A supply chain asset quarantine and disposal system based on open API and blockchain

Abstract

The application provides a supply chain asset isolation and disposal system based on an open API and a blockchain, the system comprising a trust agreement signing module, an asset registration and trust management module, a risk triggering and ownership transfer module, a disposal execution module and a blockchain storage and audit module; the trust agreement signing module realizes electronic agreement signing and blockchain storage; the asset registration and trust management module registers the assets after they meet the inclusion conditions and uniformly manages the trust asset list and state; the risk triggering and ownership transfer module triggers automatic asset ownership transfer based on the risk determination result when a risk event is detected; the disposal execution module executes disposal operations after the ownership transfer and generates disposal results; the blockchain storage and audit module stores key operations to support full-process audit tracing; the application realizes accurate risk identification through multi-source API data fusion and quality evaluation, significantly improving the intelligent level and risk control capability of supply chain asset management.

Description

Technical Field

[0001] This invention relates to the field of supply chain asset management system technology, and in particular to a supply chain asset isolation and disposal system based on open APIs and blockchain. Background Technology

[0002] With the rapid development of supply chain finance, the financing needs of distributors continue to grow. However, traditional collateral and guarantee models face problems such as unclear asset ownership, delayed risk identification, and low disposal efficiency. On the one hand, distributors may misappropriate funds or conceal assets after obtaining financing, leading to significant credit risk and asset loss risk for the funding party. On the other hand, when distributors experience operational abnormalities or defaults, funding parties often find it difficult to detect risks in a timely manner and take effective measures. By the time risks are exposed, assets have already depreciated or been lost, resulting in significant losses. Traditional risk management methods mainly rely on regular manual inspections and financial statement reviews, which suffer from problems such as information lag, data falsification, and human judgment bias, making it impossible to achieve real-time monitoring of dealers' operating conditions and asset status. At the same time, the asset disposal process after a risk is triggered also suffers from drawbacks such as long ownership confirmation cycles, limited disposal channels, reliance on human experience for decision-making, and a lack of transparency in the disposal process, leading to severe asset depreciation and low recovery rates. In addition, existing technologies lack effective integration and quality assessment mechanisms for multi-source heterogeneous data, making it impossible to comprehensively utilize multi-dimensional data such as corporate credit, judicial information, business registration, bank accounts, and logistics tracking for risk assessment. In the asset disposal process, there is also a lack of intelligent decision-making mechanisms for different asset types, different market environments, and different risk states, making it impossible to optimize the timing and method of disposal.

[0003] A review of publicly available technical solutions reveals that CN121563235A proposes a method for asset confirmation and risk control in supply chain finance based on blockchain production data. The system includes an asset digitization module, a risk monitoring module, a fund management module, and a trusted data source module. The method involves: converting production data such as orders, production progress, and quality inspection results confirmed by core enterprises into standardized digital asset certificates through smart contracts; establishing a multi-dimensional risk control model based on real-time IoT data to dynamically assess asset risk; and utilizing smart contracts to automatically issue, recover, and dispose of financing funds. This solution transforms traditionally difficult-to-finance work-in-process inventory into trusted digital assets, solving the financing difficulties of small and medium-sized suppliers, while providing financial institutions with real-time and reliable risk control evidence, achieving full-process automation and risk control in supply chain finance. However, this solution relies on confirmation from core enterprises and IoT devices, is only applicable to manufacturing scenarios, and cannot cover non-production stages such as distributor inventory; furthermore, it lacks multi-source heterogeneous data integration, data quality assessment mechanisms, and intelligent asset disposal decision-making methods. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of current systems by proposing a supply chain asset isolation and disposal system based on open APIs and blockchain.

[0005] The present invention adopts the following technical solution:

[0006] A supply chain asset isolation and disposal system based on open APIs and blockchain is disclosed. The system includes a trust agreement signing module, an asset registration and trust management module, a risk triggering and ownership transfer module, a disposal execution module, and a blockchain notarization and auditing module. The trust agreement signing module facilitates the signing of electronic trust agreements between distributors and trust institutions, and stores the agreement content on the blockchain. The asset registration and trust management module registers and includes the corresponding assets within the trust management scope based on the trust agreement after the distributor's assets meet preset inclusion conditions and are confirmed by the system, and provides unified management of the asset list and status information under the trust agreement. The risk triggering and ownership transfer module automatically transfers the ownership of assets under the trust agreement to the funding party or designated entity based on the risk assessment result when a preset risk event is detected in a distributor. The disposal execution module executes the disposal operation process according to preset disposal rules after the asset ownership transfer is completed and generates disposal result information. The blockchain notarization and auditing module stores key operations involved in the system operation on the blockchain to support the auditing and traceability of the asset isolation and disposal process.

