Electronic guarantee letter issuing verification method and system based on blockchain storage

Abstract

The application discloses a blockchain-based electronic guarantee letter issuing and checking method and system, and relates to the technical field of issuing and checking, which comprises the following steps: extracting static core commitment information and dynamic performance commitment information, performing risk degree analysis, performing commitment anchoring granularity analysis, and establishing an instantiated trusted commitment anchor point; obtaining real-time state data, generating a quantitative risk estimate value according to the real-time state data and a preset simulation deduction model; coupling and checking the quantitative risk estimate value and the instantiated trusted commitment anchor point on the blockchain, dynamically determining the guarantee letter issuing quota, the guarantee period, the counter-guarantee condition or the additional constraint clause, and generating a corresponding guarantee letter issuing instruction. The application solves the technical problems of lacking effective checking of dynamic performance commitment, insufficient accuracy of risk assessment, and lack of traceability and credibility in the guarantee letter issuing process of the prior art, and achieves the technical effects of dynamic risk monitoring, effective response to performance risk and improved checking efficiency.

Description

Technical Field

[0001] This invention relates to the field of issuance and verification technology, specifically to a method and system for issuing and verifying electronic guarantees based on blockchain-based evidence storage. Background Technology

[0002] In the process of issuing guarantees, the verification of dynamic performance commitment information typically relies on traditional manual review or fixed rules, making it impossible to reflect changes in the applicant's ability to perform in real time. Because performance commitments are affected by various external factors, such as market fluctuations and changes in the economic environment, traditional methods struggle to accurately assess these dynamic risks. Furthermore, the lack of effective traceability and verification of commitment information in the guarantee issuance process results in low credibility and transparency. If problems arise during performance, it is impossible to quickly trace relevant information or prove liability and risk in the performance process, making the issuance and enforcement of guarantees inherently risky and uncertain. Summary of the Invention

[0003] This application provides a method and system for issuing and verifying electronic guarantees based on blockchain evidence storage, which is used to address the technical problems in the existing technology of lacking effective verification of dynamic performance commitments, insufficiently accurate risk assessment, and lack of traceability and credibility in the process of issuing guarantees.

[0004] In view of the above problems, this application provides a method and system for issuing and verifying electronic guarantees based on blockchain evidence storage.

[0005] The first aspect of this application provides a method for issuing and verifying electronic guarantees based on blockchain-based evidence storage, the method comprising: The process involves analyzing the guarantee application data to extract static core commitment information and dynamic performance commitment information. It then analyzes the risk level corresponding to the commitment response, performs commitment anchoring granularity analysis based on the commitment response risk level, establishes instantiated trusted commitment anchors, and deploys these anchors on the blockchain. A risk-aware oracle network is invoked to obtain real-time status data related to the applicant's performance capability from multiple external trusted data sources, based on the static core commitment information and dynamic performance commitment information. A quantitative risk estimate is generated based on the real-time status data and a preset simulation model. The quantitative risk estimate is then coupled and verified with the instantiated trusted commitment anchors on the blockchain to dynamically determine the guarantee issuance amount, guarantee period, counter-guarantee conditions, or additional constraint clauses, and generates corresponding guarantee issuance instructions.

[0006] A second aspect of this application provides an electronic guarantee issuance and verification system based on blockchain-based evidence storage, the system comprising: The data parsing module is used to parse the guarantee application data and extract static core commitment information and dynamic performance commitment information. The risk analysis module is used to analyze the risk level corresponding to the commitment response of the static core commitment information and dynamic performance commitment information, analyze the commitment anchoring granularity according to the commitment response risk level, establish instantiated trusted commitment anchors, and deploy the instantiated trusted commitment anchors on the blockchain. The risk estimation module is used to call the risk perception oracle network, obtain real-time status data related to the applicant's performance capability from multiple external trusted data sources based on the static core commitment information and dynamic performance commitment information, and generate a quantitative risk estimate value based on the real-time status data and a preset simulation model. The instruction generation module is used to couple and verify the quantitative risk estimate value with the instantiated trusted commitment anchors on the blockchain, dynamically determine the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generate corresponding guarantee issuance instructions.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application analyzes the guarantee application data, extracting static core commitment information and dynamic performance commitment information; it analyzes the risk level corresponding to the commitment response of the static core commitment information and dynamic performance commitment information, analyzes the commitment anchoring granularity according to the commitment response risk level, establishes instantiated trusted commitment anchors, and deploys the instantiated trusted commitment anchors on the blockchain; it calls the risk perception oracle network, and based on the static core commitment information and dynamic performance commitment information, obtains real-time status data related to the applicant's performance capability from multiple external trusted data sources, and generates a quantitative risk estimate based on the real-time status data and a preset simulation model; it couples and verifies the quantitative risk estimate with the instantiated trusted commitment anchors on the blockchain, dynamically determines the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generates a corresponding guarantee issuance instruction. This invention addresses the technical problems in the existing technology of lacking effective verification of dynamic performance commitments, inaccurate risk assessment, and lack of traceability and credibility in the process of issuing guarantees. By introducing blockchain evidence storage technology, risk perception oracle network, and commitment response risk degree analysis, it achieves the technical effects of improving the accuracy and credibility of guarantee issuance, realizing dynamic risk monitoring, effectively responding to performance risks, and improving verification efficiency. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the electronic guarantee issuance and verification method based on blockchain evidence provided in this application embodiment; Figure 2 A schematic diagram of the structure of an electronic guarantee issuance and verification system based on blockchain evidence provided in this application embodiment.

[0010] Figure labeling: Data parsing module 11, risk level parsing module 12, risk estimation module 13, instruction generation module 14. Detailed Implementation

[0011] This application provides a blockchain-based electronic guarantee issuance and verification method and system. It addresses the technical problems in existing technologies, such as the lack of effective verification of dynamic performance commitments, inaccurate risk assessment, and lack of traceability and credibility during the guarantee issuance process. By introducing blockchain notarization technology, a risk perception oracle network, and commitment response risk degree analysis, it achieves the technical effects of improving the accuracy and credibility of guarantee issuance, realizing dynamic risk monitoring, effectively responding to performance risks, and improving verification efficiency.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a method for issuing and verifying electronic guarantees based on blockchain-based evidence storage, the method comprising: Step S100: Analyze the guarantee application data and extract static core commitment information and dynamic performance commitment information.

[0015] In this embodiment of the application, in the verification process of electronic guarantee issuance, the guarantee application data submitted by the applicant is first fully parsed to extract relevant business fields such as applicant information, guarantee amount, performance period, and guarantee terms. All extracted fields are then systematically organized and converted into a standardized data set according to the unified standard for electronic guarantee business processing.

