Financial asset management and securitization system based on big data, AI and block chain

Abstract

The invention relates to the technical field of asset management, and particularly discloses a financial asset management and securitization system based on big data, AI and a block chain, and the system comprises a data extraction module which is used for obtaining multi-source asset data of a target financial asset based on the big data, the AI and the block chain, and carrying out the data processing, and obtaining first processing data; the data evaluation module is used for performing data evaluation on the first processing data in combination with the evaluation data type so as to determine the first asset quality of the target financial asset; the data management module is used for performing asset maintenance on the target financial asset based on the first asset quality and the asset type of the target financial asset; the securitization module is used for judging whether the target financial assets for asset maintenance can be subjected to asset securitization or not, and determining an asset securitization scheme of the target financial assets capable of being subjected to asset securitization to perform asset securitization; the efficiency and accuracy of asset management and securitization are improved.

Description

Technical Field

[0001] The present invention relates to the field of asset management technology, and in particular to a financial asset management and securitization system based on big data, AI and blockchain. Background Art

[0002] With the continuous development of the financial market, financial institutions and investors have an increasing demand for the management and securitization of financial assets. Traditional financial asset management methods have problems such as low data processing efficiency, difficulty in accurately assessing risks, and cumbersome securitization processes. The emergence of big data, AI and blockchain technology has provided new possibilities for solving these problems.

[0003] Big data can help financial institutions comprehensively and accurately analyze market trends and asset risks, and improve the timeliness and accuracy of decision-making.

[0004] AI technology, especially machine learning algorithms, can automatically analyze historical data, discover market patterns, and improve trading efficiency and profitability.

[0005] Blockchain technology, with its decentralized and tamper-proof characteristics, ensures the security and transparency of data, and is particularly suitable for the recording and trading of financial assets.

[0006] However, in the process of managing financial assets with existing technologies, big data, AI, and blockchain technologies cannot be well integrated and used, which increases management complexity and redundancy, resulting in problems such as untimely and inaccurate management of financial assets.

[0007] Therefore, the present invention proposes a financial asset management and securitization system based on big data, AI and blockchain. Summary of the invention

[0008] The present invention provides a financial asset management and securitization system based on big data, AI and blockchain, which is used to obtain and efficiently process financial assets from multiple sources through technologies such as big data, AI and blockchain, thereby improving the efficiency and accuracy of data processing, and at the same time being able to more accurately evaluate asset quality, thereby optimizing asset maintenance strategies, making the maintenance of target financial assets more in line with user needs, and improving the overall financial risk management level.

[0009] The present invention provides a financial asset management and securitization system based on big data, AI and blockchain, including:

[0010] A data extraction module, used to obtain multi-source asset data of target financial assets based on big data, AI and blockchain, and perform data processing to obtain first processed data;

[0011] A data evaluation module, used to perform data evaluation on the first processed data in combination with the evaluation data type, so as to determine the first asset quality of the target financial asset;

[0012] A data management module, configured to perform asset maintenance on the target financial asset based on the first asset quality and the asset type of the target financial asset;

[0013] The securitization module is used to determine whether the target financial assets for asset maintenance can be securitized, and to determine the asset securitization plan for the target financial assets that can be securitized to carry out asset securitization.

[0014] Preferably, the financial asset management and securitization system based on big data, AI and blockchain, the data extraction module includes:

[0015] An asset data acquisition unit, used to acquire first asset data of a target financial asset based on big data technology, acquire second asset data of a target financial asset based on AI technology, and acquire third asset data of a target financial asset based on blockchain technology;

[0016] An asset data comparison unit, used to compare the first asset data, the second asset data and the third asset data one by one, so as to determine the similarity of the asset data between different data sources, and thus obtain a similarity set of target financial assets;

[0017] A similarity comparison unit, used to extract the maximum similarity and the minimum similarity of the asset data between different data sources in the similarity set, and determine a first difference between the maximum similarity and the minimum similarity;

[0018] an asset data optimization unit, configured to compare the first difference with a preset minimum difference, and determine the multi-source asset data of the target financial asset based on the comparison result;

[0019] The asset data processing unit is used to perform data standardization processing on multi-source asset data to obtain first processed data.

[0020] Preferably, the financial asset management and securitization system based on big data, AI and blockchain, the asset data optimization unit, includes:

[0021] an asset data optimization subunit, configured to compare the first difference with a preset minimum difference;

[0022] If the first difference is less than a preset minimum difference, obtaining multi-source asset data of the target financial asset based on the asset data of the overlapping portion of the first asset data, the second asset data, and the third asset data;

[0023] If the first difference is not less than the preset minimum difference, multi-source asset data of the target financial asset is obtained based on all asset data of the first asset data, the second asset data and the third asset data.

