A multi-level supply chain coordination and financial service management system
By using a modular architecture and standardized data interfaces, combined with a dynamic rights confirmation rule algorithm engine and data security technology, the automation and security issues of provisional rights confirmation in supply chain finance have been solved, achieving fully automated rights confirmation and data security, and generating tamper-proof electronic debt certificates.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN QIANHAIZEJIN IND & FINANCE TECH CO LTD
- Filing Date
- 2026-05-16
- Publication Date
- 2026-07-14
AI Technical Summary
In supply chain finance scenarios, provisional valuation and confirmation of rights rely on offline documents and manual entry. The system cannot automatically connect to transaction data sources, resulting in high response time and high manual intervention rate in the confirmation process. The confirmation rules are hard-coded and lack a dynamically adaptable algorithm engine, making it impossible to automatically determine based on historical transaction data, leading to poor decision consistency. Credit assessment relies on human experience and lacks standardized data processing and quantitative scoring algorithms, resulting in assessment results that are not reproducible or traceable. After the electronic debt certificate is generated, there is no encryption or anti-tampering mechanism, resulting in insufficient data security and credibility.
Adopting a modular architecture and standardized data interfaces, the pre-confirmation algorithm is embedded into the provisional data processing flow. Through the dynamic confirmation rule algorithm engine, the performance status and deviation degree are automatically identified and matched to generate tamper-proof electronic debt certificates. Combined with multi-dimensional data normalization and weighted scoring algorithms, a transaction behavior profile is constructed. Hash encryption and de-identification storage technologies are used to ensure data security.
It achieves fully automated confirmation of rights for provisional supply chain transaction data, reduces the rate of human intervention, improves decision consistency and accuracy, ensures that the confirmation results are reproducible and traceable, guarantees data security and credibility, and generates tamper-proof electronic debt certificates.
Smart Images
Figure CN122390856A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of computer data processing, and in particular to a multi-level supply chain collaboration and financial service management system. Background Technology
[0002] In supply chain finance scenarios, core enterprises and upstream suppliers often enter a provisional settlement phase after a transaction is completed. During this phase, debt confirmation is necessary to support subsequent financing. Existing technologies have the following technical shortcomings: The provisional confirmation of rights relies on offline documents and manual entry. The system cannot automatically connect to the transaction data source, resulting in high response time and high rate of manual intervention in the confirmation process. The rights confirmation rules are hard-coded and lack a dynamically adaptable algorithm engine, making it impossible to automatically determine based on historical transaction data, resulting in poor decision consistency. Credit assessment relies on human experience, lacks standardized data processing and quantitative scoring algorithms, and the assessment results are not reproducible or traceable. Electronic debt instruments lack encryption and anti-tampering mechanisms after generation, resulting in insufficient data security and reliability. There are no standardized interfaces for data interaction between multiple systems, and data transmission and storage are not anonymized, posing a risk of privacy leakage.
[0003] The aforementioned technical issues result in low automation, poor security, and insufficient credibility in supply chain provisional valuation and confirmation, failing to meet the technical requirements for efficient collaboration and financial services. Summary of the Invention
[0004] The purpose of this invention is to provide a multi-level supply chain collaboration and financial service management system to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-level supply chain collaboration and financial service management system, comprising a processor, a memory, a communication interface, and a supply chain collaboration module and a data management module that realize data interaction through the communication interface; The supply chain collaboration module and the data management module communicate bidirectionally through a standardized data interface, embedding the pre-confirmation algorithm service into the supply chain transaction provisional valuation data processing flow to realize the automated confirmation of rights for supply chain transaction provisional valuation data. The supply chain collaboration module is equipped with an online rights confirmation execution unit. The online rights confirmation execution unit has a built-in dynamic rights confirmation rule algorithm engine and transaction behavior profile quantitative processing function, which is used to automatically obtain provisional transaction data and historical transaction data, complete the automated pre-confirmation judgment of the provisional stage of supply chain transactions, and generate tamper-proof electronic debt certificates.
[0006] Preferably, the dynamic rights confirmation rule algorithm engine identifies the data stability characteristics of the core enterprise's historical performance rate and divides it into three levels: Level 1 performance status, Level 2 performance status, and Level 3 performance status; wherein: A Level 1 performance status corresponds to a performance rate of ≥98%; Level 2 performance status corresponds to a performance rate of 95% ≤ performance rate < 98%; Level 3 performance status corresponds to a performance rate of 90% ≤ performance rate < 95%.
