Electronic guarantee letter whole-process management and control method and system

Abstract

The application discloses an electronic guarantee full-process management and control method and system, relates to the technical field of electronic guarantee, and has the technical scheme as follows: obtaining business data information of an electronic guarantee business in a development process and basic information of the electronic guarantee business; performing risk level evaluation on the electronic guarantee business according to the business data information to obtain a risk level evaluation result, dividing an electronic guarantee business process according to the risk level evaluation result to obtain a business process segmentation result set; auditing the basic information of the electronic guarantee business to obtain an information auditing result, and performing node management and control on each segmented process in the business process segmentation result set according to the information auditing result and the risk level evaluation result to obtain a node management and control execution result set; and selecting a key verification node set from each segmented process in the business process segmentation result set, so that the compliance, efficiency and stability of electronic guarantee business operation are improved.

Description

Technical Field

[0001] This invention relates to the field of electronic guarantee technology, and more specifically, to a method and system for the whole-process management of electronic guarantees. Background Technology

[0002] Electronic guarantees are financial instruments issued in electronic form, utilizing digital technology to digitize the entire process from application, review, issuance, storage, and verification. They replace traditional paper guarantees and provide assurance for the performance of obligations by parties in economic activities. However, the rapid development of electronic guarantee business faces challenges such as difficult process control, insufficient accuracy in risk identification, and inadequate data utilization. Traditional control models rely heavily on human experience and have weak capabilities in integrating and analyzing data across the entire business process, making it difficult to accurately identify potential risks in complex business scenarios, such as applicant qualification fraud, abnormal cash flow, and loopholes in guarantee terms. Existing control systems lack comprehensive coverage of logical verification of process nodes and multi-dimensional effect evaluation, failing to form a complete closed loop from risk identification to control execution and effect verification, thus hindering continuous business optimization. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for full-process management of electronic guarantees.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A comprehensive management method for electronic guarantees, comprising the following steps:

[0006] To obtain business data and basic information about electronic guarantee business during its implementation;

[0007] Based on business data information, a risk level assessment is conducted on the electronic guarantee business to obtain the risk level assessment result. Based on the risk level assessment result, the electronic guarantee business process is divided to obtain a business process segmentation result set.

[0008] The basic information of the electronic guarantee business is reviewed to obtain the information review results. Based on the information review results and the risk level assessment results, the node control execution result set is obtained by performing node control execution on each segment of the business process segment result set.

[0009] Select key verification node sets from each segment of the business process segmentation result set, and extract the control and detection dataset from each segment of the business process segmentation result set.

[0010] The control effectiveness of each segment of the business process in the segmented result set is evaluated based on the control detection dataset and the node control execution result set to obtain the control evaluation result set to be verified.

[0011] The control dimension set is set according to the key verification node set, and the data collection and verification are performed on each segment of the business process segmentation result set according to the key verification node set to obtain the collection dataset.

[0012] The process control evaluation set is obtained by processing and analyzing the control dimension set, the collected dataset, the key verification node set, and the business process segmentation result set.

[0013] The evaluation results of the entire process control of electronic guarantee business are obtained by comprehensively evaluating the set of control assessment results to be verified and the set of process control assessment results.

[0014] The electronic guarantee end-to-end management system includes:

[0015] Acquisition Module: Acquires business data and basic information about electronic guarantee transactions during the process of implementation;

[0016] Assessment and segmentation module: Based on business data information, the risk level of electronic guarantee business is assessed to obtain the risk level assessment result. Based on the risk level assessment result, the electronic guarantee business process is segmented to obtain the business process segmentation result set.

[0017] Processing module: It reviews the basic information of electronic guarantee business to obtain information review results, and performs node control execution on each segment of the business process based on the information review results and risk level assessment results to obtain node control execution result set;

[0018] Module selection: Select key verification node sets from each segment of the business process segmentation result set, and extract the control and detection dataset from each segment of the business process segmentation result set.

[0019] Evaluation module: Based on the control and control detection dataset and the node control and control execution result set, the control and control effect of each segment process in the business process segment result set is evaluated to obtain the control and control evaluation result set to be verified;

[0020] The data collection and verification module sets a control dimension set based on the key verification node set, and collects and verifies data from each segment of the business process segmentation result set based on the key verification node set to obtain the data collection dataset.

[0021] Processing and Analysis Module: Processes and analyzes the control dimension set, collected dataset, key verification node set, and business process segmentation result set to obtain the process control evaluation set;

[0022] Control and Evaluation Module: After comprehensively evaluating the control and evaluation result set to be verified and the process control and evaluation set, the full-process control and evaluation result of electronic guarantee business is obtained.

[0023] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for full-process control of electronic guarantees.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] This invention constructs a refined management system covering the entire business lifecycle and multi-dimensional collaboration for the whole-process control of electronic guarantees, demonstrating significant beneficial effects. From a risk control perspective, by acquiring business data and basic information, and using a risk assessment model to accurately identify risk levels, it achieves layered risk management, proactively intercepts potential risk points, and reduces the probability of default, fraud, and other risks in electronic guarantee business from the source. In terms of process control, business processes are dynamically divided according to risk levels, and node control rule parameters are adjusted. Precise control is implemented at key nodes, ensuring sufficient control resources for high-risk businesses while releasing efficiency space for low-risk businesses, achieving a balance between risk control and business efficiency. The full-process control evaluation results generated by this method provide a clear direction for business optimization, helping enterprises accurately identify process shortcomings (such as potential problems in qualification review and fund verification), and adjust control measures accordingly to improve the compliance, efficiency, and stability of electronic guarantee business operations. Attached Figure Description

[0026] Figure 1 This is a schematic diagram illustrating the steps of the electronic guarantee full-process management method proposed in this invention;

[0027] Figure 2 This is a schematic diagram of the modules of the electronic guarantee full-process management system proposed in this invention;

[0028] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0029] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0030] Reference Figures 1 to 3 .

[0031] Example 1 further illustrates the electronic guarantee full-process management method and system proposed in this invention.

[0032] A comprehensive management method for electronic guarantees, comprising the following steps:

[0033] To obtain business data and basic information about electronic guarantee business during its implementation;

[0034] Based on business data information, a risk level assessment is conducted on the electronic guarantee business to obtain the risk level assessment result. Based on the risk level assessment result, the electronic guarantee business process is divided to obtain a business process segmentation result set.

[0035] The basic information of the electronic guarantee business is reviewed to obtain the information review results. Based on the information review results and the risk level assessment results, the node control execution result set is obtained by performing node control execution on each segment of the business process segment result set.

[0036] Select key verification node sets from each segment of the business process segmentation result set, and extract the control and detection dataset from each segment of the business process segmentation result set.

[0037] The control effectiveness of each segment of the business process in the segmented result set is evaluated based on the control detection dataset and the node control execution result set to obtain the control evaluation result set to be verified.

[0038] The control dimension set is set according to the key verification node set, and the data collection and verification are performed on each segment of the business process segmentation result set according to the key verification node set to obtain the collection dataset.

[0039] The process control evaluation set is obtained by processing and analyzing the control dimension set, the collected dataset, the key verification node set, and the business process segmentation result set.