[0024] Preferably, the financial asset management and securitization system based on big data, AI and blockchain, the data evaluation module includes:

[0025] A type determination unit, used to determine a first evaluation data type that needs to be evaluated based on the asset characteristics and asset evaluation requirements of the target financial asset;

[0026] A model building unit, configured to select, from an evaluation model database, a first evaluation method that has the highest matching degree with the target financial asset and a first evaluation model for the target financial asset built based on the first evaluation method, based on the first evaluation data type and the asset characteristics of the target financial asset;

[0027] The second model unit is used to perform model training and initial optimization on the first evaluation model based on the AI ​​algorithm to obtain a second evaluation model;

[0028] A model reference unit is used to extract a first evaluation reference model corresponding to a financial asset having the highest degree of consistency with the asset characteristics of the target financial asset and the type of the first evaluation data from a preset financial asset database, and at the same time, extract a second evaluation reference model corresponding to a financial asset having the second highest degree of consistency with the asset characteristics of the target financial asset and the type of the first evaluation data from the evaluation model database;

[0029] An evaluation difference unit, used to determine a first evaluation difference between a second evaluation method corresponding to the first evaluation reference model and a third evaluation method corresponding to the second evaluation reference model;

[0030] If the first evaluation difference is not greater than the preset standard evaluation difference, the second evaluation model is optimized based on the first evaluation reference model and the second evaluation reference model to obtain a third evaluation model;

[0031] If the first assessment difference is greater than the preset standard assessment difference, extracting a third assessment reference model corresponding to a financial asset having the third highest degree of consistency with the asset characteristics of the target financial asset and the type of the first assessment data from the preset financial asset database;

[0032] comparing the fourth evaluation method corresponding to the third evaluation reference model with the second evaluation method corresponding to the first evaluation reference model, thereby determining a second evaluation difference based on the comparison result, and re-determining the evaluation difference;

[0033] The data evaluation unit is used to input the first processed data into the third evaluation model to obtain a first evaluation result of the target financial asset, and obtain a first asset quality of the target financial asset in combination with the asset characteristics of the target financial asset.

[0034] Preferably, the financial asset management and securitization system based on big data, AI and blockchain optimizes the second evaluation model based on the first evaluation reference model and the second evaluation reference model to obtain a third evaluation model, including:

[0035] Extracting a second evaluation parameter of the second evaluation method in the first evaluation reference model to obtain a first parameter set, and at the same time, extracting a third evaluation parameter of the third evaluation method in the second evaluation reference model to obtain a second parameter set;

[0036] Extracting the first evaluation parameter of the first evaluation method in the second evaluation model to obtain an initial parameter set, and making the first parameter set, the second parameter set and the initial parameter set correspond to obtain a first parameter table;

[0037] The evaluation parameters in the initial parameter set are adjusted based on the corresponding evaluation parameters in the first parameter table to obtain a first adjusted evaluation parameter T corresponding to each first evaluation parameter in the initial parameter set. i , thereby obtaining a first adjustment evaluation parameter set;

[0038] Among them, T i For the i-th first adjustment evaluation parameter, s i is the parameter value of the i-th first evaluation parameter in the initial parameter set, s i ′ is the parameter value of the second evaluation parameter in the first parameter table that is consistent with the parameter type of the i-th first evaluation parameter, s i ″ is the parameter value of the third evaluation parameter in the first parameter table that is consistent with the parameter type of the i-th first evaluation parameter, μ i is the parameter conversion coefficient of the parameter type corresponding to the i-th first evaluation parameter, α1 is the evaluation weight of the second evaluation parameter, α2 is the evaluation weight of the third evaluation parameter, and ε is the adjustment error factor, wherein the evaluation weight of the second evaluation parameter is greater than the evaluation weight of the third evaluation parameter;

[0039] The second evaluation model is optimized based on the first set of adjusted evaluation parameters to obtain a third evaluation model.

[0040] Preferably, the financial asset management and securitization system based on big data, AI and blockchain, the data management module includes:

[0041] a strategy determination unit, configured to determine an asset maintenance strategy for the target financial asset by combining the first asset quality with the asset type and asset characteristics of the target financial asset;

[0042] A strategy judgment unit, used to judge the asset maintenance strategy based on the asset maintenance standard, so as to determine the strategy feasibility of the asset maintenance strategy;

[0043] The asset maintenance unit is used to perform asset maintenance on the target financial asset based on the asset maintenance strategy if the asset maintenance strategy is feasible.

[0044] Preferably, the financial asset management and securitization system based on big data, AI and blockchain, the securitization module, includes:

[0045] An asset judgment unit is used to obtain the real-time market demand of the financial market and judge whether the target financial asset can be securitized based on the real-time market demand;

[0046] If the target financial asset can be securitized, then the securitization is predicted based on the market demand of the real-time financial market and the asset quality of the target financial asset to obtain a first prediction result;

[0047] A scheme determination unit, used to obtain a standard securitization scheme for asset securitization, and adjust the standard securitization scheme in combination with the target financial assets to obtain a first asset securitization scheme;

[0048] The asset securitization unit is used to securitize the target financial assets based on the first asset securitization plan.

