A data security authentication sharing system based on supply chain big data

Abstract

The application discloses a data security authentication sharing system based on supply chain big data, and belongs to the technical field of supply chain financial big data security. The financial data incremental differential weighted authentication module is equipped with a self-developed incremental differential weighted hash authentication algorithm, and through field partition differential verification and incremental local calculation, the financial data small-scale implicit tampering can be accurately identified, the high-frequency iteration scene algorithm redundancy can be reduced, and the quantized output data can be trusted risk level. The risk linkage multi-level permission fuse control module dynamically calculates the multi-level node permission coefficient through the risk penetration authorization fuse algorithm based on the real-time risk level, realizes the hierarchical authorization and abnormal data global fuse, and solves the specific security pain points in the supply chain financial big data subdivision field, greatly improves the financial data authentication accuracy and multi-level sharing security, effectively avoids false financing, data leakage and credit risk, and adapts to the core business scenarios of supply chain financial incremental iteration and multi-level chain flow.

Description

Technical Field

[0001] This invention relates to the field of supply chain finance big data security authentication and trusted sharing technology, specifically a data security authentication and sharing system based on supply chain big data. Background Technology

[0002] Supply chain finance big data serves as the core data carrier supporting supply chain financing, debt confirmation, and credit risk control. Its core data includes accounts receivable confirmation data from core enterprises, multi-level supplier debt breakdown data, financial institution credit transaction data, and performance payment voucher data. Compared to general supply chain data, supply chain finance big data possesses unique characteristics such as frequent incremental iterations, highly differentiated field sensitivity, multi-level chain-like flow, and a high risk of tampering. Its requirements for the precision and real-time performance of data integrity authentication and cross-entity sharing access control are far higher than those for ordinary supply chain data. Existing general data security authentication and sharing technologies are not suitable for the specific scenarios of supply chain finance, exhibiting the following problems: 1. Traditional fixed-hash global authentication algorithms cannot detect weak tampering of incremental data in supply chain finance, posing a vulnerability in financial risk control. Supply chain finance data is characterized by incremental updates, such as accounts receivable installment splitting, phased payment record updates, and credit limit fine-tuning, which only involve minor iterative updates to some fields of the original data. Existing global fixed-hash algorithms such as MD5 and SHA-256 can only perform one-time verification of the entire data, which has two major drawbacks: First, the accuracy of detecting minor tampering of local fields and implicit tampering of incremental data in financial scenarios is extremely low. Malicious entities can evade verification by fine-tuning weak fields such as debt amount and payment time, leading to risks of fraudulent financing and double pledging. Second, each incremental data update requires global recalculation of the hash, resulting in huge computational overhead in the high-frequency iteration scenarios of financial big data. Furthermore, it does not distinguish between core sensitive financial fields and ordinary memo fields, highlighting the contradiction of missed detection of tampering of core financial fields and excessive verification of non-core fields, failing to meet the refined security verification requirements of financial scenarios.

[0003] 2. Supply chain finance, with its multi-level chain-like flow, suffers from issues of access control leakage and continuous data tampering. Supply chain finance data follows a chain-like flow architecture of "core enterprise → Tier 1 supplier → Tier 2 and multi-tier suppliers → financial institution." Traditional static hierarchical authorization technology uses a fixed permission whitelist mechanism without a risk linkage mechanism. On one hand, the traditional authorization model only distinguishes user identity levels, failing to combine data tampering risk levels with multi-level flow penetration path control permissions. Lower-level non-compliant nodes can penetrate and access upper-level core financial data through permission vulnerabilities, leading to the leakage of trade secrets and financial risk control data. On the other hand, in existing technologies, data authentication and permission control are completely decoupled. Even if abnormal incremental data tampering is detected, it is impossible to immediately suspend multi-level shared permissions. Abnormally tampered data can still flow and be reused in multiple nodes of the supply chain, directly causing financial institutions to misjudge credit granting and triggering bad debt risks. This problem is a unique security pain point in the field of supply chain finance big data, which general supply chain security solutions cannot solve.

[0004] In summary, existing technologies lack dedicated security solutions for incremental data and multi-level circulation scenarios in supply chain finance. They also lack suitable incremental refined authentication algorithms and risk-linked permission circuit breaker mechanisms, and cannot balance the accuracy of financial data authentication, computing efficiency, and multi-level sharing security. There is an urgent need to develop a dedicated security authentication and sharing system. Summary of the Invention

[0005] The purpose of this invention is to provide a data security authentication and sharing system based on supply chain big data to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data security authentication and sharing system based on supply chain big data, including a financial data cleaning module, a financial data incremental differential weighted authentication module, a risk-linked multi-level permission circuit breaker control module, an encrypted transmission module, a log tracing module, and a data sharing scheduling module; The financial data cleaning module is used to connect with the heterogeneous supply chain finance raw data from core enterprises, multi-level suppliers, and financial institutions. It cleans and standardizes the acquired financial raw data to obtain standardized supply chain finance incremental data, and then sends the standardized supply chain finance incremental data to the financial data incremental differential weighted authentication module. The incremental differential weighted authentication module for financial data is equipped with a self-developed incremental differential weighted hash authentication algorithm, which is used to perform field partitioning differential verification on standardized supply chain finance incremental data, accurately identify local weak tampering behavior, and quantify the data credibility risk level. The risk-linked multi-level permission circuit breaker control module is linked bidirectionally with the financial data incremental differential weighted authentication module in a closed loop. It is equipped with a risk penetration authorization circuit breaker algorithm, which is used to dynamically calculate the multi-level node permission coefficient based on the real-time trusted risk level and execute hierarchical authorization and risk circuit breaker. The encrypted transmission module is used to encrypt and transmit the standardized supply chain finance incremental data obtained by the financial data cleaning module to the risk-linked multi-level permission circuit breaker control module through an encryption algorithm. The log tracing module is used to record the work logs of the financial data cleaning module, the financial data incremental differential weighted authentication module, the risk linkage multi-level permission circuit breaker control module, and the encrypted transmission module, and to mark and store the work logs. It also has a tracing query interface. The data sharing and scheduling module is used to monitor the hierarchical permission judgment results of the risk linkage multi-level permission circuit breaker control module to realize the differentiated, controllable, and traceable multi-level sharing and distribution of supply chain finance data.

[0007] Preferably, the specific implementation steps of the incremental differential weighted authentication module for financial data are as follows: Step A1: Financial Data Field Partitioning and Adaptive Weight Binding Preprocessing: The incremental differential weighted authentication module receives standardized supply chain finance incremental data in real time after it has been standardized, deduplicated, and time-series aligned by the financial data cleaning module. According to financial risk control rules, the module automatically partitions and marks the standardized supply chain finance incremental data, marking the debt amount, credit line, repayment flow, and pledge status as core verification fields, and marking transaction notes, operation summaries, and auxiliary descriptions as ordinary verification fields, thus completing the pre-processing for differentiated verification. Step A2, Local Incremental Hash Calculation: Identify the incremental regions of standardized supply chain finance incremental data updates, and perform local hash calculations on the updated core fields and ordinary fields respectively to obtain incremental local hash values. Retrieve the historical baseline hash value of this field stored in the database. ; Step A3, Calculation of Time Series Deviation Coefficient: The time interval for collecting this incremental data update is compared with the supply chain finance standard update cycle to calculate the time series deviation coefficient. , It can identify abnormal operations that involve frequent, minor modifications to data within a short period of time. Step A4, Risk Level Quantification Calculation: Substitute the local incremental hash and time-series deviation coefficients obtained in steps A2 and A3 into the incremental differential weighted hash authentication algorithm formula, and combine the differentiated weights of core and ordinary fields to calculate the incremental differential verification deviation value. Further normalization calculations yield the data's reliability risk level. The formula for the incremental differential weighted hash authentication algorithm is as follows: ; ,in This is the incremental differential verification deviation value; the larger the value, the higher the risk of data tampering. This is a sensitive weight for core financial fields, a dynamically adaptively calculated value, with a value range of [range missing]. This is used to characterize the impact of different core financial fields on supply chain finance risk control. It is calculated by normalizing the field risk impact factor and update frequency factor. This is a sensitive weight for a regular remarks field; it is a dynamically calculated value, and its value range is [range missing]. This is used to characterize the degree of harm caused by tampering with auxiliary fields; The real-time local hash value of the incrementally updated field is calculated only for the updated region, without the need for global traversal; The historical baseline hash value of the corresponding field is pre-stored in the system's trusted financial database; The maximum permissible deviation threshold is preset to 0.8, based on the risk control standards of the supply chain finance industry. Data credibility risk level, value range The closer the value is to 1, the more reliable the data is; the lower the value, the higher the risk of tampering. Step A5, Risk Classification Output: Preset classification threshold: For reliable data, For low-risk suspected data tampering, To prevent high-risk data tampering, the classification results and quantitative parameters will be synchronized in real time to the multi-level permission circuit breaker control module, serving as the sole basis for permission control.