[0007] Preferably, the dynamic weighting rule algorithm engine identifies data fluctuation characteristics of the provisional estimation deviation and classifies it into low fluctuation deviation, medium fluctuation deviation, and high fluctuation deviation; wherein: Low volatility deviation corresponds to a deviation amplitude of ≤5%; Medium fluctuation deviation corresponds to 5% < deviation amplitude ≤ 10%; High volatility deviation corresponds to a deviation amplitude >10%.
[0008] Preferably, the dynamic rights confirmation rule algorithm engine automatically determines the validity of rights confirmation based on the objective matching relationship between data status and fluctuation level; the objective matching relationship specifically includes: When the core enterprise’s historical performance rate is at Level 1 and the provisional deviation is a low-fluctuation deviation, the conditions for confirmation of rights are met. When the historical performance rate of a core enterprise is at level two and the provisional deviation is at a low or medium level, the conditions for confirming ownership are met.
[0009] Preferably, when the historical performance rate of the core enterprise and the degree of provisional deviation meet the objective matching relationship for the confirmation of rights to take effect, the processor automatically executes the confirmation of rights to take effect operation; if not, the system automatically triggers the manual review interface call process.
[0010] Preferably, the transaction behavior profiling and quantitative processing function selects the core enterprise's historical transaction amount, on-time payment rate, order fulfillment rate, provisional estimate deviation rate, and number of disputes over the past year as data processing dimensions, which are automatically captured and structured by the system.
[0011] Preferably, the system performs a normalization algorithm on the data of each dimension, mapping data of different magnitudes and units to the [0,1] interval, and integrating and constructing a multi-dimensional transaction behavior data profile after eliminating the difference in units.
[0012] Preferably, the system assigns preset weight coefficients to each data processing dimension, with the following specific weights: historical transaction amount 0.2, on-time payment rate 0.3, order fulfillment rate 0.25, provisional deviation rate 0.15, and number of disputes 0.1.
[0013] Preferably, the system calculates a comprehensive score for the credibility of weak rights confirmation using a weighted summation algorithm, with a preset score threshold of 0.7. When the score is higher than the threshold, an immutable electronic debt certificate is automatically generated; when the score is lower than the threshold, the system triggers a secondary confirmation interface process for the core enterprise.
[0014] Preferably, the data management module stores the core enterprise's historical transaction data, rights confirmation-related data, and electronic debt certificate data. Sensitive data is desensitized using masking algorithms or hash encryption algorithms, and secure storage is achieved through data partitioning and access control.
[0015] The technical effects and advantages of this invention are as follows: 1. This multi-level supply chain collaboration and financial service management system adopts a modular architecture and standardized data interfaces. It embeds the pre-confirmation algorithm into the provisional data processing flow to achieve fully automated confirmation of rights, significantly reducing the rate of manual intervention and shortening the processing time. Through the dynamic confirmation rule algorithm engine, it realizes the automatic identification and objective matching of the performance rate status and deviation degree, replacing manual judgment with data-driven approach, improving the consistency and accuracy of confirmation decisions, and reducing the risk of misjudgment.
[0016] 2. This multi-level supply chain collaboration and financial service management system constructs transaction behavior profiles through multi-dimensional data normalization and weighted scoring algorithms, enabling quantifiable assessment of creditworthiness. The assessment results are reproducible and traceable, providing stable technical support for rights confirmation. It employs hash encryption, de-identified storage, and access control technologies to ensure the security of sensitive data, ensuring that electronic debt certificates are tamper-proof and enhancing data credibility and business security. Attached Figure Description
[0017] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] This invention provides, for example Figures 1-2 The multi-level supply chain collaboration and financial service management system shown includes a processor, a memory, a communication interface, and a supply chain collaboration module and a data management module that realize data interaction through the communication interface; The supply chain collaboration module and the data management module communicate bidirectionally through a standardized data interface, embedding the pre-confirmation algorithm service into the supply chain transaction provisional valuation data processing flow to realize the automated confirmation of rights for supply chain transaction provisional valuation data. The supply chain collaboration module is equipped with an online rights confirmation execution unit. The online rights confirmation execution unit has a built-in dynamic rights confirmation rule algorithm engine and transaction behavior profile quantitative processing function, which is used to automatically obtain provisional transaction data and historical transaction data, complete the automated pre-confirmation judgment of the provisional stage of supply chain transactions, and generate tamper-proof electronic debt certificates.