[0016] After field extraction and format conversion, the standardized field data set is classified. First, based on a preset change frequency threshold, the frequency of change of each field during the guarantee's lifecycle is determined. If the field's change frequency exceeds the threshold, it is classified as dynamic performance commitment information. If the field's change frequency is low or remains essentially unchanged during the guarantee's lifecycle, it is classified as static core commitment information. Simultaneously, further judgment is made based on the business attributes of each field. For example, fields such as applicant basic information and fixed guarantee amount, which are unrelated to external changes and remain constant throughout the guarantee's performance period, are classified as static core commitment information. Fields such as flexible performance limits and market-linked performance conditions, which are updated and adjusted due to economic fluctuations, market changes, and other external environmental changes, are classified as dynamic performance commitment information. Through these dual judgment criteria, the classification of all parsed fields in the guarantee application data is completed, and finally, static core commitment information and dynamic performance commitment information are extracted from the guarantee application data.

[0017] Step S200: Analyze the risk level corresponding to the commitment response of the static core commitment information and dynamic performance commitment information, analyze the commitment anchoring granularity according to the commitment response risk level, establish an instantiated trusted commitment anchor point, and deploy the instantiated trusted commitment anchor point on the blockchain.

[0018] In this embodiment, when analyzing the risk level corresponding to the commitment response of static core commitment information and dynamic performance commitment information, the static and dynamic commitment information are first decomposed into multiple commitment units in a structured manner. Then, for each commitment unit, risk characteristics such as its monetary impact, performance sensitivity, fluctuation frequency, external dependence, and trigger strength are extracted. Finally, based on preset weighting rules or risk assessment models, the commitment response risk level of each commitment unit is calculated.

[0019] Next, the commitment anchoring granularity is parsed according to the commitment response risk level to establish instantiated trusted commitment anchors. In this process, firstly, based on the preset risk granularity segmentation rules, the granularity is determined according to the commitment response risk level of each commitment unit, thus determining the anchoring granularity of each commitment unit. Different anchoring granularities correspond to different levels of evidence storage structures, such as digest level, field group level, field level, or condition chain level. Then, based on these anchoring granularities and the corresponding evidence storage structures, normalized encoding processing is performed, and corresponding hash digests are generated. Finally, an instantiated trusted commitment anchor is established for each commitment unit.

[0020] Finally, the instantiated trusted commitment anchors are deployed on the blockchain. Specifically, multiple instantiated trusted commitment anchors are first organized into a tree-like digest structure according to a preset hierarchical relationship, generating a root digest, sub-digests, and corresponding granularity indexes. The granularity index is used to identify the anchoring granularity of different commitment units and their position in the tree structure. Then, based on the root digest, granularity index, risk level identifier, version number, timestamp, and off-chain index address, anchor deployment data is generated, and the issuing entity digitally signs it and submits it to the blockchain smart contract. Finally, the blockchain smart contract completes signature verification, version verification, and anchor registration, writing the anchor digest and granularity index into the blockchain.

[0021] Furthermore, the method provided in the application embodiments, which analyzes the risk level corresponding to the commitment response of the static core commitment information and dynamic performance commitment information, also includes: The static core commitment information and dynamic performance commitment information are structurally decomposed to establish multiple commitment units. Based on the multiple commitment units, the financial impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity risk characteristics of each commitment unit are extracted. According to the financial impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity risk characteristics of each commitment unit, the corresponding commitment response risk is calculated according to the preset weighting rules or risk assessment model.

[0022] In this embodiment of the application, during the risk analysis process for issuing electronic guarantees, the static core commitment information and dynamic performance commitment information are first structurally decomposed to establish multiple commitment units. Specifically, a field splitting method is used to break down the extracted static core commitment information and dynamic performance commitment information item by item according to their field content, and the commitment units are established with one field corresponding to one commitment unit. For example, the guarantee amount field forms an amount-based commitment unit, the performance period field forms a period-based commitment unit, and the counter-guarantee condition field forms a condition-based commitment unit, thereby converting the original commitment information into multiple commitment units that can be analyzed independently.

[0023] After establishing multiple commitment units, the risk characteristics of each commitment unit—including its monetary impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity—are extracted. Specifically, a rule-based scoring method is used to assign values ​​to each commitment unit according to uniform rules. Monetary impact is calculated as the ratio of the corresponding amount of the commitment unit to the total amount of the guarantee, using the formula: commitment unit amount divided by the total guarantee amount. Performance sensitivity is assigned a graded value based on whether the commitment unit directly affects the performance outcome: 1 for direct impact, 0.5 for indirect impact, and 0 for no direct impact. Fluctuation frequency is calculated based on the number of field changes within a set statistical period, using the formula: number of changes for the commitment unit within the statistical period divided by the length of the statistical period. External dependence is assigned a value based on whether the commitment unit relies on external data updates: 1 for reliance on external data source updates and 0 for no reliance. Trigger intensity is calculated as the proportion of the number of times the commitment unit's performance status changes after being triggered to the total number of triggers, using the formula: number of performance status changes divided by the total number of triggers. The above calculations yield the monetary impact, performance sensitivity, fluctuation frequency, external dependence, and trigger strength of each commitment unit.

[0024] After obtaining the monetary impact, performance sensitivity, volatility frequency, external dependence, and trigger strength of each commitment unit, the corresponding commitment response risk level is calculated according to a preset weighting rule. In this process, a weighted summation method is used. First, the monetary impact, performance sensitivity, volatility frequency, external dependence, and trigger strength are standardized in terms of dimensions, ensuring that the values ​​of each risk characteristic fall within the same numerical range. Then, each is multiplied by its corresponding weight, and finally, the weighted results are summed to obtain the commitment response risk level. The calculation formula is: Commitment response risk level = Monetary impact multiplied by monetary weight, plus performance sensitivity multiplied by sensitivity weight, plus volatility frequency multiplied by volatility weight, plus external dependence multiplied by dependence weight, plus trigger strength multiplied by trigger weight. If the monetary weight is 0.30, the sensitivity weight is 0.25, the volatility weight is 0.20, the dependence weight is 0.15, and the trigger weight is 0.10, then the corresponding risk characteristic values ​​can be substituted into the formula for direct calculation, ultimately obtaining the commitment response risk level for each commitment unit.

[0025] When calculating the corresponding commitment response risk level according to the preset risk assessment model, the impact of amount, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity are imported into the risk assessment model as input parameters. First, each input parameter is normalized to ensure that each risk characteristic is within a uniform numerical range. Then, a weighted calculation is performed according to preset weights to obtain the commitment response risk level. The calculation formula of the risk assessment model is: commitment response risk level equals impact of amount multiplied by amount weight plus performance sensitivity multiplied by sensitivity weight plus fluctuation frequency multiplied by fluctuation weight plus external dependence multiplied by dependence weight plus trigger intensity multiplied by trigger weight. This completes the quantitative calculation of the risk level of each commitment unit.

