Intelligent contract risk real-time monitoring and early warning management platform

Abstract

The invention relates to the technical field of intelligent contract management, in particular to an intelligent contract risk real-time monitoring and early warning management platform which comprises a dynamic risk sensing module, a multi-dimensional data integration engine, an intelligent early warning generator, a distributed task scheduling center, a full-life-cycle monitoring module and a risk grading processing unit. The platform realizes accurate identification and efficient response of risks through semantic analysis, multi-source data fusion and distributed scheduling, a transparent management means is provided in combination with a time axis visualization technology, and the operation authenticity is guaranteed by using block chain evidence storage. According to the method, the intelligent level of contract risk prevention and control can be remarkably improved, enterprises are supported to quickly position problems and trace reasons, meanwhile, personalized requirements and high-concurrency operation requirements are met, and the law compliance capability and the risk management efficiency are enhanced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of information technology and risk management, specifically to a smart contract risk real-time monitoring and early warning management platform. BACKGROUND

[0002] With the widespread application of smart contracts in the business field, the importance of risk monitoring and early warning management is increasingly prominent. As a technical tool based on blockchain and automated execution, smart contracts have significant advantages in improving contract management efficiency and transparency. However, the existing smart contract risk monitoring and early warning technology still has deficiencies in real-time, intelligent level and comprehensive management ability, which is difficult to meet the needs of enterprises for efficient prevention and control of contract risks.

[0003] Patent with publication number CN112668899B discloses a contract risk identification method and device based on artificial intelligence. This technology compares the content differences of different versions of contracts through image capture technology and trained models, judges the risk level of the contract and sends early warning information. However, this scheme mainly relies on static comparison between contract versions, lacks real-time monitoring ability for dynamic risks in the contract execution process. At the same time, its risk identification range is relatively limited, and it cannot fully cover the compliance, numerical rationality and abnormal data of contract clauses and other multi-dimensional risk factors, which may lead to insufficient accuracy and comprehensiveness of risk warning.

[0004] The above problems show that the existing smart contract risk monitoring and early warning technology still has certain deficiencies in real-time, multi-dimensional risk coverage and dynamic management ability. Especially in the whole life cycle of the contract, how to realize real-time monitoring, intelligent analysis and hierarchical early warning of multi-dimensional risks is still a technical problem to be solved. Therefore, the present application aims to provide a smart contract risk real-time monitoring and early warning management platform to make up for the defects of the existing technology, improve the efficiency and accuracy of enterprise contract risk management, and meet the needs of modern business for intelligent and efficient contract management. SUMMARY

[0005] The purpose of the present application is to provide a smart contract risk real-time monitoring and early warning management platform to solve the problems raised in the background art. To achieve the above purpose, the present application provides the following technical solutions: a smart contract risk real-time monitoring and early warning management platform, the platform comprising a dynamic risk perception module, a multi-dimensional data integration engine, an intelligent early warning generator, a distributed task scheduling center, a full life cycle monitoring module and a risk grading processing unit; wherein: the dynamic risk perception module captures multi-source heterogeneous data in the contract execution process in real time, extracts key information combined with semantic analysis technology, and realizes dynamic capture of potential risks; the multi-dimensional data integration engine uses cross-system data synchronization technology to clean, map and fuse data from different sources to form a unified risk analysis view; the intelligent early warning generator generates graded early warning signals based on preset risk thresholds and user-defined rules for detected abnormal behaviors or clause conflicts; the distributed task scheduling center is responsible for coordinating the running order and resource allocation of each module in the platform, ensuring stability and response speed in a high-concurrency environment; the full life cycle monitoring module displays the state changes of the contract from drafting to execution completion through time axis visualization technology, supporting backtracking analysis; the risk grading processing unit automatically triggers the corresponding disposal process and notifies the relevant personnel according to the influence range and urgency of the risk.

