Financial consumption system verification method and device, equipment and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for verifying financial consumer regulations, relating to the field of data processing technology. The method includes: collecting multi-source regulatory information from an internal financial consumer regulations database and an external regulatory system, and performing data cleaning and standardization on the multi-source regulatory information; performing semantic analysis on the processed regulatory information based on a preset quantum semantic analysis model to obtain quantum semantic feature vectors; classifying the quantum semantic feature vectors based on preset financial classification labels to obtain classified regulatory information; and performing compliance verification on the classified regulatory information, outputting the compliance verification results. This application improves the efficiency of regulatory processing while reducing the risk of regulatory omissions by collecting and preprocessing multi-source regulatory information, combining it with a preset quantum semantic analysis model to perform deep semantic analysis on the processed regulatory information, and classifying and verifying the analyzed features.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to methods, apparatus, equipment and storage media for verifying financial consumer systems. Background Technology

[0002] Currently, consumer finance institutions rely heavily on a manual "collection-organization-classification-review-archiving" process for managing their systems. Each step is carried out through manual document transfer or sharing of basic office software. Data and document flow lacks automated connections, requiring manual screening to distinguish between internal regulations and external regulatory requirements. This results in insufficient accuracy and consistency in classification and lagging efficiency in system processing.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment and storage medium for verifying financial consumer regulations, which aims to solve the technical problem that relying on manual review processes requires manually screening and distinguishing between internal regulations and external regulatory requirements, resulting in insufficient accuracy and consistency in classification and lagging efficiency in system processing.

[0005] To achieve the above objectives, this application proposes a method for verifying financial consumer systems, the method comprising: Multi-source policy information is collected from the internal financial consumption policy database and the external regulatory system, and the multi-source policy information is cleaned and standardized to obtain the processed policy information. The processed institutional information is semantically parsed based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model built on a quantum convolutional neural network model and a large natural language processing model. The quantum convolutional neural network model includes a multidimensional quantum convolutional kernel and an adaptive residual structure. The multidimensional quantum convolutional kernel includes at least two types of quantum convolutional kernels, which are used to capture local semantic associations and long-distance dependencies in the text. The quantum semantic feature vector is classified based on a preset financial classification label to obtain the classified institutional information. Perform compliance verification on the categorized system information and output the compliance verification results.

[0006] In one embodiment, the step of performing semantic parsing on the processed institutional information based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors includes: The processed institutional information is quantum-encoded based on a preset quantum semantic parsing model to obtain a quantum-encoded feature vector. Semantic matching is performed on the quantum encoded feature vector based on a preset scene tag library to obtain a quantum semantic feature vector.

[0007] In one embodiment, the step of quantum encoding the processed institutional information based on a preset quantum semantic analysis model to obtain a quantum encoded feature vector includes: The processed institutional information is converted into word vectors based on a preset quantum semantic parsing model to obtain word vectors of the target dimension. The word vectors are quantum encoded using a quantum state mapping algorithm and a preset parameter optimization mechanism to obtain quantum encoded feature vectors.

[0008] In one embodiment, the step of semantically matching the quantum encoded feature vector based on a preset scene tag library to obtain a quantum semantic feature vector includes: Cosine similarity is calculated based on a preset scene tag library and the quantum-encoded feature vector to obtain similarity information; The weights of the quantum-encoded feature vector are adjusted based on a preset fusion attention mechanism and the similarity information to obtain a weight-adjusted quantum-encoded feature vector. Based on preset business rules, the quantum encoded feature vector after weight adjustment is optimized to obtain a quantum semantic feature vector.

[0009] In one embodiment, the classification verification includes primary classification verification and secondary classification verification. The step of classifying the quantum semantic feature vector based on a preset financial classification label to obtain the classified institutional information includes: Based on a preset logistic regression model and preset financial classification labels, the quantum semantic feature vector and the basic business features in the internal financial consumption system database are classified into primary categories to obtain primary classification results. Based on the quantum feature attention fusion mechanism, the quantum semantic feature vector is fused with the text keyword features and business scenario features in the internal financial consumption system database to obtain the feature fusion vector. The feature fusion vector is classified into two categories based on a lightweight gradient boosting model to obtain the two-level classification result. The classified institutional information is determined based on the primary classification results and the secondary classification results.

[0010] In one embodiment, the step of performing compliance verification on the categorized institutional information and outputting the compliance verification result includes: The classified institutional information is matched with the quantum features of the corresponding compliance clauses in the preset compliance clause library based on the preset quantum semantic matching verification algorithm to obtain the semantic matching result; Perform compliance verification on the semantic matching results and output the compliance verification results.

[0011] In one embodiment, after the step of performing compliance verification on the categorized institutional information and outputting the compliance verification result, the method further includes: The risk level is determined based on the compliance verification results. The source tracing management mechanism is triggered based on the risk level, and the source tracing information and the risk level are used to generate an alarm message, which is then pushed to the approver.

[0012] Furthermore, to achieve the above objectives, this application also proposes a financial consumption system verification device, which includes: The preprocessing module is used to collect multi-source regulatory information from the internal financial consumption regulatory database and the external regulatory system, and to perform data cleaning and standardization on the multi-source regulatory information to obtain the processed regulatory information. The quantum semantic parsing module is used to perform semantic parsing on the processed institutional information based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model built on a quantum convolutional neural network model and a large natural language processing model. The quantum convolutional neural network model includes a multidimensional quantum convolutional kernel and an adaptive residual structure. The multidimensional quantum convolutional kernel includes at least two types of quantum convolutional kernels, which are used to capture local semantic associations and long-distance dependencies in the text. The intelligent classification module is used to classify the quantum semantic feature vector based on preset financial classification labels to obtain classified institutional information; The compliance verification module is used to verify the compliance of the categorized system information and output the compliance verification results.

[0013] In addition, to achieve the above objectives, this application also proposes a financial consumer system verification device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the financial consumer system verification method described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the financial consumption system verification method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application collects multi-source institutional information from an internal financial consumption policy database and an external regulatory system, and performs data cleaning and standardization on this information to obtain processed institutional information. Based on a pre-defined quantum semantic parsing model, the processed institutional information is semantically parsed to obtain quantum semantic feature vectors. This pre-defined quantum semantic parsing model is a model constructed based on a quantum convolutional neural network model and a large-scale natural language processing model. The quantum convolutional neural network model includes multi-dimensional quantum convolutional kernels and an adaptive residual structure. The multi-dimensional quantum convolutional kernels contain at least two types of quantum convolutional kernels, used to capture local semantic associations and long-distance dependencies in the text. This invention classifies quantum semantic feature vectors using pre-defined financial classification labels to obtain classified institutional information. It then performs compliance verification on the classified institutional information and outputs the verification results. Compared to relying on manual review processes that require manually screening and distinguishing between internal regulations and external regulatory requirements, leading to insufficient classification accuracy and consistency, and lagging institutional processing efficiency, this application improves institutional processing efficiency while reducing the risk of institutional omissions by collecting and preprocessing multi-source institutional information, combining it with a pre-defined quantum semantic parsing model to perform deep semantic parsing on the processed institutional information, and classifying and verifying the parsed features. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the first embodiment of the financial consumer system verification method in this application. Figure 2 This is a schematic diagram of the overall process for the first embodiment of the financial consumer system verification method in this application; Figure 3 This is a flowchart illustrating the second embodiment of the financial consumer system verification method in this application. Figure 4 A detailed flowchart of quantum semantic analysis is provided for Embodiment 2 of the financial consumer system verification method in this application; Figure 5 A detailed flowchart of intelligent classification provided for Embodiment 2 of the financial consumer system verification method of this application; Figure 6This is a flowchart of compliance verification and traceability provided for Implementation Example 2 of the Financial Consumer System Verification Method in this application; Figure 7 This is a schematic diagram of the module structure of the financial consumption system verification device according to an embodiment of this application; Figure 8 This is a schematic diagram of the hardware operating environment involved in the financial consumption system verification method in this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application is as follows: This application collects multi-source institutional information from an internal financial consumption institutional database and an external regulatory system, and performs data cleaning and standardization on the multi-source institutional information to obtain processed institutional information; based on a preset quantum semantic parsing model, semantic parsing is performed on the processed institutional information to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model built on a quantum convolutional neural network model and a large-scale natural language processing model. The quantum convolutional neural network model includes multi-dimensional quantum convolutional kernels and an adaptive residual structure. The multi-dimensional quantum convolutional kernels include at least two types of quantum convolutional kernels, used to capture local semantic associations and long-distance dependencies in the text; based on preset financial classification labels, the quantum semantic feature vectors are classified to obtain classified institutional information; compliance verification is performed on the classified institutional information, and the compliance verification results are output.