[0040] The evaluation results of the entire process control of electronic guarantee business are obtained by comprehensively evaluating the set of control assessment results to be verified and the set of process control assessment results.

[0041] The electronic guarantee business involves data from multiple stages. Business data information reflects the dynamics of the process (such as application progress, fee payment records, etc.), while basic information is the premise for the business (such as applicant qualifications, guarantee subject matter, etc.). Both provide data for subsequent risk assessment and process control.

[0042] For example, when a construction company applies for an electronic bid bond, the business data information includes the time when the company submits the application, past records of bid bond usage (such as whether there have been any breaches of contract that resulted in the payment of the bid bond); the basic information includes the company's business license, qualification certificate, and basic information about the bidding project (project amount, construction period requirements), etc.

[0043] Based on business data, risk assessment models (such as logistic regression, decision tree algorithms, or industry risk rules) are used to identify the potential risks of electronic guarantee business and then classify the risk levels.

[0044] For example, decision trees break down complex risk issues into a series of simple "yes / no" judgment rules by performing "hierarchical decision-making" on business data. Data is segmented layer by layer in a tree structure, with each internal node corresponding to a business data feature (such as "whether the debt-to-asset ratio is >70%), and leaf nodes corresponding to risk levels. The optimal segmentation feature is selected based on data purity (such as the GINI coefficient and information entropy) to construct classification rules.

[0045] Select business data features that are strongly related to performance risk, such as enterprise qualification level F1 (Level 1 / Level 2 / Level 3), project delinquency rate in the past year F2 (>5% / ≤5%), guarantee amount to net assets ratio F3 (>40% / ≤40%), shareholder background F4 (state-owned enterprise / private enterprise / foreign-invested enterprise), etc.

[0046] Use historical performance guarantee default data to train decision tree generation rules. For example:

[0047] Root node: Is F1 a first-level node? → Yes → Enter node 2; No → Enter node 3.

[0048] Node 2: Is F2 ≤ 5%? → Yes → Low risk; No → Medium risk.

[0049] Node 3: Is F3 > 40%? → Yes → High risk; No → Proceed to Node 4.

[0050] Node 4: Is F4 a state-owned enterprise? → Yes → Medium risk; No → High risk.

[0051] Risk Level Assessment: A certain applicant company's business data is "Level 2 qualification (F1=Level 2), overdue rate 3% (F2≤5%), guarantee amount / net assets = 35% (F3≤40%), private enterprise (F4=private enterprise)". Substitute this into the decision tree:

[0052] F1 = Level 2 → to Node 3 → F3 = 35% ≤ 40% → to Node 4 → F4 = Private Enterprise → Determined as High Risk Level.

[0053] The business processes are then broken down according to risk levels. For high-risk businesses, the front-end process nodes are refined and control is strengthened, while for low-risk businesses, the processes are simplified to improve efficiency.

[0054] The basic information (such as the applicant's qualification documents and the compliance of the guarantee terms) is reviewed for compliance and authenticity. The control rules of each process node are dynamically adjusted based on the risk level results (such as setting stricter review standards and adding review steps for high-risk business nodes). After the node control is implemented, the execution status of each node (whether it is passed, the time taken, etc.) is recorded to form a node control execution result set.

[0055] The key verification node set is formed by selecting nodes that play a critical role in business compliance and risk control from the segmented process (such as the guarantee amount confirmation node and the beneficiary confirmation node); at the same time, data related to the control objectives in each segmented process (such as node execution time, amount involved, and participants) are extracted to form a control monitoring dataset.

[0056] The control and monitoring dataset (reflecting the actual execution data of the process) is compared and analyzed with the node control and monitoring execution result set (reflecting the node control and monitoring actions and results). Statistical methods (such as deviation rate calculation and compliance rate statistics) are used to evaluate the control and monitoring effect of each segment of the process to determine whether the risk prevention and control and process compliance goals have been achieved, thus forming a control and monitoring evaluation result set to be verified.

[0057] Based on the key verification node set (such as being divided into "funding, compliance, and qualification" according to node type), different control dimensions are set (such as the funding control dimension focusing on amount and flow; the compliance control dimension focusing on the compliance of terms and processes). Based on these dimensions, multi-perspective data collection (such as collecting transaction data of funding nodes and file archive data of compliance nodes) and verification (comparing with standard requirements) are carried out on segmented processes to form a collection dataset covering all dimensions.

[0058] The control dimension set (defining the evaluation perspective), the collected dataset (actual data), the key verification node set (core verification object), and the business process segment result set (process framework) are correlated and analyzed. The effectiveness of process control in each segment is evaluated from multiple dimensions (such as whether high-risk nodes are effectively controlled from the risk prevention dimension and whether the node time consumption is reasonable from the efficiency dimension), and the process control evaluation set is output.

[0059] By integrating the set of control assessment results to be verified (preliminary effect assessment) with the set of process control assessment (multi-dimensional in-depth assessment), the final evaluation of the overall control level of electronic guarantee is carried out, and the overall control effectiveness and improvement direction are clarified.

[0060] The process control evaluation set is obtained by processing and analyzing the control dimension set, the collected dataset, the key verification node set, and the business process segmentation result set. The specific steps include:

[0061] The first logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the first control dimension of the control dimension set and verifying the logical relationships between the key verification node sets; the second logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the second control dimension of the control dimension set and verifying the logical relationships between the key verification node sets; the third logical verification dataset is obtained by verifying the logical relationships between the nodes in the first segmented process internal data information set based on the data collection of the third key verification node;

[0062] The collected dataset includes a first collection verification set, a second collection verification set, and a third collection verification set;

[0063] The first control evaluation result set is obtained by evaluating the control effect of each segment process in the business process segmentation result set based on the first collection verification set and the first logical verification dataset. The second control evaluation result set is obtained by evaluating the control effect of each segment process in the business process segmentation result set based on the second collection verification set and the second logical verification dataset. The third control evaluation result set is obtained by evaluating the control effect of each segment process in the business process segmentation result set based on the third logical verification dataset and the third collection verification set.

[0064] Among them, the first control assessment result set, the second control assessment result set, and the third control assessment result set are combined to form the process control assessment set.

[0065] In this application's electronic guarantee full-process control system, the process control assessment set generation stage first analyzes the node execution data (such as the review status, time consumption, and results of each stage) of the segmented process based on preset control dimensions (such as qualification compliance, capital risk control, and different observation perspectives of the entire process risk). At the same time, it verifies the logical relationship between key verification nodes (for example, "qualification review passed" should precede "margin verification" to ensure the reasonable process sequence) and generates a corresponding dimension's logical verification dataset. Then, relying on the verification data collected in the early stages according to dimensions (such as scanned copies of qualification certificates and margin bank statements), the collected information is cross-compared with the logical verification results to judge the control effect of the segmented process from a single-dimensional perspective, forming a hierarchical assessment result (first control assessment result set, second control assessment result set, and third control assessment result set). Finally, it integrates multi-dimensional conclusions to output an assessment set (process control assessment set) covering the entire process control perspective, providing accurate diagnosis for risk prevention and process optimization in electronic guarantee business.

