A special asset intelligent matching method and system based on big data and artificial intelligence

Abstract

This invention discloses a method and system for intelligent matching of special assets based on big data and artificial intelligence, belonging to the field of special asset disposal technology. The system comprises six core modules: data acquisition and processing, profile construction, intelligent matching and recommendation, risk assessment and early warning, transaction management, and feedback optimization. It utilizes NLP, ML, and other technologies to standardize the processing of multi-source heterogeneous data, constructing accurate asset and investor profiles. A multi-algorithm-integrated intelligent matching engine generates personalized matching recommendations. Simultaneously, it integrates end-to-end risk management and transaction digitization tools, forming a closed-loop iterative optimization mechanism. This invention addresses the industry pain points of low efficiency, information asymmetry, and weak risk management in traditional manual matching, significantly improving the efficiency and accuracy of special asset disposal, and is applicable to non-performing asset disposal platforms.

Description

Technical Field

[0001] This invention relates to the field of special asset disposal technology, and in particular to a special asset intelligent matching method and system based on big data and artificial intelligence (AI). Background Technology

[0002] The special asset (non-performing asset) disposal industry has long relied on a manual matching model, which suffers from the following core pain points: First, there is a severe information asymmetry between assets and investors, resulting in low efficiency and accuracy in manual matching, with many high-quality assets failing to find suitable investors. Second, multi-source heterogeneous data (including asset announcements, due diligence reports, and investor behavior data) cannot be efficiently integrated, making it difficult to form standardized asset and user profiles. Third, the transaction process has a low degree of offline and digitalization, with risk management relying on manual processes, leading to low efficiency and a high risk of problems. Fourth, there is a lack of intelligent iterative optimization mechanisms, preventing the matching effect from continuously improving with transaction data. Existing technologies cannot solve these industry pain points, necessitating an intelligent, end-to-end special asset matching solution. Summary of the Invention

[0003] The purpose of this invention is to provide a special asset intelligent matching method and system based on big data and artificial intelligence (AI), which solves the problems of low efficiency, poor matching accuracy, difficulty in data integration, and weak risk control in the existing technology.

[0004] A special asset intelligent matching method based on big data and artificial intelligence includes the following steps: 1. Multi-source heterogeneous special asset data collection and standardization processing: Special asset information is collected from multiple sources, including banks, asset management companies, local financial institutions, and private channels, through automated or semi-automated methods; the collected non-standardized data is cleaned, denoised, entity-identified, and key information extracted; semantic analysis is performed using NLP (Natural Language Processing) technology to transform text information into structured data; a unified special asset data model is constructed, defining core feature fields such as asset type, geographical location, value range, ownership status, mortgage status, litigation status, and disposal progress, to achieve standardized storage and management of asset information. 2. Construction of precise asset and investor profiles: Based on standardized asset feature data, combined with historical transaction data and industry knowledge graphs, multi-dimensional and dynamically updated asset profiles are generated for each specific asset; investor browsing behavior, search history, collection preferences, transaction records, financial strength, risk preferences, and regional restrictions on the platform are recorded and analyzed, and investor profiles are constructed using ML (machine learning) algorithms. 3. Intelligent Matching Algorithm and Recommendation Engine: Design and implement an intelligent matching algorithm that integrates content matching, collaborative filtering, and deep learning (DL) technologies. This algorithm comprehensively considers various features of asset profiles and investor profiles, calculates the matching degree, and recommends specific assets that meet the needs of investors. The matching algorithm is dynamically adjusted and optimized in real time based on newly added assets on the platform, changes in investor behavior, and transaction feedback. Personalized asset recommendation lists are provided based on investor profiles and real-time behavior. 4. Risk Assessment and Early Warning Integration: Identify key factors of legal, market, and operational risks that may exist in the disposal of special assets; integrate the risk assessment model into the matching system to provide investors with the risk level, potential risk points, and early warning information of assets during the asset release and recommendation process. 5. Digitalization of Transaction Process and Feedback Optimization Mechanism: Provides digital transaction tools such as online bidding, electronic signing, and fund custody; collects transaction success rate, transaction cycle, disposal price, and transaction result data, as well as investor feedback on recommended assets, as input for the intelligent matching algorithm to form a closed-loop iterative optimization mechanism.