[0023] In this embodiment, for ease of description, the following description uses a computing service device as the execution subject.

[0024] Because the reliance on manual review processes requires manually screening and distinguishing between internal regulations and external regulatory requirements, the accuracy and consistency of classification are insufficient, resulting in a lag in the efficiency of system processing.

[0025] This application provides a solution that collects and preprocesses multi-source institutional information, combines it with a preset quantum semantic analysis model to perform in-depth semantic analysis on the processed institutional information, and classifies and verifies the compliance of the analyzed features, thereby improving the efficiency of institutional processing while reducing the risk of institutional omissions.

[0026] As can be seen from the above embodiments, this application collects multi-source institutional information from an internal financial consumption institutional database and an external regulatory system, and performs data cleaning and standardization on the multi-source institutional information to obtain processed institutional information; based on a preset quantum semantic parsing model, semantic parsing is performed on the processed institutional information to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model constructed based on a quantum convolutional neural network model and a large-scale natural language processing model. The quantum convolutional neural network model includes multi-dimensional quantum convolution kernels and an adaptive residual structure. The multi-dimensional quantum convolution kernels include at least two types of quantum convolution kernels, used to capture local semantic associations and long-distance dependencies in the text; based on preset financial classification labels, the quantum semantic feature vectors are classified to obtain classified institutional information; compliance verification is performed on the classified institutional information, and the compliance verification results are output. Compared to relying on manual review processes that require manually screening and distinguishing between internal regulations and external regulatory requirements, resulting in insufficient accuracy and consistency in classification and lagging efficiency in system processing, this application improves system processing efficiency while reducing the risk of system omissions by collecting and preprocessing multi-source system information, combining it with a pre-set quantum semantic analysis model to perform in-depth semantic analysis on the processed system information, and classifying and verifying the analyzed features.

[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, including a consumer policy verification system for financial scenarios. This application addresses the shortcomings of current policy management systems, such as low policy processing efficiency, poor classification accuracy, fragmented knowledge management, and weak compliance verification, through a full-process technical architecture of "policy acquisition, quantum semantic analysis, intelligent classification, compliance verification, and knowledge accumulation." The consumer policy verification system for financial scenarios in this application includes five core modules: a quantum semantic analysis module, a multi-source policy acquisition module, an intelligent classification and retrieval module, a compliance verification and traceability module, and a policy knowledge accumulation module. Each module achieves data interaction and instruction synchronization through a standardized data bus and service mesh, forming a closed-loop intelligent financial consumer policy management support system. The following uses a computer as an example to illustrate this embodiment and the subsequent embodiments.

[0028] Based on this, the embodiments of this application provide a method for verifying financial consumption systems, referring to... Figure 1 , Figure 1 This is a flowchart illustrating Example 1 of the financial consumer system verification method in this application.

[0029] In this embodiment, the financial consumption system verification method includes steps S10 to S40: Step S10: Collect multi-source policy information from the internal financial consumption policy database and the external regulatory system, and perform data cleaning and standardization on the multi-source policy information to obtain the processed policy information.

[0030] It should be noted that the internal financial consumption system database is a database that aggregates all financial consumption systems, while the external regulatory system refers to the system that extracts system information by connecting to external regulatory policy release platforms (such as the State Financial Supervision and Administration Bureau and the People's Bank of China) through API interfaces. The system collects multi-source system information from both the internal financial consumption system database and the external regulatory system. The multi-source system information includes two types of channels: the internal system database (PDF / WORD / TXT files) and external regulatory APIs (such as the State Financial Supervision and Administration Bureau and the People's Bank of China). The collected information includes system documents, regulatory systems, and other multi-dimensional information.

[0031] Understandably, in the policy collection stage, this application provides high-quality data input to downstream modules through unified access and standardized processing of multi-source policy data. This includes three major components: a multi-source access unit, a data cleaning unit, and a quality monitoring unit. These components perform data cleaning and standardization processing on multi-source policy information to obtain processed policy information.

[0032] It should be understood that the multi-source access unit is responsible for the collection and access of full-dimensional institutional data. Its core attributes include access channel management, protocol adaptation mechanism, and access control strategy. The access channel management is based on a data source classification system, covering two core channels: internal institutional database (PDF / WORD / TXT format files) and external regulatory APIs (State Financial Supervision and Administration Bureau, People's Bank of China, etc.). It supports adding new data sources according to business scenarios and can collect structured institutional metadata (institution number, effective date, business affiliation) and unstructured institutional text (clause content, interpretation instructions). The protocol adaptation mechanism is based on a standardized protocol library. For internal files, it uses FTP file transfer and HTTP form upload methods, and for external regulatory APIs, it uses the RESTful API protocol. It automatically identifies the data source type and matches the corresponding protocol to ensure data transmission compatibility. The access control strategy is based on the RBAC role model, distinguishing between three roles: "data administrator - business operator - auditor". Only authorized personnel can initiate data access requests. At the same time, interface authentication is achieved through API keys and tokens (validity period of 1-7 days is configurable) to prevent data leakage. The multi-source access unit uses an incremental capture mechanism of external regulatory data to calculate file hash values ​​and compare them with historical data, synchronizing only the newly added or modified parts, saving 60% of network bandwidth and storage resources compared to traditional full capture. The data cleaning unit is responsible for the standardization of raw data. Its core attributes include a cleaning rule base, an anomaly handling process, and a data standardization mechanism. The cleaning rule base, based on a data quality rule system, includes data deduplication rules (based on unique identifiers of policy numbers), missing value completion rules (related to metadata tables of the same system), and outlier filtering rules (based on business thresholds to filter invalid clauses). It supports custom rules based on data type. The anomaly handling process, based on a tiered handling mechanism, handles abnormal data in three levels: general anomalies (such as minor format deviations) are automatically corrected; important anomalies (such as missing key fields) trigger manual review work orders; and major anomalies (such as file corruption) block access and notify the responsible person via SMS. Simultaneously, the abnormal data is stored in a MongoDB anomaly pool, recording the anomaly type, discovery time, and processing status. The data standardization mechanism, based on unified data specifications, standardizes data format (date "YYYY-MM-DD", numerical values ​​to two decimal places, text encoding UTF-8), encoding rules (unified enumeration values ​​for business affiliation), and field naming ("business domain-table name-field meaning" specification), generating standardized JSON format data files.The quality monitoring unit is responsible for quality control throughout the entire data access process. Its core attributes include a quality indicator system, a real-time early warning mechanism, and quality report generation. The quality indicator system, based on data quality dimensions, defines three core indicators: data integrity (non-empty field percentage ≥95%), accuracy (format compliance rate ≥98%), and timeliness (access latency ≤5 minutes). Differentiated thresholds are set according to data source type (internal file integrity ≥95%, external API data accuracy ≥98%). The real-time early warning mechanism, based on the Prometheus monitoring system, triggers tiered alerts when indicators deviate from thresholds: integrity <95% generates system pending tasks; accuracy <98% pushes a WeChat notification; timeliness >5 minutes calls the responsible person. Simultaneously, quality trends are displayed in real-time through the Grafana dashboard. The quality report generation, based on an automated reporting engine, automatically generates daily data quality reports, listing the access volume of each data source, quality compliance rate, and anomaly handling status. These reports are converted into business-oriented descriptions using natural language generation technology and support PDF export and audit traceability.