[0006] It should be noted in the scheme that the dynamic risk perception module adopts an event-driven architecture design, which constructs a real-time risk portrait by listening to operation logs, external interface call records and user behavior trajectories generated during contract execution. The multi-dimensional data integration engine uses ETL tools to complete data extraction, conversion and loading, and improves data processing efficiency through distributed storage technology. The intelligent early warning generator has a flexible rule configuration interface built-in, allowing users to adjust the early warning conditions according to actual business needs. The distributed task scheduling center is implemented based on a microservices framework, with each sub-task independently deployed and having a fault-tolerant mechanism. The full life cycle monitoring module presents the state changes of the contract key nodes using a graphical interface and supports exporting historical records for auditing. The risk grading processing unit manages risk events of different levels through a priority queue to ensure that high-priority problems are responded to in a timely manner. Other additional functional modules can be extended according to enterprise needs, such as contract template recommendation, compliance checking, etc., to further optimize user experience.

[0007] Further worth mentioning is that the multi-dimensional data integration engine is based on a Hadoop ecosystem to build a data lake architecture, and the use of Hadoop to build a data lake includes the following steps: S1, initializing a cluster environment: using the tools provided by Hadoop to configure a distributed file system (HDFS) and a computing framework (YARN). S2, data partitioning and indexing: creating a partition table in the data lake, defining field types and indexing strategies to speed up query performance. S3, data import: writing real-time streaming data into the data lake through tools such as Flume or Kafka, while supporting batch data upload. S4, data analysis and mining: using Hive or SparkSQL for structured queries, combined with machine learning models to discover hidden risk patterns. S5, data governance: ensuring data quality through metadata management and permission control, and regularly performing storage optimization and archiving cleanup.

[0008] Further need to be explained is that the full life cycle monitoring module is used to track the key nodes in the contract execution process in real time; the contract state tracking can directly view the current stage of the contract and its detailed information; the node exception detection can quickly identify the behavior deviating from the normal path by comparing historical data and current state.

[0009] Compared with the prior art, the intelligent contract risk real-time monitoring and early warning management platform provided by the application has at least the following beneficial effects:

[0010] (1) By introducing dynamic risk perception technology, combining semantic analysis and multi-source data fusion capability, the comprehensive coverage and accurate identification of potential risks in the contract execution process are realized, and the intelligent level of risk prevention and control is significantly improved.

[0011] (2) Through distributed task scheduling and priority queue management, the efficient operation of the platform in high-concurrency scenarios is ensured, and the flexible rule configuration interface is used to meet the individual needs of enterprises.

[0012] (3) Through full life cycle monitoring and time axis visualization technology, the enterprise is provided with a transparent contract management means, which supports quick positioning of problems and tracing of reasons, and improves the decision-making efficiency.

[0013] (4) Data encryption and access control technology ensures the security of sensitive information, and block chain storage technology is used to record key operation logs, ensuring the authenticity and non-tamperability of the contract execution process, thereby enhancing the legal compliance ability and risk management level of enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The system structure diagram of the application.

[0015] Figure 2 The workflow diagram of the dynamic risk perception module.

[0016] Figure 3 Data processing flowchart for multi-dimensional data integration engine.

[0017] Figure 4 State tracking interface diagram for full life cycle monitoring module.

[0018] Figure 5 Priority queue management mechanism diagram for risk grading processing unit. DETAILED DESCRIPTION

[0019] The present application provides a smart contract risk real-time monitoring and early warning management platform, the specific implementation is combined with the attached Figure 1 to the attached Figure 5 for detailed description. The platform includes a dynamic risk perception module, a multi-dimensional data integration engine, an intelligent early warning generator, a distributed task scheduling center, a full life cycle monitoring module and a risk grading processing unit, each module works together to achieve comprehensive coverage and accurate identification of potential risks in the contract execution process. The following will be described one by one in combination with specific application scenarios and technical details.

[0020] As shown in the attached Figure 1 , the architecture of the whole system is composed of multiple functional modules, and the modules realize seamless cooperation through efficient data transmission and task scheduling mechanism. The dynamic risk perception module, as one of the core components of the system, its running principle is based on event-driven architecture design, which can collect multi-source heterogeneous data in the contract execution process in real time, including operation log, external interface call record and user behavior trajectory. These data extract key information through semantic analysis technology, and then build real-time risk portrait. For example, in a certain enterprise contract management system, when the user submits a clause that does not conform to the preset compliance rules in the contract approval link, the dynamic risk perception module will immediately listen to this operation, and identify the risk type that the clause may cause through semantic analysis technology, such as legal compliance problem or financial risk. Then, the module passes the relevant information to the subsequent processing unit, providing basic data support for risk early warning.