[0049] Preferably, the financial asset management and securitization system based on big data, AI and blockchain, the securitization module, also includes:

[0050] The securitization monitoring unit is used to monitor the securitization progress at every moment in real time during the securitization process of the target financial assets;

[0051] a securitization supervision unit, used to compare the securitization progress with the first asset securitization plan, so as to determine whether the real-time securitization progress matches the first asset securitization plan;

[0052] If there is a mismatch, the first asset securitization plan will be adjusted based on the securitization progress;

[0053] Otherwise, the asset securitization of the target assets is determined to be qualified.

[0054] The beneficial effects of the present invention compared to the prior art are as follows: by acquiring financial assets from multiple sources and efficiently processing them based on technologies such as big data, AI and blockchain, the efficiency and accuracy of data processing can be improved, and asset quality can be evaluated more accurately, thereby optimizing asset maintenance strategies, making the maintenance of target financial assets more in line with user needs and improving the overall level of financial risk management.

[0055] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0056] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 This is a schematic diagram of a financial asset management and securitization system based on big data, AI and blockchain in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] The present invention provides a financial asset management and securitization system based on big data, AI and blockchain. Figure 1 ,include:

[0062] A data extraction module, used to obtain multi-source asset data of target financial assets based on big data, AI and blockchain, and perform data processing to obtain first processed data;

[0063] A data evaluation module, used to perform data evaluation on the first processed data in combination with the evaluation data type, so as to determine the first asset quality of the target financial asset;

[0064] A data management module, configured to perform asset maintenance on the target financial asset based on the first asset quality and the asset type of the target financial asset;

[0065] The securitization module is used to determine whether the target financial assets for asset maintenance can be securitized, and to determine the asset securitization plan for the target financial assets that can be securitized to carry out asset securitization.

[0066] In this embodiment, big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain time frame. It is a massive, high-growth, and diverse information asset that requires new processing models to have stronger decision-making power, insight discovery, and process optimization capabilities.

[0067] In this embodiment, the blockchain is a distributed database that is composed of a series of data blocks in a chain in chronological order into a specific data structure, and uses cryptography to ensure the security of data transmission and access. Blockchain technology has the characteristics of decentralization, immutability, and traceability.

[0068] In this embodiment, the target financial assets refer to specific financial assets invested or held by an enterprise or individual, such as stocks, bonds, funds, futures, options, etc. These assets are the objects of investment activities and the source of investment income.

[0069] In this embodiment, multi-source asset data refers to data about target financial assets obtained from different sources, which may include financial markets, financial institutions, trading platforms, public information, etc.

[0070] In this embodiment, the first processed data refers to data obtained after preliminary processing and analysis of multi-source asset data, which provides a basis for subsequent evaluation and decision-making.

[0071] In this embodiment, the evaluation data type refers to the data type used to evaluate the quality of the target financial assets, wherein the evaluation data type includes financial data, market data, risk data, etc.

[0072] In this embodiment, the first asset quality refers to a preliminary judgment on the quality of the target financial asset obtained after evaluating the first processed data according to the evaluation data type.

[0073] In this embodiment, the asset type refers to the type of target price financial asset, wherein the asset type includes stocks, bonds, funds, etc. Different types of assets have different characteristics and risks.

[0074] In this embodiment, asset maintenance refers to the management and maintenance of the target financial assets to ensure their normal operation and maximize their value.

[0075] In this embodiment, asset securitization refers to the process of separating and reorganizing the risk and return elements of assets that lack liquidity but can generate predictable and stable cash flows through certain structural arrangements, and then converting them into securities that can be sold and circulated in the financial market.

[0076] In this embodiment, the asset securitization plan refers to the specific plan and strategy formulated to achieve asset securitization, including asset selection, structure design, risk assessment and control, etc.

[0077] The beneficial effects of the above technologies are: by acquiring and efficiently processing financial assets from multiple sources based on technologies such as big data, AI and blockchain, the efficiency and accuracy of data processing can be improved, and at the same time, the asset quality can be evaluated more accurately, thereby optimizing asset maintenance strategies, making the maintenance of target financial assets more in line with user needs and improving the overall level of financial risk management.

[0078] Embodiment 2:

[0079] Based on Example 1, the financial asset management and securitization system based on big data, AI and blockchain, the data extraction module includes:

[0080] An asset data acquisition unit, used to acquire first asset data of a target financial asset based on big data technology, acquire second asset data of a target financial asset based on AI technology, and acquire third asset data of a target financial asset based on blockchain technology;

[0081] An asset data comparison unit, used to compare the first asset data, the second asset data and the third asset data one by one, so as to determine the similarity of the asset data between different data sources, and thus obtain a similarity set of target financial assets;

[0082] A similarity comparison unit, used to extract the maximum similarity and the minimum similarity of the asset data between different data sources in the similarity set, and determine a first difference between the maximum similarity and the minimum similarity;

[0083] an asset data optimization unit, configured to compare the first difference with a preset minimum difference, and determine the multi-source asset data of the target financial asset based on the comparison result;

[0084] The asset data processing unit is used to perform data standardization processing on multi-source asset data to obtain first processed data.