[0033] Step S20: Based on a preset quantum semantic parsing model, perform semantic parsing on the processed institutional information to obtain quantum semantic feature vectors.

[0034] It should be noted that in the quantum semantic parsing stage, this application uses a quantum semantic parsing module as the core processing hub of the system. Processing is performed through a pre-defined quantum semantic parsing model, which is a model built upon a quantum convolutional neural network model and a large-scale natural language processing model. By introducing an adaptive quantum feature enhancement mechanism, deep semantic mining of institutional texts is achieved. The quantum convolutional neural network model includes multi-dimensional quantum convolutional kernels and an adaptive residual structure. The multi-dimensional quantum convolutional kernels contain at least two types of quantum convolutional kernels, used to capture local semantic associations and long-distance dependencies in the text.

[0035] In one implementation, a real-time data caching layer based on a Redis cluster is first initialized. A high-throughput data access pipeline is established by subscribing to an internal policy repository update message queue and an external regulatory policy API interface, enabling real-time acquisition of various policy documents at a processing speed of 300 documents per second. The core module adopts a layered architecture: the bottom layer is a quantum encoding engine, breaking through the limitations of traditional fixed-dimensional encoding. It employs a dynamic qubit allocation strategy, adaptively configuring the number of qubits based on the length of the policy text (4 bits for short texts, 6-8 bits for medium-to-long texts). Word vectors are encoded into quantum states through Y-gate rotation operations (angle range 0-2π, step size 0.01π), while introducing parameterized identity mapping constraints and incorporating an identity matrix into the encoding matrix to ensure that the original features are not overwhelmed by quantum state transformations. The middle layer is a QCNN feature extraction engine, designed with a multi-scale quantum convolution kernel structure. Unlike traditional single-scale convolutional kernels, this application uses a combination of 2×2 and 3×3 quantum convolutional kernels to capture local semantic associations and long-distance logical dependencies in text, respectively. It leverages quantum superposition and entanglement to generate high-dimensional feature vectors and simultaneously constructs adaptive residual connections to directly link initial word vectors with quantum feature outputs, addressing the signal degradation problem in deep networks. The top layer is a semantic matching engine that, based on a consumer finance scenario labeling system, dynamically adjusts feature weights using an attention mechanism to achieve precise matching between quantum features and business scenarios, thereby determining quantum semantic feature vectors with a semantic matching accuracy ≥95%. This module overcomes the limitations of traditional NLP in capturing deep implicit semantics and the single feature extraction of classical quantum models. Through the combination of multi-scale convolutional kernels and residual mechanisms, it makes feature representation more targeted and complete. In the quantum semantic parsing stage, this application converts institutional text into high-precision quantum feature vectors, providing feature support for subsequent classification. Step S30: Classify the quantum semantic feature vector based on the preset financial classification label to obtain the classified institutional information.

[0036] It should be noted that the preset financial classification labels are a pre-set classification label system specifically for consumer finance, covering six major categories and 15 subcategories: "Products and Services," "Risk Control," "Compliance and Supervision," "Information Technology," "Statistical Requirements," and "Others." By further subdividing the quantum semantic feature vectors using these preset financial classification labels, the categorized regulatory information is obtained.

[0037] Step S40: Perform compliance verification on the classified system information and output the compliance verification results.

[0038] It should be noted that by adopting a quantum-enhanced multi-dimensional verification framework and a full lifecycle traceability mechanism, the classified institutional information is verified in two dimensions: compliance conflict and business adaptability, and the compliance verification results are obtained and output.

[0039] Understandably, the system uses a built-in consumer finance compliance clause library (covering 300+ regulatory policies and 200+ internal regulations) and a quantum semantic matching verification algorithm to perform compliance verification on the categorized policy information. The system outputs the compliance verification results of the approved policies and updates them in the policy library. For the unapproved policies, the system marks the reasons for the problems and provides feedback for optimization.

[0040] In practice, to further illustrate the management process of this scheme, please refer to [the relevant documentation / reference]. Figure 2 The overall process diagram shown is as follows. The core of the overall process of this application is to build a full-link intelligent system for system processing, which includes "system collection, quantum semantic analysis, intelligent classification, compliance verification, effect evaluation, and knowledge accumulation". This system provides an end-to-end solution for financial consumer system management and mainly includes six core processes. The policy collection process serves as the end-to-end entry point, connecting to two channels: an internal policy repository (PDF / WORD / TXT files) and external regulatory APIs (such as the State Financial Supervision and Administration Bureau and the People's Bank of China). It collects multi-dimensional information including policy documents and regulatory policies, while simultaneously establishing data access permissions, collection frequency, format filtering rules, and interface security authentication mechanisms. The quantum semantic parsing process performs quantum encoding (word vector to quantum state), QCNN feature extraction (capturing deep semantics), and semantic transformation (associating with business tags) on the policy text. Simultaneously, a parsing verification engine checks the completeness of feature extraction; data that fails is stored in an anomaly pool and an alarm is triggered. The intelligent classification process, based on a consumer finance-specific tagging system, completes the broad category classification and precise sub-category division of policies. The compliance verification process verifies the classification results against the policy content from two dimensions: compliance conflict and business adaptability. Items that fail are marked with the reasons for the problem and feedback is provided for optimization. The effectiveness evaluation process calculates indicators such as classification accuracy, processing efficiency, and compliance coverage, generating a visual evaluation report. The knowledge accumulation process extracts knowledge assets such as classification rules and compliance cases to build a reusable knowledge system. Ultimately, through end-to-end collaboration, the system addresses the problems of "low efficiency, poor classification, difficulty in compliance, and lack of data retention" in traditional institutional management, and outputs standardized and credible institutional management results, laying the foundation for compliant operation of consumer finance business.

[0041] This embodiment collects multi-source policy information from an internal financial consumption policy database and an external regulatory system, and performs data cleaning and standardization on this information to obtain processed policy information. Based on a pre-defined quantum semantic parsing model, the processed policy information is semantically parsed to obtain quantum semantic feature vectors. This pre-defined quantum semantic parsing model is a model built upon a quantum convolutional neural network model and a large-scale natural language processing model. The quantum convolutional neural network model includes multi-dimensional quantum convolutional kernels and an adaptive residual structure. The multi-dimensional quantum convolutional kernels contain at least two types of quantum convolutional kernels, used to capture local semantic associations and long-distance dependencies in the text. This invention classifies quantum semantic feature vectors using pre-defined financial classification labels to obtain classified institutional information. It then performs compliance verification on the classified institutional information and outputs the verification results. Compared to relying on manual review processes that require manually screening and distinguishing between internal regulations and external regulatory requirements, leading to insufficient classification accuracy and consistency, and lagging institutional processing efficiency, this application improves institutional processing efficiency while reducing the risk of institutional omissions by collecting and preprocessing multi-source institutional information, combining it with a pre-defined quantum semantic parsing model to perform deep semantic parsing on the processed institutional information, and classifying and verifying the parsed features.

[0042] Based on the above Figure 1 The first embodiment shown illustrates a second embodiment of the emotion recognition method proposed in this application; refer to... Figure 3 , Figure 3 This is a flowchart illustrating Embodiment Two of the Financial Consumer System Verification Method of this Application. In Embodiment Two of this Application, the same or similar content as Embodiment One above can be referred to the above description, and will not be repeated hereafter.