[0066] Taking construction company A's application for an electronic bid bond as an example: In the first control dimension (from the perspective of qualification compliance), we first focus on the execution data of the "qualification review node" in the front-end process (company A's qualification has 3 months left on its validity period, the review took 2 hours and was conditionally approved), and at the same time verify the logical order of the qualification review node and the subsequent bid bond verification node (the qualification review is executed first, which conforms to the rule of "reviewing qualifications first and then verifying funds") to generate the first logical verification dataset (recording the compliance of qualification review, process order and time consumption issues); then, combined with the first collection verification set (including scanned copies of qualification certificates, manual review opinions, etc.), we evaluate and conclude that "under the qualification control dimension, the segmented process has a moderate control effect due to the risk of qualification validity period and the time limit exceeding the limit".

[0067] In the second control dimension (fund risk control perspective), the execution data of the "margin verification node" in the segmented process is determined (the 8 million margin is credited on time, the amount matches, and the review takes 1 hour). The logical order of the margin verification node and the risk assessment node is verified (margin verification precedes risk assessment, which complies with the pre-verification rule of funds). A second logical verification dataset is generated (reflecting the compliance of fund node execution and the smoothness of the process). The second collection verification set (covering margin bank statements and system verification logs) is linked to evaluate and obtain the second control evaluation result set of "under the fund control dimension, the segmented process has excellent control effect due to data compliance and no process deviation".

[0068] In the third control dimension (full-process risk perspective), relying on the third key verification node (risk assessment node), internal data of the front-end process (qualification validity period, margin payment information, risk scoring basis) is collected to verify the logical relationship between the risk assessment node and the qualification review and margin verification nodes (risk assessment is executed after the basic node, and the data association is reasonable) to generate the third logical verification dataset (reflecting the matching of risk assessment and basic data); combined with the third collection verification set (including risk assessment report and process node data summary), the third control assessment result set is obtained: "Under the full-process risk dimension, the segmented process has a good control effect because the risk assessment basis is sufficient but the qualification continuation needs to be paid attention to".

[0069] Finally, the results sets of the first, second, and third control assessments are integrated to form a process control assessment set, which outputs a multi-dimensional conclusion: "Qualification control needs to be optimized to reduce time consumption and validity period risks; fund control is efficient; and the overall process risk assessment is reasonable but has potential hidden dangers."

[0070] Based on the risk level assessment results, the electronic guarantee business process is divided into segments, resulting in a set of business process segments, which specifically includes the following steps:

[0071] The risk level assessment results include Level 1 risk, Level 2 risk, and Level 3 risk; among them, the risk level of Level 1 risk is greater than that of Level 2 risk, and the risk level of Level 2 risk is greater than that of Level 3 risk.

[0072] The electronic guarantee business process is divided into a front-end business process and a back-end business process.

[0073] If the risk level assessment result is Level 1 risk, then the number of nodes in the front-end business process is made greater than the number of nodes in the back-end business process to obtain the first business process segmentation result, and at this time the difference between the number of nodes in the front-end business process and the number of nodes in the back-end business process is the difference in the number of nodes in the first business process.

[0074] If the risk level assessment result is level two risk, then the number of nodes in the front-end business process is made greater than the number of nodes in the back-end business process to obtain the second business process segmentation result. At this time, the difference between the number of nodes in the front-end business process and the number of nodes in the back-end business process is the second node difference, which is less than the first node difference.

[0075] If the risk level assessment result is level three risk, then the number of nodes in the front-end business process is equal to the number of nodes in the back-end business process to obtain the third business process segmentation result.

[0076] The results of the first business process segmentation, the second business process segmentation, and the third business process segmentation are combined to form the business process segmentation result set.

[0077] In electronic guarantee business, the business process is divided based on the risk level assessment results. The core purpose is to achieve a precise match between risk and control resources, and to ensure that the business process can be properly controlled under different risk levels.

[0078] The risk assessment results are divided into three levels: Level 1, Level 2, and Level 3, with the risk level decreasing in that order. Different risk levels will determine the strategies for subsequent business process segmentation.

[0079] The electronic guarantee business process is divided into a front-end process and a back-end process. The front-end process usually involves key steps such as application review, risk assessment, and implementation of counter-guarantees, which is the stage for identifying and initially controlling business risks. The back-end process mainly includes operations such as guarantee issuance and final archiving, which are relatively less risky and focus more on the execution and completion of the process.

[0080] A risk assessment result of Level 1 indicates that the business faces high risk. In this case, to more effectively control risk, the number of nodes in the front-end business process will be greater than the number of nodes in the back-end business process, forming the Level 1 business process segmentation result. For example, if construction company A has serious credit problems, such as multiple default records and deteriorating financial condition, it is assessed as Level 1 risk. The front-end business process may include more nodes such as background checks on senior management, project feasibility assessments, and more credit checks on related parties, while the back-end business process will have fewer nodes as usual, such as only retaining basic guarantee format review and electronic signature confirmation. The difference in the number of nodes between the front-end and back-end business processes is the Level 1 node difference. This difference is relatively large, reflecting the strategy of investing more control resources in high-risk businesses upfront.

[0081] If the risk assessment result is Level 2 risk, it indicates that the business risk is at a moderate level. In this case, the number of nodes in the front-end business process will still be greater than the number of nodes in the back-end business process, resulting in a Level 2 business process segmentation. However, unlike Level 1 risk, the difference in the number of nodes in the Level 2 risk is smaller than that in the Level 1 risk. Continuing with the example of construction company A, if it only has some minor risk factors, such as delays in individual projects or less-than-ideal financial indicators, it will be assessed as Level 2 risk. The front-end business process might increase its focus on the validity of its qualifications and the review of supplementary project performance documentation. The back-end business process remains the same, but the difference in the number of nodes between the front and back ends will be smaller than in the Level 1 risk scenario, indicating that the intensity of early-stage control has been appropriately reduced under relatively controllable risk conditions.

[0082] A risk level assessment result of Level 3 indicates low business risk. In this case, the number of nodes in the front-end business process equals the number of nodes in the back-end business process, forming the Level 3 business process segmentation result. Assume construction company A has good credit, has successfully completed all past projects, has a sound financial condition, and meets all requirements, thus being assessed as Level 3 risk. In this case, both the front-end and back-end business processes might only have some basic review and operational nodes. For example, the front-end might simply verify the company's qualifications and the completeness of the tender documents, while the back-end might generate and confirm the delivery of the guarantee. The control over the front-end and back-end processes would be essentially the same to ensure efficient business progress under low-risk conditions.

[0083] The final results of the first, second, and third business process segmentations are combined to form a business process segmentation result set. This result set covers business process segmentation strategies under different risk levels, providing an important basis for subsequent implementation of node control based on specific risk situations and ensuring the safe and efficient operation of electronic guarantee business. This approach achieves refined management of the electronic guarantee business process, enabling control measures to be flexibly adjusted according to the degree of risk, achieving a balance between risk prevention and business efficiency.