[0008] Preferably, the specific implementation logic of the preprocessing of binding the financial data field partitioning mark and adaptive weight in step A1 is as follows: Step A1.1, Data Compliance Verification: First, the standardized supply chain finance incremental data received from the financial data cleaning module is verified for format compliance. Incomplete fields, garbled data, and invalid null values ​​are filtered out, while complete supply chain finance business incremental data is retained to avoid abnormal data interfering with the accuracy of subsequent authentication. Step A1.2, Preset Field Classification Standard: Based on the preset scenario definition, the debt amount, credit limit, repayment flow, and pledge status, which directly determine the risk control results of supply chain finance credit granting, pledging, and repayment, are defined as core verification fields. Transaction notes, operation summaries, and auxiliary descriptions, which do not affect core financial risk control decisions and only serve as auxiliary explanations, are defined as ordinary verification fields. Step A1.3, Adaptive Weight Dynamic Calculation: Based on the current risk control level and historical update frequency of the field, dynamically calculate the sensitive weight of the core field. Sensitive weights compared to ordinary fields It abandons the shortcomings of traditional fixed weight assignment and realizes dynamic adaptation of weights to business risks; Step A1.4, Fully Automatic Partition Tagging and Binding: A dual verification mechanism of precise keyword matching and secondary verification of financial business attributes is adopted to complete the full field accurate classification and dynamic parameter binding of the standardized supply chain finance incremental data after verification in Step A1.1, and at the same time, the tag encapsulation is completed in combination with the parameter subscript definition; Step A1.5: Generate partitioned dataset: Based on step A1.4, the final output is a differentiated incremental dataset bound with dynamic adaptive weights.

[0009] Preferably, the specific implementation logic of the fully automatic partition marking and binding in step A1.4 is as follows: Step A1.41: Full field traversal and screening: Traverse all fields of the current standardized supply chain finance incremental data after verification one by one, remove invalid empty fields left over from preprocessing, retain all valid business fields, and construct a set of fields to be classified; Step A1.42, Initial Keyword Classification and Matching: Based on a pre-defined financial field keyword library, quickly match the field names of the set of fields to be classified, and initially determine the fields of debt amount, credit limit, repayment flow, and pledge status as... Key risk control fields, including transaction notes, operation summaries, and supplementary descriptions, are initially identified as... Ordinary auxiliary field; Step A1.43, Secondary Verification of Business Attributes: Based on the initial matching and classification results of Step A1.42, a secondary verification is performed in conjunction with the supply chain finance business logic to avoid misjudgment due to identical keywords. This confirms that core fields are key fields that directly affect credit granting, pledging, and repayment risk control decisions, while ordinary fields are fields that do not play a core risk control decision role and only serve as auxiliary records, thus completing accurate partitioning and classification. Step A1.44, Attaching Dedicated Type Tags: Attach fixed type tags to the categorized fields, i.e., uniformly attach tags to core fields. Type tags and regular fields are mounted uniformly. Type tags; Step A1.45, Dynamic Weight Parameter Binding: Bind the dynamic sensitive weights of the core fields. Dynamic sensitive weights for ordinary fields Each field is bound to a corresponding tag field one-to-one, so that each valid field has a unique weight parameter that is adapted to the current risk control scenario. Step A1.46, Parameter Fixation and Locking: After completing the binding of labels and weights, temporarily fix the field parameters of the current incremental data.

[0010] Preferably, the adaptive weight dynamic calculation in step A1.3 is as follows: Core field sensitive weight Dynamic calculation: Core fields determine the results of financing, credit approval, debt pledging, and repayment settlement; their weights... The calculation is based on a two-dimensional coupling of the field's risk control impact and the field's dynamic update frequency, and the formula is as follows: ,in To determine the risk impact level of the fields, values ​​are assigned according to supply chain finance business rules, including the debt amount and pledge status fields. Credit limit field Payment receipt field The higher the value, the greater the financial risk caused by field tampering; This represents the maximum risk impact, fixed at 1.0, used for parameter normalization. The recent update frequency of a field is calculated by counting the number of incremental updates to this core field in the past 7 days. The more frequent the updates, the higher the probability that the field has been tampered with. The maximum allowed update frequency for the core field is preset to 20 times / 7 days according to the supply chain finance industry standard, which is used for frequency normalization calculation. This formula guarantees Always in Within a given range, core fields with high risk and high update frequency are automatically matched with higher verification weights to enhance anti-tampering capabilities. Sensitive weight of ordinary remarks field Linked dynamic calculation: Ordinary fields have no core risk control decision-making value. To avoid redundancy of double weights and achieve dynamic weight balancing, Based on the weighted calculation of the current core fields, the calculation formula is as follows: Among them, the linkage Dynamic iteration, when the core field risk weight The larger the value (the higher the risk of core data), the higher the weight of ordinary fields. Automatically reduce unnecessary computing power consumption; when the risk weight of core fields is low, appropriately increase the weight of ordinary fields to avoid the risk of implicit collaborative tampering, ultimately ensuring... .

[0011] Preferably, the specific implementation steps of the risk-linked multi-level permission circuit breaker control module are as follows: Step B1: Real-time synchronization and reception of risk data: The risk-linked multi-level permission circuit breaker control module monitors the output data of the incremental differential weighted authentication module of financial data throughout the entire process, and obtains the credibility risk level of the current financial data in real time. If no valid risk parameters are received, the data is judged to be abnormal by default, and the initial circuit breaker protection is triggered directly. Step B2, Multi-level Flow Parameter Initialization: Calculate the node credit coefficient for the current user's data sharing application. Obtain the current data flow level Matching the system's preset maximum trusted transfer level Complete the algorithm parameter initialization; Step B3, Dynamic Permission Coefficient Calculation: Substitute the formula of the risk penetration authorization circuit breaker algorithm to calculate the effective permission coefficient of the current node for this financial data in real time. This enables differentiated permission calculation based on "single data, single node, and single level," eliminating the vulnerabilities of unified static authorization. The formula for the risk penetration authorization circuit breaker algorithm is: ,in This is the effective coefficient for real-time permissions across multiple node levels, and its value is [value missing]. The higher the coefficient, the higher the node's data access and transfer permissions; Assess the credibility risk level of financial data; Credit coefficient for supply chain finance nodes; The current data flow hierarchy is as follows: core enterprises are at level 1, first-tier suppliers are at level 2, second-tier suppliers are at level 3, and so on, increasing progressively. To pre-determine the maximum trustworthy circulation level of supply chain finance, it is preset to 5 levels based on industry scenarios, thus restricting the unlimited penetration and circulation of data; Step B4, Tiered Authorization and Circuit Breaker Execution: Based on the effective permission coefficient threshold, execute the corresponding control strategy: normal authorization for trusted data, permission downgrade for low-risk data, and full circuit breaker for high-risk tampered data, to immediately block the chain flow of abnormal financial data in multiple nodes of the supply chain and solve the risk control vulnerability of continuous reuse of tampered data; Step B5, Two-way linkage iterative optimization: The risk linkage multi-level permission circuit breaker control module will reversely synchronize the control results of this permission circuit breaker and downgrade to the financial data incremental differential weighted authentication module; at the same time, it will record the multi-level flow path, block the permission vulnerability of lower-level nodes to penetrate and access the core financial data of the upper level, and form a closed loop of "authentication-risk control-feedback-optimization".