[0026] Furthermore, the method provided in the application embodiments, which parses the commitment anchoring granularity according to the commitment response risk level and establishes instantiated trusted commitment anchor points, also includes: According to the preset risk granularity segmentation rules, the granularity is determined based on the risk level of the commitment response of each commitment unit to obtain the anchoring granularity of each commitment unit. Different anchoring granularities correspond to digest-level, field group-level, field-level, or condition chain-level evidence storage structures. Based on the anchoring granularity and the corresponding evidence storage structure, normalized encoding processing is performed to generate corresponding hash digests, and instantiated trusted commitment anchors for each commitment unit are established.

[0027] Furthermore, in the method provided in the application embodiment, the instantiated trusted commitment anchor point includes: a static core commitment field hash, a dynamic performance commitment field hash, a commitment response risk degree identifier, and a corresponding anchoring granularity identifier, and a structure identifier representing the association between commitment units; wherein, different anchoring granularities correspond to different levels of data digest structures or field-level mapping structures.

[0028] In this embodiment, the granularity is first determined according to the risk level of each commitment unit based on a preset risk granularity segmentation rule. Specifically, an interval mapping method is used to divide the risk level of the commitment response into multiple numerical intervals. For example, a risk level of 0 to 0.3 corresponds to the summary level, 0.3 to 0.7 corresponds to the field group level, and levels above 0.7 correspond to the field level or condition chain level. Then, the risk level of each commitment unit's commitment response is matched with the above intervals to determine its corresponding interval and output the corresponding anchoring granularity. For example, if the risk level of a commitment unit is 0.25, it is determined to be at the summary level; if it is 0.55, it is determined to be at the field group level; and if it is 0.82, it is determined to be at the field level or condition chain level, thereby achieving the division of different risk levels into different granularity levels.

[0029] Next, based on the anchoring granularity and the corresponding evidence storage structure, normalized encoding processing is performed to generate a corresponding hash digest. Specifically, using normalized encoding and hash calculation methods, firstly, the corresponding data organization method is selected according to the anchoring granularity. For example, at the digest level, the relevant fields of the commitment unit are concatenated in order of field name to form a complete data string; at the field group level, multiple fields within the same business group are concatenated in a preset order to form a combined data string; at the field level, key-value pair encoding is performed directly on a single field; and at the condition chain level, multiple condition fields are arranged in sequence according to the condition triggering order to form a sequence data structure. Then, the above data is processed with a unified format encoding to ensure consistent field order and separation method. After that, a hash operation is performed on the encoded data to generate a fixed-length hash digest value, such as a 64-bit digest string, which is used to uniquely identify the data content of the commitment unit at the corresponding granularity.

[0030] After generating the hash digest, instantiated trusted commitment anchors are established for each commitment unit. Specifically, a structural encapsulation method is used to write the hashes of the static core commitment field and the dynamic performance commitment field as data digests into the anchor structure. This is used to identify the content mapping relationship of different types of commitment information. The commitment response risk level identifier is written as a numerical field to characterize the risk level of the commitment unit. The anchoring granularity identifier is written to identify the granularity level to which the current commitment unit belongs. The structure identifier is written to record the position of the commitment unit in the overall structure and its association with other commitment units, such as recording its parent node number or associated field number. Through the combination and encapsulation of the above fields, a complete data structure is formed, namely the instantiated trusted commitment anchor. Different anchoring granularities correspond to different levels of data digest structures or field-level mapping structures, thereby realizing hierarchical storage and fine-grained management of commitment data.

[0031] Furthermore, the method provided in the application embodiments, which deploys the instantiated trusted commitment anchor on the blockchain, further includes: Multiple instantiated trusted commitment anchors are organized into a tree-like digest structure according to a preset hierarchical relationship, generating a root digest, sub-digests, and corresponding granularity indexes. The granularity indexes are used to identify the anchoring granularity of different commitment units and their positions in the tree structure. Anchor deployment data is generated based on the root digest, granularity index, risk identifier, version number, timestamp, and off-chain index address. The issuing entity digitally signs the anchor deployment data and submits it to the blockchain smart contract. The blockchain smart contract completes the signature verification, version verification, and anchor registration, and writes the corresponding anchor digest and granularity index onto the blockchain.

[0032] In this embodiment, multiple instantiated trusted commitment anchors are organized into a tree-like summary structure according to a preset hierarchical relationship, generating a root summary, sub-summaries, and corresponding granularity indexes. Specifically, a hierarchical merging method is adopted. First, based on the business subordination relationship, field association relationship, and anchoring granularity hierarchy relationship between commitment units, multiple instantiated trusted commitment anchors are arranged hierarchically. The lowest-level instantiated trusted commitment anchor is taken as a leaf node. Leaf nodes in the same business group or the same field group are combined in a preset order, and the combination result is used to calculate a summary to obtain the corresponding sub-summary. Then, multiple sub-summaries are merged upwards layer by layer in the same order until a unique root summary is obtained. The sub-summary is used to represent the data summary result of a local commitment unit set, and the root summary is used to represent the overall summary result of all instantiated trusted commitment anchors. While completing the tree-like merging, a corresponding granularity index is assigned to each instantiated trusted commitment anchor, that is, it is encoded according to the tree level number plus the node position number. For example, the 5th node in the 2nd level can be recorded as 2-5, thereby identifying the anchoring granularity of different commitment units and their position in the tree-like summary structure.

[0033] After generating the root digest, sub-digests, and corresponding granularity indexes, anchor deployment data is generated based on the root digest, granularity index, risk level identifier, version number, timestamp, and off-chain index address. Specifically, a field encapsulation method is used: first, the root digest is extracted as the main digest identifier for this anchor deployment; then, the granularity index corresponding to each instantiated trusted commitment anchor is extracted as the location identifier; simultaneously, the risk level identifier is written to characterize the risk level of the corresponding commitment unit; the version number is written to identify the data version to which this anchor deployment belongs; the timestamp is written to identify the generation time of the anchor deployment data; and the off-chain index address is written to point to the original commitment data or extended data stored off-chain. Then, the above contents are arranged and concatenated into a unified data record according to the preset field order. For example, it can be encapsulated in the order of root digest, granularity index, risk level identifier, version number, timestamp, and off-chain index address to form anchor deployment data with a unified format, which facilitates subsequent signing, submission, and on-chain verification.

[0034] After the anchor deployment data is generated, the issuing entity digitally signs the data and submits it to the blockchain smart contract. This process employs an asymmetric signature method, where the issuing entity uses its private key to perform a signature operation on the anchor deployment data, generating a digital signature value that corresponds one-to-one with the anchor deployment data. The anchor deployment data and the digital signature value are then sent together as the transaction content to the blockchain smart contract. The digital signature proves that the anchor deployment data originates from the issuing entity and has not been tampered with after signing. The blockchain smart contract, acting as the on-chain execution platform, receives the transaction content and initiates the on-chain verification process.