[0084] Based on the information review results and risk level assessment results, the node control execution result set is obtained by performing node control execution on each segment of the business process segmentation result set, which specifically includes the following steps:

[0085] Based on the information review results and risk level assessment results, the node control rule parameters of each segment of the business process in the segmented result set are adjusted to obtain the parameter adjustment results;

[0086] Obtain the key node positions of each segment in the business process segmentation result set, and perform node control execution on each segment in the business process segmentation result set based on the parameter adjustment results and the key node positions of each segment. Then output the segment process node control execution result set.

[0087] The node control execution result set is output after evaluating the deviation between the node execution data and the standard execution data of the segmented process node control execution result set and the execution effect of key nodes within each segmented process.

[0088] In the electronic guarantee full-process management system, this application is a key link in achieving precise risk prevention and control by “reviewing information and risk level, executing node control and generating result set”. Its operation logic can be summarized as a closed-loop management of “rule adaptation - precise execution - effect verification”. Specifically, the process first uses the information review results (i.e., the compliance determination of the basic information of electronic guarantee business, such as whether the enterprise's qualifications are valid and whether the counter-guarantee is compliant) and the risk level assessment results (such as the business risk being level two) to dynamically adjust the node control rule parameters of each segment of the business process (such as review standards, operation permissions, time requirements, etc.) to ensure that the control intensity is adapted to the business risk and compliance status. Next, it identifies the key nodes of each segment of the process (the links that play a decisive role in risk prevention and control and business compliance), implements targeted control operations on these key nodes according to the adjusted rule parameters, and records the execution process and data to form a segment process node control execution result set. Finally, it compares the actual execution data of the nodes with the standard execution data to evaluate the deviation, and at the same time examines the execution effect of the key nodes within each segment of the process (whether it effectively prevents and controls risks and ensures business compliance), further improves and outputs the final node control execution result set, and provides accurate data support for subsequent full-process control assessment.

[0089] Taking construction company A's application for a bid bond for an urban complex project as an example: Risk assessment determined company A to be at level two risk due to its expiring qualification and the presence of certain risks associated with its affiliated companies. Information review revealed that its qualification certificate required a supplementary "Qualification Renewal Commitment Letter," but its counter-guarantee (equity pledge + corporate joint guarantee) was compliant. Based on this information review result and risk level, the control rules and parameters for the first stage of the business process (application review stage, including qualification review, bid bond verification, etc.) and the second stage (bid bond issuance stage) were adjusted. For example, the original rule for the "qualification review stage" in the first stage was "single-person review, complete and compliant materials are sufficient for approval, with a time threshold of 1 hour." The adjustment changed it to "double-person review (initial reviewer + senior reviewer), requiring an additional 'Qualification Renewal Commitment Letter,' with the time threshold relaxed to 2 hours," thereby strengthening the control of high-risk points and resulting in parameter adjustments.

[0090] The key nodes in the upstream process were identified as the "Qualification Review Node (focusing on qualification validity risk)" and the "Deposit Verification Node (focusing on the matching of amount with project budget)". Based on parameter adjustments, control measures were implemented for these key nodes. The qualification review node was reviewed by two people, with the auditor requiring Company A to supplement a commitment letter. The node was recorded as "Review passed (conditionally), time taken 1.5 hours, commitment letter attached". The deposit verification node, due to the absence of abnormalities in the information review (deposit of 8 million RMB arrived on time, compliant with the project budget ratio) and a medium risk level, was reviewed by a single person using relatively simplified rules. The record was "Review passed, time taken 0.5 hours, amount matches project requirements". These execution data were then integrated to output a set of segmented process node control execution results.

[0091] Finally, an evaluation of the execution effectiveness was conducted. Firstly, the deviations between the execution data of each node and the standard execution data were compared. The qualification review node took 1.5 hours, which did not exceed the adjusted 2-hour threshold, and the review result was "conditionally passed," meeting the information review requirements without significant deviations. The margin verification node took 0.5 hours, within the standard threshold of 1 hour, and the result was consistent with the standard. Secondly, the execution effectiveness of key nodes was examined. The qualification review node, through the attached commitment letter, effectively mitigated the risk of qualification expiration (requiring Company A to complete qualification renewal before December 2023). The margin verification node confirmed the matching amount, ensuring the compliance of the project's funding logic. The comprehensive deviation and effectiveness evaluation improved the node control execution result set, marking conclusions such as "qualification review node conditionally passed but risk is controllable" and "margin verification node execution is efficient and compliant." This provides detailed evidence for subsequent process control evaluations (such as verifying the effectiveness of qualification control dimensions), achieving a complete closed loop from risk identification to control execution and effectiveness verification, ensuring the efficient advancement of electronic guarantee business under controllable risks.

[0092] Based on the key verification node set, data is collected and verified for each segment of the business process in the segmented result set to obtain the collected dataset. The specific steps include:

[0093] The first control dimension, the second control dimension, and the third control dimension are set according to the first key verification node, the second key verification node, and the third key verification node, respectively;

[0094] Based on the first control dimension, the forward key node data direction between each segment process in the business process segmentation result set and the electronic guarantee business standard process is marked as the first data collection direction. Data collection and verification are performed on the corresponding segment processes according to the first data collection direction to obtain the first collection and verification set.

[0095] Based on the second control dimension, the reverse key node data direction between each segment process in the business process segmentation result set and the electronic guarantee business standard process is marked as the second data collection direction. Data collection and verification are performed on the corresponding segment processes according to the second data collection direction to obtain the second collection and verification set.

[0096] Based on the third control dimension, the data direction of all key nodes between the segmented processes in the business process segmentation result set and the standard process of electronic guarantee business is marked as the third data collection direction. Data collection and verification are carried out on the corresponding segmented processes according to the third data collection direction to obtain the third collection and verification set.

[0097] The first collection verification set, the second collection verification set, and the third collection verification set are combined to form the collection dataset.

[0098] In this application, the core step of constructing the "factual basis" for process control assessment within the electronic guarantee full-process control system is "setting control dimensions based on key verification nodes and collecting and verifying data to generate a collection dataset." Its operational logic can be summarized as "multi-dimensional perspective construction + differentiated data collection + full-process verification closed loop." This process first sets different control dimensions (forward, reverse, and full-dimensional) based on key verification nodes (process nodes that play a decisive role in business compliance and risk control, such as qualification review, margin verification, and risk assessment nodes) to construct a multi-perspective assessment framework. Then, for each control dimension, it clarifies the key node data collection direction (forward, reverse, and full-dimensional) between the business segment process and the electronic guarantee standard process. Data collection and verification are carried out on the segment process according to the direction, outputting the corresponding dimension's collection verification set. Finally, the verification sets of each dimension are integrated to form a collection dataset covering the entire process and multiple perspectives, providing comprehensive and accurate data support for subsequent process control assessment.

[0099] Taking construction company A's application for a bid bond for an urban complex project as an example: Assume that the key verification nodes are divided into the first key verification node (qualification review node), the second key verification node (bid bond verification node), and the third key verification node (risk assessment node), and correspondingly set the first control dimension (forward control, focusing on qualification compliance process), the second control dimension (reverse control, focusing on fund risk control and traceability), and the third control dimension (all-dimensional control, covering the risks of the entire process).