[0012] Preferably, the specific implementation details of the hierarchical authorization and circuit breaker execution in step B4 are as follows: when , For trusted data, the system grants full permissions to the supply chain data nodes that are verified to be completely trustworthy. This allows nodes to perform operations such as viewing, downloading, forwarding, chaining, and reusing data, ensuring the efficient progress of normal supply chain finance business such as core enterprise rights confirmation, supplier receivables transfer, and financial institution credit verification, and adapting to the multi-level sharing needs of compliant data. when , To downgrade permissions for low-risk data: For low-risk financial data suspected of being tampered with, the system immediately implements a permission downgrade strategy, rigidly closing the node's permissions to download, forward, multi-level circulation, and secondary editing of the current data, retaining only basic read-only viewing permissions. This control method can effectively limit the spread of low-risk data across multiple nodes in the upstream and downstream of the supply chain without interrupting basic business operations, avoiding the risk of suspected abnormal data being arbitrarily reused, tampered with, or escalated, and achieving risk-controlled fault tolerance. when , For high-risk data tampering, the system immediately triggers a full-domain access control circuit breaker mechanism. This instantly freezes all access, viewing, transfer, and reuse permissions for the current data across all multi-level circulation nodes in the supply chain, cutting off the chain-like circulation path of "core enterprise - multi-level suppliers - financial institutions". The system also marks the data as risk-blocked data, prohibiting any subsequent business calls and sharing.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes a self-developed incremental differential weighted hash authentication algorithm to achieve differentiated verification of financial data fields by partitioning. It performs hash calculations only on incremental local data, reducing computational overhead compared to traditional global hash algorithms, and perfectly adapting to high-frequency incremental iteration scenarios of financial data. At the same time, it accurately identifies minor and weak tampering of core fields such as debt amount and credit limit, completely solving the underlying risk control vulnerabilities of false pledges and tampering with financing data.

[0014] This invention achieves coordinated control of data risk and multi-level permissions through a self-developed risk penetration authorization circuit breaker algorithm, breaking the static authorization barrier. It can accurately block the permission penetration loopholes in the multi-level circulation of supply chain finance, and immediately trigger full-domain circuit breakers for high-risk tampered data, completely eliminating the problem of abnormal data being reused in the upstream and downstream chain circulation, significantly reducing the risk of bad debts in credit granting by financial institutions, and is fully adapted to the exclusive circulation scenarios of supply chain finance. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a schematic diagram of the workflow of the incremental differential weighted authentication module for financial data of the present invention; Figure 3 This is a schematic diagram of the workflow of the risk-linked multi-level permission circuit breaker control module of the present invention. Detailed Implementation

[0016] 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.

[0017] Please see Figure 1-3 The present invention provides a technical solution: a data security authentication and sharing system based on supply chain big data, including a financial data cleaning module, a financial data incremental differential weighted authentication module, a risk-linked multi-level permission circuit breaker control module, an encrypted transmission module, a log tracing module, and a data sharing scheduling module; The financial data cleaning module is used to connect with heterogeneous supply chain finance raw data from core enterprises, multi-level suppliers, and financial institutions. It cleans and standardizes the acquired raw financial data to obtain standardized incremental supply chain finance data, which is then sent to the financial data incremental differential weighted authentication module. The specific implementation logic is as follows: Step 1: Multi-source heterogeneous financial data aggregation and collection. The financial data cleaning module aggregates raw business data from the entire supply chain in real time, covering multi-source heterogeneous data such as accounts receivable confirmation data of core enterprises, multi-level supplier debt splitting data, credit line data of financial institutions, performance and payment collection data, and business operation remarks data. These data are uniformly accessed into the system's data preprocessing buffer to complete the full collection of raw data, covering all core verification fields and ordinary auxiliary field data sources of this invention.

[0018] Step 2: Filtering and Removing Dirty and Invalid Data. The financial data cleaning module performs a comprehensive compliance screening of the collected raw data, identifying and removing abnormal and invalid data in batches. This includes data with incomplete or missing fields, garbled data, null values, incorrectly formatted data, and illegal data exceeding business thresholds (such as liability amount, credit limit, etc.). This completely avoids invalid and dirty data interfering with the accuracy of subsequent field classification, weight calculation, and hash verification, matching the pre-compliance verification requirements of the financial data incremental differential weighted authentication module.

[0019] Step 3: Field Standardization and Unification. Based on the financial field classification rules preset in this invention, the retained valid data is standardized and regulated: the naming, data format, numerical precision, and unit standards of core verification fields (debt amount, credit limit, repayment flow, pledge status) and ordinary verification fields (transaction notes, operation summary, auxiliary description) are unified, eliminating heterogeneous differences between multi-source data, ensuring that the subsequent partitioning and marking mechanism of "precise keyword matching + secondary verification of business attributes" can accurately identify field types, and avoiding classification errors and weight binding confusion caused by inconsistent field formats.

[0020] Step 4: Time-series alignment and precise labeling of incremental data. Based on the system's timestamp mechanism, the standardized full data is time-series sorted and aligned to distinguish between historical existing data and real-time incremental updates. The incremental update areas and fields generated in this business iteration are precisely labeled, enabling the subsequent financial data incremental differential weighted authentication module to calculate local hash values ​​only for incremental areas. Retrieve historical baseline hash values Solving for timing deviation coefficients Providing accurate timing data is a core prerequisite for achieving incremental local verification and reducing computing power redundancy.

[0021] Step 5: Deduplication and Cleaning of Redundant Data. For redundant and duplicate data generated from multiple forwardings and uploads of supply chain finance data, deduplication is performed using a dual verification mechanism based on field content and timestamp. Only unique, valid, and up-to-date business data is retained, avoiding the increased frequency of subsequent field updates caused by duplicate data. Statistical distortion, dynamic weights , Calculate the deviation to ensure the authenticity and accuracy of the algorithm parameter statistics.

[0022] Step 6: Standardized Incremental Dataset Encapsulation and Output. After completing the full-process cleaning and preprocessing, the module encapsulates and generates a standardized supply chain finance incremental dataset with consistent time series, standardized fields, compliant data, and clear incremental tags. This dataset is pushed in real time to the financial data incremental differential weighted authentication module, providing compliant data support for subsequent core algorithm operations such as field partitioning and tagging, adaptive weight binding, incremental hash calculation, and risk level quantification.

[0023] The incremental differential weighted authentication module for financial data, equipped with a self-developed incremental differential weighted hash authentication algorithm, is used to perform field partitioning differential verification on standardized supply chain finance incremental data, accurately identify local weak tampering behavior, and quantify the data credibility risk level. The specific implementation steps are as follows: Step A1: Financial Data Field Partitioning and Adaptive Weight Binding Preprocessing: The incremental differential weighted authentication module receives standardized supply chain finance incremental data in real time after it has been standardized, deduplicated, and time-series aligned by the financial data cleaning module. According to financial risk control rules, the module automatically partitions and marks the standardized supply chain finance incremental data, designating debt amount, credit line, repayment history, and pledge status as core verification fields, and marking transaction notes, operation summaries, and auxiliary descriptions as ordinary verification fields. This completes the pre-processing for differentiated verification. The specific implementation logic is as follows: Step A1.1, Data Compliance Verification: First, the standardized supply chain finance incremental data received from the financial data cleaning module is verified for format compliance. Incomplete fields, garbled data, and invalid null values ​​are filtered out, while complete supply chain finance business incremental data is retained to avoid abnormal data interfering with the accuracy of subsequent authentication. Step A1.2, Preset Field Classification Standard: Based on the preset scenario definition, the debt amount, credit limit, repayment flow, and pledge status, which directly determine the risk control results of supply chain finance credit granting, pledging, and repayment, are defined as core verification fields. Transaction notes, operation summaries, and auxiliary descriptions, which do not affect core financial risk control decisions and only serve as auxiliary explanations, are defined as ordinary verification fields. Step A1.3, Adaptive Weight Dynamic Calculation: Based on the current risk control level and historical update frequency of the field, dynamically calculate the sensitive weight of the core field. Sensitive weights compared to ordinary fields It overcomes the shortcomings of traditional fixed-weight assignment and achieves dynamic adaptation of weights to business risks; the specific details are as follows: Core field sensitive weight Dynamic calculation: Core fields determine the results of financing, credit approval, debt pledging, and repayment settlement; their weights... The calculation is based on a two-dimensional coupling of the field's risk control impact and the field's dynamic update frequency, and the formula is as follows: ,in To determine the risk impact level of the fields, values ​​are assigned according to supply chain finance business rules, including the debt amount and pledge status fields. Credit limit field Payment receipt field The higher the value, the greater the financial risk caused by field tampering; This represents the maximum risk impact, fixed at 1.0, used for parameter normalization. The recent update frequency of a field is calculated by counting the number of incremental updates to this core field in the past 7 days. The more frequent the updates, the higher the probability that the field has been tampered with. The maximum allowed update frequency for the core field is preset to 20 times / 7 days according to the supply chain finance industry standard, which is used for frequency normalization calculation. This formula guarantees Always in Within a given range, core fields with high risk and high update frequency are automatically matched with higher verification weights to enhance anti-tampering capabilities. Sensitive weight of ordinary remarks field Linked dynamic calculation: Ordinary fields have no core risk control decision-making value. To avoid redundancy of double weights and achieve dynamic weight balancing, Based on the weighted calculation of the current core fields, the calculation formula is as follows: Among them, the linkage Dynamic iteration, when the core field risk weight The larger the value (the higher the risk of core data), the higher the weight of ordinary fields. Automatically reduce unnecessary computing power consumption; when the risk weight of core fields is low, appropriately increase the weight of ordinary fields to avoid the risk of implicit collaborative tampering, ultimately ensuring... .