[0007] Furthermore, the trust agreement signing module includes an agreement template management unit, an agreement signing unit, and an agreement notarization unit; the agreement template management unit is used to pre-configure electronic trust agreement templates and supports parameterized settings for the terms within the agreement; the agreement signing unit is used to realize the signing of electronic trust agreements between distributors and trust institutions, and to record the signing parties, signing time, and agreement status information; the agreement notarization unit is used to notarize the signed electronic trust agreements and write them into the blockchain.

[0008] Furthermore, the asset registration and trust management module includes an asset inclusion confirmation unit, an asset registration unit, a trust asset management unit, and an asset status maintenance unit. The asset inclusion confirmation unit is used to confirm whether the assets submitted by the distributor meet the preset inclusion conditions after the electronic trust agreement is signed. The asset registration unit is used to register the assets as trust assets managed based on the trust agreement after the assets have been confirmed by the system. The trust asset management unit is used to centrally manage the assets included in the scope of the trust agreement and maintain the asset list, asset identification, and the correspondence between the assets and the trust agreement. The asset status maintenance unit is used to record and update the status information of the assets during the management process.

[0009] Furthermore, the risk triggering and ownership transfer module includes a risk event acquisition unit, a risk judgment unit, an ownership transfer triggering unit, and an ownership transfer execution unit. The risk event acquisition unit is used to acquire risk event information related to the distributor and convert the risk events into risk event data that can be used for system analysis and judgment. The risk judgment unit is used to analyze the risk event data and generate risk judgment results. The ownership transfer triggering unit is used to generate an asset ownership transfer instruction based on the risk judgment results to trigger the transfer of asset ownership to a designated entity of the funding party or the distributor. The ownership transfer execution unit is used to complete the change of asset ownership status according to the asset ownership transfer instruction and generate a corresponding ownership transfer record.

[0010] Furthermore, the disposal execution module includes a disposal analysis unit, a disposal operation execution unit, and a disposal result generation unit; the disposal analysis unit is used to analyze the transferred assets and generate a disposal method applicable to the current assets after the asset ownership is completed and transferred; the disposal operation execution unit is used to execute the corresponding disposal operation process on the assets according to the asset disposal method generated by the disposal analysis unit; the disposal result generation unit is used to generate disposal result information corresponding to the assets after the disposal operation is completed.

[0011] Furthermore, the blockchain evidence storage and auditing module includes a key operation identification unit, an evidence storage writing unit, and an audit traceability unit. The key operation identification unit is used to identify key operations that require blockchain evidence storage generated by the aforementioned modules during system operation. These key operations include agreement signing, asset registration, asset ownership transfer, and asset disposal operations. The evidence storage writing unit is used to write the evidence storage data corresponding to the key operations into the blockchain, generating an immutable blockchain evidence storage record. The audit traceability unit is used to audit and trace the asset isolation and disposal process based on the blockchain evidence storage record, and output the corresponding audit result information.

[0012] Furthermore, the risk assessment unit generates risk assessment results by analyzing the data in the following manner:

[0013] ;

[0014] in, Current time Below is the risk assessment value for the distributor, which serves as the risk assessment result for the corresponding distributor; The number of risk dimensions related to risk assessment. For the first The weight coefficients corresponding to each risk dimension can be calibrated and preset based on historical risk data to assess the contribution of risk judgment. Current time Next The risk indicator values ​​corresponding to each risk dimension This is a multi-risk coupling coefficient used to adjust the impact of multiple risk coupling terms on the risk assessment value. Its value range is set through pre-experimentation. ; For the first The risk dimension and the first The risk correlation coefficient between risk dimensions represents the strength of the risk linkage between two risk dimensions, and can be determined by Pearson correlation analysis in combination with historical data. Current time Next The risk dimension and the first The coupling activation function between the risk dimensions; satisfies:

[0015] ;

[0016] in, Indicates the first The normalization function for the risk dimension is used to process the risk dimension. Risk event data weighted and fused from multiple data sources under each risk dimension is mapped to a risk indicator value with a unified dimension to ensure comparability between different risk dimensions. The normalization function... According to the The data types and value ranges corresponding to each risk dimension are preset; To obtain the first The number of data sources for risk events within each risk dimension; For the first The risk dimension corresponds to the first The validity weight of each data source. For the first The risk dimension corresponds to the first The risk event data vector obtained from each data source; satisfying:

[0017] ;

[0018] in, Current time Next The reference risk event data vector for the first risk dimension, let the first risk dimension be... Each risk dimension at the current time Depend on The set of risk event data vectors obtained from each data source is as follows: When there are at least two identical risk event data vectors in the set, the occurrence frequency of each risk event data vector is counted, and the risk event data vector with the highest occurrence frequency is taken as the reference risk event data vector; when there are no duplicate risk event data vectors in the set, the arithmetic mean vector of the risk event data vector set is taken as the reference risk event data vector. For the first The risk dimension corresponds to the first The data source obtains the time difference between the risk event data vector time and the current time. For the first The time decay parameter corresponding to each risk dimension is used to characterize the rate at which the reliability of risk event data under that risk dimension decreases over time, based on the... The risk change characteristics of each risk dimension are preset.