[0100] Based on the first control dimension (forward), the front-end processes (including qualification review, security deposit verification, etc.) of the business process segmentation result set are compared with the standard process of electronic guarantee business. In the standard process, qualification review requires "submitting materials first, then passing the review, and proceeding to the next stage." The forward key node data direction is "positive flow from the start to the end of the process," marked as the first data collection direction. Data collection and verification are carried out on the segmented processes according to this direction. Data such as "material submission time (9:00 AM on June 3, 2023), review passing time (10:30 AM on June 3), and review comments (requirement to supplement the qualification renewal commitment letter)" of the qualification review node are collected to verify whether they meet the requirements of "complete materials, recorded review, and correct process order" in the standard process, forming the first collection verification set, and recording "qualification review process forward data compliance (conditionally passed, due to supplementation of commitment letter)".

[0101] Based on the second control dimension (reverse), the business segment process and the standard process are also compared. In the standard process, the margin verification requires "confirming receipt of funds before proceeding to risk assessment." The key data direction of the reverse process is "reverse verification from the end of the process back to the beginning," marked as the second data collection direction. Data such as "receipt time (June 3, 14:00), amount (8 million yuan), and matching with the project budget (2%, meeting the requirements)" of the margin verification node are collected according to this direction. The "whether the receipt of margin funds triggers the start of risk assessment and whether the amount affects subsequent processes" is verified in reverse. For example, the "whether the risk assessment node adjusts the risk score due to sufficient margin funds" is traced back to form the second collection verification set, recording the "compliance of the reverse backtracking of the margin verification process (amount matching, process triggering normally)".

[0102] This involves a comprehensive comparison of all key nodes in the business process segments with the standard process, based on the third control dimension (full-dimensional). The full-dimensional key node data direction includes all possible verification perspectives such as "forward flow, reverse tracing, and node correlation," marked as the third data collection direction. Following this direction, a "full-process data chain" is collected for nodes such as qualification review, margin verification, and risk assessment, such as "qualification validity period (3 months remaining) → margin received (sufficient) → risk score (6.25 points, medium risk)." This verifies whether the data at each node is logically consistent and meets standards throughout the entire process (e.g., whether the risk score comprehensively considers qualification and financial data), forming a third collection verification set. The "relevance and compliance of the full-process key node data (sufficient risk assessment basis, logically reasonable)" is recorded.

[0103] Ultimately, the first collection and verification set (forward qualification control), the second collection and verification set (reverse fund control), and the third collection and verification set (all-dimensional risk control) are combined to form a collection dataset, which comprehensively presents the data compliance and process logic of the segmented process of electronic guarantee business under different control dimensions. This lays a solid data foundation for the generation of subsequent process control assessment sets (such as verifying the control effects of each dimension), realizing the implementation of control logic from key nodes to multi-dimensional collection and from single verification to the entire closed loop.

[0104] The first logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the first control dimension of the control dimension set and verifying the logical relationships between the key verification node sets; the second logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the second control dimension of the control dimension set and verifying the logical relationships between the key verification node sets; the third logical verification dataset is obtained by collecting data from the third key verification node to obtain the internal data information set of the first segmented process, and verifying the logical relationships between the nodes in the internal data information set of the first segmented process. Specifically, the following steps are included:

[0105] The first and second control dimensions are respectively compared with the execution data of each node in the segmented process node control execution result set to form the first dimension execution dataset and the second dimension execution dataset;

[0106] The first logical verification dataset is obtained by evaluating the deviation between the node execution data and the standard execution data of the segmented process node control execution result set and the execution effect of key nodes within each segmented process based on the first dimension execution dataset.

[0107] The second logical verification dataset is obtained by evaluating the deviation between the node execution data and the standard execution data of the segmented process node control execution result set and the execution effect of key nodes within each segmented process based on the second dimension execution dataset.

[0108] In this application, the "generating a logical verification dataset based on control dimensions and node execution data" is a key step in deeply verifying the effectiveness of process control within the electronic guarantee full-process management system. Its operational logic can be summarized as "multi-dimensional data association + dual verification of deviation and effect + closed-loop verification of logical relationships." This process first relies on different control dimensions (the first control dimension and the second control dimension corresponding to forward qualifications, reverse funding, etc.) to associate the execution result sets of segmented process nodes (recording the actual execution data of each node, such as review time and results) to statistically form a dimension execution dataset (aggregating node execution data focusing on specific control dimensions). Then, based on these datasets, the deviation between node execution data and standard execution data is compared (to determine whether the actual operation deviates from the preset rules), and the execution effect of key nodes within the segmented process is evaluated (to verify whether control actions effectively prevent and control risks). Furthermore, the logical relationships between key verification node sets are verified (ensuring that node flow conforms to business logic), and finally, logical verification datasets for each dimension are output, providing a basis for in-depth logical verification for process control evaluation.

[0109] Taking construction company A's application for a bid bond for an urban complex project as an example: Assume that the first control dimension is forward qualification control (focusing on qualification review nodes and related processes), and the second control dimension is reverse fund control (emphasizing deposit verification nodes and retrospective logic). The key verification node set includes qualification review, deposit verification, and risk assessment nodes.

[0110] The statistical dimension execution dataset extracts execution data for the first control dimension (forward qualification), including data such as "Qualification review node (time taken 1.5 hours, conditionally passed, with attached commitment letter)" and "Risk assessment node (time taken 3 hours, score 6.25)," and links these data with the forward qualification control dimension to form the first dimension execution dataset, focusing on the execution details of qualification-related nodes. For the second control dimension (reverse funds), execution data such as "Margin verification node (time taken 0.5 hours, passed, amount 8 million)" and "Risk assessment node (risk score adjusted due to sufficient margin)" are extracted and linked with the reverse funds control dimension to form the second dimension execution dataset, focusing on the execution and retrospective data of fund-related nodes.

[0111] A first logical verification dataset was generated. Based on the first dimension, the deviations between the node execution data and the standard execution data were compared: the standard time threshold for the qualification review node was 2 hours, while the actual time was 1.5 hours, which was within the limit; the standard review result should be "compliant or conditionally passed (requires supplementary materials)," but the actual result was "conditionally passed (supplementary commitment letter)," which met the requirements. Simultaneously, the execution effectiveness of key nodes within the segmented process was evaluated: the qualification review node effectively locked in the qualification validity risk through the additional commitment letter (requiring company A to renew its qualification before December); the risk assessment node reasonably adjusted the risk score (6.25 points, medium risk) due to the input of qualification data. Based on this, the logical relationship of the key verification node set was verified: the risk assessment node was only initiated after the qualification review node was completed (consistent with the logic of "reviewing qualifications first, then assessing risks"). The combined deviations, effects, and logical relationship verifications formed the first logical verification dataset, recording "under the forward qualification control dimension, node execution is compliant, risk control is effective, and logical relationships are smooth."