[0024] The specific implementation steps are as follows: Step a: Parameter Statistics and Collection. For the incremental financial data to be verified, identify the core field types and retrieve the preset field risk impact ratings. At the same time, the incremental update frequency of this field in the past 7 days is calculated. Read the system's preset normalization parameters , .

[0025] Step b: Solve for core weight normalization. Substitute... The calculation formula completes the two-dimensional parameter coupling normalization operation to obtain the dynamic core sensitive weight adapted to the current field.

[0026] Step c: Solve using ordinary weighted linkage iterations. The completed calculations... Substitution The linked formula generates sensitive weights for ordinary fields in real time that match the current risk control scenario, achieving dynamic balancing of dual weights.

[0027] Step d: Weight binding and solidification. The calculated weights will be... , Each field in the current partition is bound to it and used as the subsequent incremental deviation value. The core parameters of the weighted operation are used to complete the adaptive initialization of the weights.

[0028] Step A1.4, Fully Automated Partition Tagging and Binding: A dual-verification mechanism combining precise keyword matching and secondary verification of financial business attributes is employed to complete the precise classification of all fields and dynamic parameter binding of the standardized supply chain finance incremental data after verification in Step A1.1. Simultaneously, tag encapsulation is completed by combining parameter subscript definitions. The specific implementation logic is as follows: Step A1.41: Full field traversal and screening: Traverse all fields of the current standardized supply chain finance incremental data after verification one by one, remove invalid empty fields left over from preprocessing, retain all valid business fields, and construct a set of fields to be classified; Step A1.42, Initial Keyword Classification and Matching: Based on a pre-defined financial field keyword library, quickly match the field names of the set of fields to be classified, and initially determine the fields of debt amount, credit limit, repayment flow, and pledge status as... Key risk control fields, including transaction notes, operation summaries, and supplementary descriptions, are initially identified as... Ordinary auxiliary field; Step A1.43, Secondary Verification of Business Attributes: Based on the initial matching and classification results of Step A1.42, a secondary verification is performed in conjunction with the supply chain finance business logic to avoid misjudgment due to identical keywords. This confirms that core fields are key fields that directly affect credit granting, pledging, and repayment risk control decisions, while ordinary fields are fields that do not play a core risk control decision role and only serve as auxiliary records, thus completing accurate partitioning and classification. Step A1.44, Attaching Dedicated Type Tags: Attach fixed type tags to the categorized fields, i.e., uniformly attach tags to core fields. Type tags and regular fields are mounted uniformly. Type tags; Step A1.45, Dynamic Weight Parameter Binding: Bind the dynamic sensitive weights of the core fields. Dynamic sensitive weights for ordinary fields Each field is bound to a corresponding tag field one-to-one, so that each valid field has a unique weight parameter that is adapted to the current risk control scenario. Step A1.46, Parameter Fixation and Locking: After completing the binding of labels and weights, temporarily fix the field parameters of the current incremental data.

[0029] Step A1.5: Generate partitioned dataset: Based on step A1.4, the final output is a differentiated incremental dataset bound with dynamic adaptive weights.