[0035] After the anchor deployment data is submitted to the blockchain smart contract, the blockchain smart contract completes the signature verification, version verification, and anchor registration, and writes the corresponding anchor digest and granularity index onto the blockchain. Specifically, using the contract verification method, the blockchain smart contract first calls the public key corresponding to the issuing entity to verify the digital signature, confirming the identity and integrity of the submitted anchor deployment data. After the signature verification is successful, the version number in the anchor deployment data is read and compared with the version record already registered on the chain. If the version number is valid and there is no duplicate registration, the anchor registration continues. When the anchor registration is completed, the root digest, sub-digest, and corresponding granularity index are written into the blockchain ledger, and the association between the risk level identifier, version number, timestamp, and off-chain index address is registered simultaneously, thus forming a traceable and tamper-proof on-chain anchor record. This enables subsequent data consistency verification based on the anchor digest and rapid location and hierarchical verification of different commitment units based on the granularity index.

[0036] Step S300: Invoke the risk perception oracle network, based on the static core commitment information and dynamic performance commitment information, obtain real-time status data related to the applicant's performance capability from multiple external trusted data sources, and generate a quantitative risk estimate based on the real-time status data and a preset simulation model.

[0037] In this embodiment, a risk-aware oracle network first acquires real-time status data related to the applicant's performance capability from multiple external trusted data sources. This real-time status data is then timestamped and its source consistency verified to form unified risk characteristic data. This risk characteristic data includes features related to monetary impact, performance sensitivity, status fluctuations, and external dependencies. Subsequently, the risk characteristic data is input into a simulation model, and combined with static core commitment information and dynamic performance commitment information, risk simulation analysis is performed. Based on the current risk characteristic data, the trend of the applicant's performance capability changes within a preset performance period is simulated, outputting a default probability or risk score, thereby generating a quantitative risk estimate.

[0038] Furthermore, in the method provided in the application embodiments, generating a quantitative risk estimate further includes: The risk-aware oracle network acquires real-time status data from multiple external data sources, timestamps and verifies the consistency of the sources of this data, and generates unified risk characteristic data. This risk characteristic data includes at least one of the following: monetary impact characteristics, performance sensitivity characteristics, state fluctuation characteristics, and external dependency characteristics. The risk-aware oracle network is an off-chain service network deployed outside the blockchain. The risk characteristic data is input into a simulation model, and combined with the static core commitment information and dynamic performance commitment information, risk simulation analysis is performed. Based on the current risk characteristic data, the model simulates the applicant's performance capability change trend within a preset performance period, outputting the default probability or risk score, and generating the quantitative risk estimate.

[0039] In this embodiment, real-time status data is first obtained from multiple external data sources through a risk-aware oracle network, and the real-time status data is timestamped and its source consistency is verified. In this process, a multi-source acquisition method is adopted. The risk-aware oracle network initiates data acquisition requests to bank account data sources, transaction flow data sources, contract performance record data sources, and judicial enforcement record data sources respectively. After obtaining consent, it acquires real-time status data related to the applicant's ability to perform its obligations. After each piece of real-time status data is returned, the corresponding data acquisition time is recorded as a timestamp. Subsequently, source consistency verification is performed on similar data from multiple external data sources. The values ​​of the same data item in different data sources are compared item by item. Data with numerical differences not exceeding a preset range is retained, while data exceeding the range is discarded. After completing the timestamp and source consistency verification... After consistency verification, valid data is merged according to preset field rules and unified risk characteristic data is formed through calculation. Among them, the amount impact characteristic is calculated by the ratio of account balance data to credit limit data; the performance sensitivity characteristic is calculated by the proportion of historical default records to the total number of contract performance records; the status fluctuation characteristic is calculated by the rate of change of transaction amount data in continuous statistical period; and the external dependency characteristic is calculated by counting the number of records in the contract performance records where the applicant's performance is delayed, interrupted or failed due to the counterparty's failure to perform as agreed, and dividing the number of records by the total number of contract performance records, thereby completing the construction of unified risk characteristic data.

[0040] After generating unified risk characteristic data, this data is input into a simulation model, combining static core commitment information and dynamic performance commitment information for risk simulation analysis. The simulation model is constructed by organizing historical contract performance records, historical default records, and historical transaction flow data. Specifically, various historical data from the applicant's past performance processes are first collected. Then, the risk characteristic data in the historical data is labeled with the corresponding performance results to form model training data. Based on this, the relationship between the risk characteristic data and the performance results is determined, forming the simulation model. In practice, a time series simulation method is used. Static core commitment information is input as a fixed constraint into the simulation model, dynamic performance commitment information is input as a variable, and unified risk characteristic data is used as an input variable. Simulation calculations are performed periodically within a preset performance period. By updating the values ​​of the risk characteristic data at different time points, a sequence of simulation results at each time point is obtained, thus forming the applicant's risk change trend within the preset performance period.

[0041] After completing the risk simulation analysis, a quantitative risk estimate is generated based on the risk change trend. In this process, interval mapping and linear interpolation methods are used to quantify the magnitude of risk changes. First, the difference between the simulation results at each time point and the initial state is calculated, and this difference is normalized to a range of 0 to 1. Then, the normalized difference is divided into intervals according to a preset interval division rule. For example, the interval 0 to 0.2 corresponds to the scoring interval 20 to 40, the interval 0.2 to 0.5 corresponds to the scoring interval 40 to 70, and the interval 0.5 to 1 corresponds to the scoring interval 70 to 100. When the difference falls within a certain interval, linear interpolation is used... Interpolation is used to calculate the specific score corresponding to the change difference. This involves subtracting the lower limit of the interval from the change difference, dividing by the interval width, multiplying by the span of the corresponding score interval, and adding the lower limit of the score interval. For example, if the change difference is 0.3, falling within the 0.2 to 0.5 interval, the calculation process is as follows: first, subtract 0.2 from 0.3 to get 0.1; then, divide by 0.3 to get the proportion value 0.333; then, multiply by the score span of 30 to get 10; finally, add the lower limit of 40 to get the final risk score of 50. Simultaneously, the probability of default is obtained by statistically analyzing the number of performance failures during the simulation and dividing by the total number of simulations. The final calculation results are used as the quantitative risk estimate, where the quantitative risk estimate is either the probability of default or the risk score.

[0042] Step S400: Based on the quantitative risk estimate and the instantiated trusted commitment anchor on the blockchain, perform coupling verification to dynamically determine the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generate the corresponding guarantee issuance instruction.

[0043] In this embodiment, when performing coupled verification based on the quantified risk estimate and the instantiated trusted commitment anchor on the blockchain, the quantified risk estimate and the instantiated trusted commitment anchor on the blockchain are correlated and verified. Using the commitment constraints and corresponding anchoring granularity recorded in the instantiated trusted commitment anchor, a set of constraint clauses matching the quantified risk estimate is identified. Then, based on the parameter mapping relationship between the constraint clause set and the quantified risk estimate, parameters for the guarantee issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses are corrected and consistency verified. This enables dynamic adjustment of the issuance parameters, thereby dynamically determining the guarantee issuance amount, guarantee period, counter-guarantee conditions, or additional constraint clauses, and forming a set of issuance parameters that satisfy the constraint conditions. Finally, combining the issuance parameter set and the verification granularity constraints corresponding to the anchoring granularity, a corresponding guarantee issuance instruction is generated.

