Full-chain data monitoring and risk early warning management system

Abstract

The invention provides a full-chain data monitoring and risk early warning management system, and the system is characterized in that the system comprises a data obtaining module which obtains risk data through employing an ETL tool, a log collection tool, and an interface calling mode; the data preprocessing module is used for carrying out data preprocessing on the risk data; the risk assessment module is used for carrying out deep mining and analysis on the preprocessed risk data by utilizing a big data analysis technology and a machine learning algorithm, establishing various data analysis models to find potential rules and abnormal conditions in the data, and according to a result of the data analysis layer, combining a preset risk index and a threshold value, and carrying out risk assessment on the risk data; identifying and evaluating a potential risk, and when a risk index exceeds a threshold value, triggering an early warning mechanism; and the display module provides personalized operation interfaces for different user roles, and monitoring and early warning of full-chain data can be realized according to the method.

Description

Technical Field

[0001] This document relates to the technical field of risk early warning management, and particularly to a full-chain data monitoring and risk early warning management system. Background Art

[0002] With the rapid development of information technology, especially the wide application of emerging technologies such as big data, cloud computing, and the Internet of Things, enterprises and organizations have generated a vast amount of data in their daily operations. These data come from a wide range of sources and cover all aspects of business processes, including transaction records, production data, customer information, equipment operation status, etc. Traditional data management methods have become difficult to cope with the data processing requirements of such a large scale and diversity, prompting enterprises to seek more efficient and intelligent data management solutions to explore the value behind the data and timely discover potential risk factors. Traditional risk early warning methods mainly rely on manual experience and simple statistical analysis tools, and there are many limitations. First, the efficiency of manually collecting and analyzing data is low, and it is difficult to process a vast amount of complex data, resulting in a significant reduction in the timeliness and accuracy of risk early warning. Second, traditional methods often can only analyze a single data source or a limited number of indicators, unable to comprehensively consider and deeply explore the full-chain business data, and prone to missing potential risk factors. In addition, the adaptability of traditional risk early warning models is poor, and it is difficult to adjust and optimize in a timely manner with the changes in the business environment and the evolution of data characteristics, unable to meet the increasingly complex and changeable risk prevention and control needs of enterprises.

[0003] In summary, in the context of the development of information technology and the urgent need for risk prevention and control in various industries, the limitations of traditional risk early warning methods are becoming increasingly prominent. Developing a full-chain data monitoring and risk early warning management system has become an inevitable choice for enterprises and organizations to improve their risk management level and ensure the stable operation of their businesses. By integrating advanced information technology means, real-time collecting, deeply analyzing, and intelligently warning the full-chain business data can help enterprises timely discover and respond to various potential risks and achieve sustainable development. Summary of the Invention

[0004] According to an embodiment of the present invention, there is provided a full-chain data monitoring and risk early warning management system, including:

[0005] A data acquisition module, which acquires risk data by using ETL tools, log collection tools, and interface call methods;

[0006] A data preprocessing module, which is used to preprocess the risk data;

[0007] The risk assessment module utilizes big data analysis techniques and machine learning algorithms to deeply mine and analyze the preprocessed risk data, establish various data analysis models to discover potential patterns and anomalies in the data, and based on the results of the data analysis layer, combined with preset risk indicators and thresholds, identify and evaluate potential risks. When the risk indicators exceed the thresholds, it triggers the early warning mechanism;

[0008] The display module provides personalized operation interfaces for different user roles, including functional modules such as data visualization reports, risk monitoring dashboards, early warning notification lists, and risk handling processes.

[0009] The full-chain data monitoring and risk early warning management system of the present invention mainly includes a data acquisition module, a data preprocessing module, a risk assessment module, a display module, and a data lineage management module. Each module works in coordination to achieve all-round and precise monitoring and early warning of enterprise risks, and through data lineage technology, realizes the full-life cycle tracking and management of data, provides scientific decision-making support for enterprise management, helps enterprises respond to potential risks in a timely manner, and ensures the stable operation and sustainable development of enterprises. Brief Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of the full-chain data monitoring and risk early warning management system of the embodiment of the present invention. Detailed Embodiments

[0012] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only some of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0013] System Embodiment

[0014] According to an embodiment of the present invention, a full-chain data monitoring and risk early warning management system is provided, Figure 1Schematic diagram of the full-chain data monitoring and risk early warning management system according to the embodiments of the present invention. According to Figure 1 As shown, the full-chain data monitoring and risk early warning management system according to the embodiments of the present invention specifically includes:

[0015] Data acquisition module 10. The data acquisition module uses ETL tools, log collection tools, and interface call methods to acquire risk data, and further expands the data collection scope, including collecting data related to enterprise risks from social media, industry research reports, and government public economic data, and using natural language processing and data mining technologies to extract risk features therein; for the relational database within the enterprise, use the connection driver of the corresponding database to establish a connection with the database, and use SQL query statements to extract the required data from the database table; for non-relational databases, use the provided API or corresponding client tools, and write query statements according to the storage structure and business requirements of the data to obtain the data; for CSV files, use the file reading function provided by the programming language to read the file content into a data table form; for XML and JSON files, use the corresponding parsing libraries to parse and extract the required data elements and structures; for the log files of the business system, use specialized log collection tools to collect the log files in real time or at regular intervals, extract key information, and convert it into a structured data format; for external data sources, send requests to the data source system through API interfaces using the HTTP / HTTPS protocol and receive the returned data. The risk data includes: ERP data, production management system data, Internet of Things data, social media data, industry research report data, and government public economic data; the ERP data includes: procurement, finance, and inventory information of assets; the production management system data includes: real-time operation parameter data during operation; the Internet of Things data: includes operation condition data collected in real time through sensors.

[0016] In the data acquisition module, not only ERP, production management system, and Internet of Things data are acquired, but also the collection of external multi-source data such as social media data, industry research report data, and government public economic data is increased. Using natural language processing and data mining technologies, features related to enterprise risks are extracted from these unstructured and semi-structured data, such as extracting the emotional tendency of the public towards the enterprise brand from social media, extracting competitor dynamics and other information from industry reports, as supplementary dimensions for risk assessment, enriching the sources and information content of risk data, so as to make the risk assessment of the entire system more comprehensive and accurate. In the data preprocessing module, preprocessing steps for these new types of data are added, such as operations of word segmentation, stop word removal, and word vector conversion for text data, to adapt to the subsequent analysis process.

[0017] The data preprocessing module 12 is used to preprocess the risk data, including performing operations such as word segmentation, stop word removal, and word vector conversion on newly collected data from social media, industry reports, etc. At the same time, data cleaning is performed on the risk data, specifically including: missing value processing, duplicate value processing, and error value processing; data conversion is performed on the cleaned data, including: data type conversion, data standardization, and data aggregation.

[0018] The risk assessment module 14 uses big data analysis technology and machine learning algorithms to deeply mine and analyze the preprocessed risk data, establish various data analysis models to discover potential patterns and anomalies in the data, and based on the results of the data analysis layer, combined with preset risk indicators and thresholds, identify and evaluate potential risks. When the risk indicator exceeds the threshold, the warning mechanism is triggered. The risk assessment module 14 specifically includes:

[0019] The risk indicator construction sub-module is used to set financial risk indicators, market risk indicators, operation risk indicators, and compliance risk indicators, and regularly perform correlation analysis on each risk indicator. When strong correlations are found between certain indicators, the relevant indicators are integrated or screened to avoid duplicate calculations and redundant information in the risk assessment process, improve the efficiency and accuracy of risk assessment, and at the same time, according to the results of the correlation analysis, adjust the variable relationship in the warning model to ensure the scientificity and reliability of the warning; the risk indicator construction sub-module can also automatically trigger the dynamic adjustment mechanism of risk indicators according to major fluctuations in the external market (such as a large increase in raw material prices, a major adjustment of industry policies, etc.). For example, for enterprises sensitive to raw material prices, when the raw material price increase exceeds a certain threshold, increase the weight of the supply chain cost-related indicators in the operation risk indicators and correspondingly adjust the threshold range in the risk threshold setting sub-module; in addition, in the risk threshold setting sub-module, machine learning algorithms such as clustering analysis and decision tree algorithms are introduced to automatically learn and divide risk levels according to historical data and market dynamics, and set a more reasonable threshold range for each risk level;

[0020] The risk threshold setting sub-module is used to set risk thresholds for the risk indicator construction sub-module. By considering the risk preference of the enterprise, determine the benchmark value and reasonable range of risk indicators according to the strategic objectives, industry standards, and historical data of the enterprise, and the risk indicators are dynamically adjusted with the changes in the internal and external environment of the enterprise.