[0005] A special asset intelligent matching system based on big data and artificial intelligence includes: The data acquisition and processing module is used to acquire, clean, structure, and standardize multi-source heterogeneous special asset data; The profile building module is used to create profiles of specific assets and investors; The intelligent matching and recommendation module executes intelligent matching algorithms to generate personalized asset recommendations; the risk assessment and early warning module integrates risk assessment models to provide asset risk levels and early warning information. The transaction management module supports digital transaction processes such as online bidding, electronic signing, and fund custody. The feedback optimization module is used to collect transaction feedback data and optimize the intelligent matching algorithm.

[0006] The beneficial effects of this invention are as follows: 1. Achieve standardized processing of multi-source heterogeneous data to build accurate asset and investor profiles and solve the problem of information asymmetry; 2. Through AI-powered intelligent matching algorithms, the efficiency and accuracy of matchmaking are significantly improved, while reducing labor costs; 3. Achieve full-process digital management of transactions, and enhance risk control capabilities by combining risk assessment and early warning modules; 4. The model is iterated through the feedback optimization module to continuously improve the matching effect and form a business closed loop. Attached Figure Description

[0007] Figure 1This is a diagram of the overall system architecture of the present invention; Figure 2 This is a flowchart of the overall method of the present invention; Figure 3 This is a flowchart of the data acquisition and processing module of the present invention; Figure 4 This is a flowchart of the intelligent matching and recommendation module of the present invention. Detailed Implementation

[0008] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0009] Example 1: System Architecture like Figure 1 As shown, the system of this invention is divided into a 5-layer architecture: 1. User layer: This includes service providers, investors, and asset owners, who access the system through the Teziquan APP / Web to provide services, browse purchase and sale transactions, and publish assets, respectively. 2. Infrastructure Layer: The big data platform is responsible for storing and computing all data; the AI ​​algorithm platform provides algorithmic support for profile building, matching algorithms, and risk models; and the cloud computing platform provides elastic computing power to support the operation of all upper-layer modules. 3. Application Layer: The user interface service serves as a unified entry point, connecting to user layer requests and distributing them to the asset release management, investor management, transaction matching service, and service provider management modules to achieve business management; 4. Core Service Layer: - Data Acquisition and Processing Module: Performs multi-source data acquisition, cleaning, parsing, and structured transformation to generate standardized asset data; - Profile Building Module: Builds asset and investor profiles based on standardized data; - Intelligent Matching and Recommendation Module: Calculates matching degree based on profiles and generates personalized recommendations; - Risk Assessment and Early Warning Module: Provides end-to-end risk monitoring and early warning; - Transaction Management Module: Provides end-to-end digital management of transactions; - Feedback and Optimization Module: Collects transaction feedback and iteratively optimizes the model; 5. Data Layer: Stores all process data and provides data access services for each module.

[0010] Example 2: Data Acquisition and Processing Flow like Figure 3 As shown, the data acquisition and processing module performs the following steps: S1: Multi-source heterogeneous data collection, acquiring data from multiple sources such as asset announcements, due diligence reports, investor behavior, and transaction records; S2: Data cleaning and preprocessing, including deduplication, completion, format standardization, and removal of invalid data; S3: Unstructured data parsing, extracting key information from text data through NLP analysis, and obtaining effective information from image / video data through feature extraction; S4: Key information extraction, integrating text and image features to extract key information such as core asset attributes and investor preferences; S5: Structured data transformation, converting unstructured data into standardized structured data; S6: Asset Characteristic Engineering, extracting core characteristics such as asset type, value, risk, and disposal requirements; S7: Standardized asset data storage, storing the processed data into a multi-source heterogeneous data storage and asset profiling database. Example 3: Intelligent matching process as follows... Figure 4 As shown, the intelligent matching and recommendation module performs the following steps: S1: Asset side: Extract features based on standardized asset data, construct asset profiles, and store them in the asset profile database; S2: Investor side: Based on investor behavior data and investment history analysis preferences, investor profiles are constructed and stored in the investor profile database; S3: The intelligent matching algorithm calls up asset profiles and investor profiles to calculate the matching degree; S4: Sort by matching degree and generate a personalized recommendation list; S5: Push the recommendation list to investors and receive investor feedback; S6: Optimize the matching algorithm model based on feedback data to continuously improve matching accuracy.