[0021] The multi-dimensional data integration engine is another key module, which mainly functions to clean, map and fuse data from different sources to form a unified risk analysis view. As shown in the attached Figure 3As shown, the module builds a data lake architecture based on the Hadoop ecosystem, and the specific implementation steps are as follows: First, initialize the cluster environment, and use the tools provided by Hadoop to configure the distributed file system (HDFS) and computing framework (YARN). Second, create partition tables and define field types and index strategies to speed up query performance. Next, use tools such as Flume or Kafka to write real-time streaming data into the data lake, while supporting batch data upload. Then, use Hive or SparkSQL for structured queries, combined with machine learning models to discover hidden risk patterns. Finally, ensure data quality through metadata management and permission control, and regularly perform storage optimization and archival cleanup. For example, in a certain scenario, an enterprise needs to integrate and analyze contract-related data from ERP systems, CRM systems, and third-party suppliers. The multi-dimensional data integration engine processes scattered data sources into structured data through the above process, providing reliable data support for subsequent risk assessment and early warning.

[0022] The intelligent early warning generator is responsible for generating graded early warning signals based on preset risk thresholds and user-defined rules. This module has a flexible rule configuration interface built-in, allowing users to adjust early warning conditions according to actual business needs. For example, an enterprise sets a rule in contract management: when the contract amount exceeds 1 million yuan and has not been reviewed by the legal department, the system needs to generate a high-level early warning signal. The intelligent early warning generator monitors contract status changes in real time, and once it detects abnormal behavior that meets the conditions, it will trigger the corresponding level of early warning signal. The generation process of the early warning signal can be described as the following formula:

[0023] W = f(T, R);

[0024] Where W represents the early warning signal level, T represents the current contract state parameter, and R represents the preset risk rule set. The specific implementation of function f depends on the rule matching algorithm, which determines the final early warning level by comparing the contract state parameter with the rule set item by item. In addition, the intelligent early warning generator also supports dynamic adjustment of rule weights to adapt to the individual needs of different enterprises.

[0025] The distributed task scheduling center ensures the stability and response speed of the platform in a high-concurrency environment. This module is implemented based on a micro-service framework, with each sub-task independently deployed and with fault tolerance mechanisms. For example, in a certain peak scenario, the system needs to handle real-time risk monitoring tasks for hundreds of contracts simultaneously. The distributed task scheduling center coordinates the running order and resource allocation of each module to ensure that each task is completed on time. Specifically, the task scheduling center uses a priority queue management mechanism to prioritize high-priority tasks while dynamically adjusting the execution time of low-priority tasks. The specific algorithm for task scheduling can be represented as:

[0026]

[0027] where P i represents the priority of task i, U i represents the importance score of the task, C i represents the estimated completion time of the task. The task priority calculated by this formula is used to guide the task allocation strategy of the scheduling center, thereby improving the overall efficiency of the system.

[0028] The full life cycle monitoring module displays the state changes of the contract from drafting to execution through timeline visualization technology, supporting backtracking analysis. As shown in FIG. 8, this module uses a graphical interface to present the state changes of the key nodes of the contract and supports exporting historical records for auditing. For example, in a certain contract management scenario, the enterprise wants to know whether there are any abnormal behaviors in the execution process of a certain contract. Through the full life cycle monitoring module, the user can intuitively view the current stage of the contract and its detailed information, and quickly identify behaviors deviating from the normal path by comparing historical data. The module also supports node anomaly detection function, whose core algorithm is based on the deviation analysis of historical data and current state, and the specific formula is as follows: Figure 4

[0029]

[0030] where D represents the deviation value, S j represents the current state parameter, H j represents the historical average state parameter, and n represents the number of state parameters. When the deviation value exceeds the preset threshold, the system will automatically mark the node as an abnormal state and notify the relevant personnel for processing.