[0112] A second logical verification dataset was generated. Based on this second-dimensional dataset, deviation and effectiveness evaluations were conducted. The standard time threshold for the margin verification node was 1 hour, but the actual time was 0.5 hours, which was within the limit. The standard review result should be "pass if the amount matches," and it was indeed passed (the 8 million matched 2% of the project budget of 400 million). The margin verification node confirmed the funds had arrived, providing valid data for risk assessment (because the funds were fully received, the risk score was not penalized for insufficient funds). After the margin verification node was completed, the risk assessment node was initiated (consistent with the logic of "verify funds first, then assess risk"). These elements combined to form the second logical verification dataset, recording "efficient node execution, compliant fund logic, and reasonable process relationships under the reverse fund control dimension."

[0113] The third logical verification dataset is obtained by verifying the logical relationships between nodes in the data information set within the first segment process. This includes the following steps:

[0114] The third-dimensional execution dataset is formed by the execution data of each node in the statistical third control dimension and the segmented process node control execution result set.

[0115] The third logic verification dataset is obtained by evaluating the deviation between the execution data of nodes in the data information set within the first segment process and the standard execution data, as well as the execution effect of key nodes within each segment process, based on the third dimension execution dataset.

[0116] In the electronic guarantee full-process control system, "based on the third control dimension, verifying the logical relationship between the internal nodes of the first segment process to generate a third logic verification dataset" is a key link in deeply verifying the rigor of process control from a full-dimensional perspective. Its operation logic can be summarized as "full-dimensional data aggregation + in-depth verification of deviations and effects + internal logic closed-loop verification". Specifically, the process first relies on the third control dimension (a comprehensive perspective covering all key nodes of the entire process, such as simultaneously focusing on the logic of qualifications, funds, and risks) to associate the execution result set of segmented process node control (recording the actual execution data of each node, such as the time spent on qualification review, the status of deposit receipt, and risk scoring results) to form a third-dimensional execution dataset (aggregating the execution data of key nodes of the entire process). Then, based on this dataset, the deviation between the node execution data in the data information set of the first segmented process and the standard execution data is compared (to determine whether the operation of the entire process deviates from the preset rules), and the execution effect of key nodes within the segmented process is evaluated (to verify whether the control actions of the entire process are coordinated to prevent and control risks). In addition, the logical relationship between each node within the first segmented process is verified (to ensure that the node flow conforms to the business logic and risk control requirements in the entire process). Finally, the third logic verification dataset is output, providing a comprehensive and in-depth logical verification basis for process control evaluation.

[0117] Taking construction company A's application for a bid bond for an urban complex project as an example: Assuming the third control dimension is a full-process risk control perspective (covering all key nodes such as qualification review, bid bond verification, and risk assessment), the first segment of the process is the front-end application review process (including the above-mentioned key nodes), and the set of key verification nodes involves qualification, funding, and risk-related nodes.

[0118] First, we statistically analyzed the execution dataset for the third dimension. We extracted execution data from key nodes throughout the entire process, such as the "Qualification Review Node (1.5 hours, conditionally passed, with an attached commitment letter)," "Deposit Verification Node (0.5 hours, passed, amount 8 million)," and "Risk Assessment Node (3 hours, score 6.25)," and correlated them with the third control dimension (overall process risk) to form the third-dimensional execution dataset.

[0119] Based on the third-dimensional execution dataset, the deviations between the execution data of internal nodes in the first segment process (front-end application and review process) and the standard execution data were compared. The standard time threshold for the qualification review node was 2 hours, while the actual time was 1.5 hours, within the limit. The standard time threshold for the deposit verification node was 1 hour, while the actual time was 0.5 hours, within the limit. The standard time threshold for the risk assessment node was 4 hours, while the actual time was 3 hours, within the limit. Regarding the standard review results, the qualification was conditionally approved (supplementary commitment letter), the deposit was approved (amount matched), and the risk score was moderate (6.25 points), all meeting the preset rules. Simultaneously, the execution effectiveness of key nodes within the segment process was evaluated: the qualification review node secured the qualification validity period risk through an additional commitment letter (requiring company A to renew the qualification before December); the deposit verification node confirmed sufficient funds to provide basic data for the risk assessment (because there was no funding gap, the risk score did not increase additionally); the risk assessment node reasonably output a moderate risk score (6.25 points) based on the comprehensive qualification and funding data. These three elements synergistically controlled the potential risks of the front-end process.

[0120] Finally, the internal node logic relationships were verified. Based on the deviation and effect evaluation results, the logical relationships of each node within the first segment of the process (the front-end application and review process) were verified. After the qualification review node was completed, the deposit verification node was initiated (consistent with the business logic of "reviewing qualifications first, then verifying funds"); after the deposit verification node was completed, the risk assessment node was initiated (consistent with the risk control logic of "verifying funds first, then assessing risks"); and the scoring data of the risk assessment node was strongly correlated with the execution results of the qualification and fund nodes (conditional qualification approval and sufficient funds jointly affect the risk score), and the logical flow of each node was smooth and the data association was reasonable. The above deviation verification, effect evaluation, and logical relationship verification were combined to form a third logical verification dataset, recording "the compliance of the front-end application and review process node execution, the effective coordination of risk control, and the tight and self-consistent internal logical relationships under the third control dimension (full-process risk)".

[0121] Through the above steps, the third logic verification dataset deeply verifies the execution deviation, collaborative control effect, and internal node logical relationship of the first segment of the electronic guarantee process from a full-dimensional perspective.

[0122] The comprehensive evaluation of the control assessment result set and the process control assessment set is used to obtain the full-process control evaluation result of the electronic guarantee business, which is as follows:

[0123] The difference between the set of control assessment results to be verified and the first set of control assessment results is used to obtain the first set of verification difference results;

[0124] The difference between the set of control assessment results to be verified and the second set of control assessment results is used to obtain the second set of verification difference results.

[0125] The difference between the set of control assessment results to be verified and the set of third control assessment results is used to obtain the third verification difference result set;

[0126] After adjusting the control assessment results based on the first verification difference result set, the second verification difference result set, and the third verification difference result set, the control assessment adjustment result set is obtained.

[0127] The control and management evaluation results of each segment of the process are comprehensively processed to obtain the full-process control and management evaluation results of the electronic guarantee business.

[0128] In the full-process control system for electronic guarantees, "integrating the set of control assessment results to be verified with the set of process control assessment results to generate a full-process control evaluation result" is a key step in the final diagnosis and calibration of the business control effectiveness. This process first forms a verification difference result set (quantifying the discrepancies between different assessment perspectives) by statistically analyzing the differences between the set of control assessment results to be verified (the data set for preliminary assessment of the segmented process control effectiveness) and the assessment result sets of each dimension in the process control assessment set (the first, second, and third control assessment result sets, corresponding to different control perspectives). Then, based on these difference results, the set of control assessment results to be verified is calibrated and adjusted to output a more accurate control assessment adjustment result set. Finally, a comprehensive evaluation of the adjusted result set is conducted to output the full-process control evaluation result for electronic guarantee business.

[0129] Taking construction company A's application for a bid bond for an urban complex project as an example: Assume that the preliminary assessment of the control and management results set to be verified is "medium" in the control and management effect of the application review process, and the first control and management results set (forward qualification dimension) in the process control and management assessment set is "medium", the second control and management results set (reverse funding dimension) is "excellent", and the third control and management results set (all-dimensional risk) is "good".

