, intelligent generation, automatic review, and feedback optimization, can automatically break down data silos and achieve intelligent fusion and unified management of multi-source heterogeneous data. Based on this, through template-based and intelligent technologies, it can quickly and accurately generate standardized appeal reports automatically. Furthermore, it utilizes rules and AI models to automatically audit the reports, ensuring report quality. Finally, it can self-learn and optimize based on the audit results, continuously improving the overall efficiency and quality of data processing and report generation, thereby solving the efficiency bottlenecks and quality control challenges of manual processing. Attached Figure Description

[0016] Figure 1 A structural block diagram of a certificate center management platform based on AI-driven multi-source data fusion processing provided in this application embodiment; Figure 2 A flowchart illustrating the AI-based multi-source data fusion processing credential center management method provided in this application embodiment. Detailed Implementation

[0017] To facilitate understanding of the technical solutions provided in the embodiments of this application, the background technology involved in the embodiments of this application will be described below.

[0018] In industries such as telecommunications and financial services, handling user complaints and generating formal, standardized complaint reports is a crucial business process and a typical data-intensive and knowledge-intensive task. In current practice, the complete chain of facts and evidence (i.e., credentials) required to support a complaint report is often not stored in a single system. For example, user identity information is in a Customer Relationship Management (CRM) system, transaction records are in an order management system, complaint interaction history is in a customer service system, and audio and video recordings may be stored on a dedicated media platform. These systems are independent of each other, with heterogeneous data models, forming significant data silos.

[0019] Faced with a specific appeal form, processing personnel are forced to perform a large amount of inefficient and repetitive manual operations: they need to log into multiple systems separately, identify key entities (such as user IDs and order numbers) based on experience, repeatedly query, filter, and compare in each system, and manually copy, paste, and combine fragmented results into a document. This process is not only extremely time-consuming (usually taking tens of minutes or even longer), but it is also highly susceptible to introducing human errors or omitting key evidence during transcription and translation, resulting in a weak report foundation.

[0020] Furthermore, the quality of compiling these original voucher data into the final report highly depends on the personal experience and writing habits of the personnel involved. Due to the lack of mandatory structured templates and intelligent assistance, the quality of the reports varies greatly in terms of the completeness of factual statements, the rigor of logical argumentation, and the standardization of format. The resulting compliance audits also face significant challenges, forcing auditors to invest considerable time in manual word-by-word review and cross-validation, resulting in low efficiency and difficulty in ensuring consistency.

[0021] Therefore, the core bottleneck of existing technologies lies in the inefficiency and high error rate of manually acquiring and integrating original voucher data from multi-source, heterogeneous, and isolated systems. Furthermore, relying on human experience to compile and review reports results in cumbersome processes, inconsistent quality, and a lack of continuous improvement mechanisms. This constitutes a comprehensive technical challenge across the entire chain, from the data layer to the application layer. This embodiment aims to provide a systematic solution to overcome the aforementioned shortcomings.

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Furthermore, all processes involving the collection, processing, storage, and analysis of user data provided in this embodiment are conducted in strict compliance with relevant laws, regulations, and industry standards. Before accessing any business system, the platform has obtained explicit authorization for system access and data use. For the collected user complaint-related data, the platform employs technical means to desensitize or anonymize sensitive personal information during processing. All data is used only within the scope of necessary business purposes and is protected by security measures such as encrypted storage and access control to ensure the legality, legitimacy, and necessity of data processing activities.

[0023] Firstly, this embodiment provides an AI-based multi-source data fusion processing credential center management platform, applicable to scenarios such as operator complaint processing and financial services that require intelligent integration, analysis, and report generation of multi-source business credentials. For example... Figure 1 As shown, the platform includes: The data fusion and processing module is configured to access multiple heterogeneous business systems, collect user complaint-related data scattered across multiple business systems, clean and standardize the collected data, and build a unified voucher element library. The intelligent report generation module is configured to access a pre-built standardized report template library, respond to report generation requests, retrieve data elements from the voucher element library and fill them into the corresponding positions of the selected report template to generate an initial appeal report; The intelligent review module is configured to automatically audit initial appeal reports. The automated audit includes comparing the report content with a pre-set business rule base to output error indicators, and performing AI analysis on the report text to identify logical contradictions and potential risk points. The feedback optimization module is configured to analyze the root causes of report defects based on the automated audit results output by the intelligent audit module, and drive the data association logic of the voucher element library and / or the template logic of the standardized report template library to perform adaptive optimization.