[0044] Furthermore, in the method provided in the application embodiment, the issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses of the guarantee are dynamically determined by coupling and verifying the quantitative risk estimate with the instantiated trusted commitment anchor point on the blockchain, and a corresponding guarantee issuance instruction is generated. This method also includes: The quantitative risk estimate is correlated and verified with the instantiated trusted commitment anchor on the blockchain. Using the commitment constraints and corresponding anchoring granularity recorded in the commitment anchor, a set of constraint clauses matching the quantitative risk estimate is identified. Based on the parameter mapping relationship between the constraint clause set and the quantitative risk estimate, parameters for the guarantee issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses are corrected and consistency verified to generate a set of issuance parameters that meet the constraint conditions. Finally, a guarantee issuance instruction is generated based on the issuance parameter set and the verification granularity constraints.

[0045] In this embodiment of the application, the quantitative risk estimate is first correlated with the instantiated trusted commitment anchor on the blockchain. The commitment constraints and corresponding anchoring granularity recorded in the commitment anchor are used to identify the set of constraint clauses that match the quantitative risk estimate. In this process, a condition matching method is employed. First, instantiated trusted commitment anchors are extracted from the blockchain, and the commitment constraints, anchoring granularity identifiers, and commitment response risk level identifiers recorded within them are read. Then, the quantified risk estimate is compared item by item with the corresponding value range of each commitment constraint. When the quantified risk estimate is the probability of default, it is matched with the probability threshold range in the commitment constraints. For example, a default probability between 0 and 0.2 matches a low-risk constraint, between 0.2 and 0.5 matches a medium-risk constraint, and above 0.5 matches a high-risk constraint. When the quantified risk estimate is the risk score, it is matched with the score range in the commitment constraints. For example, a risk score between 20 and 40 matches a low-risk constraint, between 40 and 70 matches a medium-risk constraint, and between 70 and 100 matches a high-risk constraint. After completing the range matching, the commitment constraints belonging to the summary level, field group level, field level, or condition chain level are then hierarchically filtered based on the corresponding anchoring granularity to identify the set of constraint clauses that match the quantified risk estimate.

[0046] Next, based on the parameter mapping relationship between the set of constraint clauses and the quantitative risk estimate, the issuance amount of the guarantee, the guarantee period, the counter-guarantee conditions, and the additional constraint clauses are adjusted and their consistency verified. In this process, a parameter mapping method is used. First, the pre-set mapping rules for the issuance amount of the guarantee, the guarantee period, the counter-guarantee conditions, and the additional constraint clauses in the constraint clause set are read. Then, the quantitative risk estimate is substituted into the corresponding mapping rules for calculation. For the issuance amount of the guarantee, it is adjusted according to the amount adjustment coefficient. For example, if the original issuance amount of the guarantee is RMB 1 million, and the amount adjustment coefficient corresponding to the quantitative risk estimate is 0.8, then the adjusted issuance amount of the guarantee is RMB 800,000. For the guarantee period, it is adjusted according to the period range mapping rule. For example, if the quantitative risk estimate is in the medium-risk range, the guarantee period is limited to within 6 months; if it is in the high-risk range, the guarantee period is limited to within 3 months. For the counter-guarantee conditions, a threshold is used... The trigger rules are modified accordingly. For example, when the quantitative risk estimate exceeds a preset threshold, an increase in the margin ratio or additional collateral is required. For additional constraint clauses, modifications are made according to the clause loading rules. For example, when the quantitative risk estimate is in a high-risk range, a clause restricting the use of funds or a clause reporting the performance status is added. After the above parameter modifications are completed, the consistency of the modified guarantee issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses and constraint clauses is checked item by item to determine whether all modified parameters fall within the scope of the commitment constraint conditions. If there are parameters that do not meet the constraint conditions, the modification is continued according to the mapping rules until all parameters meet the constraint conditions, thereby generating a set of issuance parameters that meet the constraint conditions.

[0047] Finally, based on the issuance parameter set and verification granularity constraints, a guarantee issuance instruction is generated. Specifically, a structured encapsulation method is used to first extract the guarantee issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses from the issuance parameter set. Then, the verification granularity constraints corresponding to the issuance parameter set are read, where the verification granularity constraints are used to determine the granularity level and scope of subsequent verification. Subsequently, according to the preset field order, the guarantee issuance amount, guarantee period, counter-guarantee conditions, additional constraint clauses, and verification granularity constraints are sequentially written into the instruction data structure to form complete instruction content. For example, when the verification granularity constraint corresponds to the field level, the range of fields that need to be verified field by field is recorded in the guarantee issuance instruction; when the verification granularity constraint corresponds to the condition chain level, the range of condition chains that need to be verified according to the condition triggering order is recorded in the guarantee issuance instruction. After completing the data writing, a standardized guarantee issuance instruction is output for subsequent electronic guarantee issuance processing.

[0048] Furthermore, the method provided in the application embodiments also includes: During the execution of the guarantee, a verification path is determined based on the anchoring granularity and the corresponding granularity index; based on the verification path, the summary data of the corresponding commitment unit is extracted from the blockchain, and the performance event data is selectively verified; based on the selective verification matching result, the performance event data and the issuance parameter set are verified for consistency, and the guarantee execution verification result is generated.

[0049] In this embodiment of the application, during the execution of the guarantee, the verification path is determined based on the anchoring granularity and the corresponding granularity index. Specifically, firstly, the anchoring granularity identifier and granularity index of the instantiated trusted commitment anchors registered on the blockchain are read. The anchoring granularity is used to indicate the data storage level corresponding to the commitment unit, and the granularity index is used to identify the specific position of the commitment unit in the tree digest structure. Subsequently, based on the business fields involved in the performance event data, the corresponding commitment unit is matched, and the target node path is located in the tree digest structure in combination with the granularity index. For example, when the anchoring granularity is at the field level, the corresponding field node path is directly located; when the anchoring granularity is at the field group level, the corresponding field combination node path is located, thereby determining the verification path corresponding to the current performance event data.

[0050] Next, based on the verification path, the digest data of the corresponding commitment units is extracted from the blockchain, and selective verification is performed on the performance event data. In this process, according to the determined verification path, the digest data of the corresponding nodes is read layer by layer from the blockchain ledger, including leaf node digests and their upper-level sub-digests, forming a complete digest verification chain. Subsequently, the performance event data is processed according to the same standardized encoding method as the commitment units, generating corresponding data digest values, and compared item by item with the digest data extracted from the blockchain. In this process, only the commitment units involved in the verification path are verified, rather than traversing all commitment units, thus achieving selective verification. Selective verification is used to filter commitment units directly related to the performance event data and complete matching verification.