[0030] Step A2, Local Incremental Hash Calculation: Identify the incremental regions of standardized supply chain finance incremental data updates, and perform local hash calculations on the updated core fields and ordinary fields respectively to obtain incremental local hash values. Retrieve the historical baseline hash value of this field stored in the database. ; Step A3, Calculation of Time Series Deviation Coefficient: The time interval for collecting this incremental data update is compared with the supply chain finance standard update cycle to calculate the time series deviation coefficient. , It can identify abnormal operations that involve frequent, minor modifications to data within a short period of time. Step A4, Risk Level Quantification Calculation: Substitute the local incremental hash and time-series deviation coefficients obtained in steps A2 and A3 into the incremental differential weighted hash authentication algorithm formula, and combine the differentiated weights of core and ordinary fields to calculate the incremental differential verification deviation value. Further normalization calculations yield the data's reliability risk level. The formula for the incremental differential weighted hash authentication algorithm is as follows: ; ,in This is the incremental differential verification deviation value; the larger the value, the higher the risk of data tampering. This is a sensitive weight for core financial fields, a dynamically adaptively calculated value, with a value range of [range missing]. This is used to characterize the impact of different core financial fields on supply chain finance risk control. It is calculated by normalizing the field risk impact factor and update frequency factor. This is a sensitive weight for a regular remarks field; it is a dynamically calculated value, and its value range is [range missing]. This is used to characterize the degree of harm caused by tampering with auxiliary fields; The real-time local hash value of the incrementally updated field is calculated only for the updated region, without the need for global traversal; The historical baseline hash value of the corresponding field is pre-stored in the system's trusted financial database; The maximum permissible deviation threshold is preset to 0.8, based on the risk control standards of the supply chain finance industry. Data credibility risk level, value range The closer the value is to 1, the more reliable the data is; the lower the value, the higher the risk of tampering. Step A5, Risk Classification Output: Preset classification threshold: For reliable data, For low-risk suspected data tampering, To prevent high-risk data tampering, the classification results and quantitative parameters will be synchronized in real time to the multi-level permission circuit breaker control module, serving as the sole basis for permission control. The risk-linked multi-level permission circuit breaker control module, in a two-way closed-loop linkage with the aforementioned financial data incremental differential weighted authentication module, is equipped with a risk penetration authorization circuit breaker algorithm. This algorithm dynamically calculates multi-level node permission coefficients based on real-time trusted risk levels, and executes tiered authorization and risk circuit breaker mechanisms. The specific implementation steps are as follows: Step B1: Real-time synchronization and reception of risk data: The risk-linked multi-level permission circuit breaker control module monitors the output data of the incremental differential weighted authentication module of financial data throughout the entire process, and obtains the credibility risk level of the current financial data in real time. If no valid risk parameters are received, the data is judged to be abnormal by default, and the initial circuit breaker protection is triggered directly. Step B2, Multi-level Flow Parameter Initialization: Calculate the node credit coefficient for the current user's data sharing application. Obtain the current data flow level Matching the system's preset maximum trusted transfer level Complete algorithm parameter initialization; supply chain finance node credit coefficient. The specific steps to obtain it are as follows: Step 1: Periodic Data Collection and Statistics: The system automatically retrieves the full risk control logs and authentication records from the financial data cleaning module for the past 30 days, and calculates the total amount of compliant data from the data sharing nodes. Total amount of data uploaded Number of times data was tampered with Retrieve the system's preset maximum number of tolerable anomalies. With balance coefficient Complete the initialization of all parameters; Step 2, Data Compliance Rate Calculation: Solve for the compliance pass rate of financial data at each node. This indicator directly reflects the overall credibility of the financial data uploaded by the nodes, preventing inferior data nodes from obtaining high privileges. Step 3, Calculation of Anomaly Penalty Factor: Solving for Node Anomaly Compliance Factor The more times a node is tampered with abnormally, the lower this factor value becomes, thus implementing dynamic credit penalties for maliciously tampering nodes; if Forced zeroing out and complete blocking of credit permissions for high-risk nodes; Step 4, Two-Dimensional Weighted Fusion: Through balancing coefficients The weighted fusion of data compliance rate and anomaly penalty factor yields a comprehensive compliance score for each node. Step 5: Value Range Mapping to Solve for Final Coefficients: By fixing the base and floating range through the dynamic calculation formula of the node credit coefficient, the comprehensive compliance score is linearly mapped to... The interval is used to obtain the real-time dynamic credit coefficient of the current node. Substitute the permission circuit breaker algorithm to complete the permission coefficient The calculation enables dynamic linkage across the entire chain of data authentication results, node credit, and sharing permissions. The dynamic calculation formula for node credit coefficient is as follows: The constant 0.3 is the baseline coefficient for node credit protection. This value represents the minimum credit baseline threshold for supply chain nodes. Unlike the extreme logic of general risk control systems that "abnormalities result in zeroing out and complete blocking," this invention designs a baseline mechanism based on the unique characteristics of long-term chain cooperation and strong business binding in supply chain finance. Upstream and downstream enterprises in the supply chain are long-term cooperating entities. Occasional data update errors or minor, non-malicious anomalies are within the tolerable range and do not require direct stripping of all permissions. The fixed baseline of 0.3 ensures that even if the number of abnormal modifications reaches the upper limit within a node's cycle and the compliance score reaches zero, the basic minimum credit permissions are still retained. This avoids excessive risk control that could paralyze normal supply chain credit granting and confirmation of rights, eliminates the possibility of negative or invalid values ​​in the algorithm, and adapts to the needs of stable financial business operations. The constant 0.7 is the dynamic credit floating scaling coefficient. Step B3, Dynamic Permission Coefficient Calculation: Substitute the formula of the risk penetration authorization circuit breaker algorithm to calculate the effective permission coefficient of the current node for this financial data in real time. This enables differentiated permission calculation based on "single data, single node, and single level," eliminating the vulnerabilities of unified static authorization. The formula for the risk penetration authorization circuit breaker algorithm is: ,in This is the effective coefficient for real-time permissions across multiple node levels, and its value is [value missing]. The higher the coefficient, the higher the node's data access and transfer permissions; Assess the credibility risk level of financial data; Credit coefficient for supply chain finance nodes; The current data flow hierarchy is as follows: core enterprises are at level 1, first-tier suppliers are at level 2, second-tier suppliers are at level 3, and so on, increasing progressively. To pre-determine the maximum trustworthy circulation level of supply chain finance, it is preset to 5 levels based on industry scenarios, thus restricting the unlimited penetration and circulation of data; Step B4, Tiered Authorization and Circuit Breaker Execution: Based on the validity coefficient threshold of permissions, execute the corresponding control strategies: normal authorization for trusted data, permission downgrade for low-risk data, and full circuit breaker for high-risk tampered data, immediately blocking the chain flow of abnormal financial data across multiple nodes in the supply chain, and addressing the risk control vulnerability of continuous reuse of tampered data; the specific implementation details are as follows: when , For trusted data, the system grants full permissions to the supply chain data nodes that are verified to be completely trustworthy. This allows nodes to perform operations such as viewing, downloading, forwarding, chaining, and reusing data, ensuring the efficient progress of normal supply chain finance business such as core enterprise rights confirmation, supplier receivables transfer, and financial institution credit verification, and adapting to the multi-level sharing needs of compliant data. when , To downgrade permissions for low-risk data: For low-risk financial data suspected of being tampered with, the system immediately implements a permission downgrade strategy, rigidly closing the node's permissions to download, forward, multi-level circulation, and secondary editing of the current data, retaining only basic read-only viewing permissions. This control method can effectively limit the spread of low-risk data across multiple nodes in the upstream and downstream of the supply chain without interrupting basic business operations, avoiding the risk of suspected abnormal data being arbitrarily reused, tampered with, or escalated, and achieving risk-controlled fault tolerance. when , For high-risk data tampering, the system immediately triggers a full-domain access control circuit breaker mechanism. This instantly freezes all access, viewing, transfer, and reuse permissions for the current data across all multi-level nodes in the supply chain, severing the chain-like transfer path from "core enterprise - multi-level suppliers - financial institutions." Simultaneously, the data is marked as risk-blocked data, prohibiting any subsequent business calls and sharing. Step B5, Two-way Linkage Iterative Optimization: The risk-linked multi-level permission circuit breaker control module will reverse-synchronize the control results of this permission circuit breaker and downgrade to the financial data incremental differential weighted authentication module; at the same time, it will record the multi-level flow path, block the permission vulnerability of lower-level nodes penetrating to access the core financial data of the upper level, and form a closed loop of "authentication-risk control-feedback-optimization"; the specific implementation steps are as follows: (1) Full reverse synchronization of risk control results: After the risk linkage multi-level permission circuit breaker control module completes a single hierarchical authorization, permission downgrade or full-domain circuit breaker operation, it captures the full-dimensional related data of this control in real time, including the credibility risk level of the current financial data. Real-time credit rating of nodes Data flow hierarchy The system records control types (normal authorization / permission downgrade / global circuit breaker) and abnormal access behavior of nodes. These parameters are then completely reverse-synchronized to the risk control log database of the financial data incremental differential weighted authentication module. A unique binding ledger of "node-data-risk-control" is established to provide real business sample data for algorithm parameter iteration and optimization.

[0031] (2) Adaptive optimization of authentication module algorithm parameters: After receiving the reverse synchronization data, the incremental differential weighted authentication module for financial data optimizes the core verification parameters in a targeted manner to achieve precise iteration: First, the timing deviation coefficient First, threshold optimization: For nodes that repeatedly trigger permission downgrades and full-domain circuit breakers, the system automatically tightens the timing deviation judgment criteria for those nodes, improving the verification sensitivity for their high-frequency, small-scale incremental update behavior, and accurately identifying malicious probing tampering operations. Second, field weight adaptation optimization: Based on the node's historical management records, for nodes that frequently tamper with core financial fields, the system dynamically increases the sensitivity weight of their core fields. The calculation benchmark strengthens subsequent data verification and simultaneously adaptively lowers the weight of ordinary fields. This allows for precise adaptation to the risk characteristics of the node, reducing unnecessary computing power consumption.

[0032] (3) Dynamic updates of node credit and permission rules: The risk-linked multi-level permission circuit breaker control module will include node violations in the number of abnormal modifications in the cycle based on the current abnormal control record. In subsequent nodes, credit coefficient In the dynamic calculation, the credit score of the node is automatically reduced by the abnormal penalty factor to achieve the dynamic constraint of "downgrading the right to violate the rules". At the same time, the system automatically marks high-risk penetration nodes, updates the multi-level circulation permission verification rules, and blocks the permission vulnerability of lower-level supplier nodes to penetrate and access the confidential financial data of the upper-level core enterprise, thus solving the problem of permission penetration and data leakage in the background technology from the root.

[0033] (4) Closed-loop iteration solidification effect: After completing parameter optimization and rule update, the system solidifies the latest algorithm threshold, weight calculation rules and permission control strategy, which are applied to the next round of supply chain finance incremental data authentication and sharing process, so as to realize the data verification accuracy and the strictness of permission control dynamically adapt to the risk behavior of nodes, continuously iterate and improve the overall risk control capability of the system, and ensure the dynamism and accuracy of the whole process security control.

[0034] The encrypted transmission module is used to encrypt and transmit the standardized supply chain finance incremental data obtained by the financial data cleaning module to the risk-linked multi-level access control module via encryption algorithms. The specific implementation steps are as follows: Step 1: Data Transmission, Classification, and Encapsulation. The encrypted transmission module receives standardized incremental data output from the cleaning module and, following the pre-defined field classification rules of this invention, performs secondary classification and encapsulation on the data to be transmitted: binding data with high dynamic weights... Core financial fields such as debt amount, credit line, repayment history, and pledge status are classified as ultra-high-security data and will be bound to low dynamic weights. Transaction notes, operation summaries, and auxiliary instructions, among other common fields, are classified as regular confidential data for transmission. At the same time, the original incremental markers, timestamps, and field parameter bindings are preserved to prevent encapsulation operations from damaging the data structure and affecting subsequent weight calculations and timing deviation coefficients. Solution accuracy.

[0035] Step 2: Adaptive Layered Encryption Key Initialization. Differentiated encryption key systems are adapted for the two types of tiered data to meet the supply chain finance security and computing power balance requirements: For core classified financial data, the existing high-strength AES-256 symmetric encryption key is used to ensure the absolute security of core risk control data transmission; for ordinary classified auxiliary data, the existing lightweight AES-128 symmetric encryption key is used to reduce the computing power overhead of high-frequency incremental data transmission. Simultaneously, the symmetric key is encrypted and stored using an asymmetric RSA algorithm to prevent key leakage during transmission.