[0024] In practical implementation, the data fusion processing module can be implemented through a data integration service deployed on a server. This service is configured with application programming interfaces (APIs) to connect with various business systems (such as CRM systems, customer service ticket systems, and billing systems). The data collection process can be triggered periodically by a task scheduler or in real time by listening for event notifications from business systems via a message queue. Data cleaning and standardization processing includes: removing duplicate records, correcting obvious formatting errors (such as inconsistent date formats), and extracting key information (such as user IDs and order numbers) from unstructured text into structured fields using regular expressions or named entity recognition technology. Building a unified voucher element library specifically refers to associating and storing standardized data from all business systems according to the core dimension of "user-appeal event," forming a centralized, queryable data set. This set can be stored in a relational database or a graph database.

[0025] The intelligent report generation module can be a standalone microservice. Its pre-built standardized report template library defines the report structure, style, and data placeholders in XML or JSON format. When a report generation request (containing the appeal ticket ID and the target template ID) is received from the user interface or workflow engine, the module's template rendering engine first loads the template definition from the library based on the template ID, then parses the data placeholders in the template, generates the corresponding data query statement, initiates a query to the voucher element library to retrieve the required data elements, and finally fills the query results into the corresponding positions in the template to generate a complete initial appeal report in Word or PDF format.

[0026] The intelligent review module comprises two parallel review engines: a rules engine and an AI analysis engine. The rules engine loads a pre-built business rules library (e.g., rules could be expressed as "the report must include the last four digits of the user's ID number" or "the compensation amount must not exceed X times the standard rate") and scans the generated initial complaint report item by item. Any violation of the rules is marked as an error and an error label is generated. The AI ​​analysis engine, on the other hand, calls a pre-trained deep learning model (such as a text classification model based on the Transformer architecture) to perform semantic analysis on the entire report, identifying logical contradictions such as "the preceding text describes the user requesting a refund, but the conclusion does not provide a refund processing opinion," or potential risk points such as "the evidence has a weak correlation with the complaint issue," and pinpointing specific paragraphs to these points.

[0027] The feedback optimization module is a continuously running background service that receives batch audit results (including error identifiers, risk points, and corresponding report IDs, template IDs, and related data element IDs) from the intelligent audit module. It then uses data analysis algorithms (such as frequent pattern mining) to identify frequently occurring defect patterns. For example, it discovers that a large number of reports are rejected by the rule engine due to "missing screenshots of business processing vouchers." This module then analyzes the root cause: if the problem stems from a missing or weak correlation between a certain type of screenshot data in the voucher element library and the user entity, it generates instructions to drive the data fusion processing module to optimize the data association logic (e.g., adjusting the confidence level of the association rules); if the problem stems from a report template unreasonably requiring the inclusion of this screenshot in a specific scenario, it generates instructions to drive the intelligent report generation module to optimize the template's logic (e.g., changing mandatory fields to optional fields).

[0028] Based on the above, the data fusion processing module, intelligent report generation module, intelligent review module, and feedback optimization module are interconnected to form a complete automated closed loop of "data-report-review-optimization". The data fusion processing module solves the problem of scattered and isolated data sources, providing downstream users with unified and high-quality data raw materials; the intelligent report generation module uses templates to replace manual writing, achieving rapid and standardized report output; the intelligent review module, through a dual engine of rules and AI, achieves automated and multi-dimensional quality control of reports; and the feedback optimization module, based on review feedback, enables the platform to have self-diagnosis and continuous improvement capabilities. This closed-loop working method changes the drawbacks of traditional manual processing modes, realizing full-process intelligentization from data to high-quality reports, and ensuring the continuous evolution of platform efficiency.

[0029] To clarify how to accurately identify the same entity in different business systems (such as the same user having different IDs in different business systems) and establish meaningful relationships between them, in one embodiment, the data fusion processing module includes: The entity merging unit is configured to identify and extract core business entities from different business data through a recognition model pre-trained based on domain knowledge, and semantically merge heterogeneous representations pointing to the same real object to form standardized entities; The relationship building unit is configured to establish relationships between standardized entities after merging based on business rules and data co-occurrence analysis, and assign a quantitative relationship confidence level to each relationship to form a voucher element library.

[0030] In its implementation, the core of the entity merging unit is an entity recognition model pre-trained based on domain knowledge. This model can use pre-trained language models such as BERT or ERNIE as a base and be fine-tuned on labeled data in a specific business domain (such as telecommunications complaints). The labeled data includes text fragments extracted from logs and work order descriptions of various business systems, labeled with entity types such as "user," "complaint number," "product package," and "time." The model can accurately identify these entities from unstructured work order descriptions. The semantic merging process is implemented through entity parsing technology: for the identified entities, the platform extracts their features (such as name, mobile phone number, ID card fragments, time window, etc.) and compares and clusters them in an entity parsing service. For example, the user "Zhang San (ID: 1001)" in the CRM system and the complainant "Mr. Zhang (Mobile Number: 138xxxx)" in the customer service system, when the platform confirms through mobile phone number or ID card information matching algorithms that they refer to the same person, merges these two heterogeneous expressions into a unique standardized entity ID, such as "User_ZHANGSAN_001."