[0051] After selective validation is completed, consistency verification is performed on the performance event data and the issuance parameter set based on the selective validation matching results. Specifically, it is first determined whether the performance event data passes the summary matching verification in selective validation. If the matching passes, the key fields in the performance event data are compared item by item with the guarantee issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses in the issuance parameter set. For example, it is determined whether the performance event occurrence time falls within the guarantee period, whether the performance amount exceeds the guarantee issuance amount, whether the counter-guarantee conditions are met, and whether the additional constraint clauses are met. When all fields meet the constraints of the issuance parameter set, a guarantee execution verification result is generated indicating that the execution has passed. When any field does not meet the constraints, a guarantee execution verification result of restricted execution, delayed execution, or refusal execution is generated according to the type of non-compliance, thereby completing the verification process of the guarantee execution stage.

[0052] Furthermore, in the method provided in the application embodiments, after generating the corresponding guarantee issuance instruction, it further includes: During the execution of the guarantee, the guarantee trigger request and corresponding performance event data are obtained. Based on the performance event data, the risk perception oracle network is invoked to obtain the applicant's current real-time status data, and a risk estimate for the execution stage is generated in conjunction with a preset simulation and deduction model. The performance event data and the risk estimate for the execution stage are correlated and verified with the instantiated trusted commitment anchor on the blockchain. The corresponding level of verification strategy is executed according to the granularity of the commitment anchor. The performance event data is also verified for consistency with the issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses determined in the guarantee issuance instruction. Based on the correlation verification results and the consistency verification results, a guarantee execution decision is determined. The execution decision includes execution, restricted execution, delayed execution, or refusal to execute, and the execution result is written into the blockchain for evidence storage.

[0053] In this embodiment, during the execution of the guarantee, when the guarantee trigger request and corresponding performance event data are obtained, the risk perception oracle network is first invoked based on the performance event data to obtain the applicant's current real-time status data, and a risk estimate for the execution stage is generated in conjunction with a preset simulation model. Specifically, the trigger type, trigger time, performance amount, performance object, and performance status contained in the guarantee trigger request are extracted to form performance event data; subsequently, the risk perception oracle network initiates real-time query requests to bank account data sources, transaction flow data sources, contract performance record data sources, and judicial enforcement record data sources to obtain the applicant's current real-time status data, and timestamps and verifies the consistency of the source of the current real-time status data, and then forms execution stage risk characteristic data according to preset field rules; then, the execution stage risk characteristic data is input into the preset simulation model, and static core commitment information is input as a fixed constraint condition, and dynamic performance commitment information is input as a changing condition condition, and simulation calculation is performed at the execution time node corresponding to the current performance event to output the applicant's default probability or risk score at the current performance stage, thereby generating the execution stage risk estimate.

[0054] After obtaining the risk estimate for the execution phase, the performance event data, the risk estimate for the execution phase, and the instantiated trusted commitment anchors on the blockchain are correlated and verified, and the corresponding level of verification strategy is executed according to the granularity of the commitment anchor. In this process, the instantiated trusted commitment anchor point corresponding to the current guarantee is first extracted from the blockchain, and the static core commitment field hash, dynamic performance commitment field hash, commitment response risk level identifier, anchoring granularity identifier, structure identifier, and granularity index recorded therein are read. Then, based on the business fields involved in the performance event data and the risk range corresponding to the risk estimate value of the execution stage, the corresponding commitment units are matched item by item, and the position of the commitment unit in the tree summary structure is located using the granularity index. After the position is located, the corresponding level of verification strategy is selected according to the anchoring granularity. When the anchoring granularity is at the summary level, the overall summary corresponding to the performance event data is verified. When the anchoring granularity is at the field group level, the field group summary involved in the performance event data is verified. When the anchoring granularity is at the field level, the single field summary involved in the performance event data is verified. When the anchoring granularity is at the condition chain level, the condition chain order and condition logic summary involved in the performance event data are verified. Under the selected verification strategy, The performance event data is standardized and encoded to generate an event summary. Then, the risk estimate of the execution phase is compared with the commitment constraints in the commitment anchor point item by item. The preset risk threshold range in the commitment constraints is read, and the risk estimate of the execution phase is substituted into the corresponding range for judgment. When the risk estimate of the execution phase falls into a certain risk threshold range, the commitment constraint is determined to be satisfied; otherwise, it is determined to be unsatisfied, and the corresponding matching result is recorded. Then, the event summary is recalculated according to the same encoding rules as when it is stored on the blockchain, and compared with the summary data of the corresponding commitment unit position in the blockchain one by one. When the two are completely consistent, it is determined that the commitment unit data has not changed; when there is a discrepancy, it is determined that the commitment unit data has changed or is mismatched. Finally, the risk threshold range matching result and the summary comparison result are combined for judgment. When both are satisfied, a passing association verification result is generated; when either result is not satisfied, a failing association verification result is generated, thus obtaining the association verification result.

[0055] After completing the correlation verification, the performance event data is compared with the issuance amount, guarantee period, counter-guarantee conditions, and additional binding clauses specified in the guarantee issuance instruction for consistency verification. Specifically, a rule-based verification method is used. First, the issuance amount, guarantee period, counter-guarantee conditions, and additional binding clauses recorded in the guarantee issuance instruction are parsed and converted into verification rules that can be compared item by item. Then, the performance amount in the performance event data is compared with the issuance amount to determine whether the performance amount is less than or equal to the issuance amount. The occurrence time of the performance event is compared with the guarantee period to determine whether the occurrence time of the performance event is within the guarantee period. The guarantee implementation status in the performance event data is compared with the counter-guarantee conditions to determine whether the counter-guarantee conditions are met. Finally, the execution status, reporting records, or usage information in the performance event data are compared item by item with the additional binding clauses to determine whether the additional binding clause requirements are met. After completing the above item-by-item comparison, the comparison results are summarized to form a consistency verification result.

[0056] Finally, based on the correlation verification results and the consistency verification results, the guarantee execution decision is determined. The execution decision includes execution, restricted execution, delayed execution, or refusal to execute, and the execution result is written to the blockchain for notarization. Specifically, a combination judgment rule for the correlation verification result and the consistency verification result is first set. When both the correlation verification result and the consistency verification result pass, the guarantee execution decision is determined to be execution; when the correlation verification result passes but some non-critical items in the consistency verification result fail, the guarantee execution decision is determined to be restricted execution; when the risk threshold in the correlation verification result exceeds the preset range but there is still a possibility of subsequent correction, the guarantee execution decision is determined to be delayed execution; when the correlation verification result fails or the critical items in the consistency verification result fail, the guarantee execution decision is determined to be refusal to execute. After the guarantee execution decision is determined, the guarantee number, performance event data summary, execution stage risk estimate, correlation verification result, consistency verification result, guarantee execution decision, and execution timestamp are encapsulated into execution result data according to the preset field order and submitted to the blockchain for writing. The blockchain completes on-chain registration and notarization, thereby forming a traceable execution result record.