[0020] The core enterprise / supplier system pushes preliminary transaction data to the system through a standardized interface; the system's processor receives the data through a communication interface and wakes up the supply chain collaboration module; the supply chain collaboration module and the data management module interact in real time through a standardized data interface to obtain historical transaction data of the core enterprise; the online rights confirmation execution unit in the supply chain collaboration module is activated, calling the built-in dynamic rights confirmation rule algorithm engine and transaction behavior profile quantitative processing function; the dynamic rights confirmation rule algorithm engine and transaction behavior profile quantitative processing function work together to execute automated pre-confirmation rights determination; after the determination is completed, the system automatically generates an immutable electronic debt certificate and sends the rights confirmation result back to the core enterprise / supplier system interface.
[0021] By constructing a system architecture that includes processors, memory, and communication interfaces, with the supply chain collaboration module and data management module at its core, the pre-confirmation algorithm service is embedded into the transaction provisional valuation data processing flow, realizing the fully automated execution of the provisional valuation confirmation process. Through the dynamic confirmation rule algorithm engine and transaction behavior profile quantitative processing function built into the online confirmation execution unit, the automated pre-confirmation determination of the provisional valuation stage is realized, and an immutable electronic debt certificate is generated, providing reliable and efficient technical support for multi-level supply chain collaborative financial services.
[0022] Furthermore, the dynamic rights confirmation rule algorithm engine identifies the data stability characteristics of the core enterprise's historical performance rate, classifying it into three levels: Level 1 performance status, Level 2 performance status, and Level 3 performance status; wherein: A Level 1 performance status corresponds to a performance rate of ≥98%; Level 2 performance status corresponds to a performance rate of 95% ≤ performance rate < 98%; Level 3 performance status corresponds to a performance rate of 90% ≤ performance rate < 95%.
[0023] By identifying the data stability characteristics of the historical performance rate of core enterprises, and classifying them into Level 1, Level 2, and Level 3 performance status, an objective and quantitative classification of the historical performance data of core enterprises is achieved.
[0024] Furthermore, the dynamic weighting rule algorithm engine identifies data fluctuation characteristics of the provisional estimation deviation and classifies it into low-fluctuation deviation, medium-fluctuation deviation, and high-fluctuation deviation; wherein: Low volatility deviation corresponds to a deviation amplitude of ≤5%; Medium fluctuation deviation corresponds to 5% < deviation amplitude ≤ 10%; High volatility deviation corresponds to a deviation amplitude >10%.
[0025] By identifying the data fluctuation characteristics of the provisional estimate deviation, it is divided into low fluctuation deviation, medium fluctuation deviation, and high fluctuation deviation, thus achieving an objective and quantitative classification of the difference between provisional estimate data and settlement data.
[0026] Furthermore, the dynamic rights confirmation rule algorithm engine automatically determines the validity of rights confirmation based on the objective matching relationship between data status and fluctuation level; the objective matching relationship specifically includes: When the core enterprise’s historical performance rate is at Level 1 and the provisional deviation is a low-fluctuation deviation, the conditions for confirmation of rights are met. When the historical performance rate of a core enterprise is at level two and the provisional deviation is at a low or medium level, the conditions for confirming ownership are met.
[0027] The requirement is to establish an objective matching relationship between data status and fluctuation level, thereby enabling the dynamic rights confirmation rule algorithm engine to automatically determine the conditions for rights confirmation to take effect.
[0028] Furthermore, when the historical performance rate of the core enterprise and the degree of provisional deviation meet the objective matching relationship for the confirmation of rights to take effect, the processor automatically executes the confirmation of rights to take effect operation; if not, the system automatically triggers the manual review interface call process.