[0019] The beneficial effects achieved by this invention are:

[0020] This solution improves the comprehensiveness and accuracy of supply chain asset risk identification by constructing a data acquisition mechanism based on open APIs to achieve real-time acquisition and integrated analysis of multi-source risk event information. By establishing a risk assessment and automatic ownership transfer mechanism, it enables automatic isolation and ownership transfer of assets under risk-triggered conditions, enhancing the timeliness of risk response and asset security. Simultaneously, by introducing asset disposal suitability analysis and value prediction mechanisms, it achieves intelligent selection of disposal channels and optimized control of the disposal process, improving asset disposal efficiency and recovery value. Furthermore, through blockchain-based evidence storage and auditing mechanisms, it achieves reliable recording and traceable management of the entire process of asset registration, ownership transfer, and disposal, enhancing the transparency and reliability of system operation. Attached Figure Description

[0021] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0022] Figure 1 This is a schematic diagram of the overall module workflow of the present invention.

[0023] Figure 2 This is a schematic diagram of the workflow of the trust agreement signing module of the present invention.

[0024] Figure 3 This is a schematic diagram of the workflow of the risk triggering and ownership transfer module of the present invention.

[0025] Figure 4 This is a schematic diagram of the workflow of the processing execution module of the present invention.

[0026] Figure 5 This is a schematic diagram comparing the key performance indicators of the system of the present invention with those of traditional systems.

[0027] Figure 6 This diagram illustrates the improvement in key performance indicators of the system of the present invention compared to traditional systems. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to its embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. Other systems, methods, and / or features of this embodiment will become apparent to those skilled in the art after reviewing the following detailed description. It is intended that all such additional systems, methods, features, and advantages are included within this specification, are included within the scope of the present invention, and are protected by the appended claims. Further features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0029] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0030] Example 1:

[0031] like Figure 1 , Figure 2 , Figure 3 , Figure 4As shown, this embodiment provides a supply chain asset isolation and disposal system based on open APIs and blockchain. The system includes a trust agreement signing module, an asset registration and trust management module, a risk triggering and ownership transfer module, a disposal execution module, and a blockchain notarization and auditing module. The trust agreement signing module is used to realize the signing of electronic trust agreements between distributors and trust institutions, and to store the agreement content on the blockchain. The asset registration and trust management module is used to register the corresponding assets and include them in the trust management scope based on the trust agreement after the distributor's assets meet the preset inclusion conditions and are confirmed by the system, and to uniformly manage the list and status information of the assets under the trust agreement. The risk triggering and ownership transfer module is used to trigger the automatic transfer of asset ownership under the trust agreement to the funding party or designated entity based on the risk assessment result when a preset risk event is detected in the distributor. The disposal execution module is used to execute the disposal operation process of the assets according to the preset disposal rules after the asset ownership transfer is completed, and to generate disposal result information. The blockchain notarization and auditing module is used to store the key operations involved in the system operation on the blockchain to support the auditing and traceability of the asset isolation and disposal process.

[0032] Furthermore, the trust agreement signing module includes an agreement template management unit, an agreement signing unit, and an agreement storage unit; the agreement template management unit is used to pre-configure electronic trust agreement templates and supports parameterized settings for the terms within the agreement; the agreement signing unit is used to realize the signing of electronic trust agreements between distributors and trust institutions, and to record the signing parties, signing time, and agreement status information; the agreement storage unit is used to store the signed electronic trust agreements and write them into the blockchain.

[0033] Furthermore, the asset registration and trust management module includes an asset inclusion confirmation unit, an asset registration unit, a trust asset management unit, and an asset status maintenance unit. The asset inclusion confirmation unit is used to confirm whether the assets submitted by the distributor meet the preset inclusion conditions after the electronic trust agreement is signed. The asset registration unit is used to register the assets as trust assets managed based on the trust agreement after system confirmation. The trust asset management unit is used to centrally manage assets included in the trust agreement's management scope, maintaining the asset list, asset identifiers, and the correspondence between assets and the trust agreement. The asset status maintenance unit is used to record and update the asset status information during the management process.

[0034] Furthermore, the risk triggering and ownership transfer module includes a risk event acquisition unit, a risk judgment unit, an ownership transfer triggering unit, and an ownership transfer execution unit. The risk event acquisition unit is used to acquire risk event information related to the distributor and convert the risk events into risk event data that can be used for system analysis and judgment. The risk judgment unit is used to analyze the risk event data and generate a risk judgment result. The ownership transfer triggering unit is used to generate an asset ownership transfer instruction based on the risk judgment result to trigger the transfer of asset ownership to a designated entity of the funding party or the distributor. The ownership transfer execution unit is used to complete the change of asset ownership status according to the asset ownership transfer instruction and generate a corresponding ownership transfer record.