[0085] In this embodiment, big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain time frame. It is a massive, high-growth, and diverse information asset that requires new processing models to have stronger decision-making power, insight discovery, and process optimization capabilities.

[0086] In this embodiment, the blockchain is a distributed database that is composed of a series of data blocks in a chain in chronological order into a specific data structure, and uses cryptography to ensure the security of data transmission and access. Blockchain technology has the characteristics of decentralization, immutability, and traceability.

[0087] In this embodiment, the target financial assets refer to specific financial assets invested or held by an enterprise or individual, such as stocks, bonds, funds, futures, options, etc. These assets are the objects of investment activities and the source of investment income.

[0088] In this embodiment, the similarity is used to measure the similarity between asset data from different data sources, wherein the asset data obtained from different data sources may be the same or different.

[0089] In this embodiment, the similarity set refers to a set including multiple similarity values, and these similarity values ​​reflect the similarity between asset data of different data sources.

[0090] In this embodiment, the maximum similarity refers to the maximum value among all similarity values ​​in the similarity set, which represents the degree to which the asset data between different data sources are most similar.

[0091] In this embodiment, the minimum similarity refers to the minimum value among all similarity values ​​in the similarity set, which represents the least similarity between asset data of different data sources.

[0092] In this embodiment, the first difference refers to the difference between the maximum similarity and the minimum similarity, which reflects the difference range of the similarity of asset data between different data sources.

[0093] In this embodiment, the preset minimum difference refers to a preset minimum difference threshold when comparing data. When the first difference is less than the preset minimum difference, it can be considered that the asset data between different data sources have a high degree of similarity, and the corresponding first asset data or second asset data or third asset data can be integrated to obtain multi-source asset data of the target financial asset, which can be used for data processing and analysis.

[0094] In this embodiment, multi-source asset data refers to a data set of target financial assets obtained from different data sources, and these data may include transaction records, market trends, financial reports, etc.

[0095] In this embodiment, data standardization processing refers to the process of converting data from different sources and in different formats into a unified format and standard to facilitate subsequent data analysis and processing.

[0096] In this embodiment, the first processed data refers to the financial asset data obtained after performing data standardization processing on the multi-source asset data.

[0097] The beneficial effect of the above technology is: by comparing the extracted data, data processing is performed based on the comparison results, so that the obtained multi-source asset data is more consistent with the asset data of the target financial assets, thereby making the analysis and management of the target financial assets more accurate.

[0098] Embodiment 3:

[0099] Based on Example 2, the financial asset management and securitization system based on big data, AI and blockchain, the asset data optimization unit, includes:

[0100] an asset data optimization subunit, configured to compare the first difference with a preset minimum difference;

[0101] If the first difference is less than a preset minimum difference, obtaining multi-source asset data of the target financial asset based on the asset data of the overlapping portion of the first asset data, the second asset data, and the third asset data;

[0102] If the first difference is not less than the preset minimum difference, multi-source asset data of the target financial asset is obtained based on all asset data of the first asset data, the second asset data and the third asset data.

[0103] In this embodiment, the first difference refers to the difference between the maximum similarity and the minimum similarity, which reflects the difference range of the similarity of asset data between different data sources.

[0104] In this embodiment, the preset minimum difference refers to a preset minimum difference threshold when comparing data. When the first difference is less than the preset minimum difference, it can be considered that the asset data between different data sources have a high degree of similarity, and the corresponding first asset data or second asset data or third asset data can be integrated to obtain multi-source asset data of the target financial asset, which can be used for data processing and analysis.

[0105] In this embodiment, multi-source asset data refers to a data set of target financial assets obtained from different data sources, and these data may include transaction records, market trends, financial reports, etc.

[0106] The beneficial effect of the above technology is: by comparing the extracted data, data processing is performed based on the comparison results, so that the obtained multi-source asset data is more consistent with the asset data of the target financial assets, thereby making the analysis and management of the target financial assets more accurate.

[0107] Embodiment 4:

[0108] Based on Example 2, the financial asset management and securitization system based on big data, AI and blockchain, the data evaluation module includes:

[0109] A type determination unit, used to determine a first evaluation data type that needs to be evaluated based on the asset characteristics and asset evaluation requirements of the target financial asset;

[0110] A model building unit, configured to select, from an evaluation model database, a first evaluation method that has the highest matching degree with the target financial asset and a first evaluation model for the target financial asset built based on the first evaluation method, based on the first evaluation data type and the asset characteristics of the target financial asset;

[0111] The second model unit is used to perform model training and initial optimization on the first evaluation model based on the AI ​​algorithm to obtain a second evaluation model;