[0043] In this embodiment, step S20 further includes: Step S201: Based on a preset quantum semantic analysis model, the processed institutional information is quantum encoded to obtain a quantum encoded feature vector.

[0044] It should be noted that, to further illustrate the quantum semantic analysis process in this application, reference is made to... Figure 4 The detailed flowchart of quantum semantic parsing is shown. The quantum encoding component serves as the module entry point, receiving the preprocessed institutional text and converting it into a low-dimensional word vector sequence using Word2Vec. Then, it encodes the word vectors into quantum states (the number of qubits is dynamically configured from 4 to 8) using Y-gate operations, incorporating parameterized identity mapping to ensure the preservation of original features. The QCNN feature extraction component constructs a multi-scale convolutional kernel (2×2+3×3) based on 4 qubits. Through a combination of rotation gates and CNOT gates, combined with adaptive residual connections, it captures deep semantic associations using quantum superposition and entanglement properties, generating high-precision quantum feature vectors.

[0045] Understandably, this application uses a quantum coding engine and a quantum feature extraction unit (QCNN feature extraction unit) in a pre-defined quantum semantic parsing model to quantum code the processed institutional information to obtain quantum-coded feature vectors. In this application, quantum coding processing breaks through the limitations of traditional fixed-dimensional coding and adopts a dynamic quantum bit allocation strategy. The number of quantum bits is adaptively configured according to the length of the institutional text (4 bits for short texts and 6-8 bits for medium-to-long texts). The word vectors are encoded into quantum states through Y-gate rotation operations (angle range 0-2π, step size 0.01π). At the same time, parameterized identity mapping constraints are introduced to incorporate an identity matrix into the coding matrix to ensure that the original features are not submerged by quantum state transformations, thereby obtaining more accurate quantum-coded feature vectors.

[0046] Furthermore, step S201 further includes: performing word vector conversion on the processed institutional information based on a preset quantum semantic parsing model to obtain word vectors of the target dimension; and performing quantum encoding on the word vectors based on a quantum state mapping algorithm and a preset parameter optimization mechanism to obtain quantum encoded feature vectors.

[0047] Understandably, this application performs word vector conversion on the processed institutional information based on a preset quantum semantic parsing model to obtain word vectors of the target dimension; and performs quantum encoding on the word vectors based on a quantum feature extraction unit, a quantum state mapping algorithm, and a preset parameter optimization mechanism to obtain quantum encoded feature vectors. This application performs quantum encoding through a quantum encoding unit and a quantum feature extraction unit, and completes quantum encoding through word vector conversion rules, a quantum state mapping algorithm, a preset parameter optimization mechanism, quantum convolution kernel construction rules, quantum operation sequences, and a measurement mechanism to obtain quantum encoded feature vectors. The word vector conversion rule uses the Word2Vec model to convert the institutional text into 128-512 dimensional word vectors after word segmentation, and the window size is set to 5-10 (dynamically adjusted according to the text length). The quantum state mapping algorithm innovatively adopts a dynamic qubit allocation strategy, allocating 4, 6, and 8 qubits respectively based on the text length (less than or equal to 500 characters for short text, 500-2000 characters for medium text, and more than 2000 characters for long text). Each word vector feature is encoded into a quantum state using a Y-gate operation, with the rotation angle determined by training parameter optimization (range 0-2π, step size 0.01π). Simultaneously, an identity matrix is ​​incorporated into the encoding matrix to achieve parameterized identity mapping, ensuring original feature retention ≥98%. The preset parameter optimization mechanism is based on gradient descent to minimize the mapping error between quantum states and classical features, reducing feature loss rate by 40% compared to traditional fixed-bit encoding. The QCNN feature extraction unit is used for deep mining of quantum features. Its core attributes include quantum convolution kernel construction rules, quantum operation sequences, and measurement mechanisms. The quantum convolution kernel construction rules innovatively design multi-scale quantum convolution kernels, constructing 2×2 and 3×3 scale kernels based on four qubits. The 2×2 kernel captures local semantic relationships in the text (such as adjacent clause logic), while the 3×3 kernel captures long-distance dependencies (such as cross-chapter semantic echoes). Each kernel contains 12 random quantum operations (a combination of rotation gates and CNOT gates), distributed across all qubits to achieve global operation. Entanglement; the quantum operation sequence first adjusts the state of individual qubits through a rotation gate, and then realizes entanglement between qubits through a CNOT gate to generate complex feature representations. At the same time, an adaptive residual connection is constructed to directly associate the initial word vector with the quantum feature output of the current layer, solving the signal degradation problem of deep networks; the measurement mechanism uses PauliZ measurement to observe qubits, obtains the measurement results of quantum states and converts them into 256-1024 dimensional classical feature vectors, with a feature extraction accuracy of ≥95%, which improves the deep semantic recognition capability by 15% compared with the traditional single-scale QCNN.

[0048] Step S202: Semantic matching is performed on the quantum encoded feature vector based on a preset scene tag library to obtain a quantum semantic feature vector.

[0049] It should be noted that the preset scenario tag library is a pre-set database covering 6 major categories and 15 subcategories of consumer finance scenarios, including tag business descriptions, applicable scope, and related keywords, and is updated once a week. The quantum semantic feature vector is obtained by semantic transformation and matching of the quantum encoded feature vector through the preset scenario tag library.

[0050] In its specific implementation, this application uses a semantic transformation unit to perform business-oriented mapping on the quantum encoded feature vector, thereby determining the quantum semantic feature vector.

[0051] Furthermore, step S202 further includes: performing cosine similarity calculation based on a preset scene tag library and the quantum encoded feature vector to obtain similarity information; adjusting the weights of the quantum encoded feature vector based on a preset fusion attention mechanism and the similarity information to obtain a weight-adjusted quantum encoded feature vector; and performing feature optimization on the weight-adjusted quantum encoded feature vector based on preset business rules to obtain a quantum semantic feature vector.

[0052] Understandably, cosine similarity is calculated on quantum-encoded feature vectors using a preset semantic matching algorithm and a preset scenario tag library to obtain similarity information. Based on the similarity information, configurable quantum-encoded feature vectors are determined. The weights of the configurable quantum-encoded feature vectors and the preset fusion attention mechanism are adjusted to obtain weight-adjusted quantum-encoded feature vectors. Furthermore, the weight-adjusted quantum-encoded feature vectors are optimized using preset business rules to retain feature dimensions that are strongly correlated with consumer finance scenarios, thereby obtaining quantum semantic feature vectors.

[0053] In its specific implementation, the semantic transformation unit's core attributes include a preset scenario tag library, a semantic matching algorithm, and a feature optimization mechanism. The preset scenario tag library covers 6 major categories and 15 subcategories of consumer finance scenarios, including tag business descriptions, applicable scope, and associated keywords, and is updated weekly. The preset semantic matching algorithm is based on the cosine similarity calculation between quantum feature vectors and tag features (threshold configurable from 0.7 to 0.9), and dynamically adjusts the weights of different feature dimensions through a preset fusion attention mechanism, assigning higher weights to feature dimensions strongly related to business scenarios (such as "credit scoring" and "overdue" features in the "risk control" scenario). The feature optimization mechanism filters redundant features through preset business rules, retaining feature dimensions strongly related to consumer finance scenarios, thereby improving subsequent classification efficiency.