[0036] Step 3: Layered Data Encryption Processing. The encrypted transmission module performs layered encryption operations on the encapsulated incremental data according to the hierarchical standard. Throughout the process, the incremental data update identifier and unique field label are preserved, without modifying the original field values, weight parameters, or timing information. This ensures data consistency before and after encryption, prevents data deviations introduced by the encryption operation, and guarantees the local hash value of the subsequent financial data incremental differential weighted authentication module. Calculation, risk level The quantitative results are accurate and effective.

[0037] Step 4: Establish a multi-level node secure transmission channel. Based on the chain-like circulation architecture of supply chain finance ("core enterprise - multi-level suppliers - financial institutions"), establish a dedicated encrypted transmission channel for each node, combined with the subsequent permission-based circuit breaker module's circulation hierarchy. The rules restrict unauthorized cross-level transmission and only allow data to be transmitted within the compliant and trusted node link, thus initially blocking the vulnerability of lower-level nodes obtaining core financial data from higher-level nodes without authorization at the transmission link level.

[0038] Step 5: Incremental Data Targeted Encrypted Transmission. Based on business workflow requirements, the system transmits layered encrypted incremental financial data to the backend financial data incremental differential weighted authentication module via a dedicated secure channel. During transmission, node transmission logs, workflow hierarchy records, and timestamp information are bound in real time, preserving the entire transmission trajectory for subsequent bidirectional iterative optimization, log tracing, and node anomaly statistics. Provides support for raw data transmission.

[0039] Step 6: Precise Decryption and Data Restoration at the Receiving End. After receiving the encrypted data, the incremental differential weighted authentication module of financial data performs layered precise decryption using a preset pairing key. It restores the complete standardized incremental dataset according to the inverse encryption operation, strictly restoring all core parameters such as field partitioning structure, dynamic weight parameters, incremental tags, and time series information. This ensures that the decrypted data is completely consistent with the original data output from the cleaning process, with no missing data, no tampering, and no parameter offset.

[0040] Step 7: Release Compliant Data. After decryption, a transmission integrity verification is performed. If the verification passes, the standardized incremental dataset is officially pushed to the financial data incremental differential weighted authentication module, initiating subsequent core processes such as field partitioning, adaptive weight binding, and risk authentication. If the verification fails, it is immediately determined that illegal tampering has occurred during transmission, the data is proactively intercepted, and the anomaly log is reported, which is recorded as the node's abnormal tampering count and contributes to the node's credit score. Dynamic calculations provide evidence for abnormal data.

[0041] The log tracing module records the work logs of the financial data cleaning module, the financial data incremental differential weighted authentication module, the risk-linked multi-level permission circuit breaker control module, and the encrypted transmission module. It also marks and stores these work logs and provides a tracing query interface. The specific implementation steps are as follows: Step 1: Real-time collection of multi-dimensional logs across the entire process. The log tracing module activates a round-the-clock monitoring mechanism to capture real-time operation data of the entire system, covering key behaviors and parameter information of all front-end, core, and back-end modules. The collected content includes: field normalization, deduplication, and incremental marking records from the financial data cleaning module; layered encryption, transmission channel, and decryption verification records from the encrypted transmission module; and field partitioning and dynamic weighting from the financial data incremental differential weighted authentication module. , Calculation, hash value comparison, timing deviation Solution, Risk Level Quantitative recording; node credit coefficient of the risk-linked multi-level permission circuit breaker control module. Calculation and permission validity coefficient Records of solution processing, hierarchical authorization, permission downgrading, and full-domain circuit breaker control; records of data access, forwarding, downloading, and flow penetration operations at each supply chain node, achieving comprehensive collection of all operations across the entire chain.

[0042] Step 2: Log Structured Classification, Encapsulation, and Parameter Binding. For the massive amounts of fragmented log data collected, a structured classification and organization is performed according to the dimensions of node-data-operation-risk-parameter, abandoning the traditional unordered log storage model. Logs are divided into three main categories: compliant operation logs, suspected risk logs, and malicious anomaly logs. A unique traceability identifier is bound to each log entry, synchronously associating corresponding core parameters: anomaly logs are bound to a node periodic anomaly count statistics identifier, risk level value, and permission circuit breaker type; compliant logs are bound to a data compliance identifier and a node compliance data statistics identifier, for subsequent... , Accurate statistics provide a basis for classification and prevent data discrepancies.

[0043] Step 3: Log Encryption, Solidification, and Partitioned Storage. To prevent logs from being tampered with, deleted, or forged, the module uses existing mature irreversible encryption algorithms to encrypt and solidify the structured log data, ensuring that the traceability logs themselves are authentic, trustworthy, and tamper-proof. Simultaneously, a partitioned storage mechanism is employed to distinguish between short-term operational logs and periodic statistical logs. Dynamic statistical logs from the past 30 days are stored in a separate partition, specifically for supporting node credit ratings. The system performs periodic iterative calculations and permanently retains historical archived logs, achieving a balance between short-term algorithm iteration adaptation and long-term traceability and accountability.

[0044] Step 4: Real-time linkage and algorithm parameter statistics updates. The log tracing module automatically and periodically calculates core algorithm parameters based on structured log data and optimizes these parameters. On one hand, it automatically calculates the total amount of compliant data for each node over the past 30 days. Total amount of data uploaded Number of abnormal modifications Real-time synchronization to the node credit coefficient calculation logic ensures On the one hand, it ensures the authenticity and accuracy of dynamic calculations; on the other hand, it continuously records the high-frequency incremental update behavior and minor tampering behavior of each node, and synchronizes them in reverse to the incremental differential weighted authentication module of financial data, providing real sample data for tightening the time series deviation threshold, optimizing the field weight benchmark, and iterating the verification sensitivity, thus supporting the upgrade of the system's dynamic risk control capabilities.

[0045] Step 5: Abnormal Behavior Tracing, Location, and Marking. When the system triggers permission downgrade, global circuit breaker, or detects data tampering or permission penetration, the module automatically activates a precise tracing mechanism. Using a unique tracing identifier, it reverse-engineers the source of the anomaly, accurately identifying the abnormal supply chain node, the time of the tampering operation, the type of tampered field, the unauthorized access path, and the data flow level. This information is used to simultaneously mark abnormal nodes with unique risk tags and update node risk control labels, providing a basis for tracing the source of subsequent multi-level permission control and node credit penalties, thus fundamentally solving the problem of being unable to locate abnormal behavior and assign responsibility in multi-level chain flow scenarios.

[0046] Step 6: Compliant Archiving and Long-Term Retention of Logs. The log traceability module, in accordance with supply chain finance industry compliance requirements, regularly archives, categorizes, and cleans up expired log data. This ensures that all risk control operations, data flows, and algorithm iteration records are traceable, auditable, and accountable in the long term, while preventing massive log accumulation from consuming system computing resources, thus meeting the compliance and regulatory requirements of the financial system.

[0047] Step 7: Data Source Traceability Output and Closed-Loop Support. The log traceability module provides an external traceability query interface, allowing core enterprises, financial institutions, and regulators to query data flow trajectories, risk control records, and node credit change records as needed. Simultaneously, it continuously provides a constant stream of real traceability data for the system's dynamic closed loop of "authentication-risk control-feedback-optimization," ensuring the system's continuous and accurate adaptive iterative optimization and improving the overall security and risk control system.

[0048] The data sharing and scheduling module is used to monitor the hierarchical permission judgment results of the risk-linked multi-level permission circuit breaker control module, enabling differentiated, controllable, and traceable multi-level sharing and distribution of supply chain finance data. The specific implementation logic is as follows: Step 1: Receive full-dimensional risk control scheduling parameters. The module monitors the output of Innovation Module 2 in real time, accurately receiving the full-dimensional core parameters corresponding to a single data sharing request, including the data trust risk level. Node permission validity coefficient Node dynamic credit coefficient Current data flow level Maximum Trusted Transfer Level The control type (full authorization / permission downgrade / circuit breaker across the entire domain) will be used as the sole criterion for determining data sharing and scheduling, thereby preventing blind data sharing without parameters and risk control.

[0049] Step 2: Precise Matching of Sharing Scenarios and Permission Policies. Based on the received risk control parameters, the data sharing scheduling module automatically matches a dedicated sharing scheduling policy adapted to the current node and data status, according to the system's preset three-level authorization circuit breaker thresholds. It distinguishes three differentiated scheduling modes: fully authorized compliant scheduling mode, permission-downgraded restricted scheduling mode, and full-domain circuit breaker interception scheduling mode. This distinguishes the flow rules of compliant data, low-risk data, and high-risk data from the source, achieving a refined scheduling capability of one policy per data and one control per node.