[0035] Specifically, the risk event acquisition unit is used to acquire multi-dimensional risk event information related to distributors in real time from multiple open API data sources, and convert raw risk events of different formats and types into standardized risk event data. The risk event information includes, but is not limited to, financial risk events, such as debt-to-equity ratio, current ratio, revenue growth rate, net profit margin, abnormal account balance, and tax declaration delays; legal risk events, such as litigation cases, enforcement records, marking as dishonest judgment debtors, asset freezes, and administrative penalties; operational risk events, such as changes in legal representatives, changes in registered addresses, changes in business scope, significant revenue fluctuations, delayed submission of reports, and major contract changes; asset risk events, such as fluctuations in commodity market prices, abnormal changes in inventory, decline in asset valuation, mortgage and pledge records, and abnormal warehousing environment; and public opinion risk events, such as negative news reports, negative social media reviews, decline in industry reputation, consumer complaints, and media exposure.

[0036] The risk event acquisition unit concurrently initiates data requests to multiple data sources, such as enterprise credit reporting platforms, judicial information platforms, business registration platforms, bank account monitoring systems, tax declaration systems, logistics tracking systems, warehouse management systems, commodity exchanges, and public opinion monitoring platforms, through pre-configured open API interfaces to obtain real-time or near-real-time risk event information related to the target distributor. After receiving the raw data returned by each API data source, it first identifies the data type of the risk event information, including count data, percentage data, exponential data, tag data, graded data, text data, and time series data. After completing the data type identification, it uses corresponding standardization methods to convert and generate standardized risk event data according to different data types. For example, for count data, a normalization method based on historical maximum values ​​is used; for percentage data, it is directly divided by 100 to convert to the [0,1] interval; for exponential and graded data, a linear mapping method is used; for tag data, it is converted to corresponding values ​​through a preset mapping table; for text data, a sentiment index is extracted through natural language processing and sentiment analysis technology; and for time series data, its statistical characteristics are calculated.

[0037] Furthermore, the disposal execution module includes a disposal analysis unit, a disposal operation execution unit, and a disposal result generation unit; the disposal analysis unit is used to analyze the transferred assets and generate a disposal method applicable to the current assets after the asset ownership is completed and transferred; the disposal operation execution unit is used to execute the corresponding disposal operation process on the assets according to the asset disposal method generated by the disposal analysis unit; the disposal result generation unit is used to generate disposal result information corresponding to the assets after the disposal operation is completed.

[0038] Furthermore, the blockchain evidence storage and auditing module includes a key operation identification unit, an evidence storage writing unit, and an audit tracing unit. The key operation identification unit is used to identify key operations that require blockchain evidence storage generated by the aforementioned modules during system operation. These key operations include agreement signing, asset registration, asset ownership transfer, and asset disposal operations. The evidence storage writing unit is used to write the evidence storage data corresponding to the key operations into the blockchain, generating an immutable blockchain evidence storage record. The audit tracing unit is used to audit and trace the asset isolation and disposal process based on the blockchain evidence storage record, and output the corresponding audit result information.

[0039] This system is built on an open API mechanism and blockchain technology. It collects data related to asset management, risk identification and disposal from external business systems, risk control systems or data services through open APIs, and on this basis, it realizes automated control of asset registration, risk triggering, ownership transfer and disposal processes.

[0040] Furthermore, the risk assessment unit generates risk assessment results by analyzing the data in the following manner:

[0041] ;

[0042] in, Current time Below is the risk assessment value for the distributor, which serves as the risk assessment result for the corresponding distributor; The number of risk dimensions related to risk assessment. For the first The weight coefficients corresponding to each risk dimension can be calibrated and preset based on historical risk data to assess the contribution of risk judgment. Current time Next The risk indicator values ​​corresponding to each risk dimension This is a multi-risk coupling coefficient used to adjust the impact of multiple risk coupling terms on the risk assessment value. Its value range is set through pre-experimentation. ; For the first The risk dimension and the first The risk correlation coefficient between risk dimensions represents the strength of the risk linkage between two risk dimensions, and can be determined by Pearson correlation analysis in combination with historical data. Current time Next The risk dimension and the first The coupling activation function between the risk dimensions; satisfies:

[0043] ;

[0044] in, Indicates the first The normalization function for the risk dimension is used to process the risk dimension. Risk event data weighted and fused from multiple data sources under each risk dimension is mapped to a risk indicator value with a unified dimension to ensure comparability between different risk dimensions. The normalization function... According to the The data types and value ranges corresponding to each risk dimension are preset; To obtain the first The number of data sources for risk events within each risk dimension; For the first The risk dimension corresponds to the first The validity weight of each data source. For the first The risk dimension corresponds to the first The risk event data vector obtained from each data source; satisfying:

[0045] ;

[0046] in, Current time Next The reference risk event data vector for the first risk dimension, let the first risk dimension be... Each risk dimension at the current time Depend on The set of risk event data vectors obtained from each data source is as follows: When there are at least two identical risk event data vectors in the set, the occurrence frequency of each risk event data vector is counted, and the risk event data vector with the highest occurrence frequency is taken as the reference risk event data vector; when there are no duplicate risk event data vectors in the set, the arithmetic mean vector of the risk event data vector set is taken as the reference risk event data vector. For the first The risk dimension corresponds to the first The data source obtains the time difference between the risk event data vector time and the current time. For the first The time decay parameter corresponding to each risk dimension is used to characterize the rate at which the reliability of risk event data under that risk dimension decreases over time, based on the... The risk change characteristics of each risk dimension are pre-defined;

[0047] ;

[0048] in, The threshold value for a single-dimensional risk indicator represents the critical value state of the single-dimensional risk indicator, and can preferably be set to 0.6.