[0021] An early warning model module is used to set different early warning models for different types of risk indicators, regularly backtest and verify the prediction results of the early warning models, optimize and adjust the parameters and structure of the models according to the backtest results to adapt to the dynamic fluctuations of enterprise business changes and market environment, and improve the accuracy and stability of the early warning models; at the same time, establish a version management mechanism for the early warning models to record the reasons, times and operator information of each model adjustment for traceability and auditing; the early warning model module also includes:

[0022] Construction of financial risk early warning, including: using solvency indicators, profitability indicators and operating capacity indicators as independent variables, fitting a regression model through historical data, and combining a neural network model to deeply mine complex financial data relationships to obtain the coefficients of each indicator for predicting the probability of future financial crises of enterprises;

[0023] Construction of market risk early warning model, specifically including: using ARCH / GARCH model to obtain market price fluctuation risks in the case of clustering and heteroscedasticity of market price fluctuations, and adopting a market sentiment analysis model integrating deep learning technology. By collecting text information about enterprise products or markets from channels such as news media and social media, using convolutional neural network for feature extraction and sentiment classification to more accurately capture the changing trend of market sentiment;

[0024] Construction of operation risk early warning, including: using Bayesian network model to calculate the probability of risks occurring in the entire supply chain operation when there are multiple interrelated operation risk factors, and using fault tree analysis model to analyze the operation risks of production equipment;

[0025] Construction of compliance risk early warning model, including: building a rule engine to convert compliance rules into rule statements recognizable by computers. When the business behaviors of enterprises violate these rules, the rule engine immediately issues a compliance risk warning;

[0026] The risk propagation analysis sub-module, based on the graph database and graph algorithms, models and analyzes the propagation relationships between different risk indicators. When a certain risk indicator deteriorates, it analyzes through the risk propagation model the possible impacts on other risk indicators and provides a visual display of the risk propagation path in the early warning notification. Through the risk propagation analysis sub-module, using the graph database and graph algorithms, it models and analyzes the propagation relationships between different risk indicators. For example, when the debt-servicing ability indicator in financial risk deteriorates, it analyzes through the risk propagation model the possible impacts on the supply chain stability indicator in operational risk (such as delivery delays caused by suppliers' concerns about the enterprise's credit) and the market share indicator in market risk (such as a decline in market share due to the enterprise's inability to conduct effective market promotion due to tight funds), and provides a visual display of the risk propagation path in the early warning notification, helping enterprise managers more comprehensively understand the potential chain reactions of risks, formulate targeted risk response strategies in advance, and effectively reduce the overall impact of risks.

[0027] The display module 16 provides personalized operation interfaces for different user roles, including functional modules such as data visualization reports, risk monitoring dashboards, early warning notification lists, and risk handling processes. The display module supports cross-platform access, including the desktop and mobile terminals. A dedicated application is developed for the mobile terminal to facilitate users to obtain risk early warning information and conduct risk monitoring and handling operations anytime and anywhere. In the display module, the dynamic visual display of risk indicators is added, presenting the change trends and distribution of risk indicators in the form of time series graphs, heat maps, etc. Users can conduct in-depth analysis of risk data through mouse interaction operations (such as zooming, filtering, associated queries, etc.). At the same time, in the risk handling process module, an intelligent decision-making recommendation function is introduced. According to the risk assessment results and early warning information, combined with the historical case library and expert experience library, machine learning algorithms are used to recommend the best risk handling solutions for users and provide the expected effect evaluation and implementation step guidance for each solution.

[0028] The embodiments of the present invention further include: a data lineage management module. During the data acquisition process, the data lineage management module records the source information of each piece of data, including the name, address, acquisition time, acquisition method, and the query statements or collection rules used, etc., providing basic data for subsequent data lineage tracing and analysis. During the data preprocessing stage, it continuously tracks the changes of data and records the detailed information of each data cleaning and transformation operation, such as which data were filled with missing values, what filling methods were used, which data were standardized and the parameter settings for standardization, etc., forming a complete data preprocessing lineage chain, making the data flow and processing process traceable. During the risk assessment process, the data lineage management module records the association relationship between risk indicators and the original data, as well as the data sources and transformation processes of each variable in the early warning model, enabling quick tracing to the source and processing steps of relevant data during risk analysis and early warning, facilitating the verification and interpretation of risk assessment results, and also helping to discover data quality problems or potential data risk hazards, improving the reliability and transparency of risk assessment. The data lineage management module provides data lineage query and display functions for the display module. Users can obtain the complete data lineage information of corresponding data items or indicators, including the origin of the data, the processing steps it has gone through, and its association relationship with other data, etc., when viewing risk data and assessment results, to enhance users' understanding and trust in the data and assist users in conducting more in-depth risk analysis and decision-making.