[0031] Based on the above, the entity merging unit solves the problem of user identity consistency across business system data, ensuring the uniqueness and accuracy of data subjects. The relationship building unit, on the basis of unified entities, constructs a network (i.e., a voucher element library) expressing business logic and data relationships, transforming discrete data points into interconnected knowledge. Assigning confidence levels to relationships provides important quality references for downstream report generation and review. For example, when generating reports, high-confidence related data can be prioritized, while during review, conclusions supported by low-confidence related data can be carefully examined. This voucher element library, constructed in this way, not only integrates data but also injects business semantics and confidence metrics, enhancing the intelligence level of the data layer.

[0032] To accurately reflect the reliability of the relationship and avoid subjective assumptions, in one embodiment, the association confidence score is calculated through the following process: Three aspects of evidence were obtained, including the semantic relevance of entity pairs calculated based on a pre-trained language model. The frequency of entity pairs co-occurring across systems in historical complaint data And relationship existence scoring based on a predefined business rule base. ; The three pieces of evidence were weighted and fused, and then normalized using the Sigmoid function to obtain the association confidence level. : in, , and This is a dynamic adjustment coefficient used to balance different evidence weights.

[0033] In practical implementation, entity-to-semantic relevance The calculation method involves inputting the standardized names, descriptions, or contexts of two entities into a pre-trained sentence semantic similarity model (such as Sentence-BERT). This model outputs a score representing the semantic relevance between the two entities, typically ranging from 0 to 1. For example, the entities "5G unlimited data plan" and "data package subscription record" may have a high semantic relevance.

[0034] Entity co-occurrence frequency across systems This is a statistical value. The platform counts the number of times the two entities appear simultaneously in records across different business systems based on historical data (such as appeal ticket data from the past year). This involves smoothing the original frequency to prevent high-frequency co-occurrence from having an excessive linear impact on the results, while ensuring that... This item is meaningful at that time.

[0035] Relationship Existence Score It originates from a predefined business rule base. Each rule in the rule base, in addition to logical judgments, can also be assigned an authority weight (for example, a rule explicitly stipulated by the Ministry of Industry and Information Technology has a weight of 1.0, while an internal enterprise regulation has a weight of 0.7). When a pair of entities satisfies a certain rule, the weight of that rule is used as the score for this match. If no rules are met, then .

[0036] Dynamic adjustment coefficient , and The settings can be dynamically adjusted based on the business scenario or data quality. For example, in scenarios with high data quality and complete records, the settings can be appropriately increased. The weight of (co-occurrence frequency) can be significantly increased in scenarios with extremely strict compliance requirements. The weights of (rule-based scoring). These coefficients can be determined through grid search combined with validation set results, or adjusted by administrators based on experience during platform operation. Specifically, in scenarios with a large amount of historical appeal reports and review result data, the determination of dynamically adjusted coefficients can be as follows: First, based on the experience of experts in the business domain, set an initial value for each coefficient; for example, initially, it could be set to... , , Then, using the set of initial values ​​to form a coefficient set, the complete platform process (including report generation and review scoring) is run on a historical dataset. The logical risk score calculated by the platform is compared and analyzed with the actual risk level marked by humans (such as calculating the correlation coefficient), or the template fit is correlated with the final adoption of the report. Finally, with the goal of improving the above comparison indicators, parameter tuning methods such as grid search or Bayesian optimization are used to conduct multiple rounds of iterative testing on the coefficient combination within a reasonable value range, and finally a set of coefficient values ​​that make the overall system performance optimal is determined.

[0037] Based on the above, the calculation process of association confidence nonlinearly fuses evidence from three different dimensions: semantic association (reflecting inherent business connections), statistical evidence (reflecting historical experience patterns), and rule authority (reflecting rigid constraints). Furthermore, the Sigmoid function maps the weighted sum to the (0,1) interval, consistent with the probabilistic interpretation of confidence. This calculation method makes association confidence a comprehensive and interpretable reliability measure that integrates multi-source information. Downstream applications (such as selecting data sources during report generation and assessing evidence strength during review) can make more refined and intelligent decisions based on association confidence, thereby improving the overall accuracy and credibility of the platform's output.