[0049] This solution constructs a comprehensive risk assessment model based on multiple risk dimensions, weightedly integrates and normalizes multi-source risk event data from distributors, achieving a unified quantitative assessment of risk status across different risk dimensions. Simultaneously, by introducing a correlation coupling mechanism between risk dimensions, the risk assessment results are synergistically amplified when multiple risks are activated simultaneously, thereby improving the sensitivity to identify complex operational risk scenarios. Furthermore, by introducing vector similarity and time decay mechanisms into risk event data processing, the risk assessment results can balance the consistency and timeliness of multi-source data, effectively reducing the interference of outdated or abnormal data on risk assessment results and improving the accuracy and stability of risk assessment results.

[0050] Furthermore, the ownership transfer triggering unit compares the risk assessment result with a pre-set ownership transfer triggering threshold to determine whether the asset ownership transfer conditions are met. When the risk assessment result reaches or exceeds the ownership transfer triggering threshold, the ownership transfer triggering unit generates an asset ownership transfer instruction to trigger the transfer of asset ownership under the trust agreement to a designated entity of the funder or distributor. When the risk assessment result does not reach the ownership transfer triggering threshold, the original ownership status of the asset remains unchanged.

[0051] Example 2:

[0052] This embodiment should be understood to include at least all the features of any of the foregoing embodiments, and to further improve upon them;

[0053] This embodiment provides a supply chain asset isolation and disposal system based on open APIs and blockchain. The system includes a trust agreement signing module, an asset registration and trust management module, a risk triggering and ownership transfer module, a disposal execution module, and a blockchain evidence storage and auditing module. The trust agreement signing module is used to realize the signing of electronic trust agreements between distributors and trust institutions, and to store the agreement content on the blockchain. The asset registration and trust management module is used to register the corresponding assets and include them in the trust management scope based on the trust agreement after the distributor's assets meet the preset inclusion conditions and are confirmed by the system, and to uniformly manage the list and status information of the assets under the trust agreement. The risk triggering and ownership transfer module is used to trigger the automatic transfer of asset ownership under the trust agreement to the funding party or designated entity based on the risk assessment result when a preset risk event is detected in the distributor. The disposal execution module is used to execute the disposal operation process of the assets according to the preset disposal rules after the asset ownership transfer is completed, and to generate disposal result information. The blockchain evidence storage and auditing module is used to store the key operations involved in the system operation on the blockchain to support the auditing and traceability of the asset isolation and disposal process.

[0054] Furthermore, the disposal execution module includes a disposal analysis unit, a disposal operation execution unit, and a disposal result generation unit; the disposal analysis unit is used to analyze the transferred assets and generate a disposal method applicable to the current assets after the asset ownership is completed and transferred; the disposal operation execution unit is used to execute the corresponding disposal operation process on the assets according to the asset disposal method generated by the disposal analysis unit; the disposal result generation unit is used to generate disposal result information corresponding to the assets after the disposal operation is completed.

[0055] Furthermore, the disposal analysis unit is used to perform quantitative analysis on the transferred assets based on asset attribute characteristics, disposal channel characteristics, and disposal time constraints after the asset ownership is completed and transferred, and to generate a disposal method suitable for the current assets; the disposal analysis unit specifically generates a disposal method suitable for the current assets in the following ways:

[0056] ;

[0057] in, Indicates current assets In time Through channels The suitability value for disposal is used to characterize the relative advantages and disadvantages of disposing of assets through different disposal channels at different times; Assets With channels The matching index between the two is used to characterize the degree of compatibility between asset attributes and disposal channels; Assets In time Through channels The value index for disposal is used to characterize the relative quality of asset disposal value obtained through different channels; To use through channels The time urgency index during disposal is used to characterize the degree of influence of disposal cycle factors on the choice of disposal method. Indicates through channels The processing time period is the length of time from the start of the processing operation to the confirmation of the processing result. , and The weights assigned to the matching index, value index, and time urgency score are pre-set based on asset type, market demand, and timeliness requirements to meet [the criteria]. ;