[0112] A model reference unit is used to extract a first evaluation reference model corresponding to a financial asset having the highest degree of consistency with the asset characteristics of the target financial asset and the type of the first evaluation data from a preset financial asset database, and at the same time, extract a second evaluation reference model corresponding to a financial asset having the second highest degree of consistency with the asset characteristics of the target financial asset and the type of the first evaluation data from the evaluation model database;

[0113] An evaluation difference unit, used to determine a first evaluation difference between a second evaluation method corresponding to the first evaluation reference model and a third evaluation method corresponding to the second evaluation reference model;

[0114] If the first evaluation difference is not greater than the preset standard evaluation difference, the second evaluation model is optimized based on the first evaluation reference model and the second evaluation reference model to obtain a third evaluation model;

[0115] If the first assessment difference is greater than the preset standard assessment difference, extracting a third assessment reference model corresponding to a financial asset having the third highest degree of consistency with the asset characteristics of the target financial asset and the type of the first assessment data from the preset financial asset database;

[0116] comparing the fourth evaluation method corresponding to the third evaluation reference model with the second evaluation method corresponding to the first evaluation reference model, thereby determining a second evaluation difference based on the comparison result, and re-determining the evaluation difference;

[0117] The data evaluation unit is used to input the first processed data into the third evaluation model to obtain a first evaluation result of the target financial asset, and obtain a first asset quality of the target financial asset in combination with the asset characteristics of the target financial asset.

[0118] In this embodiment, asset characteristics refer to the unique properties or features of the target financial assets, such as scale, liquidity, risk level, return potential, etc.

[0119] In this embodiment, the asset evaluation demand refers to the specific requirements or purposes for evaluating the target financial assets, such as determining their value, risk level, investment potential, etc.

[0120] In this embodiment, the first evaluation data type is a specific data type that needs to be evaluated and is determined based on the asset characteristics and evaluation requirements of the target financial asset, such as financial data, market data, credit data, etc.

[0121] In this embodiment, the valuation model database is a database for storing various valuation models, and the valuation models in the valuation model database can be used for valuation of different types of financial assets.

[0122] In this embodiment, the first evaluation method is an evaluation method selected from the evaluation model database and having the highest matching degree with the target financial asset.

[0123] In this embodiment, the first valuation model is a target financial asset valuation model constructed based on the first valuation method.

[0124] In this embodiment, the second evaluation model is an evaluation model trained and optimized by an AI algorithm.

[0125] In this embodiment, the first evaluation reference model refers to the evaluation model corresponding to the financial asset with the highest degree of consistency between the target financial asset characteristics and the first evaluation data type, which is extracted from the preset financial asset database; the second evaluation reference model refers to the evaluation model corresponding to the financial asset with the second highest degree of consistency between the target financial asset characteristics and the first evaluation data type, which is extracted from the preset financial asset database.

[0126] In this embodiment, the second evaluation method refers to the evaluation method corresponding to the first evaluation reference model, and the third evaluation method refers to the evaluation method corresponding to the second evaluation reference model.

[0127] In this embodiment, the first evaluation difference refers to the evaluation difference between the second evaluation method and the third evaluation method.

[0128] In this embodiment, the preset standard evaluation difference is a preset standard for determining whether the evaluation difference is acceptable.

[0129] In this embodiment, the third evaluation model is an evaluation model obtained by comprehensively optimizing the second evaluation model based on the first evaluation reference model and the second evaluation reference model.

[0130] In this embodiment, the third evaluation reference model refers to an evaluation model corresponding to a financial asset with the third highest degree of consistency with the target financial asset characteristics and the first evaluation data type, which is extracted from a preset financial asset database when the evaluation difference is greater than a preset standard.

[0131] In this embodiment, the fourth evaluation method refers to the evaluation method corresponding to the third evaluation reference model.

[0132] In this embodiment, the second evaluation difference refers to an evaluation difference between the fourth evaluation method corresponding to the third evaluation reference model and the second evaluation method corresponding to the first evaluation reference model.

[0133] In this embodiment, the first evaluation result is a result obtained by evaluating the target financial asset using the third evaluation model.

[0134] In this embodiment, the first asset quality refers to an asset quality assessment result obtained by combining the asset characteristics of the target financial asset and the first assessment result.

[0135] The beneficial effects of the above technology are: by adjusting and optimizing the valuation model, the determined valuation model can be made more accurate, better able to meet asset valuation needs, and make the asset management of the target financial assets more efficient and accurate.