[0038] In the process of automated report generation, fixed report templates may not be fully adaptable to all changing appeal scenarios and data availability conditions. Mechanically applying templates may lead to generation failures or incomplete reports. To address this, in one embodiment, the intelligent report generation module includes a template adaptation unit. This unit is configured to evaluate the fillability of the selected report template with the currently available data set when retrieving data elements from the credential element library. If the fillability is lower than a preset threshold, the template is dynamically adjusted to generate a derived template adapted to the current data situation by combining preset template components to complete the generation of the initial appeal report.

[0039] In practice, the template adaptation unit is a decision-making component within the intelligent report generation module. When data retrieval for a report template begins, this unit simultaneously initiates an evaluation process. The evaluation not only checks the existence of the data but also assesses its quality and its match with the template. Preset thresholds can be set based on the importance of the report type; for example, a threshold of 0.9 can be set for critical reports, and 0.6 for general reports.

[0040] The dynamic template adjustment mechanism relies on a pre-built template component library. Standard report templates are designed to consist of multiple logically independent components (or "paragraph templates" or "fragments"), such as "User Information Statement Paragraph," "Problem Description Paragraph," "Evidence Listing Paragraph," "Responsibility Determination Paragraph," and "Handling Conclusion Paragraph." Each component has a clearly defined input data interface. When dynamic adjustment is triggered, the platform first analyzes which aspects of the currently successfully acquired data set are best suited for description. Then, it selects components from the component library that maximize the use of this data and combines them according to a certain business logic order (such as "Problem-Evidence-Conclusion") to form a new, derived report template. This derived template is immediately used for the current report generation and may be recorded for future reference in similar scenarios.

[0041] Based on the above, the template adaptation unit transforms the report generation process from rigid filling to intelligent adaptation. It predicts problems by assessing support and provides solutions through dynamic template adjustments. This ensures that even when the data is not ideal, the platform can still generate a report that is as complete and usable as possible, rather than directly reporting errors or generating meaningless and useless content. This improves the platform's robustness and applicability, enabling it to better cope with the complexity and incompleteness of data in real-world business scenarios.

[0042] To measure the completeness of data quantity and the extent to which missing data disrupts the logical structure of the report, in one embodiment, the fill support is calculated through the following process: Calculate the data completeness score: ,in The total number of key data elements required by the selected report template; For the first Preset weights for key data elements; It is an indicator function, and when the element The value is 1 if the request is successfully made, and 0 otherwise. Penalty for computational logic uncertainty: ,in This represents a set of template logic judgments where the execution path cannot be determined due to missing key data. Indicates the logical path under the condition that data has been retrieved. conditional entropy, This is a penalty factor for logical uncertainty. Calculate the difference between the data completeness score and the logical uncertainty penalty: in, This represents the final calculated fill support.

[0043] In the specific implementation, the numerator of the data completeness score is the weighted sum of the actual acquired data, and the denominator is the magnitude of the weight vector under the ideal template state (all data acquired). This ensures that the score value is in the [0,1] interval and takes into account the differences in importance (weights) of different data elements. For example, the weight of "user identification" might be set to 1.0, while the weight of "detailed notes of a customer service call" might be set to 0.3.

[0044] The penalty for logical uncertainty aims to quantify the confusion in template logic caused by missing data. Report templates often contain conditional statements, such as: "If the [complaint type] is 'fee dispute,' then the [fee details] and [deduction rules] must be presented." If the "complaint type" data is missing, the platform cannot determine which logical branch to follow, thus creating uncertainty. A set is a collection of all logical judgments that cannot be evaluated due to missing precondition data. It refers to conditional entropy in information theory, calculated when the acquired data is known. Under the condition of logical path The greater the uncertainty, the more critical the impact of missing data on logical judgment, and the greater the penalty. It is a factor greater than 0, used to control the penalty for logical uncertainty in the overall score. The larger the logical uncertainty penalty factor, the greater the penalty for logical confusion when evaluating template fit. Its value is usually set according to the business requirements for the rigor of the report logic, and can range from 0.1 to 1.0.

[0045] Based on the above, the calculation process for imputation support decomposes it into two parts: data completeness score and logical uncertainty penalty. It focuses not only on "how much data was obtained" but also on "whether the missing data leads to logical inconsistencies in the report." This makes the evaluation results more reflective of the actual feasibility of generating the report. For example, even if 90% of the data is obtained, if the missing 10% is a crucial basis for logical judgment... The value might be very low, triggering template adjustments; conversely, even if only 70% of the data is retrieved, the missing information is only minor supplementary information. The value may still be acceptable. This evaluation mechanism enhances the intelligence level of report generation decisions, ensuring that the generated report is not only relatively complete in content, but also has a clear and reasonable logical structure.