[0054] In this embodiment, the classification verification includes primary classification verification and secondary classification verification. Step S30 further includes: performing primary classification on the quantum semantic feature vector and the basic business features in the internal financial consumption system database based on a preset logistic regression model and preset financial classification labels to obtain primary classification results; performing feature fusion on the quantum semantic feature vector and the text keyword features and business scenario features in the internal financial consumption system database based on a quantum feature attention fusion mechanism to obtain feature fusion vectors; performing secondary classification on the feature fusion vectors based on a lightweight gradient boosting model to obtain secondary classification results; and determining the classified system information based on the primary classification results and the secondary classification results.

[0055] It should be noted that this application also completes the quantum feature classification through two components: a classification rule engine and a hierarchical classification component. The classification rule engine loads a pre-defined consumer finance classification label system (6 major categories and 15 subcategories) and pre-defined business rules (such as keyword matching logic) to provide a standard basis for classification. The hierarchical classification component first completes the coarse classification of major categories using a pre-defined logistic regression model, i.e., the first-level classification. Then, it achieves precise sub-classification using the LightGBM model, a lightweight gradient boosting model that fuses quantum features and multi-source features, i.e., the second-level classification. The classification results are output and synchronized to the compliance verification module. Data that fails the classification verification is fed back to the QCNN feature extraction component for reprocessing. Ultimately, this module solves the problem of "shallow semantic capture and low accuracy" in traditional classification, providing support for subsequent compliance verification and retrieval.

[0056] It should be understood that, for further explanation of the classification process of this application, reference is made to... Figure 5The detailed flowchart of the intelligent classification process is shown. Based on quantum semantic parsing, the intelligent classification module constructs a full-process classification system centered on "hierarchical classification" and "quantum feature fusion." This module takes the high-dimensional semantic vectors output from quantum encoding and QCNN feature extraction as input. First, it uses a logistic regression model to perform a coarse classification of six categories: "Products and Services," "Risk Control," "Compliance and Supervision," "Information Technology," "Statistical Requirements," and "Others," completing the first-level classification verification. Then, it enters the feature fusion stage, employing a quantum feature attention fusion mechanism to dynamically weight and fuse quantum features (weight 0.7), text keyword features from the internal financial consumption policy database (weight 0.2), and business scenario features from the internal financial consumption policy database (weight 0.1) to form an enhanced semantic representation and obtain a feature fusion vector. Next, based on the LightGBM model, it accurately divides the feature fusion vector into 15 subclasses to complete the second-level classification verification. During this process, a mandatory verification mechanism based on business rules is embedded (e.g., clauses containing keywords such as "credit reporting" and "overdue" are automatically classified into the "risk control" category), achieving a classification accuracy of ≥92%. The system has closed-loop optimization capabilities; data that fails verification is automatically fed back to the QCNN module for reprocessing, enabling continuous model iteration. This process effectively solves the problems of "shallow semantic capture and low accuracy" in traditional classification methods, providing high-quality structured input for subsequent compliance verification and retrieval.

[0057] Understandably, this application achieves automated and accurate classification of institutional documents during the intelligent classification stage. This classification is accomplished through three main components: a classification label management unit, a hierarchical classification unit, and a classification optimization unit. The structure, connections, and functional rules of each component include the classification label management unit, which defines and manages classification standards. Its core attributes include label system design rules, a version control mechanism, and a label maintenance process. The label system design rules are designed with a two-level architecture of "major category" and "sub-category." Major categories include six types such as "Products and Services" and "Risk Prevention," while sub-categories include 15 types such as "Credit Risk" and "Personal Loans." Each label includes a business definition, applicable institutional type, and associated rules. The version control mechanism is based on Git-like version management, recording label additions, modifications, and deletions. The version number format is "V major version. minor version. revision number." The label maintenance process involves business experts initiating label change requests, which take effect after review by the compliance department, ensuring the authority and accuracy of the label system. The hierarchical classification unit is used for two-level classification processing of policy texts. Its core attributes include a first-level classification verification model and a second-level classification verification model. The feature fusion rule uses a logistic regression model in the first-level classification verification model, inputting quantum feature vectors and basic business features (such as the policy issuing department) to output a major category classification result, completing the first-level classification. The regularization coefficient is configurable from 0.01 to 0.1. The second-level classification verification model uses the LightGBM model, introducing a quantum feature attention fusion mechanism. It fuses QCNN quantum features, text keyword features, and business scenario features with weights of 0.7, 0.2, and 0.1, respectively, inputting them into the model to achieve accurate subclass division and complete the second-level classification. The tree depth is configurable from 3 to 10, and the learning rate is 0.01 to 0.1. The feature fusion rule dynamically adjusts feature weights through an attention network, improving multi-label classification accuracy by 8% compared to traditional single-feature input, achieving a classification accuracy ≥92%. The classification optimization unit is used for continuous iteration of the classification model. Its core attributes include model evaluation metrics, iteration mechanism, and anomaly handling process. The model evaluation metrics use three types of metrics: accuracy, F1_macro, and F1_micro, with target values ​​of ≥92%, ≥90%, and ≥91%, respectively. The iteration mechanism automatically iterates model parameters every 7 days and retrains the model every 30 days based on user feedback and classification performance data. The anomaly handling process triggers model diagnosis (analyzing data quality and feature effectiveness) when the classification accuracy of a certain system falls below 85%, and optimizes the model by supplementing training data and adjusting model hyperparameters to ensure stable classification performance.

[0058] Furthermore, step S40 also includes: matching the classified institutional information with the quantum features of the compliance clauses corresponding to the preset compliance clause library based on a preset quantum semantic matching verification algorithm to obtain a semantic matching result; performing compliance verification on the semantic matching result and outputting the compliance verification result.

[0059] It should be noted that this application also includes a compliance verification and traceability stage. The core of this stage is to ensure the compliance and traceability of the system, and it comprises three main components: a compliance verification unit, a traceability management unit, and an alarm handling unit. To further illustrate the compliance verification and traceability process, please refer to [reference needed]. Figure 6 The flowchart shown illustrates the compliance verification and traceability process. Based on intelligent classification, the compliance verification and traceability module constructs a comprehensive compliance management system centered on "quantum semantic matching" and "blockchain traceability." This module takes the classified regulatory clauses and their quantum feature vectors as input. First, it uses a quantum semantic matching algorithm to perform multi-path, high-dimensional matching between the clause features and the quantum features of corresponding compliance clauses in a pre-set compliance clause library. This library includes over 300 regulatory policies and over 200 internal regulations. Similarity is calculated, and the semantic matching result is determined based on the similarity calculation. Conflict detection is then performed on the semantic matching result to determine the compliance verification result and output it, accurately identifying explicit compliance conflicts and implicit semantic contradictions.

[0060] Furthermore, after step S40, the method further includes: determining the risk level based on the compliance verification result; triggering the traceability management mechanism based on the risk level; generating alarm information by combining the traceability information and the risk level; and pushing the alarm information to the approver.

[0061] It should be noted that a three-tiered risk assessment is used to determine the risk level based on the compliance verification results. This includes: high-risk conflicts (such as violations of core regulatory requirements) are automatically blocked and trigger alarms; medium-risk deviations (such as inconsistencies in non-critical clauses) are pushed for manual review; and low-risk issues (such as non-standard wording) are given optimization suggestions. The overall compliance identification coverage rate is ≥98%. Simultaneously, this application introduces a blockchain traceability management mechanism to generate tamper-proof hash records for key nodes throughout the entire lifecycle of the regulation, including drafting, revision, review, and effectiveness, linking regulatory basis with revised versions to achieve "traceable clauses and verifiable compliance." The system also establishes a closed-loop handling mechanism, automatically generating handling work orders for medium- and high-risk issues and tracking the closure process to ensure that all compliance risks are handled within 24 hours. This process effectively solves the problems of "weak identification of implicit conflicts and easy tampering of traceability records" in traditional compliance verification, providing a solid guarantee for the compliance and credibility of financial consumer regulation management.