[0029] By setting system execution logic under different conditions, the system automatically executes the confirmation of rights operation when the objective matching relationship is met, and automatically triggers the manual review interface call process when the relationship is not met, thus realizing the automated branching process of the confirmation of rights. This not only ensures the efficiency of confirmation of rights in high-confidence scenarios, but also provides a technical interface for manual intervention in low-confidence scenarios, thus balancing the automation and flexibility of confirmation of rights.
[0030] Furthermore, the transaction behavior profiling and quantitative processing function selects the core enterprise's historical transaction amount, on-time payment rate, order fulfillment rate, provisional estimate deviation rate, and number of disputes over the past year as data processing dimensions, which are automatically captured and structured by the system.
[0031] By selecting the core enterprise's historical transaction amount, on-time payment rate, order fulfillment rate, provisional estimate deviation rate, and number of disputes over the past year as data processing dimensions, and having the system automatically capture and structure the data, a comprehensive and objective foundation of core enterprise transaction behavior data was constructed; providing multi-dimensional and high-quality data support for the subsequent construction of transaction behavior profiles and credibility scoring.
[0032] Furthermore, the system performs normalization algorithms on the data of each dimension, mapping data of different magnitudes and units to the [0,1] interval, eliminating the difference in units, and then integrating and constructing a multi-dimensional transaction behavior data profile.
[0033] Normalization was performed on the data for each dimension separately, mapping data of different magnitudes and units to the [0,1] interval to eliminate dimensional differences. Positive metrics (such as historical transaction amount, on-time payment rate, and order fulfillment rate) are normalized using a min-max linear normalization method.
[0034] in: : The original data value of the i-th dimension; The minimum value in this dimension of the dataset; The maximum value in this dimension of the dataset; : Normalized data value, with a range of [0,1].
[0035] Negative indicators (such as provisional estimate deviation rate, number of disputes, the higher the value, the higher the risk) are normalized using inverse normalization:
[0036] The normalized multi-dimensional data is integrated to construct a multi-dimensional transaction behavior data profile, providing a standardized data foundation for subsequent weighted scoring calculations.
[0037] By performing normalization algorithms on the data of each dimension, data of different magnitudes and units are uniformly mapped to the [0,1] interval, eliminating the impact of differences in scale on data processing and realizing the standardized integration of multi-dimensional data; providing a fair and unified data foundation for subsequent weighted scoring calculations, and improving the scientificity and credibility of transaction behavior profile construction.
[0038] Furthermore, the system assigns preset weight coefficients to each data processing dimension, with the following specific weights: historical transaction amount 0.2, on-time payment rate 0.3, order fulfillment rate 0.25, provisional estimate deviation rate 0.15, and number of disputes 0.1.
[0039] The processor reads the preset weight coefficient vector from memory. ,in: (Historical transaction amount); (On-time payment rate); (Order fulfillment rate); (Preliminary deviation rate); (Number of disputes).
[0040] By assigning preset weight coefficients to each data processing dimension, the contribution of different dimensions of data to the credibility score is clarified. The weight coefficients are stored in memory and executed by the processor, avoiding the subjectivity of manually adjusting the weights and ensuring the stability and reproducibility of the credibility score model.
[0041] Furthermore, the system calculates a comprehensive score for the credibility of weak rights confirmation using a weighted summation algorithm, with a preset score threshold of 0.7. When the score is higher than the threshold, an immutable electronic debt certificate is automatically generated; when the score is lower than the threshold, the system triggers a secondary confirmation interface process for the core enterprise.
[0042] The processor reads the normalized data vector and weight coefficient vector ; The processor executes a weighted summation algorithm to calculate the comprehensive confidence score S for weak weighting, as shown in the following formula:
[0043] in: S: Overall credibility score, with a value range of [0,1]; : The weight coefficient of the i-th dimension; : The normalized data value of the i-th dimension.
[0044] The processor compares the calculated score S with a preset threshold of 0.7: like The system automatically generates tamper-proof electronic debt certificates; like The system triggers the core enterprise's secondary confirmation interface process; Based on the judgment result, the system proceeds to the corresponding subsequent process (voucher generation or secondary confirmation). By calculating a comprehensive score for the credibility of weak rights confirmation using a weighted summation algorithm and setting a preset scoring threshold, a quantitative assessment of the credibility of core enterprise transaction behavior is achieved. When the score is higher than the threshold, an immutable electronic debt certificate is automatically generated; when it is lower than the threshold, a secondary confirmation interface process for the core enterprise is triggered, thus realizing the automation and hierarchical processing of the rights confirmation process, balancing the efficiency of rights confirmation with risk control.