[0046] Rule-based review can only detect formal errors. For deeper quality issues such as the report's inherent logic and sufficiency of argumentation, an intelligent review method capable of understanding natural language semantics is needed. Therefore, in one embodiment, the intelligent review module includes an AI risk analysis unit. This AI risk analysis unit is configured to perform end-to-end semantic understanding of the initial appeal report using a deep learning model trained on historical appeal reports. It quantifies the completeness of the evidence chain and the logical consistency of the arguments in the report, and outputs a logical risk score and risk paragraph location.

[0047] In its implementation, the AI ​​risk analysis unit deploys a dedicated deep learning model. This model employs a hierarchical neural network architecture; for example, the bottom layer uses models like BERT to encode sentences, the middle layer uses recurrent neural networks or attention mechanisms to model semantic relationships within and between paragraphs, and the top layer connects fully connected layers to output risk scores and risk labels. The model's training data comes from a large number of historical appeal reports, which are labeled with risk levels (e.g., high risk, medium risk, low risk, no risk) and the location of risky paragraphs by senior review experts. Through end-to-end learning, the model acquires the ability to identify complex patterns in report texts, such as "insufficient evidence to support the conclusion," "time inconsistencies in descriptions," and "inconsistencies between liability determination and cited clauses."

[0048] Based on the above, the AI ​​risk analysis unit simulates the deep reading and logical judgment process of human experts. Through semantic understanding, it transforms unstructured report text into a structured assessment of the quality of its arguments (logical risk scoring) and accurately pinpoints problems. This compensates for the shortcomings of rule-based review, enabling the discovery of complex logical fallacies and argumentative flaws that cannot be described by simple rules. Working in conjunction with the rule engine, this unit constitutes a dual guarantee system for the formal and substantive quality of reports, enhancing the depth and breadth of automated review and reducing the quality risks of reports.

[0049] To quantify the correspondence between claims and evidence and the focus of arguments in the report text, in one embodiment, the logical risk score is calculated through the following process: For each user claim and its supporting evidence in the report, a pre-trained semantic encoding model is used to map its text content into a high-dimensional semantic vector, denoted as the i-th. The semantic vector of each claim is The semantic vector of the corresponding evidence is ; Calculate the semantic consistency measure between claims and evidence: ,in Represents the cosine similarity function; Calculate the deviation between the semantics of the paragraph containing the claim and the overall semantics of the entire text: ,in Indicates based on the first The semantic probability distribution calculated from the text of the paragraph containing each claim. This represents the semantic probability distribution calculated based on the entire appeal report document. Represents the KL divergence function; Based on all user claims and their supporting evidence, the final logical risk score is calculated using the following formula. : in, This indicates the number of supporting pieces of evidence for each user claim identified in the report. This is a temperature coefficient used to adjust the intensity of the impact of local deviations on the overall risk.

[0050] In practice, the identification of supporting evidence for a user's claim can be accomplished using a pre-trained sequence labeling model or dependency parsing combined with rules. For example, the sentence following "The user claims..." can be identified as the claim, and the sentence following "After verification..." can be identified as evidence. A pre-trained semantic encoding model (such as Sentence-BERT) is responsible for converting the text into semantically representative vectors.

[0051] The semantic consistency metric calculates the cosine distance between the claim and the evidence vector. The larger the distance, the less semantically related the two are, and the greater the risk contribution.

[0052] Deviation metrics are calculated using either a topic model (such as LDA) or a deep document representation model, respectively, for the entire document and for the contained claims. We model the paragraphs to obtain their probability distributions on semantic topics. and KL divergence measures the difference between the distribution of paragraphs and the distribution of the entire text. The greater the difference, the more likely the paragraph may have deviated from the core theme of the report (for example, suddenly describing a large section about network signal problems while discussing fee disputes), and its risk needs to be amplified. The term acts as an amplifier, and the strength of the amplification effect is controlled by the temperature coefficient. The larger the temperature coefficient, the smoother the change of the exponent, which means that the amplification effect of the deviation of the paragraph topic on the overall risk is weaker; the smaller the temperature coefficient, the stronger the amplification effect, which is usually adjusted between 0.5 and 2.0.

[0053] Based on the above, the calculation process of the logical risk score comprehensively assesses logical risk from two levels: micro-level relevance (the degree of matching between claims and evidence) and macro-level consistency (the degree to which local arguments fit the overall theme). It not only checks "whether there is evidence," but also "whether the evidence is relevant to the topic"; it not only checks local logic, but also whether the local logic serves the whole. (Index amplification item) This means that a seriously off-topic argument, even if its internal claims are only slightly related to the evidence, will be judged as high-risk, and an off-topic argument is more dangerous than a closely related but weaker one. This calculation method makes logical risk scoring a refined, multi-dimensional, and cognitively sound quantitative indicator, providing a powerful and interpretable analytical tool for automated review.