[0011] Example 4: Overall Method Flow like Figure 2 As shown, the overall method flow of this invention is as follows: S1: Begin, perform data acquisition and standardization processing; S2: Parallel construction of asset profiles and investor profiles; S3: Calculates the matching degree using an intelligent matching algorithm to generate a recommendation list; S4: Present recommended assets to investors; S5: Investors browse, request, and trade. S6: Complete the entire transaction management process and generate transaction records through the transaction management module; S7: Conduct risk assessments and early warnings, and monitor risks throughout the entire process; S8: Collect transaction feedback, optimize matching algorithms and profile models to form a closed loop; S9: End.

Claims

1. A special asset intelligent matching method based on big data and artificial intelligence, characterized in that, Includes the following steps: Data collection and standardization processing of multi-source heterogeneous special assets; Precise profiling of assets and investors; intelligent matching algorithms and recommendation engines; integrated risk assessment and early warning; digitalization of transaction processes and feedback optimization mechanisms.

2. The method according to claim 1, characterized in that, The multi-source heterogeneous special asset data collection and standardization processing includes: collecting special asset information from multiple sources such as banks, asset management companies, local financial institutions, and private channels through automated or semi-automated methods; cleaning, denoising, entity recognition, and key information extraction of the collected non-standardized data, and using natural language processing technology for semantic analysis to transform text information into structured data; constructing a unified special asset data model, defining core feature fields such as asset type, geographical location, value range, ownership status, mortgage status, litigation status, and disposal progress, to achieve standardized storage and management of asset information.

3. The method according to claim 1, characterized in that, The construction of precise asset and investor profiles includes: generating multi-dimensional, dynamically updated asset profiles for each specific asset based on standardized asset feature data, combined with historical transaction data and industry knowledge graphs; recording and analyzing investors' browsing behavior, search history, collection preferences, transaction records, financial strength, risk preferences, and regional restrictions on the platform, and using machine learning algorithms to construct investor profiles.

4. The method according to claim 1, characterized in that, The intelligent matching algorithm and recommendation engine include: designing and implementing an intelligent matching algorithm that integrates content matching, collaborative filtering, and deep learning technologies; comprehensively considering various features of asset profiles and investor profiles; calculating the matching degree; and recommending specific assets that meet the needs of investors; the matching algorithm is dynamically adjusted and optimized in real time based on newly added assets on the platform, changes in investor behavior, and transaction feedback; and providing a personalized asset recommendation list based on investor profiles and real-time behavior.

5. The method according to claim 1, characterized in that, The integrated risk assessment and early warning system includes: identifying key factors of legal, market, and operational risks that may exist during the disposal of special assets; and integrating the risk assessment model into the matching system to provide investors with the risk level, potential risk points, and early warning information of assets during the asset release and recommendation process.

6. The method according to claim 1, characterized in that, The digital transaction process and feedback optimization mechanism includes: providing digital transaction tools such as online bidding, electronic signing, and fund custody; collecting transaction success rate, transaction cycle, disposal price, and transaction result data, as well as investor feedback on recommended assets, as optimization inputs for the intelligent matching algorithm, forming a closed-loop iterative optimization mechanism.

7. A special asset intelligent matching system based on big data and artificial intelligence, characterized in that, include: The data acquisition and processing module is used to acquire, clean, structure, and standardize multi-source heterogeneous special asset data; The profile building module is used to build profiles of specific assets and investors; the intelligent matching and recommendation module is used to execute intelligent matching algorithms to generate personalized asset recommendations; and the risk assessment and early warning module is used to integrate risk assessment models to provide asset risk levels and early warning information. The transaction management module supports digital transaction processes such as online bidding, electronic signing, and fund custody. The feedback optimization module is used to collect transaction feedback data and optimize the intelligent matching algorithm.

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