[0045] Furthermore, the data management module stores the core enterprise's historical transaction data, rights confirmation-related data, and electronic debt certificate data. Sensitive data is desensitized using masking algorithms or hash encryption algorithms, and secure storage is achieved through data partitioning and access control.
[0046] The data management module stores historical transaction data, rights confirmation data, and electronic debt certificate data of core enterprises. Sensitive data is anonymized using masking or hash encryption algorithms. Secure storage is achieved through data partitioning and access control, ensuring the security and privacy of supply chain finance data.
[0047] By adopting a modular architecture and standardized data interfaces, the pre-confirmation algorithm is embedded into the provisional data processing flow to achieve fully automated confirmation of rights, significantly reducing the rate of manual intervention and shortening the processing time. Through the dynamic confirmation rule algorithm engine, the automatic identification and objective matching of the performance rate status and deviation degree are realized. Data-driven decision-making replaces manual judgment, improving the consistency and accuracy of confirmation decisions and reducing the risk of misjudgment.
[0048] By constructing transaction behavior profiles through multidimensional data normalization and weighted scoring algorithms, a quantitative assessment of creditworthiness is achieved. The assessment results are reproducible and traceable, providing stable technical support for rights confirmation. Hash encryption, de-identified storage, and access control technologies are adopted to ensure the security of sensitive data and the immutability of electronic debt certificates, thereby improving data credibility and business security.
[0049] The overall system workflow is as follows: Data Triggering and Receiving: The core enterprise ERP / supply chain platform pushes transaction provisional information to the system through a standardized data interface. The system receives the data through a communication interface, activating the online rights confirmation execution unit of the supply chain collaboration module.
[0050] Historical data loading and preprocessing: The supply chain collaboration module initiates a data query to the data management module through the communication interface. The data management module returns structured historical transaction data of core enterprises and completes data verification and preprocessing.
[0051] The dynamic rights confirmation rule engine automatically determines whether the conditions for rights confirmation are met. The dynamic rights confirmation rule algorithm engine obtains the historical performance rate of the core enterprise and the deviation of the current provisional estimate from the data management module, and performs data stability feature identification and data fluctuation feature identification respectively to obtain the corresponding performance status and deviation degree. Then, based on the objective matching relationship between the data status and the fluctuation degree, it automatically determines whether the conditions for rights confirmation to take effect are met.
[0052] Quantitative processing of transaction behavior profiles: The quantitative processing function of transaction behavior profiles automatically captures multi-dimensional data of core enterprises in the past year, such as transaction amount, on-time payment rate, order fulfillment rate, provisional estimate deviation rate, and number of disputes. It performs normalization algorithm processing on the data of each dimension to eliminate the difference in units and integrates them to construct a multi-dimensional transaction behavior data profile. Based on preset weight coefficients, it calculates the comprehensive score of weak confirmation of rights credibility through weighted summation algorithm.
[0053] Branch execution of the property rights confirmation process: When the credibility score is ≥0.7 and the objective matching relationship for the confirmation of rights is met, the system automatically generates an tamper-proof electronic debt certificate; When the credibility score is ≥0.7 but does not meet the objective matching relationship, the system automatically triggers the core enterprise secondary confirmation interface process, and generates electronic debt certificates after the confirmation is passed. When the credibility score is less than 0.7, the system automatically rejects the claim and records the risk in the log.
[0054] Data security storage and traceability: All data from the ownership confirmation process is stored in the data management module. Sensitive data is desensitized using masking or hash encryption algorithms, and secure storage is achieved through data partitioning and access control. At the same time, tamper-proof operation logs are generated to support full-process traceability.
[0055] Multi-system data synchronization: The status of electronic debt instruments is transmitted back to the core enterprise and supplier systems through standardized data interfaces, supporting subsequent financing, transfer and other business connections.