[0130] For the first control assessment result set (forward qualification), the differences between the control assessment result set to be verified and the first control assessment result set are compared. Both assess the front-end application review process as "medium," with small differences, forming the first verification difference result set, recorded as "small discrepancy in forward qualification dimension assessment, low difference value." For the second control assessment result set (reverse funds), the result to be verified is "medium," while the second control result is "excellent," showing a significant difference, forming the second verification difference result set, recorded as "large discrepancy in reverse funds dimension assessment, high difference value." For the third control assessment result set (all-dimensional risk), the result to be verified is "medium," while the third control result is "good," showing a difference, forming the third verification difference result set, recorded as "disagreement in all-dimensional risk dimension assessment, medium difference value."

[0131] Secondly, adjustments were made to the control assessment results. Based on the difference results sets of the first, second, and third verifications, the control assessment result set to be verified was calibrated: for the first dimension with small differences, the original assessment result was maintained (moderate); for the second dimension with large differences, the assessment basis was traced (e.g., checking whether the execution data of the reverse fund control dimension was omitted by the assessment to be verified), and it was found that the assessment to be verified did not fully consider the positive impact of the efficient execution of the margin verification node on the process, so the assessment result was adjusted to "good"; for the third dimension with differences, the effectiveness of collaborative prevention and control at each node under the all-dimensional risk control was supplemented, and the assessment result was adjusted to "good". After comprehensive adjustments, a control assessment adjustment result set was formed, recording "Control effect of the front-end application review process: moderate in qualification dimension, good in fund dimension, and good in all dimensions".

[0132] Finally, a comprehensive evaluation of the entire process is implemented. The results of the control assessment and adjustment are integrated from the control assessment results of each segment of the process (such as the front-end application and review process and the back-end guarantee issuance process). Assuming the control assessment result for the back-end guarantee issuance process is "Excellent" (efficient execution of nodes, no risk deviation), combined with the front-end process's "medium qualifications, good funding, and good overall performance," a comprehensive evaluation from a holistic perspective is conducted. The front-end process needs optimization due to qualification risks, but funding and overall control are effective; the back-end process is efficient and compliant. The final output is the overall control assessment result for the electronic guarantee business, such as "The overall control effect is good; attention needs to be paid to the continuation risk in the front-end qualification stage; the overall risk is controllable, and process collaboration is effective."

[0133] The evaluation results of the electronic guarantee full-process management approach, taking a multi-dimensional perspective, achieve a precise diagnosis of business management effectiveness through assessment, calibration, and full-process integration. Taking the scenario of construction company A as an example, the evaluation results not only pointed out potential optimization points in the upstream qualification process but also affirmed the collaborative effectiveness of the full-process management. This provides a clear decision-making direction for the continuous optimization of electronic guarantee business (such as targeted strengthening of qualification management) and risk prevention and control (such as monitoring qualification renewal), completing closed-loop management from segmented assessment to full-process evaluation, ensuring that the business operates in a compliant, efficient, and risk-controllable state.

[0134] Example 2 adds the following technical features based on Example 1:

[0135] The electronic guarantee end-to-end management system includes:

[0136] Acquisition Module: Acquires business data and basic information about electronic guarantee transactions during the process of implementation;

[0137] Assessment and segmentation module: Based on business data information, the risk level of electronic guarantee business is assessed to obtain the risk level assessment result. Based on the risk level assessment result, the electronic guarantee business process is segmented to obtain the business process segmentation result set.

[0138] Processing module: It reviews the basic information of electronic guarantee business to obtain information review results, and performs node control execution on each segment of the business process based on the information review results and risk level assessment results to obtain node control execution result set;

[0139] Module selection: Select key verification node sets from each segment of the business process segmentation result set, and extract the control and detection dataset from each segment of the business process segmentation result set.

[0140] Evaluation module: Based on the control and control detection dataset and the node control and control execution result set, the control and control effect of each segment process in the business process segment result set is evaluated to obtain the control and control evaluation result set to be verified;

[0141] The data collection and verification module sets a control dimension set based on the key verification node set, and collects and verifies data from each segment of the business process segmentation result set based on the key verification node set to obtain the data collection dataset.

[0142] Processing and Analysis Module: Processes and analyzes the control dimension set, collected dataset, key verification node set, and business process segmentation result set to obtain the process control evaluation set;

[0143] Control and Evaluation Module: After comprehensively evaluating the control and evaluation result set to be verified and the process control and evaluation set, the full-process control and evaluation result of electronic guarantee business is obtained.

[0144] Electronic devices, including memory, processor, and computer programs stored in memory and capable of running on the processor, wherein the processor executes the program to implement a method for full-process control of electronic guarantees.

[0145] like Figure 3 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute the electronic guarantee full-process management method.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for full-process control of electronic guarantees, characterized in that, The method includes the following steps: To obtain business data and basic information about electronic guarantee business during its implementation; Based on business data, a risk level assessment is conducted on the electronic guarantee business to obtain the risk level assessment result. Based on the risk level assessment result, the electronic guarantee business process is divided into segments to obtain a business process segmentation result set, specifically including the following steps: The risk level assessment results include Level 1 risk, Level 2 risk, and Level 3 risk; wherein, the risk level of Level 1 risk is greater than that of Level 2 risk, and the risk level of Level 2 risk is greater than that of Level 3 risk; The electronic guarantee business process is divided into a front-end business process and a back-end business process. If the risk level assessment result is Level 1 risk, then the number of nodes in the front-end business process is made greater than the number of nodes in the back-end business process to obtain the first business process segmentation result, and at this time the difference between the number of nodes in the front-end business process and the number of nodes in the back-end business process is the difference in the number of nodes in the first business process. If the risk level assessment result is level two risk, then the number of nodes in the front-end business process is made greater than the number of nodes in the back-end business process to obtain the second business process segmentation result. At this time, the difference between the number of nodes in the front-end business process and the number of nodes in the back-end business process is the second node difference, which is less than the first node difference. If the risk level assessment result is level three risk, then the number of nodes in the front-end business process is equal to the number of nodes in the back-end business process to obtain the third business process segmentation result. The first business process segmentation result, the second business process segmentation result, and the third business process segmentation result are combined to form a business process segmentation result set. The basic information of the electronic guarantee business is reviewed to obtain the information review results. Based on the information review results and the risk level assessment results, the node control execution result set is obtained by performing node control execution on each segment of the business process segment result set. Select key verification node sets from each segment of the business process segmentation result set, and extract the control and detection dataset from each segment of the business process segmentation result set. The control effectiveness of each segment of the business process in the segmented result set is evaluated based on the control detection dataset and the node control execution result set to obtain the control evaluation result set to be verified. The control dimension set is set according to the key verification node set, and the data collection and verification are performed on each segment of the business process segmentation result set according to the key verification node set to obtain the collection dataset. The process control evaluation set is obtained by processing and analyzing the control dimension set, the collected dataset, the key verification node set, and the business process segmentation result set. The evaluation results of the entire process control of electronic guarantee business are obtained by comprehensively evaluating the set of control assessment results to be verified and the set of process control assessment results.