[0062] Understandably, the compliance verification unit is responsible for checking the compliance of the institutional clauses. Its core attributes include a compliance clause library, a verification algorithm, and a risk grading mechanism. The compliance clause library is based on a two-tiered "regulatory-internal" architecture. The regulatory clauses include over 300 articles such as the "Pilot Management Measures for Consumer Finance Companies," while the internal clauses include over 200 institutional regulations. It is dynamically updated through API integration with the compliance system. The verification algorithm is a quantum semantic matching verification algorithm that utilizes the high-dimensional representation capability of quantum features to perform multi-path matching between the quantum features of institutional clauses and the quantum features of compliance clauses. This accurately identifies implicit conflicts, with a similarity threshold configurable from 0.6 to 0.8. Combined with the Rete algorithm, it achieves rapid rule matching, improving the implicit conflict identification rate by 25% compared to traditional semantic matching. The risk grading mechanism divides risks into three levels based on the severity of the violation: low risk (e.g., non-standard wording) prompts for optimization, medium risk (e.g., deviations from non-core clauses) requires manual review, and high risk (e.g., violation of core regulatory requirements) results in blocking the violation. The misjudgment rate is ≤1%. The traceability management unit is responsible for the traceability management of the entire lifecycle of the system. Its core attributes include a traceability information system, index construction rules, and a traceability display mechanism. The traceability information system innovatively introduces blockchain technology to record the drafter of the system, revision records, revision basis (related regulatory documents / internal notices), and verification records. Each operation node generates an immutable hash value to ensure the traceability of key nodes. The index construction rules build a traceability index in Elasticsearch, including document ID, chapter ID, data source identifier, and original data snapshot, supporting reverse retrieval of the source by system content. The traceability display mechanism allows users to click on a clause of the system to pop up a traceability pop-up window, displaying data source details, verification records, and de-identification instructions. It also supports downloading the original data source file, achieving "content traceability and data auditability." The alarm handling unit is responsible for timely response to compliance risks. Its core attributes include alarm triggering conditions, alarm synchronization mechanisms, and closed-loop handling processes. The alarm triggering conditions include high-risk compliance conflicts, data quality anomalies (deviation > 5%), and a sharp drop in classification accuracy (> 10%). The alarm synchronization mechanism is based on the Apollo distributed configuration center to achieve second-level synchronization of alarm commands, supporting three methods: system pending tasks, enterprise WeChat notifications, and telephone alarms. The closed-loop handling process records the alarm recipient, handling measures, and handling results, requiring handling to be completed within 24 hours, with automatic archiving after handling, and a closure rate of ≥ 98%.

[0063] In one possible implementation, this application further includes an effect evaluation stage and a knowledge accumulation stage. The effect evaluation stage, whose core function is to quantitatively analyze the overall effect of the system processing, provides data support for system optimization. It comprises three main components: an indicator calculation unit, a multi-dimensional analysis unit, and an evaluation report generation unit. The structure, connection relationships, and functional rules of each component are as follows: The indicator calculation unit is responsible for the automated calculation of core evaluation indicators. Its core attributes include the indicator system, calculation rules, and real-time update mechanism. The indicator system, based on the evaluation dimension framework, considers efficiency (generation time, retrieval response time), quality (classification accuracy, compliance coverage), and business adaptability (scenario matching). Ten core metrics are set across three dimensions: accuracy, user satisfaction, and performance. The calculation rules are as follows: efficiency metrics are calculated based on log timestamps (generation time = classification completion time - collection time); quality metrics are calculated based on comparison results (accuracy = number of fields conforming to standards / total number of fields); and business adaptability metrics are calculated by combining user feedback and business data. The real-time update mechanism is based on a stream-batch fusion architecture. FlinkStreaming processes real-time data (generation time is calculated in real-time), SparkBatch performs full daily calculations of historical data (monthly accuracy), and the metric results are stored in the InfluxDB time-series database, supporting multi-granularity queries. The multi-dimensional analysis unit is responsible for evaluating the in-depth breakdown and insight of the data. Its core attributes include analysis dimensions, drill-down mechanisms, and anomaly localization algorithms. The analysis dimensions are based on a multi-dimensional slicing system, supporting slicing analysis by system type (product / risk control), business department (personal loan department / compliance department), time (day / week / month), and user (new / old user), such as "monthly product system classification accuracy rate of the personal loan department". The drill-down mechanism is based on hierarchical decomposition rules, drilling down from the summary indicators to the detailed data layer by layer ("low overall accuracy → low accuracy of a certain type of document → specific field error") to locate the root cause of the problem. The anomaly localization algorithm is based on a time series model, using the ARIMA model to identify abnormal fluctuations in indicators (sudden drop in accuracy), and combining correlation analysis to find influencing factors (quality decline after a template update), generating anomaly analysis conclusions.The report generation unit is responsible for the visualization and business-oriented output of the evaluation results. Its core attributes include report templates, dynamic layout, and an export mechanism. The report templates are based on a periodic template system, with templates set according to the evaluation cycle (day / week / month), and include "Overview of Core Indicators - Dimensional Analysis - Anomaly Insights - Optimization Suggestions," supporting custom structures. The dynamic layout is based on importance ranking rules, adjusting the chapter order according to indicator weights (business impact × data significance), placing important conclusions at the top, and using ECharts to generate visual charts such as line charts (trends) and bar charts (comparisons). The export mechanism is based on multi-format output functionality, supporting PDF / PPT export, with charts accompanied by data source markers (time range, calculation rules). The report content is transformed into concise business descriptions through NLP, supporting online sharing or email delivery.