[0136] Embodiment 5:

[0137] On the basis of Example 4, the financial asset management and securitization system based on big data, AI and blockchain optimizes the second evaluation model based on the first evaluation reference model and the second evaluation reference model to obtain a third evaluation model, including:

[0138] Extracting a second evaluation parameter of the second evaluation method in the first evaluation reference model to obtain a first parameter set, and at the same time, extracting a third evaluation parameter of the third evaluation method in the second evaluation reference model to obtain a second parameter set;

[0139] Extracting the first evaluation parameter of the first evaluation method in the second evaluation model to obtain an initial parameter set, and making the first parameter set, the second parameter set and the initial parameter set correspond to obtain a first parameter table;

[0140] The evaluation parameters in the initial parameter set are adjusted based on the corresponding evaluation parameters in the first parameter table to obtain a first adjusted evaluation parameter T corresponding to each first evaluation parameter in the initial parameter set. i , thereby obtaining a first adjustment evaluation parameter set;

[0141] Among them, T i For the i-th first adjustment evaluation parameter, s i is the parameter value of the i-th first evaluation parameter in the initial parameter set, s i ′ is the parameter value of the second evaluation parameter in the first parameter table that is consistent with the parameter type of the i-th first evaluation parameter, s i ″ is the parameter value of the third evaluation parameter in the first parameter table that is consistent with the parameter type of the i-th first evaluation parameter, μ iis the parameter conversion coefficient of the parameter type corresponding to the i-th first evaluation parameter, α1 is the evaluation weight of the second evaluation parameter, α2 is the evaluation weight of the third evaluation parameter, and ε is the adjustment error factor, wherein the evaluation weight of the second evaluation parameter is greater than the evaluation weight of the third evaluation parameter;

[0142] The second evaluation model is optimized based on the first set of adjusted evaluation parameters to obtain a third evaluation model.

[0143] In this embodiment, the second evaluation parameter refers to a parameter included in the second evaluation method in the first evaluation parameter model, and the third evaluation parameter refers to a parameter included in the third evaluation method in the second evaluation parameter model.

[0144] In this embodiment, the first parameter set refers to a set consisting of second evaluation parameters, and the second parameter set refers to a set consisting of third evaluation parameters.

[0145] In this embodiment, the initial parameter set is a parameter set formed according to the evaluation parameters of the first evaluation method in the second evaluation model.

[0146] In this embodiment, the first parameter table is a parameter table obtained by making the first parameter set, the second parameter set and the evaluation parameters in the initial parameter set correspond to each other.

[0147] In this embodiment, the first adjusted evaluation parameter is an evaluation parameter obtained by adjusting the first evaluation parameter according to the first parameter table.

[0148] In this embodiment, the third evaluation model refers to an evaluation model obtained by adjusting the corresponding evaluation parameters in the second evaluation model by the first adjusted evaluation parameters in the first adjusted evaluation parameter set.

[0149] The beneficial effects of the above technology are: by adjusting and optimizing the valuation model, the determined valuation model can be made more accurate, better able to meet asset valuation needs, and make the asset management of the target financial assets more efficient and accurate.

[0150] Embodiment 6:

[0151] Based on Example 4, the financial asset management and securitization system based on big data, AI and blockchain, the data management module includes:

[0152] a strategy determination unit, configured to determine an asset maintenance strategy for the target financial asset by combining the first asset quality with the asset type and asset characteristics of the target financial asset;

[0153] A strategy judgment unit, used to judge the asset maintenance strategy based on the asset maintenance standard, so as to determine the strategy feasibility of the asset maintenance strategy;

[0154] The asset maintenance unit is used to perform asset maintenance on the target financial asset based on the asset maintenance strategy if the asset maintenance strategy is feasible.

[0155] In this embodiment, the first asset quality refers to a preliminary judgment on the quality of the target financial asset obtained after evaluating the first processed data according to the evaluation data type.

[0156] In this embodiment, the asset type refers to the type of target price financial asset, wherein the asset type includes stocks, bonds, funds, etc. Different types of assets have different characteristics and risks.

[0157] In this embodiment, the asset maintenance strategy refers to a series of maintenance and management measures formulated to ensure that the target financial assets can continuously and stably generate value. These strategies may include regular inspections, preventive maintenance, risk management, asset allocation, and other aspects.

[0158] In this embodiment, asset maintenance refers to the management and maintenance of the target financial assets to ensure their normal operation and maximize their value.

[0159] The beneficial effects of the above technology are: by determining and judging the asset maintenance strategy, the asset maintenance of the target financial assets can be made more timely and effective, thereby improving the overall financial risk management level of the target financial assets.

[0160] Embodiment 7:

[0161] Based on Example 6, the financial asset management and securitization system based on big data, AI and blockchain, the securitization module, includes:

[0162] An asset judgment unit is used to obtain the real-time market demand of the financial market and judge whether the target financial asset can be securitized based on the real-time market demand;

[0163] If the target financial asset can be securitized, then the securitization is predicted based on the market demand of the real-time financial market and the asset quality of the target financial asset to obtain a first prediction result;

[0164] A scheme determination unit, used to obtain a standard securitization scheme for asset securitization, and adjust the standard securitization scheme in combination with the target financial assets to obtain a first asset securitization scheme;

[0165] The asset securitization unit is used to securitize the target financial assets based on the first asset securitization plan.

[0166] In this embodiment, real-time market demand refers to the immediate demand of investors or consumers in the financial market for certain financial assets or financial products. This demand is constantly changing with changes in market conditions, economic situation, investor expectations and other factors.