[0050] Step 3: Fully Authorized Data Sharing and Scheduling ( , For verified, compliant, and tamper-proof trusted financial data, the system implements a full-function shared scheduling mechanism. This allows the current compliant node to fully exercise all permissions, including data access, download, editing, chain forwarding, multi-level circulation, and business reuse. This supports the efficient operation of normal supply chain finance businesses such as debt confirmation, credit approval, repayment verification, and debt splitting. Simultaneously, it strictly adheres to the maximum trusted circulation level constraint, allowing data to circulate normally only within the 1-5 level compliant links, prohibiting unlimited cross-level sharing, thus ensuring efficient circulation of compliant data while maintaining the bottom line of hierarchical security.

[0051] Step 4: Permission downgrade and restricted data sharing scheduling ( , For low-risk financial data suspected of being tampered with, the system activates a restricted scheduling mechanism, rigidly locking data flow permissions and permanently disabling download, secondary editing, multi-level forwarding, and chain-like flow permissions for the current node, granting only basic read-only viewing permissions. The module marks this type of data to prevent its reuse in subsequent core financial transactions such as debt pledging and financing; it only supports staff risk verification and data checking. This achieves fault-tolerant scheduling of low-risk data, ensuring it is "verifiable but not transmittable, visible but not usable," preventing the spread of potentially tampered data and its escalation into malicious tampering risks.

[0052] Step 5: High-risk data full-domain interception and scheduling ( , For high-risk financial data confirmed to have been maliciously tampered with, the system implements a comprehensive interception and scheduling strategy, immediately terminating all sharing requests and freezing all channels for sharing, accessing, transferring, and reusing the current data across the entire supply chain, completely severing the data chain propagation path. Simultaneously, the risky data is globally blocked and added to the system's risk blacklist, prohibiting any subsequent financial business calls and sharing scheduling, thus completely resolving the core risk control vulnerability of continuously circulating and reusing tampered data at the terminal level.

[0053] Step 6: Multi-level data flow compliance verification and anti-penetration control. Throughout the entire data scheduling and sharing process, the module verifies the data flow hierarchy in real time. Compare with the system's preset maximum trusted transfer level Once data is detected to be penetrating and flowing to unknown lower-level nodes or higher-level nodes, abnormal sharing requests are immediately and automatically blocked, thus preventing lower-level nodes from unauthorizedly accessing confidential financial data of higher-level core enterprises. This is combined with node credit coefficients. Further tighten shared scheduling permissions for low-credit nodes to achieve multi-level linkage scheduling and control based on hierarchy, credit, and risk.

[0054] Step 7: Real-time Synchronization and Log Consolidation of Sharing Activities. The data sharing scheduling module synchronizes each data sharing scheduling result, node operation behavior, access control status, and data flow trajectory to the log tracing module in real time, completing structured log encapsulation and encrypted retention. This compliant sharing activity is included in the node's total compliant data volume for the period. Abnormal interception and unauthorized access behavior are counted as abnormal tampering counts within a given period. This serves as the credit coefficient for subsequent nodes. Dynamic calculation, incremental differential weighted authentication module for financial data, and risk-linked multi-level permission circuit breaker control module are iteratively optimized to provide real and effective scheduling sample data.

[0055] Step 8: Shared Session Closure and Status Update. After a single data sharing schedule is completed, the system automatically terminates the temporary shared session, updates the current financial data flow status, access records, and permission status in real time, and clears temporary access permissions to avoid hidden security vulnerabilities caused by long-term authorization. Simultaneously, the scheduling strategy is solidified, completing the entire closed-loop process of "data authentication - permission circuit breaking - differentiated scheduling - log tracing - algorithm optimization," continuously iterating and improving the system's multi-level shared security control capabilities.

[0056] This invention discloses a data security authentication and sharing system based on supply chain big data, belonging to the field of supply chain finance big data security technology. The incremental differential weighted authentication module for financial data is equipped with a self-developed incremental differential weighted hash authentication algorithm. Through field partitioning differential verification and incremental local calculation, it accurately identifies minor, hidden tampering of financial data, reduces computational redundancy in high-frequency iteration scenarios, and quantifies the data credibility risk level. The risk-linked multi-level permission circuit breaker control module dynamically calculates multi-level node permission coefficients based on real-time risk levels through a risk penetration authorization circuit breaker algorithm, achieving hierarchical authorization and full-domain circuit breaker for abnormal data. This system specifically addresses the unique security pain points of the supply chain finance big data sub-sector, significantly improving the accuracy of financial data authentication and the security of multi-level sharing, effectively avoiding risks such as fraudulent financing, data leakage, and bad debts, and is suitable for the core business scenarios of incremental iteration and multi-level chain-like circulation in supply chain finance.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data security authentication and sharing system based on supply chain big data, characterized in that, include: The system includes a financial data cleaning module, a financial data incremental differential weighted authentication module, a risk-linked multi-level permission circuit breaker control module, an encrypted transmission module, a log tracing module, and a data sharing and scheduling module. The financial data cleaning module is used to connect with the heterogeneous supply chain finance raw data from core enterprises, multi-level suppliers, and financial institutions. It cleans and standardizes the acquired financial raw data to obtain standardized supply chain finance incremental data, and then sends the standardized supply chain finance incremental data to the financial data incremental differential weighted authentication module. The incremental differential weighted authentication module for financial data is equipped with a self-developed incremental differential weighted hash authentication algorithm, which is used to perform field partitioning differential verification on standardized supply chain finance incremental data, accurately identify local weak tampering behavior, and quantify the data credibility risk level. The risk-linked multi-level permission circuit breaker control module is linked bidirectionally with the financial data incremental differential weighted authentication module in a closed loop. It is equipped with a risk penetration authorization circuit breaker algorithm, which is used to dynamically calculate the multi-level node permission coefficient based on the real-time trusted risk level and execute hierarchical authorization and risk circuit breaker. The encrypted transmission module is used to encrypt and transmit the standardized supply chain finance incremental data obtained by the financial data cleaning module to the risk-linked multi-level permission circuit breaker control module through an encryption algorithm. The log tracing module is used to record the work logs of the financial data cleaning module, the financial data incremental differential weighted authentication module, the risk linkage multi-level permission circuit breaker control module, and the encrypted transmission module, and to mark and store the work logs. It also has a tracing query interface. The data sharing and scheduling module is used to monitor the hierarchical permission judgment results of the risk linkage multi-level permission circuit breaker control module to realize the differentiated, controllable, and traceable multi-level sharing and distribution of supply chain finance data.

2. The data security authentication and sharing system based on supply chain big data according to claim 1, characterized in that: The specific implementation steps of the incremental differential weighted authentication module for financial data are as follows: Step A1: Financial Data Field Partitioning and Adaptive Weight Binding Preprocessing: The incremental differential weighted authentication module receives standardized supply chain finance incremental data in real time after it has been standardized, deduplicated, and time-series aligned by the financial data cleaning module. According to financial risk control rules, the module automatically partitions and marks the standardized supply chain finance incremental data, marking the debt amount, credit line, repayment flow, and pledge status as core verification fields, and marking transaction notes, operation summaries, and auxiliary descriptions as ordinary verification fields, thus completing the pre-processing for differentiated verification. Step A2, Local Incremental Hash Calculation: Identify the incremental regions of standardized supply chain finance incremental data updates, and perform local hash calculations on the updated core fields and ordinary fields respectively to obtain incremental local hash values. Retrieve the historical baseline hash value of this field stored in the database. ; Step A3, Calculation of Time Series Deviation Coefficient: The time interval for collecting this incremental data update is compared with the supply chain finance standard update cycle to calculate the time series deviation coefficient. , It can identify abnormal operations that involve frequent, minor modifications to data within a short period of time. Step A4, Risk Level Quantification Calculation: Substitute the local incremental hash and time-series deviation coefficients obtained in steps A2 and A3 into the incremental differential weighted hash authentication algorithm formula, and combine the differentiated weights of core and ordinary fields to calculate the incremental differential verification deviation value. Further normalization calculations yield the data reliability risk level. The formula for the incremental differential weighted hash authentication algorithm is as follows: ; ,in This is the incremental differential verification deviation value; the larger the value, the higher the risk of data tampering. This is a sensitive weight for core financial fields, a dynamically adaptively calculated value, with a value range of [range missing]. This is used to characterize the impact of different core financial fields on supply chain finance risk control. It is calculated by normalizing the field risk impact factor and update frequency factor. This is a sensitive weight for a regular remarks field; it is a dynamically calculated value, and its value range is [range missing]. This is used to characterize the degree of harm caused by tampering with auxiliary fields; To update the real-time local hash value of the field incrementally; This corresponds to the historical stock baseline hash value of the field; The preset maximum allowable deviation threshold; Data credibility risk level, value range The closer the value is to 1, the more reliable the data is; the lower the value, the higher the risk of tampering. Step A5, Risk Classification Output: Preset classification threshold: For reliable data, For low-risk suspected data tampering, To prevent high-risk data tampering, the classification results and quantitative parameters will be synchronized in real time to the multi-level permission circuit breaker control module.