[0064] In one possible implementation, the core function of the knowledge accumulation stage is to realize the full lifecycle management and reuse of institutional management knowledge. It includes four major components: a knowledge extraction unit, a model training unit, an intelligent recommendation unit, and a version management unit. The structure, connection relationships, and functional rules of each component are as follows: The knowledge extraction unit is responsible for the collection and transformation of high-quality knowledge during the creation process. Its core attributes include a knowledge type library, extraction algorithm, and standardization mechanism. The knowledge type library, based on a knowledge classification system, includes template knowledge (high-quality classification rules, format standards), rule knowledge (cleaning rules, verification rules), and case knowledge (high-pass-rate institutional fragments, problem-solving solutions), covering the entire... The process involves the following extraction algorithm: Based on knowledge mining technology, template knowledge is extracted through structural comparison (filtering rules with high usage and low modification rates), rule knowledge is extracted through effect analysis (retaining rules that improve accuracy), and case knowledge is extracted through user annotation and semantic analysis. An innovative dual-screening mechanism of "effect quantification + manual annotation" is designed, achieving a knowledge extraction accuracy rate of ≥90%. The standardization mechanism is based on a unified knowledge format: template knowledge is stored as an XML structure, rule knowledge as JSON rules, and case knowledge as "scenario-content-effect" triples. Tags are added to the knowledge (e.g., "Loan Report - Risk Control Analysis - 2024 Version") for convenient classification and management. The model training unit is responsible for the training iteration of the knowledge recommendation and optimization model. Its core attributes include model architecture, training data, and performance verification mechanism. The model architecture is based on a hybrid model design, integrating XGBoost (template structure matching) and BERT (semantic case recommendation). XGBoost takes the document type and scenario as input and outputs recommendation weights, while BERT takes the topic as input and outputs similar cases. The model is deployed on TensorFlowServing. The training data is based on a dual-source data system, using "historical knowledge + user feedback" data. Historical knowledge includes accumulated templates, rules, and cases, while user feedback includes template reuse rate and case adoption rate. The data volume is ≥100,000 records, and the sample is expanded through data augmentation. The performance verification mechanism is based on a dual-line verification system. Offline testing evaluates the AUC and accuracy (target AUC ≥ 0.92), and online A / B testing compares the efficiency of the recommendation group and the manual selection group (target improvement of 20%). After achieving the target, the production model is updated. An innovative knowledge decay mechanism is introduced, which automatically reduces the recommendation weight by 50% for knowledge with a reuse rate of <10% for three consecutive months.The intelligent recommendation unit is responsible for the accurate delivery and reuse of knowledge. Its core attributes include a demand understanding algorithm, a recommendation strategy, and a feedback mechanism. The demand understanding algorithm, based on semantic understanding technology, uses the BERT model to extract user-created demands ("Generate classification rules for personal credit systems in October 2024"), identifies key features (time, type, scenario), and generates a demand vector. The recommendation strategy is based on a three-level recommendation process: in the recall stage, similar knowledge is matched through FAISS vector retrieval; in the coarse ranking stage, the Top 100 are selected using LightGBM; and in the fine ranking stage, DeepFM is used to comprehensively evaluate matching degree, effect, and novelty, outputting the Top 3 recommendation results. The feedback mechanism, based on user behavior records, records user operations on recommended knowledge (adoption / ignoring / modification). Knowledge with high adoption rates has increased weight, while knowledge with high ignore rates has decreased weight. It also supports users marking "high-quality recommendations" for model iteration. The version management unit is responsible for the secure iteration and traceability of knowledge assets. Its core attributes include a version control mechanism, a rollback process, and a difference comparison tool. The version control mechanism is based on semantic versioning specifications. Major updates increment the main version (V1.0→V2.0), feature optimizations increment the secondary version (V1.0→V1.1), and bug fixes increment the revision number (V1.0→V1.0.1), recording the version number, update content, personnel, and time. The rollback process is based on a secure rollback mechanism, supporting rollback to any historical version. After a user submits an application, the system verifies the version's legality and completes the rollback within one minute, generating a rollback log (reason, scope of impact). The difference comparison tool is based on text comparison technology and uses Levenshtein distance to display version differences (rule condition adjustments, case content additions and deletions). Difference content is marked with different colors for easy and quick identification. The system is deployed on a Kubernetes cluster (≥6 nodes, 1 master and 5 slaves, with dual-machine hot standby for the master node), using the Istio service mesh for microservice governance. Each node has at least 16 CPU cores (Intel Xeon Gold 6330 or equivalent); at least 64GB of DDR4 memory; and at least 1TB of SSD storage (NVMe protocol). The operating system is CentOS 7.9 or Ubuntu 20.04 LTS, supporting containerized elastic scaling to ensure high availability of microservices. The backend application is based on a J2EE architecture and MVC pattern, with the service layer relying on Spring Boot and Mybatis for core component support. The frontend web application uses the Vue framework and Element-UI components for page design. The algorithm service is developed in Python, using the Flask framework to build a RESTful API service, and relies on libraries such as transformers, PyTorch, and Qiskit to implement core functionality.The database uses MySQL (one master and one slave deployment) to store business data, Redis (Sentinel architecture) to cache frequently used data, and MongoDB to store unstructured knowledge units, ensuring high availability and high reliability of the system. Real-world testing data shows that under pressure to process an average of 300 regulatory documents per day, the system's average processing time is ≤2 hours, classification accuracy is ≥92%, and compliance coverage is ≥98%, fully meeting the regulatory management needs of the consumer finance industry.

[0065] This embodiment collects multi-source regulatory information from an internal financial consumption regulatory database and an external regulatory system, and performs data cleaning and standardization on this information to obtain processed regulatory information. Based on a pre-defined quantum semantic parsing model, the processed regulatory information is quantum-encoded to obtain quantum-encoded feature vectors. Semantic matching of these quantum-encoded feature vectors is then performed on them using a pre-defined scenario label library to obtain quantum semantic feature vectors. The pre-defined quantum semantic parsing model is a model constructed based on a quantum convolutional neural network model and a large-scale natural language processing model. The quantum convolutional neural network model includes multi-dimensional quantum convolutional kernels and an adaptive residual structure. The multi-dimensional quantum convolutional kernels contain at least two types of quantum convolutional kernels used to capture local semantic associations and long-distance dependencies in the text. Based on pre-defined financial classification labels, the quantum semantic feature vectors are classified to obtain classified regulatory information. Finally, compliance verification is performed on the classified regulatory information, and the compliance verification results are output. Compared to relying on manual review processes that require manually screening and distinguishing between internal regulations and external regulatory requirements, leading to insufficient accuracy and consistency in classification and lagging efficiency in system processing, this embodiment collects and preprocesses multi-source system information, combines it with a preset quantum semantic parsing model to perform deep semantic parsing on the processed system information, and classifies and verifies the compliance of the parsed features, thereby improving the efficiency of system processing while reducing the risk of system omissions.

[0066] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the financial consumer system verification method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0067] This application also provides a financial consumption system verification device, please refer to... Figure 7 The financial consumption system verification device includes: The institutional preprocessing module 10 is used to collect multi-source institutional information from the internal financial consumption institutional database and the external regulatory system, and to perform data cleaning and standardization on the multi-source institutional information to obtain processed institutional information. The quantum semantic parsing module 20 is used to perform semantic parsing on the processed institutional information based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model built on a quantum convolutional neural network model and a large natural language processing model. The quantum convolutional neural network model includes a multidimensional quantum convolutional kernel and an adaptive residual structure. The multidimensional quantum convolutional kernel includes at least two types of quantum convolutional kernels, which are used to capture local semantic associations and long-distance dependencies in the text. The intelligent classification module 30 is used to classify the quantum semantic feature vector based on a preset financial classification label to obtain the classified institutional information; The compliance verification module 40 is used to perform compliance verification on the classified system information and output the compliance verification results.

[0068] Furthermore, the quantum semantic parsing module 20 is also used to perform quantum encoding on the processed institutional information based on a preset quantum semantic parsing model to obtain a quantum encoded feature vector; and to perform semantic matching on the quantum encoded feature vector based on a preset scene tag library to obtain a quantum semantic feature vector.

[0069] Furthermore, the quantum semantic parsing module 20 is also used to perform word vector conversion on the processed institutional information based on a preset quantum semantic parsing model to obtain word vectors of the target dimension; and to perform quantum encoding on the word vectors based on a quantum state mapping algorithm and a preset parameter optimization mechanism to obtain quantum encoded feature vectors.

[0070] Furthermore, the quantum semantic parsing module 20 is also used to perform cosine similarity calculation based on a preset scene tag library and the quantum encoded feature vector to obtain similarity information; adjust the weights of the quantum encoded feature vector based on a preset fusion attention mechanism and the similarity information to obtain a weight-adjusted quantum encoded feature vector; and perform feature optimization on the weight-adjusted quantum encoded feature vector based on preset business rules to obtain a quantum semantic feature vector.

[0071] Furthermore, the classification verification includes primary classification verification and secondary classification verification. The intelligent classification module 30 is also used to perform primary classification on the quantum semantic feature vector and the basic business features in the internal financial consumption system database based on a preset logistic regression model and preset financial classification labels to obtain primary classification results; to perform feature fusion on the quantum semantic feature vector and the text keyword features and business scenario features in the internal financial consumption system database based on a quantum feature attention fusion mechanism to obtain feature fusion vector; to perform secondary classification on the feature fusion vector based on a lightweight gradient boosting model to obtain secondary classification results; and to determine the classified system information based on the primary classification results and the secondary classification results.

[0072] Furthermore, the compliance verification module 40 is also used to match the classified institutional information with the quantum features of the compliance clauses corresponding to the preset compliance clause library based on a preset quantum semantic matching verification algorithm to obtain a semantic matching result; to perform compliance verification on the semantic matching result and output the compliance verification result.