[0057] In summary, the embodiments of this application have at least the following technical effects: This application analyzes the guarantee application data, extracting static core commitment information and dynamic performance commitment information; it analyzes the risk level corresponding to the commitment response of the static core commitment information and dynamic performance commitment information, analyzes the commitment anchoring granularity according to the commitment response risk level, establishes instantiated trusted commitment anchors, and deploys the instantiated trusted commitment anchors on the blockchain; it calls the risk perception oracle network, and based on the static core commitment information and dynamic performance commitment information, obtains real-time status data related to the applicant's performance capability from multiple external trusted data sources, and generates a quantitative risk estimate based on the real-time status data and a preset simulation model; it couples and verifies the quantitative risk estimate with the instantiated trusted commitment anchors on the blockchain, dynamically determines the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generates a corresponding guarantee issuance instruction. This invention addresses the technical problems in the existing technology of lacking effective verification of dynamic performance commitments, inaccurate risk assessment, and lack of traceability and credibility in the process of issuing guarantees. By introducing blockchain evidence storage technology, risk perception oracle network, and commitment response risk degree analysis, it achieves the technical effects of improving the accuracy and credibility of guarantee issuance, realizing dynamic risk monitoring, effectively responding to performance risks, and improving verification efficiency.

[0058] Example 2 is based on the same inventive concept as the blockchain-based electronic guarantee issuance and verification method described in the previous examples, such as... Figure 2 As shown, this application provides an electronic guarantee issuance and verification system based on blockchain evidence storage. The system and method embodiments in this application are based on the same inventive concept. The system includes: The data parsing module 11 is used to parse the guarantee application data and extract static core commitment information and dynamic performance commitment information; the risk analysis module 12 is used to analyze the risk level corresponding to the commitment response of the static core commitment information and dynamic performance commitment information, analyze the commitment anchoring granularity according to the commitment response risk level, establish an instantiated trusted commitment anchor point, and deploy the instantiated trusted commitment anchor point on the blockchain; the risk estimation module 13 is used to call the risk perception oracle network, obtain real-time status data related to the applicant's performance capability from multiple external trusted data sources based on the static core commitment information and dynamic performance commitment information, and generate a quantitative risk estimate value according to the real-time status data and the preset simulation and deduction model; the instruction generation module 14 is used to perform coupling verification based on the quantitative risk estimate value and the instantiated trusted commitment anchor point on the blockchain, dynamically determine the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generate the corresponding guarantee issuance instruction.

[0059] Furthermore, the system is also used to implement the following functions: During the execution of the guarantee, the guarantee trigger request and corresponding performance event data are obtained. Based on the performance event data, the risk perception oracle network is invoked to obtain the applicant's current real-time status data, and a risk estimate for the execution stage is generated in conjunction with a preset simulation and deduction model. The performance event data and the risk estimate for the execution stage are correlated and verified with the instantiated trusted commitment anchor on the blockchain. The corresponding level of verification strategy is executed according to the granularity of the commitment anchor. The performance event data is also verified for consistency with the issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses determined in the guarantee issuance instruction. Based on the correlation verification results and the consistency verification results, a guarantee execution decision is determined. The execution decision includes execution, restricted execution, delayed execution, or refusal to execute, and the execution result is written into the blockchain for evidence storage.

[0060] Furthermore, the system is also used to implement the following functions: The static core commitment information and dynamic performance commitment information are structurally decomposed to establish multiple commitment units. Based on the multiple commitment units, the financial impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity risk characteristics of each commitment unit are extracted. According to the financial impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity risk characteristics of each commitment unit, the corresponding commitment response risk is calculated according to the preset weighting rules or risk assessment model.

[0061] Furthermore, the system is also used to implement the following functions: According to the preset risk granularity segmentation rules, the granularity is determined based on the risk level of the commitment response of each commitment unit to obtain the anchoring granularity of each commitment unit. Different anchoring granularities correspond to digest-level, field group-level, field-level, or condition chain-level evidence storage structures. Based on the anchoring granularity and the corresponding evidence storage structure, normalized encoding processing is performed to generate corresponding hash digests, and instantiated trusted commitment anchors for each commitment unit are established.

[0062] Furthermore, the system is also used to implement the following functions: The instantiated trusted commitment anchor points include: static core commitment field hash, dynamic performance commitment field hash, commitment response risk level identifier and corresponding anchoring granularity identifier, and structural identifier representing the relationship between commitment units; wherein, different anchoring granularities correspond to different levels of data digest structure or field-level mapping structure.

[0063] Furthermore, the system is also used to implement the following functions: Multiple instantiated trusted commitment anchors are organized into a tree-like digest structure according to a preset hierarchical relationship, generating a root digest, sub-digests, and corresponding granularity indexes. The granularity indexes are used to identify the anchoring granularity of different commitment units and their positions in the tree structure. Anchor deployment data is generated based on the root digest, granularity index, risk identifier, version number, timestamp, and off-chain index address. The issuing entity digitally signs the anchor deployment data and submits it to the blockchain smart contract. The blockchain smart contract completes the signature verification, version verification, and anchor registration, and writes the corresponding anchor digest and granularity index onto the blockchain.

[0064] Furthermore, the system is also used to implement the following functions: The risk-aware oracle network acquires real-time status data from multiple external data sources, timestamps and verifies the consistency of the sources of this data, and generates unified risk characteristic data. This risk characteristic data includes at least one of the following: monetary impact characteristics, performance sensitivity characteristics, state fluctuation characteristics, and external dependency characteristics. The risk-aware oracle network is an off-chain service network deployed outside the blockchain. The risk characteristic data is input into a simulation model, and combined with the static core commitment information and dynamic performance commitment information, risk simulation analysis is performed. Based on the current risk characteristic data, the model simulates the applicant's performance capability change trend within a preset performance period, outputting the default probability or risk score, and generating the quantitative risk estimate.

[0065] Furthermore, the system is also used to implement the following functions: The quantitative risk estimate is correlated and verified with the instantiated trusted commitment anchor on the blockchain. Using the commitment constraints and corresponding anchoring granularity recorded in the commitment anchor, a set of constraint clauses matching the quantitative risk estimate is identified. Based on the parameter mapping relationship between the constraint clause set and the quantitative risk estimate, parameters for the guarantee issuance amount, guarantee period, counter-guarantee conditions, and additional constraint clauses are corrected and consistency verified to generate a set of issuance parameters that meet the constraint conditions. Finally, a guarantee issuance instruction is generated based on the issuance parameter set and the verification granularity constraints.

[0066] Furthermore, the system is also used to implement the following functions: During the execution of the guarantee, a verification path is determined based on the anchoring granularity and the corresponding granularity index; based on the verification path, the summary data of the corresponding commitment unit is extracted from the blockchain, and the performance event data is selectively verified; based on the selective verification matching result, the performance event data and the issuance parameter set are verified for consistency, and the guarantee execution verification result is generated.