[0056] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A multi-level supply chain collaboration and financial service management system, characterized in that, It includes a processor, memory, communication interface, and a supply chain collaboration module and a data management module that realize data interaction through the communication interface; The supply chain collaboration module and the data management module communicate bidirectionally through a standardized data interface, embedding the pre-confirmation algorithm service into the supply chain transaction provisional valuation data processing flow to realize the automated confirmation of rights for supply chain transaction provisional valuation data. The supply chain collaboration module is equipped with an online rights confirmation execution unit. The online rights confirmation execution unit has a built-in dynamic rights confirmation rule algorithm engine and transaction behavior profile quantitative processing function, which is used to automatically obtain provisional transaction data and historical transaction data, complete the automated pre-confirmation judgment of the provisional stage of supply chain transactions, and generate tamper-proof electronic debt certificates.
2. The multi-level supply chain collaboration and financial service management system according to claim 1, characterized in that, The dynamic rights confirmation rule algorithm engine identifies the data stability characteristics of the core enterprise's historical performance rate, classifying it into three levels: Level 1 performance status, Level 2 performance status, and Level 3 performance status; among which: A Level 1 performance status corresponds to a performance rate of ≥98%; Level 2 performance status corresponds to a performance rate of 95% ≤ performance rate < 98%; Level 3 performance status corresponds to a performance rate of 90% ≤ performance rate < 95%.
3. The multi-level supply chain collaboration and financial service management system according to claim 2, characterized in that, The dynamic weighting rule algorithm engine identifies data fluctuation characteristics of the provisional estimation deviation and classifies it into low-fluctuation deviation, medium-fluctuation deviation, and high-fluctuation deviation; wherein: Low volatility deviation corresponds to a deviation amplitude of ≤5%; Medium fluctuation deviation corresponds to 5% < deviation amplitude ≤ 10%; High volatility deviation corresponds to a deviation amplitude >10%.
4. The multi-level supply chain collaboration and financial service management system according to claim 3, characterized in that, The dynamic rights confirmation rule algorithm engine automatically determines the validity of rights confirmation based on the objective matching relationship between data status and fluctuation level; the objective matching relationship is specifically as follows: When the core enterprise’s historical performance rate is at Level 1 and the provisional deviation is a low-fluctuation deviation, the conditions for confirmation of rights are met. When the historical performance rate of a core enterprise is at level two and the provisional deviation is at a low or medium level, the conditions for confirming ownership are met.
5. A multi-level supply chain collaboration and financial service management system according to claim 4, characterized in that, When the historical performance rate of a core enterprise and the degree of provisional deviation meet the objective matching relationship for the confirmation of rights to take effect, the processor automatically executes the confirmation of rights to take effect operation; if not, the system automatically triggers the manual review interface call process.
6. The multi-level supply chain collaboration and financial service management system according to claim 1, characterized in that, The transaction behavior profiling and quantitative processing function selects the core enterprise's historical transaction amount, on-time payment rate, order fulfillment rate, provisional estimate deviation rate, and number of disputes over the past year as data processing dimensions, which are automatically captured and structured by the system.
7. A multi-level supply chain collaboration and financial service management system according to claim 6, characterized in that, The system performs a normalization algorithm on the data of each dimension, mapping data of different magnitudes and units to the [0,1] interval, and after eliminating the differences in dimensions, it integrates and constructs a multi-dimensional transaction behavior data profile.
8. A multi-level supply chain collaboration and financial service management system according to claim 7, characterized in that, The system assigns preset weight coefficients to each data processing dimension. The specific weights are: historical transaction amount 0.2, on-time payment rate 0.3, order fulfillment rate 0.25, provisional deviation rate 0.15, and number of disputes 0.
1.
9. A multi-level supply chain collaboration and financial service management system according to claim 8, characterized in that, The system calculates a comprehensive score for the credibility of weak rights confirmation using a weighted summation algorithm, with a preset score threshold of 0.
7. When the score is higher than the threshold, an immutable electronic debt certificate is automatically generated; when the score is lower than the threshold, the system triggers a secondary confirmation interface process for the core enterprise.
10. A multi-level supply chain collaboration and financial service management system according to claim 1, characterized in that, The data management module stores the core enterprise's historical transaction data, rights confirmation data, and electronic debt certificate data. Sensitive data is desensitized using masking or hash encryption algorithms, and secure storage is achieved through data partitioning and access control.