[0058] Furthermore, the matching index The matching rules are obtained by querying a pre-established asset type-disposal channel matching rule table. This table assigns different normalized values ​​as matching indices based on the degree of compatibility between different assets and each disposal channel. Preferably, the matching rule table is established based on statistical analysis of historical disposal cases, specifically including: collecting data from more than 500 historical disposal cases, statistically analyzing the transaction rate, average premium rate, and average disposal cycle of each type of asset in each disposal channel, comprehensively evaluating and determining the matching level, and adjusting it according to the standardization degree of the asset type, timeliness requirements, and market liquidity characteristics. For example, the corresponding matching rule tables for some types of assets and disposal channels are as follows:

[0059]

[0060] Furthermore, the value index The value is obtained through a pre-established LSTM prediction model. This model combines historical market data, asset attributes, and relevant factors to predict the market value of an asset at a specified point in time. For example, by inputting historical price sequences for the past 60 days, multiple macroeconomic indicators, and multiple industry supply and demand indicators, combined with the asset's depreciation rate, the market value of the asset at a specified point in time is predicted. Based on this, by comprehensively calculating the channel transaction price ratio, real-time liquidity coefficient, and transaction cost rate, the proportion of net value that can be actually recovered when disposing of the asset through this channel is obtained, thus deriving a normalized value index. The transaction price ratio is statistically derived based on the ratio of actual transaction price to appraised price in historical disposal cases. The liquidity coefficient is obtained by querying the market activity and availability of each disposal channel in real time via an open API. The transaction cost rate is determined based on the platform commission and service fees of each channel.

[0061] Furthermore, the time urgency index Specifically, it can be obtained through the following methods:

[0062] ;

[0063] in, The maximum acceptable disposal timeframe is predetermined based on the asset's characteristics; For assets The risk assessment value corresponding to the triggering of ownership transfer is obtained through the risk assessment unit; The preset maximum risk assessment value is used for normalization. ;

[0064] Furthermore, the disposal operation execution unit selects the time point and disposal channel corresponding to the maximum fit value to ensure that the assets are efficiently disposed of through the most suitable disposal channel within a given time, thereby maximizing the recovery value and reducing the risk.

[0065] This solution achieves intelligent selection of disposal channels by establishing asset-channel matching rules, value prediction models, and risk adaptive urgency index mechanisms. It automatically selects the optimal disposal method through a three-dimensional comprehensive index, ensuring efficient and flexible asset disposal in a dynamic market environment and improving the system's intelligence and real-time response capabilities.

[0066] Example 3:

[0067] This embodiment should be understood to include at least all the features of any of the foregoing embodiments, and to further improve upon them;

[0068] This embodiment further provides a supply chain asset isolation and disposal system based on open APIs and blockchain, which enhances the reliability of data collection and the accuracy of disposal execution. The system is deployed on a server with an eight-core processor, a main frequency of 2.6GHz, a memory capacity of 32GB, and a solid-state storage capacity of 1TB. The server runs a service environment based on a containerized architecture and is configured with multi-threaded concurrent processing capabilities to support no less than 200 API call requests per second. The system establishes data communication connections with external open API interfaces via the HTTPS protocol and parses and processes risk event data in JSON or XML formats. The open API interfaces include enterprise credit data interfaces, judicial risk query interfaces, asset price query interfaces, warehouse status query interfaces, and market trading platform interfaces. The average response time of the interfaces is controlled within 150ms, and the data update cycle is preferably set within the range of 60 seconds to 600 seconds. Through the above-mentioned multi-source data real-time collection mechanism, the asset risk monitoring frequency is increased from once a day in traditional manual monitoring to once per minute, thereby significantly improving the real-time performance of risk identification.

[0069] Furthermore, the trust agreement signing module uses an electronic signature component that supports asymmetric encryption algorithms to implement the trust agreement signing process. The electronic signature component uses an encryption key with a preferred length of 256 bits to generate digital signature data and generate a unique agreement identifier code. After the agreement data is generated, it is written into the blockchain network through the blockchain evidence storage interface. The number of blockchain network nodes is preferably 5 to 15 and the block generation time is preferably controlled within the range of 2 to 10 seconds.

[0070] Furthermore, during the asset inclusion phase, the asset registration and trust management module generates a unique identifier code for each asset through an asset identification unit. This unique identifier code is a 32-bit string and together with the asset type code, asset quantity code, and asset registration time code, constitutes the asset's digital identity information. Simultaneously, the asset status maintenance unit updates the asset status every 300 seconds and synchronously writes the updated status data into the blockchain evidence storage module.

[0071] Furthermore, the risk triggering and ownership transfer module synchronously acquires risk event data from at least 8 open API data sources through the risk event collection unit and performs standardized processing on the data, including credit data sources, judicial data sources, logistics data sources, asset price data sources, tax data sources, bank account data sources, and public opinion data sources.

[0072] Furthermore, the disposal execution module includes a disposal analysis unit, a disposal operation execution unit, and a disposal result generation unit. The disposal analysis unit determines a set of candidate disposal channels by querying a pre-established asset type and disposal channel matching database. The matching database is established based on no less than 800 historical disposal records and includes asset type parameters, disposal channel type parameters, and historical transaction rate parameters. In this embodiment, the disposal channels include online auction platform interfaces, electronic trading platform interfaces, and targeted transfer interfaces. The disposal operation execution unit sends disposal requests to the corresponding disposal channels through open API interfaces and monitors the disposal status in real time.