[0067] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for issuing and verifying electronic guarantees based on blockchain-based evidence storage, characterized in that, include: Analyze the guarantee application data to extract static core commitment information and dynamic performance commitment information; The static core commitment information and dynamic performance commitment information are analyzed for the corresponding risk degree of the commitment response. The commitment anchoring granularity is analyzed according to the commitment response risk degree. An instantiated trusted commitment anchor points are established and deployed on the blockchain. The risk perception oracle network is invoked to obtain real-time status data related to the applicant's performance capability from multiple external trusted data sources based on the static core commitment information and dynamic performance commitment information. Based on the real-time status data and the preset simulation and deduction model, a quantitative risk estimate is generated. The quantitative risk estimate is coupled and verified with the instantiated trusted commitment anchor on the blockchain to dynamically determine the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generate the corresponding guarantee issuance instruction.

2. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 1, characterized in that, After generating the corresponding guarantee issuance instruction, the following is also included: During the execution of the guarantee, the guarantee trigger request and corresponding performance event data are obtained. Based on the performance event data, the risk perception oracle network is called to obtain the applicant's current real-time status data, and a risk estimate value for the execution stage is generated in combination with a preset simulation and deduction model. The performance event data and the risk estimate of the execution stage are correlated and verified with the instantiated trusted commitment anchor on the blockchain. The corresponding level of verification strategy is executed according to the granularity of the commitment anchor. The performance event data is also verified for consistency with the issuance amount, guarantee period, counter-guarantee conditions and additional constraint clauses determined in the guarantee issuance instruction. Based on the correlation verification results and the consistency verification results, the execution decision of the guarantee is determined. The execution decision includes execution, restricted execution, delayed execution, or refusal to execute, and the execution result is written into the blockchain for evidence storage.

3. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 1, characterized in that, The static core commitment information and dynamic performance commitment information are analyzed for corresponding risk levels in the commitment response, including: The static core commitment information and dynamic performance commitment information are decomposed into a structured form to establish multiple commitment units; Based on multiple commitment units, the risk characteristics of each commitment unit, including its monetary impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity, are extracted. Based on the financial impact, performance sensitivity, fluctuation frequency, external dependence, and trigger intensity risk characteristics of each commitment unit, the corresponding commitment response risk level is calculated according to preset weighting rules or risk assessment models.

4. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 3, characterized in that, The commitment anchoring granularity is analyzed according to the risk level of the commitment response, and instantiated trustworthy commitment anchors are established, including: According to the preset risk granularity segmentation rules, the granularity is determined based on the risk level of the commitment response of each commitment unit to obtain the anchoring granularity of each commitment unit. Different anchoring granularities correspond to the summary level, field group level, field level or condition chain level evidence storage structure. Based on the anchoring granularity and the corresponding evidence storage structure, normalized encoding processing is performed and a corresponding hash digest is generated to establish an instantiated trusted commitment anchor point for each commitment unit.

5. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 4, characterized in that, The instantiated trusted commitment anchor points include: static core commitment field hash, dynamic performance commitment field hash, commitment response risk level identifier and corresponding anchoring granularity identifier, and structural identifier representing the relationship between commitment units; wherein, different anchoring granularities correspond to different levels of data digest structure or field-level mapping structure.

6. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 5, characterized in that, And deploy the instantiated trusted commitment anchor on the blockchain, including: Multiple instantiated trusted commitment anchors are organized into a tree-structured summary according to a preset hierarchical relationship, generating a root summary, sub-summaries, and corresponding granularity indexes. The granularity indexes are used to identify the anchoring granularity of different commitment units and their positions in the tree structure. Anchor deployment data is generated based on the root digest, granularity index, risk level identifier, version number, timestamp, and off-chain index address. The issuing entity digitally signs the anchor deployment data and submits it to the blockchain smart contract. The signature verification, version verification, and anchor registration are completed by the blockchain smart contract, and the corresponding anchor summary and granularity index are written to the blockchain.

7. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 2, characterized in that, Generate quantitative risk estimates, including: The risk-aware oracle network obtains real-time status data from multiple external data sources, timestamps the real-time status data and verifies the consistency of the sources, and generates unified risk feature data. The risk feature data includes at least one of the following: monetary impact feature, performance sensitivity feature, status fluctuation feature and external dependency feature. The risk-aware oracle network is an off-chain service network deployed outside the blockchain. The risk characteristic data is input into the simulation model, and risk simulation analysis is carried out in combination with the static core commitment information and dynamic performance commitment information. Specifically, the applicant's performance capability change trend is simulated based on the current risk characteristic data within the preset performance period, and the default probability or risk score is output to generate the quantitative risk estimate.

8. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 7, characterized in that, Based on the coupled verification of the quantitative risk estimate and the instantiated trusted commitment anchor point on the blockchain, the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional binding clauses are dynamically determined, and corresponding guarantee issuance instructions are generated, including: The quantitative risk estimate is correlated and verified with the instantiated trusted commitment anchor on the blockchain. The commitment constraints and corresponding anchoring granularity recorded in the commitment anchor are used to identify the set of constraint clauses that match the quantitative risk estimate. Based on the parameter mapping relationship between the set of constraint clauses and the quantitative risk estimate, the parameters of the guarantee issuance amount, guarantee period, counter-guarantee conditions and additional constraint clauses are corrected and consistency verified to generate a set of issuance parameters that meet the constraint conditions. Based on the set of issuance parameters and the verification granularity constraints, a guarantee issuance instruction is generated.

9. The method for issuing and verifying electronic guarantees based on blockchain evidence storage according to claim 8, characterized in that, Also includes: During the execution of the guarantee, the verification path is determined based on the anchoring granularity and the corresponding granularity index; Based on the verification path, the summary data of the corresponding commitment unit is extracted from the blockchain, and the performance event data is selectively verified. Based on the selective verification matching results, the consistency verification between the performance event data and the issuance parameter set is performed to generate the guarantee execution verification result.

10. An electronic guarantee issuance and verification system based on blockchain evidence storage, characterized in that, The system is used to execute the electronic guarantee issuance and verification method based on blockchain evidence storage as described in any one of claims 1-9, and the system includes: The data parsing module is used to parse the guarantee application data and extract static core commitment information and dynamic performance commitment information; The risk analysis module is used to analyze the risk level of the static core commitment information and dynamic performance commitment information corresponding to the commitment response, analyze the commitment anchoring granularity according to the commitment response risk level, establish instantiated trusted commitment anchors, and deploy the instantiated trusted commitment anchors on the blockchain. The risk estimation module is used to call the risk perception oracle network, based on the static core commitment information and dynamic performance commitment information, to obtain real-time status data related to the applicant's performance capability from multiple external trusted data sources, and to generate a quantitative risk estimate value based on the real-time status data and the preset simulation and deduction model. The instruction generation module is used to perform coupled verification based on the quantitative risk estimate and the instantiated trusted commitment anchor on the blockchain, dynamically determine the guarantee issuance amount, guarantee period, counter-guarantee conditions or additional constraint clauses, and generate corresponding guarantee issuance instructions.

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