[0167] In this embodiment, the first prediction result refers to the prediction result of asset securitization based on the market demand of the real-time financial market and the asset quality of the target financial assets.

[0168] In this embodiment, the standard securitization scheme refers to a general, standardized asset securitization operation process and scheme. For example, the standard securitization scheme includes multiple links such as asset selection, structure design, credit enhancement, securities issuance, etc., and is applicable to various types of financial assets.

[0169] In this embodiment, asset securitization refers to the process of separating and reorganizing the risk and return elements of assets that lack liquidity but can generate predictable and stable cash flows through certain structural arrangements, and then converting them into securities that can be sold and circulated in the financial market.

[0170] In this embodiment, the first asset securitization plan refers to a specific plan and strategy formulated to achieve asset securitization, including aspects such as asset selection, structure design, risk assessment and control, etc.

[0171] The beneficial effect of the above technology is: by combining real-time market demand to judge the target financial assets, asset securitization forecasts can be made to obtain more accurate asset securitization plans.

[0172] Embodiment 8:

[0173] On the basis of Example 7, the financial asset management and securitization system based on big data, AI and blockchain, the securitization module, further includes:

[0174] The securitization monitoring unit is used to monitor the securitization progress at every moment in real time during the securitization process of the target financial assets;

[0175] a securitization supervision unit, used to compare the securitization progress with the first asset securitization plan, so as to determine whether the real-time securitization progress matches the first asset securitization plan;

[0176] If there is a mismatch, the first asset securitization plan will be adjusted based on the securitization progress;

[0177] Otherwise, the asset securitization of the target assets is determined to be qualified.

[0178] In this embodiment, the securitization progress refers to the real-time securitization progress of the target financial assets.

[0179] In this embodiment, asset securitization refers to the process of separating and reorganizing the risk and return elements of assets that lack liquidity but can generate predictable and stable cash flows through certain structural arrangements, and then converting them into securities that can be sold and circulated in the financial market.

[0180] In this embodiment, the first asset securitization plan refers to a specific plan and strategy formulated to achieve asset securitization, including aspects such as asset selection, structure design, risk assessment and control, etc.

[0181] The beneficial effect of the above technology is: by real-time monitoring of the securitization progress and comparing it with the first asset securitization plan, the asset securitization plan can be adjusted in a timely and accurate manner, so that asset securitization can more accurately meet user needs.

[0182] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A financial asset management and securitization system based on big data, AI and blockchain, characterized by: include: A data extraction module, used to obtain multi-source asset data of target financial assets based on big data, AI and blockchain, and perform data processing to obtain first processed data; A data evaluation module, used to perform data evaluation on the first processed data in combination with the evaluation data type, so as to determine the first asset quality of the target financial asset; A data management module, configured to perform asset maintenance on the target financial asset based on the first asset quality and the asset type of the target financial asset; The securitization module is used to determine whether the target financial assets for asset maintenance can be securitized, and to determine the asset securitization plan for the target financial assets that can be securitized to carry out asset securitization.

2. The financial asset management and securitization system based on big data, AI and blockchain according to claim 1 is characterized in that: Data extraction module, including: An asset data acquisition unit, used to acquire first asset data of a target financial asset based on big data technology, acquire second asset data of a target financial asset based on AI technology, and acquire third asset data of a target financial asset based on blockchain technology; An asset data comparison unit, used to compare the first asset data, the second asset data and the third asset data one by one, so as to determine the similarity of the asset data between different data sources, and thus obtain a similarity set of target financial assets; A similarity comparison unit, used to extract the maximum similarity and the minimum similarity of the asset data between different data sources in the similarity set, and determine a first difference between the maximum similarity and the minimum similarity; an asset data optimization unit, configured to compare the first difference with a preset minimum difference, and determine the multi-source asset data of the target financial asset based on the comparison result; The asset data processing unit is used to perform data standardization processing on multi-source asset data to obtain first processed data.

3. The financial asset management and securitization system based on big data, AI and blockchain according to claim 2 is characterized in that: Asset Data Optimization Unit, including: an asset data optimization subunit, configured to compare the first difference with a preset minimum difference; If the first difference is less than a preset minimum difference, obtaining multi-source asset data of the target financial asset based on the asset data of the overlapping portion of the first asset data, the second asset data, and the third asset data; If the first difference is not less than a preset minimum difference, multi-source asset data of the target financial asset is obtained based on all asset data of the first asset data, the second asset data and the third asset data.