[0029] The data lineage management module is responsible for constructing and maintaining the data lineage relationship graph of the entire system and storing the data lineage information in the form of a database or a file system. In each link of data acquisition, preprocessing, assessment, and display, a complete data lineage chain is constructed by recording information such as the source, transformation, use, and association of data. It provides a data lineage query interface, allowing users and other system modules to query the lineage information of any data element, including tracing upwards to its data source and acquisition path, tracing downwards to its evolution and use in each processing link, and horizontally associating its relationship with other relevant data. When the system performs data updates, model adjustments, or there are data quality problems, the data lineage management module can quickly locate the affected data scope and relevant business processes, providing strong support for system maintenance and optimization, ensuring data consistency, accuracy, and integrity, and at the same time providing a solid data foundation guarantee for the reliability of risk assessment and early warning.

[0030] In practical applications, the data acquisition module is first started. According to the predetermined collection strategy and frequency, it obtains risk data from various data sources and transmits it to the data preprocessing module. At the same time, the data lineage management module records the data acquisition information. After the data preprocessing module cleans, transforms, and performs special preprocessing operations on the received data for new types of data, it provides the processed data to the risk assessment module. During this process, the data lineage management module continuously updates the data processing lineage information. The risk indicator construction sub-module in the risk assessment module constructs a risk indicator system according to the actual situation of the enterprise and the characteristics of the data, and determines the threshold range of each indicator through the risk threshold setting sub-module. The early warning model module evaluates and predicts risks using the constructed risk indicators and thresholds, and transmits the early warning results to the display module. The data lineage management module synchronously records the data association and usage during the risk assessment process. The display module displays risk data, early warning information, and disposal processes in a personalized interface according to the user's role and permissions, and through the data lineage query function provided by the data lineage management module, enables users to deeply understand the data source and evolution process. Users can select appropriate risk disposal solutions according to the intelligent decision-making recommendation function, so as to achieve full-chain monitoring and effective response to enterprise risks, and realize refined management and traceability of data with the help of data lineage technology.

[0031] During the operation of the system, each module will be automatically updated and optimized according to the preset rules and algorithms. For example, the risk threshold setting sub-module will regularly recalculate the threshold range of risk indicators based on new historical data and market changes; the early warning model module will regularly conduct backtesting verification and adjust the model parameters and structure according to the results; the risk propagation analysis sub-module will monitor the changes of risk indicators in real time and update the analysis results of the risk propagation path in a timely manner; the data lineage management module will continuously improve and update the data lineage relationship graph as the data is updated and the processing process progresses, ensuring that the system can always accurately reflect the enterprise's risk status, provide timely and effective early warnings and decision-making support, and at the same time ensure the traceability and reliability of the data.

[0032] The full-chain data monitoring and risk early warning management system of the embodiments of the present invention solves many problems existing in the existing risk early warning systems through innovative data acquisition, preprocessing, evaluation, and display methods, and integrates data lineage technology, has remarkable creativity and practicality, can provide strong technical support for the risk management of enterprises, enhance the competitiveness and risk resistance ability of enterprises in the complex market environment, and at the same time provide effective means for the data governance and quality assurance of enterprises.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A full-chain data monitoring and risk early warning management system, characterized in that include: The data acquisition module uses ETL tools, log collection tools, and interface calls to acquire risk data; A data preprocessing module, used for performing data preprocessing on the risk data; The risk assessment module uses big data analysis technology and machine learning algorithms to conduct in-depth mining and analysis of pre-processed risk data, and establish various data analysis models to discover potential patterns and anomalies in the data. Based on the results of the data analysis layer, combined with preset risk indicators and thresholds, potential risks are identified and assessed. When the risk indicator exceeds the threshold, the early warning mechanism is triggered; The display module provides a personalized operation interface for different user roles, including data visualization reports, risk monitoring dashboards, early warning notification lists, risk disposal processes and other functional modules.

2. The system according to claim 1, characterized in that The risk data includes: ERP data, production management system data and Internet of Things data; The ERP data includes: procurement, finance, and inventory information of assets; The production management system data includes: real-time operation parameter data during operation; The IoT data includes operating condition data collected in real time by sensors.

3. The system according to claim 1, characterized in that The data acquisition module is specifically used for: For the internal relational database of the enterprise, use the connection driver of the corresponding database to establish a connection with the database, and use SQL query statements to extract the required data from the database table; For non-relational databases, use the API or corresponding client tools provided by them to write query statements to obtain data based on the data storage structure and business requirements; For CSV files, use the file reading function provided by the programming language to read the file content into a data table format; for XML and JSON files, use the corresponding parsing library to parse and extract the required data elements and structures; For the log files of the business system, a special log collection tool is used to collect the log files in real time or at a fixed time, extract key information, and convert it into a structured data format; For external data sources, API calls are made using the HTTP / HTTPS protocol to send requests to the data source system and receive returned data.