[0073] Furthermore, the compliance verification module 40 is also used to determine the risk level based on the compliance verification result; trigger the traceability management mechanism based on the risk level, generate alarm information by combining the traceability information and the risk level, and push it to the approver.

[0074] The financial consumer regulations verification device provided in this application, employing the financial consumer regulations verification method described in the above embodiments, can solve the technical problem that relying on manual review processes requires manually screening and distinguishing between internal regulations and external regulatory requirements, leading to insufficient classification accuracy and consistency, and lagging system processing efficiency. Compared with the prior art, the beneficial effects of the financial consumer regulations verification device provided in this application are the same as those of the financial consumer regulations verification method provided in the above embodiments, and other technical features in the financial consumer regulations verification device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0075] This application provides a financial consumer system verification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the financial consumer system verification method in the above embodiment 1.

[0076] The following is for reference. Figure 8 The diagram illustrates a structural schematic of a financial consumer policy verification device suitable for implementing embodiments of this application. The financial consumer policy verification device in this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The financial consumption system verification device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 8As shown, the financial consumer policy verification device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the financial consumer policy verification device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the financial consumer insurance verification device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows financial consumer insurance verification devices with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0078] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0079] The financial consumer regulations verification device provided in this application, employing the financial consumer regulations verification method described in the above embodiments, can solve the technical problem that relying on manual review processes requires manually screening and distinguishing between internal regulations and external regulatory requirements, leading to insufficient classification accuracy and consistency, and lagging system processing efficiency. Compared with the prior art, the beneficial effects of the financial consumer regulations verification device provided in this application are the same as those of the financial consumer regulations verification method provided in the above embodiments, and other technical features in this financial consumer regulations verification device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0080] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0082] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the financial consumption system verification method in the above embodiments.

[0083] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0084] The aforementioned computer-readable storage medium may be included in the financial consumer system verification device; or it may exist independently and not be assembled into the financial consumer system verification device.

[0085] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the financial consumer system verification device, the financial consumer system verification device: sequentially polls the motion control chip corresponding to each axis within a preset control cycle via a high-speed serial bus, and obtains the encoder data corresponding to each axis fed back by the motion control chip; determines the encoder type corresponding to each axis based on the encoder data; selects a target control strategy from preset control strategies based on the encoder type corresponding to each axis, and performs closed-loop control on each axis according to the target control strategy.

[0086] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0089] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the aforementioned financial consumer regulations verification method. This solves the technical problem that relying on manual review processes requires manually screening and distinguishing between internal regulations and external regulatory requirements, leading to insufficient accuracy and consistency in classification and lagging system processing efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the financial consumer regulations verification method provided in the above embodiments, and will not be elaborated upon here.

[0090] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the financial consumption system verification method described above.

[0091] The computer program product provided in this application can solve the technical problem that relying on manual review processes requires manually screening and distinguishing between internal regulations and external regulatory requirements, resulting in insufficient accuracy and consistency in classification and lagging efficiency in system processing. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the financial consumer system verification method provided in the above embodiments, and will not be repeated here.

[0092] The above description is only a part of the embodiments of this application and does not limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

Claims

1. A method for verifying a financial consumption system, characterized in that, The verification methods for the financial consumption system include: Multi-source policy information is collected from the internal financial consumption policy database and the external regulatory system, and the multi-source policy information is cleaned and standardized to obtain the processed policy information. The processed institutional information is semantically parsed based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model built on a quantum convolutional neural network model and a large natural language processing model. The quantum convolutional neural network model includes a multidimensional quantum convolutional kernel and an adaptive residual structure. The multidimensional quantum convolutional kernel includes at least two types of quantum convolutional kernels, which are used to capture local semantic associations and long-distance dependencies in the text. The quantum semantic feature vector is classified based on a preset financial classification label to obtain the classified institutional information. Perform compliance verification on the categorized system information and output the compliance verification results.

2. The financial consumption system verification method as described in claim 1, characterized in that, The step of performing semantic parsing on the processed institutional information based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors includes: The processed institutional information is quantum-encoded based on a preset quantum semantic parsing model to obtain a quantum-encoded feature vector. Semantic matching is performed on the quantum encoded feature vector based on a preset scene tag library to obtain a quantum semantic feature vector.

3. The financial consumption system verification method as described in claim 2, characterized in that, The step of quantum encoding the processed institutional information based on a preset quantum semantic analysis model to obtain a quantum encoded feature vector includes: The processed institutional information is converted into word vectors based on a preset quantum semantic parsing model to obtain word vectors of the target dimension. The word vectors are quantum encoded using a quantum state mapping algorithm and a preset parameter optimization mechanism to obtain quantum encoded feature vectors.

4. The financial consumption system verification method as described in claim 3, characterized in that, The step of semantically matching the quantum encoded feature vector based on a preset scene tag library to obtain a quantum semantic feature vector includes: Cosine similarity is calculated based on a preset scene tag library and the quantum-encoded feature vector to obtain similarity information; The weights of the quantum-encoded feature vector are adjusted based on a preset fusion attention mechanism and the similarity information to obtain a weight-adjusted quantum-encoded feature vector. Based on preset business rules, the quantum encoded feature vector after weight adjustment is optimized to obtain a quantum semantic feature vector.

5. The financial consumption system verification method as described in any one of claims 1 to 4, characterized in that, The classification verification includes primary classification verification and secondary classification verification. The step of classifying the quantum semantic feature vector based on preset financial classification labels to obtain the classified institutional information includes: Based on a preset logistic regression model and preset financial classification labels, the quantum semantic feature vector and the basic business features in the internal financial consumption system database are classified into primary categories to obtain primary classification results. Based on the quantum feature attention fusion mechanism, the quantum semantic feature vector is fused with the text keyword features and business scenario features in the internal financial consumption system database to obtain the feature fusion vector. The feature fusion vector is classified into two categories based on a lightweight gradient boosting model to obtain the two-level classification result. The classified institutional information is determined based on the primary classification results and the secondary classification results.

6. The financial consumption system verification method as described in any one of claims 1 to 4, characterized in that, The step of performing compliance verification on the categorized system information and outputting the compliance verification results includes: The classified institutional information is matched with the quantum features of the corresponding compliance clauses in the preset compliance clause library based on the preset quantum semantic matching verification algorithm to obtain the semantic matching result; Perform compliance verification on the semantic matching results and output the compliance verification results.

7. The financial consumption system verification method as described in claim 6, characterized in that, After the step of performing compliance verification on the categorized system information and outputting the compliance verification results, the method further includes: The risk level is determined based on the compliance verification results. The source tracing management mechanism is triggered based on the risk level, and the source tracing information and the risk level are used to generate an alarm message, which is then pushed to the approver.

8. A financial consumption system verification device, characterized in that, The financial consumption system verification device includes: The preprocessing module is used to collect multi-source regulatory information from the internal financial consumption regulatory database and the external regulatory system, and to perform data cleaning and standardization on the multi-source regulatory information to obtain the processed regulatory information. The quantum semantic parsing module is used to perform semantic parsing on the processed institutional information based on a preset quantum semantic parsing model to obtain quantum semantic feature vectors. The preset quantum semantic parsing model is a model built on a quantum convolutional neural network model and a large natural language processing model. The quantum convolutional neural network model includes a multidimensional quantum convolutional kernel and an adaptive residual structure. The multidimensional quantum convolutional kernel includes at least two types of quantum convolutional kernels, which are used to capture local semantic associations and long-distance dependencies in the text. The intelligent classification module is used to classify the quantum semantic feature vector based on preset financial classification labels to obtain classified institutional information; The compliance verification module is used to verify the compliance of the categorized system information and output the compliance verification results.

9. A financial consumption system verification device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the financial consumer system verification method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the financial consumption system verification method as described in any one of claims 1 to 7.

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