2. The method for full-process control of electronic guarantees according to claim 1, characterized in that, The process control evaluation set is obtained by processing and analyzing the control dimension set, the collected dataset, the key verification node set, and the business process segmentation result set. The specific steps include: The first logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the first control dimension of the control dimension set and verifying the logical relationships between the key verification node sets; the second logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the second control dimension of the control dimension set and verifying the logical relationships between the key verification node sets; the third logical verification dataset is obtained by verifying the logical relationships between the nodes in the first segmented process internal data information set based on the data collection of the third key verification node; The collected dataset includes a first collection verification set, a second collection verification set, and a third collection verification set; The first control evaluation result set is obtained by evaluating the control effect of each segment process in the business process segmentation result set based on the first collection verification set and the first logical verification dataset. The second control evaluation result set is obtained by evaluating the control effect of each segment process in the business process segmentation result set based on the second collection verification set and the second logical verification dataset. The third control evaluation result set is obtained by evaluating the control effect of each segment process in the business process segmentation result set based on the third logical verification dataset and the third collection verification set. The first control assessment result set, the second control assessment result set, and the third control assessment result set are combined to form the process control assessment set.

3. The method for full-process control of electronic guarantees according to claim 2, characterized in that, Based on the information review results and risk level assessment results, the node control execution result set is obtained by performing node control execution on each segment of the business process segmentation result set, which specifically includes the following steps: Based on the information review results and risk level assessment results, the node control rule parameters of each segment of the business process in the segmented result set are adjusted to obtain the parameter adjustment results; Obtain the key node positions of each segment in the business process segmentation result set, and perform node control execution on each segment in the business process segmentation result set based on the parameter adjustment results and the key node positions of each segment. Then output the segment process node control execution result set. The node control execution result set is output after evaluating the deviation between the node execution data and the standard execution data of the segmented process node control execution result set and the execution effect of key nodes within each segmented process.

4. The method for full-process control of electronic guarantees according to claim 3, characterized in that, Based on the key verification node set, data is collected and verified for each segment of the business process in the segmented result set to obtain the collected dataset. The specific steps include: The first control dimension, the second control dimension, and the third control dimension are set according to the first key verification node, the second key verification node, and the third key verification node, respectively; Based on the first control dimension, the forward key node data direction between each segment process in the business process segmentation result set and the electronic guarantee business standard process is marked as the first data collection direction. Data collection and verification are performed on the corresponding segment processes according to the first data collection direction to obtain the first collection and verification set. Based on the second control dimension, the reverse key node data direction between each segment process in the business process segmentation result set and the electronic guarantee business standard process is marked as the second data collection direction. Data collection and verification are performed on the corresponding segment processes according to the second data collection direction to obtain the second collection and verification set. Based on the third control dimension, the data direction of all key nodes between the segmented processes in the business process segmentation result set and the standard process of electronic guarantee business is marked as the third data collection direction. Data collection and verification are performed on the corresponding segmented processes according to the third data collection direction to obtain the third collection and verification set. The first collection verification set, the second collection verification set, and the third collection verification set are combined to form the collection dataset.

5. The method for full-process control of electronic guarantees according to claim 4, characterized in that, The first logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the first control dimension of the control dimension set and verifying the logical relationship between the key verification node sets. The second logical verification dataset is obtained by analyzing the node execution data of the segmented process node control execution result set under the second control dimension of the control dimension set and verifying the logical relationship between the key verification node sets. Based on the data collected from the third key verification node, the internal data information set of the first segment process is obtained. The logical relationships between the nodes in the internal data information set of the first segment process are verified to obtain the third logical verification dataset, which specifically includes the following steps: The first and second control dimensions are respectively compared with the execution data of each node in the segmented process node control execution result set to form the first dimension execution dataset and the second dimension execution dataset; The first logical verification dataset is obtained by evaluating the deviation between the node execution data and the standard execution data of the segmented process node control execution result set and the execution effect of key nodes within each segmented process based on the first dimension execution dataset. The second logical verification dataset is obtained by evaluating the deviation between the node execution data and the standard execution data of the segmented process node control execution result set and the execution effect of key nodes within each segmented process based on the second dimension execution dataset.

6. The method for full-process control of electronic guarantees according to claim 5, characterized in that, The third logical verification dataset is obtained by verifying the logical relationships between nodes in the data information set within the first segment process. This process includes the following steps: The third-dimensional execution dataset is formed by the execution data of each node in the statistical third control dimension and the segmented process node control execution result set. The third logic verification dataset is obtained by evaluating the deviation between the execution data of nodes in the data information set within the first segment process and the standard execution data, as well as the execution effect of key nodes within each segment process, based on the third dimension execution dataset.

7. The method for full-process control of electronic guarantees according to claim 6, characterized in that, The comprehensive evaluation of the control assessment result set and the process control assessment set is used to obtain the full-process control evaluation result of the electronic guarantee business, which is as follows: The difference between the set of control assessment results to be verified and the first set of control assessment results is used to obtain the first set of verification difference results; The difference between the set of control assessment results to be verified and the second set of control assessment results is used to obtain the second set of verification difference results. The difference between the control assessment result set to be verified and the third control assessment result set is used to obtain the third verification difference result set; After adjusting the control assessment results based on the first verification difference result set, the second verification difference result set, and the third verification difference result set, the control assessment adjustment result set is obtained. The control and management evaluation results of each segment of the process are comprehensively processed to obtain the full-process control and management evaluation results of the electronic guarantee business.

8. An electronic guarantee end-to-end management system, applied to the electronic guarantee end-to-end management method described in claims 1-7, characterized in that, include: Acquisition Module: Acquires business data and basic information about electronic guarantee transactions during the process of implementation; Assessment and segmentation module: Based on business data information, the risk level of electronic guarantee business is assessed to obtain the risk level assessment result. Based on the risk level assessment result, the electronic guarantee business process is segmented to obtain the business process segmentation result set. Processing module: The module reviews the basic information of the electronic guarantee business to obtain the information review results. Based on the information review results and risk level assessment results, the module performs node control and execution on each segment of the business process in the segmented result set to obtain the node control and execution result set. Module selection: Select key verification node sets from each segment of the business process segmentation result set, and extract the control and detection dataset from each segment of the business process segmentation result set. Evaluation module: Based on the control and control detection dataset and the node control and control execution result set, the control and control effect of each segment process in the business process segment result set is evaluated to obtain the control and control evaluation result set to be verified; The data collection and verification module sets a control dimension set based on the key verification node set, and collects and verifies data from each segment of the business process segmentation result set based on the key verification node set to obtain the data collection dataset. Processing and Analysis Module: Processes and analyzes the control dimension set, collected dataset, key verification node set, and business process segmentation result set to obtain the process control evaluation set; Control and Evaluation Module: After comprehensively evaluating the control and evaluation result set to be verified and the process control and evaluation set, the full-process control and evaluation result of electronic guarantee business is obtained.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the electronic guarantee full-process management method as described in any one of claims 1 to 7.

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