4. The system according to claim 1, characterized in that The data preprocessing module is specifically used for: Performing data cleaning on the risk data, specifically including: processing missing values, duplicate values, and erroneous values; Perform data conversion on the cleaned data, including data type conversion, data standardization, and data aggregation.

5. The system according to claim 1, characterized in that The risk assessment module specifically includes: The risk indicator construction submodule is used to set financial risk indicators, market risk indicators, operational risk indicators, and compliance risk indicators; A risk threshold setting submodule, used to set a risk threshold for the risk indicator construction submodule; The early warning model module is used to set different early warning models for different types of risk indicators.

6. The system according to claim 5, characterized in that The risk indicator construction submodule is specifically used for: The construction of financial risk indicators specifically includes: Obtain debt repayment capacity indicators by collecting balance sheet data of enterprises and calculating debt-to-asset ratio and current ratio; Based on the income statement data, obtain the profitability indicators of the enterprise; Obtain operating capacity indicators by analyzing accounts receivable turnover and inventory turnover; The construction of market risk indicators specifically includes: Obtain market share indicators by calculating the share of a company's products or services in a specific market through sales data; Analyze the price volatility of products or services to obtain price volatility indicators; Obtain customer demand indicators based on customer feedback data, purchase frequency, and customer churn rate; The construction of operational risk indicators specifically includes: Production efficiency index: Calculate the utilization rate of production equipment through production data, and calculate the per capita output to obtain the production efficiency index; Obtain supply chain stability indicators based on supplier on-time delivery rates and inventory out-of-stock rates; Obtain quality control indicators by calculating product defective rate and customer complaint rate; The construction of compliance risk indicators specifically includes: Through the legal department's recorded data and external regulatory monitoring, the number of corporate violations and the amount involved are counted to obtain compliance indicators for laws and regulations; Calculate the contract default rate by reviewing the contract execution of the enterprise and obtain the contract execution index; Obtain industry regulatory indicators based on regulatory requirements issued by industry regulators and the compliance status of enterprises.

7. The system according to claim 5, characterized in that The risk threshold setting submodule is specifically used for: By considering the risk preferences of the enterprise, the benchmark value and reasonable range of risk indicators are determined according to the enterprise's strategic goals, industry standards and historical data. The risk indicators are dynamically adjusted as the internal and external environment of the enterprise changes.

8. The system according to claim 6, characterized in that The early warning model module is specifically used for: Financial risk early warning construction, including: using debt repayment ability indicators, profitability indicators and operating ability indicators as independent variables, fitting regression models through historical data, and obtaining the coefficients of each indicator to predict the probability of future financial crisis of enterprises; The construction of market risk early warning model includes: using ARCH / GARCH model to obtain market price volatility risk when market price volatility has clustering and heteroscedasticity, and using market sentiment analysis model to collect text information about enterprise products or markets from news media, social media and other channels, and using natural language processing technology to conduct sentiment analysis; Operational risk early warning construction, including: using the Bayesian network model to calculate the probability of risk in the entire supply chain operation when there are multiple interrelated operational risk factors, and using the fault tree analysis model to analyze the operational risks of production equipment; The construction of a compliance risk warning model includes: building a rule engine to convert compliance rules into computer-recognizable rule statements. When the company's business behavior violates these rules, the rule engine immediately issues a compliance risk warning.

9. The system according to claim 5, characterized in that The risk indicator construction submodule is also used for: Regularly conduct correlation analysis on various risk indicators. When strong correlation is found between certain indicators, integrate or screen the relevant indicators to avoid repeated calculations and redundant information in the risk assessment process, improve the efficiency and accuracy of risk assessment, and adjust the variable relationship in the early warning model based on the correlation analysis results to ensure the scientificity and reliability of the early warning.

10. The system according to claim 8, characterized in that The early warning model module is also used for: The prediction results of the early warning model are backtested and verified regularly, and the parameters and structure of the model are optimized and adjusted according to the backtesting results to adapt to the changes in corporate business and the dynamic fluctuations of the market environment, and to improve the accuracy and stability of the early warning model. At the same time, a version management mechanism for the early warning model is established to record the reasons, time and operator information of each model adjustment for traceability and auditing.

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