[0031] The risk grading processing unit automatically triggers the corresponding disposal process and notifies the relevant personnel according to the impact range and urgency of the risk. As shown in FIG. 9, this module manages different levels of risk events through a priority queue to ensure that high-priority problems are responded to in a timely manner. For example, in a certain scenario, the system detects that a contract has a serious legal compliance problem, and the risk grading processing unit will immediately mark this event as the highest priority and trigger the corresponding disposal process. Specifically, the disposal process includes the following steps: first, the system automatically generates a risk report and sends it to the head of the relevant department; second, the head formulates countermeasures according to the report content and feeds back the processing progress through the system; finally, the system records the entire disposal process and generates an audit log for future reference. Figure 5

[0032] ​​In addition, the platform also integrates a variety of additional functional modules to further optimize user experience. For example, the contract template recommendation module provides industry-standard contract templates for enterprises by analyzing historical contract data; the compliance check module uses natural language processing technology to automatically review contract clauses to ensure compliance with legal requirements. These additional functional modules can be flexibly extended according to enterprise needs, meeting application needs in different scenarios.

[0033] In summary, the present application realizes comprehensive coverage and accurate identification of potential risks in the contract execution process by introducing dynamic risk perception technology, multi-dimensional data integration capability, intelligent early warning mechanism, and distributed task scheduling strategy. At the same time, through full life cycle monitoring and timeline visualization technology, the present application provides transparent contract management means for enterprises, significantly improving the intelligent level of risk prevention and control and decision-making efficiency.

[0034] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0035] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A smart contract risk real-time monitoring and early warning management platform, characterized by: The platform includes a dynamic risk perception module, a multi-dimensional data integration engine, an intelligent early warning generator, a distributed task scheduling center, a full life cycle monitoring module, and a risk classification processing unit; wherein: The dynamic risk perception module collects multi-source heterogeneous data in real time during the contract execution process and extracts key information using semantic parsing technology to dynamically capture potential risks. The multi-dimensional data integration engine uses cross-system data synchronization technology to clean, map, and fuse data from different sources to form a unified risk analysis view; The intelligent warning generator generates graded warning signals for detected abnormal behaviors or clause conflicts based on preset risk thresholds and user-defined rules; The distributed task scheduling center is responsible for coordinating the operation sequence and resource allocation of each module in the platform; The full life cycle monitoring module uses timeline visualization technology to display the status changes of the entire contract process from drafting to execution; The risk classification processing unit automatically triggers the corresponding disposal process and notifies relevant personnel based on the scope of risk impact and urgency.

2. The smart contract risk real-time monitoring and early warning management platform according to claim 1, characterized in that: The dynamic risk perception module adopts an event-driven architecture design and builds a real-time risk profile by monitoring the operation logs, external interface call records and user behavior trajectories generated during the contract execution process.

3. The smart contract risk real-time monitoring and early warning management platform according to claim 1 is characterized by: The multidimensional data integration engine utilizes ETL tools to complete data extraction, conversion, and loading, and improves data processing efficiency through distributed storage technology.

4. The smart contract risk real-time monitoring and early warning management platform according to claim 1, characterized in that: The intelligent warning generator has a built-in flexible rule configuration interface, allowing users to adjust warning conditions according to actual business needs.

5. The smart contract risk real-time monitoring and early warning management platform according to claim 1 is characterized by: The distributed task scheduling center is implemented based on a microservice framework, and each subtask is deployed independently and has a fault-tolerant mechanism.

6. The smart contract risk real-time monitoring and early warning management platform according to claim 1, characterized in that: The full life cycle monitoring module uses a graphical interface to present the status changes of key nodes of the contract and supports the export of historical records for auditing.

7. The smart contract risk real-time monitoring and early warning management platform according to claim 1, characterized in that: The risk classification processing unit manages risk events of different levels through priority queues to ensure that high-priority issues receive timely responses.

8. The smart contract risk real-time monitoring and early warning management platform according to claim 3 is characterized by: The multidimensional data integration engine builds a data lake architecture based on the Hadoop ecosystem. The specific steps include: initializing the cluster environment, configuring the distributed file system and computing framework; creating partition tables and defining field types and indexing strategies; writing real-time streaming data to the data lake through Flume or Kafka tools; using Hive or Spark SQL for structured queries; and ensuring data quality through metadata management and permission control.

9. The smart contract risk real-time monitoring and early warning management platform according to claim 6, characterized in that: The full life cycle monitoring module supports node anomaly detection by real-time tracking of key nodes in the contract execution process. The detection method is based on deviation analysis between historical data and current status.

10. The smart contract risk real-time monitoring and early warning management platform according to claim 1, characterized in that: The platform also includes additional functional modules, including a contract template recommendation module and a compliance check module, to meet the personalized needs of enterprises.

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