[0054] To translate macro-level audit results into specific optimization actions targeting the data layer or template layer, in one embodiment, the feedback optimization module includes a pattern analysis unit and an optimization instruction unit: The pattern analysis unit is configured to perform statistical analysis on the batch audit results output by the intelligent audit module to identify frequently occurring defect patterns. The optimized instruction unit configuration is to trace the defect pattern back to the entity relationship in the data fusion processing module where the associated confidence level is lower than a preset low threshold, and / or map it to the report template in the intelligent report generation module where the statistical mean of its template adaptability is lower than a preset statistical threshold under a preset appeal scenario, and generate the corresponding data association optimization instruction or template logic adjustment instruction.

[0055] In practice, the pattern analysis unit can use association rule learning (such as the Apriori algorithm) or cluster analysis algorithm in data mining. It processes all audit results within a period of time (such as a week) to find frequently occurring error combinations or high-frequency single-point errors, forming defect patterns such as "template T in scenario S often fails to trigger rule R due to missing data item D".

[0056] The optimization instruction unit includes a source mapping rule engine. For each defect pattern, the engine attempts to map it to its root cause. A mapping rule could be: "If the defect involves data item D that cannot be obtained, query the data fusion processing module for the relationships between entities related to the current work order that can provide data item D, and check if the association confidence is lower than a preset low threshold (e.g., 0.3)." If it is lower, it is determined to be a data association problem, and an instruction to "increase the confidence of relationship R" or "establish a new relationship" is generated. Another mapping rule could be: "If the defect manifests as template T frequently triggering dynamic adjustments or having a high audit risk in scenario S, calculate the average template adaptability of the template in scenario S over multiple historical executions, and check if it is lower than a preset statistical threshold (e.g., 0.5)." If it is lower, it is determined to be a template adaptability problem, and an instruction to "optimize the logic of template T in scenario S" is generated.

[0057] Based on the above, the pattern analysis unit and optimization instruction unit statistically analyze scattered, superficial audit errors to identify clear error targets. Then, using intelligent mapping rules based on quantitative indicators (association confidence and fill support), they precisely determine the cause of the error (whether it's weak data correlation or improper template design). This makes platform optimization no longer blind or global, but targeted and data-driven. This precise feedback mechanism is the core of the platform's efficient self-evolution, ensuring that each optimization effectively solves a known, high-frequency quality problem.

[0058] After the optimization instructions are generated, they need to be executed, and the execution effect needs to be verified to form a truly closed-loop and reliable self-optimization process, avoiding the accumulation of invalid or erroneous optimizations. Therefore, in one embodiment, the feedback optimization module also includes a closed-loop control unit; the closed-loop control unit is configured to execute the following process: Confidence Optimization Sub-process: When the optimization instruction unit generates a data association optimization instruction, the closed-loop control unit instructs the relationship construction unit of the data fusion processing module to recalculate and improve the association confidence of the target entity relationship based on new evidence or revised business rules involved in the defect pattern. When the optimization instruction unit generates a template logic adjustment instruction, the closed-loop control unit instructs the template adaptation unit of the intelligent report generation module to modify the element combination logic or condition judgment path of the target report template based on historical instances of successfully generating high-quality reports in preset appeal scenarios, so as to improve its template adaptability in that scenario. Risk mitigation verification sub-process: After the confidence optimization sub-process or template reconstruction sub-process is executed, the closed-loop control unit monitors the new appeal reports generated subsequently involving the optimized objects; obtains their logical risk score through the intelligent review module and compares it with the average logical risk score of similar reports before optimization; if the score does not drop to the expected level, a new round of defect pattern analysis and optimization instruction generation is triggered.

[0059] In its implementation, the closed-loop control unit is a workflow engine. In the confidence optimization sub-process, it calls the API of the relationship building unit, passing in the relationship ID specified in the instruction, along with new evidence (such as newly discovered high co-occurrence frequency data) or rule weight parameters, triggering a recalculation of the association confidence. In the template reconstruction sub-process, it calls the analysis service of the template adaptation unit, providing a batch of historical report cases that have succeeded in the same scenario (high fill support value and low logical risk score value). The template adaptation unit analyzes the data usage patterns and paragraph structure of these cases, adjusting the component selection logic or conditional branches of the original template to generate a new version of the template.

[0060] The risk mitigation verification subprocess sets an observation period (e.g., processing 50 similar complaints next). It collects the logical risk scores of these new reports, calculates their mean, and compares it with the mean logical risk scores of similar reports in the previous statistical period. If the new mean is not significantly lower than the old mean (e.g., the decrease does not exceed 10%), the optimization is considered to have failed to achieve the expected results. The closed-loop control unit then creates a new optimization task, feeds the problem back to the pattern analysis unit, and starts a new round of "analysis-optimization" cycle.