3. The data security authentication and sharing system based on supply chain big data according to claim 2, characterized in that: The specific implementation logic of the preprocessing step A1, which involves binding the financial data field partitioning tags and adaptive weights, is as follows: Step A1.1, Data Compliance Verification: First, the standardized incremental supply chain finance data received from the financial data cleaning module is verified for format compliance. Incomplete fields, garbled data, and invalid null values ​​are filtered out, while complete incremental supply chain finance business data is retained. Step A1.2, Preset Field Classification Standard: Based on the preset scenario definition, the debt amount, credit limit, repayment flow, and pledge status, which directly determine the risk control results of supply chain finance credit granting, pledging, and repayment, are defined as core verification fields. Transaction notes, operation summaries, and auxiliary descriptions, which do not affect core financial risk control decisions and only serve as auxiliary explanations, are defined as ordinary verification fields. Step A1.3, Adaptive Weight Dynamic Calculation: Based on the current risk control level and historical update frequency of the field, dynamically calculate the sensitive weight of the core field. Sensitive weights compared to ordinary fields ; Step A1.4, Fully Automatic Partition Tagging and Binding: A dual verification mechanism of precise keyword matching and secondary verification of financial business attributes is adopted to complete the full field accurate classification and dynamic parameter binding of the standardized supply chain finance incremental data after verification in Step A1.1, and at the same time, the tag encapsulation is completed in combination with the parameter subscript definition; Step A1.5: Generate partitioned dataset: Based on step A1.4, the final output is a differentiated incremental dataset bound with dynamic adaptive weights.

4. A data security authentication and sharing system based on supply chain big data according to claim 3, characterized in that: The specific implementation logic of the fully automatic partition marking and binding in step A1.4 is as follows: Step A1.41: Full field traversal and screening: Traverse all fields of the current standardized supply chain finance incremental data after verification one by one, remove invalid empty fields left over from preprocessing, retain all valid business fields, and construct a set of fields to be classified; Step A1.42, Initial Keyword Classification and Matching: Based on a pre-defined financial field keyword library, quickly match the field names of the set of fields to be classified, and initially determine the fields of debt amount, credit limit, repayment flow, and pledge status as... Key risk control fields, including transaction notes, operation summaries, and supplementary descriptions, are initially identified as... Ordinary auxiliary field; Step A1.43, Secondary Verification of Business Attributes: Based on the initial matching and classification results of Step A1.42, a secondary verification is performed in conjunction with the supply chain finance business logic to avoid misjudgment due to keyword homonyms; Step A1.44, Attaching Dedicated Type Tags: Attach fixed type tags to the categorized fields, i.e., uniformly attach tags to core fields. Type tags and regular fields are mounted uniformly. Type tags; Step A1.45, Dynamic Weight Parameter Binding: Bind the dynamic sensitive weights of the core fields. Dynamic sensitive weights for ordinary fields Each field is bound to a corresponding tag field one-to-one, so that each valid field has a unique weight parameter that is adapted to the current risk control scenario. Step A1.46, Parameter Fixation and Locking: After completing the binding of labels and weights, temporarily fix the field parameters of the current incremental data.

5. A data security authentication and sharing system based on supply chain big data according to claim 3, characterized in that: The specific details of the adaptive weight dynamic calculation in step A1.3 are as follows: Core field sensitive weight Dynamic calculation: Core fields determine the results of financing, credit approval, debt pledging, and repayment settlement; their weights... The calculation is based on a two-dimensional coupling of the field's risk control impact and the field's dynamic update frequency, and the formula is as follows: ,in To determine the risk impact level of the fields, values ​​are assigned according to supply chain finance business rules, including the debt amount and pledge status fields. Credit limit field Payment receipt field ; This represents the maximum risk impact, fixed at 1.0, used for parameter normalization. To calculate the recent update frequency of a field, count the number of incremental updates to this core field in the past 7 days. The maximum allowed update frequency for core fields; Sensitive weight of ordinary remarks field Linked dynamic calculation: Ordinary fields have no core risk control decision-making value. To avoid redundancy of double weights and achieve dynamic weight balancing, Based on the weighted calculation of the current core fields, the calculation formula is as follows: Among them, the linkage Dynamic iteration, when the core field risk weight The larger the value, the higher the weight of the ordinary field. Automatically reduce unnecessary computing power consumption.

6. The data security authentication and sharing system based on supply chain big data according to claim 1, characterized in that: The specific implementation steps of the risk-linked multi-level permission circuit breaker control module are as follows: Step B1: Real-time synchronization and reception of risk data: The risk-linked multi-level permission circuit breaker control module monitors the output data of the incremental differential weighted authentication module of financial data throughout the entire process, and obtains the credibility risk level of the current financial data in real time. If no valid risk parameters are received, the data is judged to be abnormal by default, and the initial circuit breaker protection is triggered directly. Step B2, Multi-level Flow Parameter Initialization: Calculate the node credit coefficient for the current user's data sharing application.

1. Obtain the current data flow level Matching the system's preset maximum trusted transfer level Complete the algorithm parameter initialization; Step B3, Dynamic Permission Coefficient Calculation: Substitute the formula of the risk penetration authorization circuit breaker algorithm to calculate the effective permission coefficient of the current node for this financial data in real time. ; The formula for the risk penetration authorization circuit breaker algorithm is: ,in This is the effective coefficient for real-time permissions across multiple node levels, and its value is [value missing]. The higher the coefficient, the higher the node's data access and transfer permissions; Assess the credibility risk level of financial data; Credit coefficient for supply chain finance nodes; The current data flow hierarchy is as follows: core enterprises are at level 1, first-tier suppliers are at level 2, second-tier suppliers are at level 3, and so on, increasing progressively. To pre-determine the maximum trustworthy circulation level of supply chain finance, it is preset to 5 levels based on industry scenarios, thus restricting the unlimited penetration and circulation of data; Step B4, Tiered Authorization and Circuit Breaker Execution: Based on the effective permission coefficient threshold, execute the corresponding control strategy: normal authorization for trusted data, downgrade of permissions for low-risk data, and full circuit breaker for high-risk tampered data, to immediately block the chain flow of abnormal financial data in multiple nodes of the supply chain; Step B5, Two-way linkage iterative optimization: The risk linkage multi-level permission circuit breaker control module will reversely synchronize the control results of this permission circuit breaker and downgrade to the financial data incremental differential weighted authentication module; at the same time, it will record the multi-level flow path and block the permission vulnerability of lower-level nodes to penetrate and access the upper-level core financial data.

7. A data security authentication and sharing system based on supply chain big data as described in claim 6, characterized in that: The specific implementation details of the hierarchical authorization and circuit breaker execution in step B4 are as follows: when , For trusted data, the system grants full permissions to the supply chain data nodes that are verified to be completely trustworthy, allowing them to perform operations including but not limited to viewing, downloading, forwarding, chaining, and reusing data. when , For low-risk data, the system immediately implements a permission downgrade strategy for low-risk financial data that is suspected of being tampered with. This strategy rigidly closes the node's permissions to download, forward, multi-level transfer, and secondary editing of the current data, retaining only basic read-only viewing permissions. when , For high-risk data tampering, the system immediately triggers a full-domain access control circuit breaker mechanism. This instantly freezes all access, viewing, transfer, and reuse permissions for the current data across all multi-level circulation nodes in the supply chain, cutting off the chain-like circulation path of "core enterprise - multi-level suppliers - financial institutions". The system also marks the data as risky and blocked data, prohibiting any subsequent business calls and sharing.

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