[0073] Furthermore, such as Figure 5 , Figure 6As shown, to verify the practical application effect of the supply chain asset isolation and disposal system based on open API and blockchain described in this embodiment, 12 distributors were selected as pilot subjects for continuous 10-month system operation verification. These included 4 vehicle asset distributors, 3 industrial material distributors, 2 electronic product distributors, 2 machinery and equipment distributors, and 1 agricultural product distributor. A control group of 12 distributors using traditional manual risk management and collateral guarantee methods were selected for comparative testing during the same period. The asset size of both the pilot and control groups ranged from 6 million to 48 million yuan, and their asset types were similar. The system was tested for efficiency in risk identification, accuracy in risk identification, efficiency in ownership transfer, asset disposal cycle, and asset recovery. Statistical analysis of key indicators such as efficiency revealed that the system in this embodiment significantly outperforms traditional methods in terms of the timeliness of risk identification. The pilot group's average risk event identification time was 3.5 hours after the actual occurrence of the risk, while the control group's average identification time was over 32 days, representing an improvement of approximately 99.5% in risk identification response efficiency. Regarding risk identification accuracy, the pilot group achieved 91.8%, an increase of approximately 44.8% compared to the control group's 63.4%, significantly reducing the probability of missed risk identification. In terms of the efficiency of ownership transfer execution, the pilot group's average execution time for asset ownership transfer ranged from 8 to 35 minutes, while the control group's average time for manual confirmation and legal procedures ranged from 90 to 180 days, demonstrating superior efficiency in ownership transfer execution. The efficiency improvement exceeded 99%; in terms of asset disposal efficiency, the pilot group's average asset disposal cycle was 9.6 days, while the control group's average disposal cycle was 142 days, a reduction of approximately 93.2%; in terms of asset recovery rate, the pilot group's average asset recovery rate was 91.2%, while the control group's average asset recovery rate was 61.5%, an increase of approximately 48.3%; in terms of risk loss control, the pilot group's actual asset loss rate was 0.9% of the total credit assets, while the control group's loss rate was 4.2%, a reduction of approximately 78.6%; in terms of system operation efficiency, the pilot group's average asset registration time was 1.8 working days, while the control group's average manual registration time was 6.5 working days, an improvement of approximately [missing information]. 72.3%; Regarding the frequency of asset status monitoring, the system in this embodiment achieves minute-level automatic monitoring, while the traditional manual monitoring frequency is monthly, increasing the monitoring frequency by more than 40,000 times, thereby significantly improving asset risk control capabilities; The comprehensive comparison results above show that the system described in this embodiment is significantly superior to traditional technical solutions in key performance indicators such as timeliness of risk identification, accuracy of risk identification, efficiency of ownership transfer, asset disposal cycle, and asset recovery rate. It achieves technical effects such as improving supply chain asset risk control capabilities by more than 40%, improving asset disposal efficiency by more than 90%, and reducing asset loss rate by more than 70%, thus verifying the effectiveness and superiority of the system in the application scenario of supply chain asset isolation and disposal.

[0074] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A supply chain asset isolation and disposal system based on open APIs and blockchain, characterized in that, The system includes a trust agreement signing module, an asset registration and trust management module, a risk triggering and ownership transfer module, a disposal execution module, and a blockchain evidence storage and auditing module. The trust agreement signing module enables the signing of electronic trust agreements between distributors and trust institutions, and stores the agreement content on the blockchain. The asset registration and trust management module registers and includes the corresponding assets within the trust management scope based on the trust agreement after the distributor's assets meet the preset inclusion conditions and are confirmed by the system, and uniformly manages the list and status information of the assets under the trust agreement. The risk triggering and ownership transfer module automatically transfers the ownership of the assets under the trust agreement to the funding party or a designated entity based on the risk assessment result when a preset risk event is detected in the distributor. The disposal execution module is used to perform disposal operations on assets according to preset disposal rules after the transfer of asset ownership is completed, and to generate disposal result information; the blockchain evidence storage and auditing module is used to store key operations involved in the system operation on the blockchain to support the auditing and traceability of asset isolation and disposal processes.

2. The supply chain asset isolation and disposal system based on open APIs and blockchain as described in claim 1, characterized in that, The trust agreement signing module includes an agreement template management unit, an agreement signing unit, and an agreement notarization unit. The agreement template management unit is used to pre-configure electronic trust agreement templates and supports parameterized settings for the terms within the agreement. The agreement signing unit is used to realize the signing of electronic trust agreements between distributors and trust institutions and to record the signing parties, signing time, and agreement status information. The agreement notarization unit is used to notarize the signed electronic trust agreements and write them into the blockchain.