4. The financial asset management and securitization system based on big data, AI and blockchain according to claim 2 is characterized in that: Data evaluation module, including: A type determination unit, used to determine a first evaluation data type that needs to be evaluated based on the asset characteristics and asset evaluation requirements of the target financial asset; A model building unit, configured to select, from an evaluation model database, a first evaluation method that has the highest matching degree with the target financial asset and a first evaluation model for the target financial asset built based on the first evaluation method, based on the first evaluation data type and the asset characteristics of the target financial asset; The second model unit is used to perform model training and initial optimization on the first evaluation model based on the AI ​​algorithm to obtain a second evaluation model; A model reference unit is used to extract a first evaluation reference model corresponding to a financial asset having the highest degree of consistency with the asset characteristics of the target financial asset and the type of the first evaluation data from a preset financial asset database, and at the same time, extract a second evaluation reference model corresponding to a financial asset having the second highest degree of consistency with the asset characteristics of the target financial asset and the type of the first evaluation data from the evaluation model database; An evaluation difference unit, used to determine a first evaluation difference between a second evaluation method corresponding to the first evaluation reference model and a third evaluation method corresponding to the second evaluation reference model; If the first evaluation difference is not greater than the preset standard evaluation difference, the second evaluation model is optimized based on the first evaluation reference model and the second evaluation reference model to obtain a third evaluation model; If the first assessment difference is greater than the preset standard assessment difference, extracting a third assessment reference model corresponding to a financial asset having the third highest degree of consistency with the asset characteristics of the target financial asset and the type of the first assessment data from the preset financial asset database; comparing the fourth evaluation method corresponding to the third evaluation reference model with the second evaluation method corresponding to the first evaluation reference model, thereby determining a second evaluation difference based on the comparison result, and re-determining the evaluation difference; The data evaluation unit is used to input the first processed data into the third evaluation model to obtain a first evaluation result of the target financial asset, and obtain a first asset quality of the target financial asset in combination with the asset characteristics of the target financial asset.

5. The financial asset management and securitization system based on big data, AI and blockchain according to claim 4 is characterized in that: The second evaluation model is optimized based on the first evaluation reference model and the second evaluation reference model to obtain a third evaluation model, including: Extracting a second evaluation parameter of the second evaluation method in the first evaluation reference model to obtain a first parameter set, and at the same time, extracting a third evaluation parameter of the third evaluation method in the second evaluation reference model to obtain a second parameter set; Extracting the first evaluation parameter of the first evaluation method in the second evaluation model to obtain an initial parameter set, and making the first parameter set, the second parameter set and the initial parameter set correspond to obtain a first parameter table; The evaluation parameters in the initial parameter set are adjusted based on the corresponding evaluation parameters in the first parameter table to obtain a first adjusted evaluation parameter T corresponding to each first evaluation parameter in the initial parameter set. i , thereby obtaining a first adjustment evaluation parameter set; Among them, T i For the i-th first adjustment evaluation parameter, s i is the parameter value of the i-th first evaluation parameter in the initial parameter set, s′ i is the parameter value of the second evaluation parameter in the first parameter table that is consistent with the parameter type of the i-th first evaluation parameter, s″ i is the parameter value of the third evaluation parameter in the first parameter table that is consistent with the parameter type of the i-th first evaluation parameter, μ i is the parameter conversion coefficient of the parameter type corresponding to the i-th first evaluation parameter, α1 is the evaluation weight of the second evaluation parameter, α2 is the evaluation weight of the third evaluation parameter, and ε is the adjustment error factor, wherein the evaluation weight of the second evaluation parameter is greater than the evaluation weight of the third evaluation parameter; The second evaluation model is optimized based on the first set of adjusted evaluation parameters to obtain a third evaluation model.

6. The financial asset management and securitization system based on big data, AI and blockchain according to claim 4 is characterized in that: Data management module, including: a strategy determination unit, configured to determine an asset maintenance strategy for the target financial asset by combining the first asset quality with the asset type and asset characteristics of the target financial asset; A strategy judgment unit, used to judge the asset maintenance strategy based on the asset maintenance standard, so as to determine the strategy feasibility of the asset maintenance strategy; The asset maintenance unit is used to perform asset maintenance on the target financial asset based on the asset maintenance strategy if the asset maintenance strategy is feasible.

7. The financial asset management and securitization system based on big data, AI and blockchain according to claim 6 is characterized in that: Securitization module, including: An asset judgment unit is used to obtain the real-time market demand of the financial market and judge whether the target financial asset can be securitized based on the real-time market demand; If the target financial asset can be securitized, then the securitization is predicted based on the market demand of the real-time financial market and the asset quality of the target financial asset to obtain a first prediction result; A scheme determination unit, used to obtain a standard securitization scheme for asset securitization, and adjust the standard securitization scheme in combination with the target financial assets to obtain a first asset securitization scheme; The asset securitization unit is used to securitize the target financial assets based on the first asset securitization plan.

8. The financial asset management and securitization system based on big data, AI and blockchain according to claim 7 is characterized in that: The securitization module also includes: The securitization monitoring unit is used to monitor the securitization progress at every moment in real time during the securitization process of the target financial assets; a securitization supervision unit, used to compare the securitization progress with the first asset securitization plan, so as to determine whether the real-time securitization progress matches the first asset securitization plan; If there is a mismatch, the first asset securitization plan will be adjusted based on the securitization progress; Otherwise, the asset securitization of the target assets is determined to be qualified.

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