[0061] Based on the above, the closed-loop control unit connects problem analysis and optimization instruction execution, and verifies the execution effect, ensuring that every optimization instruction is executed and that the execution effect is objectively evaluated in a quantitative way (changes in logical risk scores). If the execution effect is unsatisfactory, the platform automatically initiates a secondary analysis-optimization. This achieves a complete, negative feedback "perception-decision-execution-evaluation" adaptive control closed loop. This closed loop enables the platform to continuously learn from experience, constantly correct itself, and steadily improve output quality.

[0062] On the other hand, this embodiment provides an AI-based multi-source data fusion processing-based voucher center management method, applied to the AI-based multi-source data fusion processing-based voucher center management platform described above, such as... Figure 2 As shown, the method includes the following steps in sequence: S1 - Integrates with multiple heterogeneous business systems, collects user complaint-related data scattered across multiple business systems, cleans and standardizes the collected data, and builds a unified voucher element library; S2 - Based on a pre-built standardized report template library, responding to report generation requests, it retrieves data elements from the voucher element library and fills them into the corresponding positions of the selected report template to generate an initial appeal report; S3 - Automated auditing of initial complaint reports. Automated auditing includes comparing the report content with a pre-set business rule base to output error indicators, and performing AI analysis on the report text to identify logical contradictions and potential risk points. S4 - Based on the results of automated audits, analyze the root causes of report defects and drive adaptive optimization of the data association logic of the voucher element library and / or the template logic of the standardized report template library.

[0063] In practice, this method can be implemented using software programs running on a server cluster. Step S1, the process of building the credential element library, may include entity merging and association confidence calculation in the aforementioned platform. Step S2, the process of generating the initial appeal report, may include template adaptability assessment and dynamic adjustment in the aforementioned platform. Step S3, the process of automated auditing, may include AI risk analysis and logical risk scoring calculation in the aforementioned platform. Step S4, the process of adaptive optimization, may include defect pattern analysis, optimization instruction generation, closed-loop execution, and effect verification in the aforementioned platform. The specific implementation details of each of the above steps correspond to the implementation methods of each module in the aforementioned platform, and will not be repeated here.

[0064] Based on the above, this method summarizes the technical solution of this embodiment from a process perspective. It achieves the same technical effect as the technical solution corresponding to the aforementioned platform by sequentially executing four major steps: data fusion (step S1), intelligent generation (step S2), automatic review (step S3), and feedback optimization (step S4). That is, it breaks down data silos, automatically and intelligently generates high-quality reports, and achieves continuous self-optimization of the platform through closed-loop feedback.

[0065] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0066] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A voucher center management platform based on AI-driven multi-source data fusion processing, characterized in that, The platform includes: The data fusion and processing module is configured to access multiple heterogeneous business systems, collect user complaint-related data scattered across these systems, and clean and standardize the collected data to build a unified credential element library. The intelligent report generation module is configured to access a pre-set standardized report template library, respond to report generation requests, retrieve data elements from the voucher element library and fill them into the corresponding positions of the selected report template to generate an initial appeal report; The intelligent review module is configured to automatically audit the initial appeal report. The automated audit includes comparing the report content with a pre-set business rule base to output error indicators, and performing AI analysis on the report text to identify logical contradictions and potential risk points. The feedback optimization module is configured to analyze the root causes of report defects based on the automated audit results output by the intelligent audit module, and drive the data association logic of the voucher element library and / or the template logic of the standardized report template library to perform adaptive optimization.

2. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 1, characterized in that, The data fusion processing module includes: The entity merging unit is configured to identify and extract core business entities from different business data through a recognition model pre-trained based on domain knowledge, and semantically merge heterogeneous representations pointing to the same real object to form standardized entities; The relationship building unit is configured to establish relationships between standardized entities after merging based on business rules and data co-occurrence analysis, and to assign a quantitative relationship confidence level to each relationship in order to form the voucher element library.

3. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 2, characterized in that, The association confidence level is calculated through the following process: Three aspects of evidence were obtained, including the semantic relevance of entity pairs calculated based on a pre-trained language model. The frequency of co-occurrence of the entity pairs across systems in historical complaint data. And relationship existence scoring based on a predefined business rule base. ; The three pieces of evidence obtained are weighted and fused, and then normalized using the Sigmoid function to obtain the association confidence level. : in, , and This is a dynamic adjustment coefficient used to balance the weights of different pieces of evidence.

4. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 1, characterized in that, The intelligent report generation module includes a template adaptation unit, which is configured to evaluate the fill support of the currently available data set for the selected report template when retrieving data elements from the voucher element library; if the support is lower than a preset threshold, the template is dynamically adjusted to generate a derived template adapted to the current data situation by combining preset template components to complete the generation of the initial appeal report.

5. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 4, characterized in that, The fill support is calculated through the following process: Calculate the data completeness score: ,in The total number of key data elements required by the selected report template; For the first Preset weights for key data elements; It is an indicator function, and when the element The value is 1 if the request is successfully made, and 0 otherwise. Penalty for computational logic uncertainty: ,in This represents a set of template logic judgments where the execution path cannot be determined due to missing key data. Indicates the logical path under the condition that data has been retrieved. conditional entropy, This is a penalty factor for logical uncertainty. Calculate the difference between the data completeness score and the logical uncertainty penalty: in, This is the final calculated fill support.

6. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 1, characterized in that, The intelligent review module includes an AI risk analysis unit, which is configured to perform end-to-end semantic understanding of the initial appeal report using a deep learning model trained on historical appeal reports, quantify the completeness of the evidence chain and the logical consistency of the arguments in the report, and output a logical risk score and risk paragraph location.

7. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 6, characterized in that, The logical risk score is calculated through the following process: For each user claim and its supporting evidence in the report, a pre-trained semantic encoding model is used to map its text content into a high-dimensional semantic vector, denoted as the i-th. The semantic vector of each claim is The semantic vector of the corresponding evidence is ; Calculate the semantic consistency measure between the claim and the evidence: ,in Represents the cosine similarity function; Calculate the deviation measure between the semantics of the paragraph containing the claim and the overall semantics of the entire text: ,in Indicates based on the first The semantic probability distribution calculated from the text of the paragraph containing each claim. This represents the semantic probability distribution calculated based on the entire appeal report document. Represents the KL divergence function; Based on all user claims and their supporting evidence, the final logical risk score is calculated using the following formula. : in, This indicates the number of supporting pieces of evidence for each user claim identified in the report. This is a temperature coefficient used to adjust the intensity of the impact of local deviations on the overall risk.

8. The voucher center management platform based on AI-driven multi-source data fusion processing according to any one of claims 1-7, characterized in that, The feedback optimization module includes a pattern analysis unit and an optimization instruction unit: The pattern analysis unit is configured to perform statistical analysis on the batch audit results output by the intelligent audit module to identify frequently occurring defect patterns. The optimization instruction unit is configured to trace the defect pattern to entity relationships in the data fusion processing module with a confidence level lower than a preset low threshold, and / or map it to report templates in the intelligent report generation module whose template adaptation average is lower than a preset statistical threshold under a preset appeal scenario, and generate corresponding data association optimization instructions or template logic adjustment instructions.

9. The voucher center management platform based on AI-driven multi-source data fusion processing according to claim 8, characterized in that, The feedback optimization module further includes a closed-loop control unit; the closed-loop control unit is configured to execute the following process: Confidence optimization sub-process: When the optimization instruction unit generates the data association optimization instruction, the closed-loop control unit instructs the relationship construction unit of the data fusion processing module to recalculate and improve the association confidence of the target entity relationship based on the new evidence or the revised business rules involved in the defect pattern. In the template reconstruction subprocess, when the optimization instruction unit generates the template logic adjustment instruction, the closed-loop control unit instructs the template adaptation unit of the intelligent report generation module to modify the element combination logic or condition judgment path of the target report template based on historical instances of successfully generating high-quality reports in the preset appeal scenario, so as to improve its template adaptability in the scenario. Risk mitigation verification sub-process: After the confidence optimization sub-process or template reconstruction sub-process is executed, the closed-loop control unit monitors the newly generated appeal reports involving the optimized objects. The intelligent audit module obtains the logical risk score and compares it with the average logical risk score of similar reports before optimization. If the score does not drop to the expected level, a new round of defect pattern analysis and optimization instruction generation is triggered.

10. A voucher center management method based on AI-driven multi-source data fusion processing, characterized in that, The method includes the following steps: The system connects to multiple heterogeneous business systems, collects user complaint-related data scattered across these systems, cleans and standardizes the collected data, and constructs a unified credential element library. Based on a pre-built standardized report template library, in response to a report generation request, data elements are retrieved from the voucher element library and filled into the corresponding positions of the selected report template to generate an initial appeal report; The initial appeal report is automatically audited. The automatic audit includes comparing the report content with a pre-set business rule base to output error indicators, and performing AI analysis on the report text to identify logical contradictions and potential risk points. Based on the results of automated audits, the root causes of report defects are analyzed, and the data association logic of the voucher element library and / or the template logic of the standardized report template library are adaptively optimized.

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