3. The supply chain asset isolation and disposal system based on open APIs and blockchain as described in claim 1, characterized in that, The asset registration and trust management module includes an asset inclusion confirmation unit, an asset registration unit, a trust asset management unit, and an asset status maintenance unit. The asset inclusion confirmation unit is used to confirm whether the assets submitted by the distributor meet the preset inclusion conditions after the electronic trust agreement is signed. The asset registration unit is used to register the assets as trust assets managed based on the trust agreement after the assets have been confirmed by the system. The trust asset management unit is used to centrally manage assets included in the scope of the trust agreement, and to maintain the asset list, asset identification, and the correspondence between assets and the trust agreement; The asset status maintenance unit is used to record and update the status information of assets during the management process.

4. A supply chain asset isolation and disposal system based on open APIs and blockchain as described in claim 1, characterized in that, The risk triggering and ownership transfer module includes a risk event acquisition unit, a risk judgment unit, an ownership transfer triggering unit, and an ownership transfer execution unit; the risk event acquisition unit is used to acquire risk event information related to the dealer and convert the risk events into risk event data that can be used for system analysis and judgment. The risk assessment unit is used to analyze the risk event data and generate risk assessment results; The ownership transfer triggering unit is used to generate an asset ownership transfer instruction based on the risk assessment result, so as to trigger the transfer of asset ownership to the designated entity of the funder or distributor; The ownership transfer execution unit is used to complete the change of asset ownership status according to the asset ownership transfer instruction and generate the corresponding ownership transfer record.

5. A supply chain asset isolation and disposal system based on open APIs and blockchain as described in claim 1, characterized in that, The disposal execution module includes a disposal analysis unit, a disposal operation execution unit, and a disposal result generation unit. The disposal analysis unit is used to analyze the transferred assets and generate a disposal method applicable to the current assets after the asset ownership is completed and transferred. The disposal operation execution unit is used to execute the corresponding disposal operation process on the assets according to the asset disposal method generated by the disposal analysis unit. The disposal result generation unit is used to generate disposal result information corresponding to the assets after the disposal operation is completed.

6. A supply chain asset isolation and disposal system based on open APIs and blockchain as described in claim 1, characterized in that, The blockchain evidence storage and auditing module includes a key operation identification unit, an evidence storage writing unit, and an audit traceability unit. The key operation identification unit is used to identify key operations that require blockchain evidence storage generated by the aforementioned modules during system operation. These key operations include agreement signing, asset registration, asset ownership transfer, and asset disposal. The evidence storage writing unit is used to write the evidence storage data corresponding to the key operations into the blockchain, generating an immutable blockchain evidence storage record. The audit traceability unit is used to audit and trace the asset isolation and disposal process based on the blockchain evidence storage record, and output the corresponding audit result information.

7. A supply chain asset isolation and disposal system based on open APIs and blockchain as described in claim 1, characterized in that, The risk assessment unit generates risk assessment results through analysis in the following manner: ； in, Current time Below is the risk assessment value for the distributor, which serves as the risk assessment result for the corresponding distributor; The number of risk dimensions related to risk assessment. For the first The weight coefficients corresponding to each risk dimension can be calibrated and preset based on historical risk data to assess the contribution of risk judgment. Current time Next Risk indicator values ​​corresponding to each risk dimension This is a multi-risk coupling coefficient used to adjust the impact of multiple risk coupling terms on the risk assessment value. Its value range is set through pre-experimentation. ; For the first The risk dimension and the first The risk correlation coefficient between risk dimensions represents the strength of the risk linkage between two risk dimensions, and can be determined by Pearson correlation analysis in combination with historical data. Current time Next The risk dimension and the first The coupling activation function between the risk dimensions; satisfies: ； in, Indicates the first The normalization function for the risk dimension is used to process the risk dimension. Risk event data weighted and fused from multiple data sources under each risk dimension is mapped to a risk indicator value with a unified dimension to ensure comparability between different risk dimensions. The normalization function... According to the The data types and value ranges corresponding to each risk dimension are preset; To obtain the first The number of data sources for risk events within each risk dimension; For the first The risk dimension corresponds to the first The validity weight of each data source. For the first The risk dimension corresponds to the first The risk event data vector obtained from each data source; satisfying: ； in, Current time Next The reference risk event data vector for the first risk dimension, let the first risk dimension be... Each risk dimension at the current time Depend on The set of risk event data vectors obtained from each data source is as follows: When there are at least two identical risk event data vectors in the set, the occurrence frequency of each risk event data vector is counted, and the risk event data vector with the highest occurrence frequency is taken as the reference risk event data vector; when there are no duplicate risk event data vectors in the set, the arithmetic mean vector of the risk event data vector set is taken as the reference risk event data vector. For the first The risk dimension corresponds to the first The data source obtains the time difference between the risk event data vector time and the current time. For the first The time decay parameter corresponding to each risk dimension is used to characterize the rate at which the reliability of risk event data under that risk dimension decreases over time, based on the... The risk change characteristics of each risk dimension are preset.

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