A process automation monitoring system applied to data asset management

Abstract

The application relates to the technical field of data asset management, in particular to a process self-automation supervision system applied to data asset management. The system comprises a process link data mining module, a whole life cycle management process modeling module, a process compliance abnormality supervision module and a process abnormality early warning optimization suggestion module. The system can acquire data flow transfer execution rules and condition trigger analysis of each data asset management process link, and perform whole life cycle management process modeling to generate a data asset whole life cycle management process model; the system can acquire data asset management compliance rule standards, and perform management behavior rule detection engine design and compliance automatic abnormality supervision, and simultaneously perform abnormality early warning response processing and management process abnormality optimization suggestion to generate a data asset management process abnormality optimization suggestion scheme to execute corresponding data asset management process abnormality improvement work. The application can improve the efficiency and reliability of data asset management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data asset management, and particularly relates to a process automation supervision system applied to data asset management. BACKGROUND

[0002] In the field of data asset management, with the rapid increase in data volume and the increasing complexity of management, how to efficiently and accurately supervise and manage data assets has become an important challenge. By constructing an automated process based on data asset management needs, including data collection, storage, analysis and use, etc., by defining process rules and automated tasks, the management and monitoring of the whole life cycle of data assets is realized. By using advanced data monitoring technology, the flow and use of data assets are tracked in real time, and by deploying real-time data collection systems and data flow analysis tools, the real-time status of data assets is ensured, and an intelligent rule engine is introduced to automatically generate and adjust management rules according to the actual needs of data asset management. The engine can automatically update and optimize management strategies according to data flow and usage patterns, thereby improving the flexibility and accuracy of supervision. However, traditional data asset management methods usually rely on manual supervision and static processes, which are prone to vulnerabilities when dealing with complex data asset processes, often lacking real-time tracking of data flow and usage, resulting in low compliance and supervision efficiency of data asset management. SUMMARY

[0003] Therefore, it is necessary to provide a process automation supervision system applied to data asset management to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a process automation supervision system applied to data asset management comprises the following modules:

[0005] A process link data mining module is used to obtain each data asset management process link, wherein each data asset management process link includes a data acquisition process link, a data storage process link, a data access process link, a data analysis process link, a data sharing process link and a data destruction process link; data flow execution rules and condition triggering analysis are performed on each data asset management process link to obtain data flow execution rules and data flow condition triggering nodes between each management process link;

[0006] A whole life cycle management process modeling module is used to model each data asset management process link based on the data flow execution rules and data flow condition triggering nodes between each management process link to generate a data asset whole life cycle management process model;

[0007] A process compliance anomaly supervision module is configured to acquire data asset management compliance rule standards, and design a management behavior rule detection engine according to the data asset management compliance rule standards, to generate a data asset management operation behavior compliance detection engine; and automatically supervise the compliance of corresponding data asset management process operation behaviors in a data asset lifecycle management process model according to the data asset management operation behavior compliance detection engine, to obtain data asset management process non-compliant abnormal operation behaviors.

[0008] A process anomaly early warning optimization suggestion module is configured to perform abnormal early warning response processing on the data asset management process non-compliant abnormal operation behaviors, to generate data asset management process anomaly early warning response data; perform management process anomaly optimization suggestions on the data asset management process anomaly early warning response data, to generate data asset management process anomaly optimization suggestion schemes, to perform corresponding data asset management process anomaly improvement work.

[0009] Further, the process link data mining module includes the following functions:

[0010] Each data asset management process link is acquired from a data asset management process, wherein each data asset management process link includes a data acquisition process link, a data storage process link, a data access process link, a data analysis process link, a data sharing process link, and a data destruction process link.

[0011] Data flow tracking analysis is performed between each data asset management process link, to obtain actual data flow routes between each management process link.

[0012] Based on the actual data flow routes between each management process link, data flow interaction mode recognition analysis is performed on each data asset management process link, to generate a data flow transfer interaction mode graph between each management process link.

[0013] Data flow transfer interaction mode graph analysis is performed on the data flow transfer interaction mode graph between each management process link, to obtain data flow transfer execution rules between each management process link.

[0014] Based on the data flow transfer execution rules between each management process link, data flow condition trigger node recognition analysis is performed on the actual data flow routes between each management process link, to obtain data flow condition trigger nodes between each management process link.

[0015] Further, the data flow interaction mode recognition analysis performed on each data asset management process link based on the actual data flow routes between each management process link includes:

[0016] mapping connection graph of the data transfer transmission path between the management process links is obtained based on the data transfer transmission path between the management process links;

[0017] The data transfer interaction relationship between the management process links is obtained by mining and analyzing the interaction relationship between the data transfer actual routes of the management process links;

[0018] The data transfer transmission path interaction feature data between the management process links is obtained by analyzing the transfer path interaction features of the mapping connection graph of the data transfer transmission path between the management process links based on the data transfer interaction relationship between the management process links;

[0019] The data transfer transmission path interaction feature data between the management process links is obtained by analyzing the transfer path interaction features of the mapping connection graph of the data transfer transmission path between the management process links based on the data transfer interaction relationship between the management process links;

[0020] Further, the data transfer execution rule mining analysis of the data transfer transmission path interaction mode graph between the management process links includes:

[0021] The data transfer transmission path interaction behavior logic between the management process links is obtained by analyzing the interaction behavior logic of each data transfer transmission path in the data transfer transmission path interaction mode graph between the management process links;

[0022] The data transfer transmission path interaction behavior logic between the management process links is obtained by analyzing the interaction behavior logic of each data transfer transmission path in the data transfer transmission path interaction mode graph between the management process links;

[0023] The data transfer operation standard between the management process links is obtained by determining the data transfer operation standard of each data transfer transmission path in the data transfer transmission path interaction mode graph between the management process links based on the data transfer interaction behavior logic constraint condition between the management process links;

[0024] The data transfer execution rule between the management process links is obtained by mining and analyzing the data transfer execution rule of the data transfer interaction behavior operation standard between the management process links.

[0025] Further, the data transfer execution rule mining analysis of the data transfer execution rule between the management process links includes:

[0026] Data flow event identification analysis is performed on the data flow transfer execution rules between the various management process links to obtain data flow transfer process execution event relationships between the various management process links;

[0027] Trigger condition node candidate set processing is performed on the data flow transfer actual routes between the various management process links based on the data flow transfer process execution event relationships between the various management process links to obtain a data flow transfer trigger condition node candidate set between the various management process links;

[0028] Data flow transfer frequency and impact degree statistical analysis is performed on each data flow transfer trigger condition candidate node in the data flow transfer trigger condition node candidate set between the various management process links to obtain the frequency and impact degree of each trigger condition candidate node between the various management process links;

[0029] Trigger key degree quantitative calculation is performed on each data flow transfer trigger condition candidate node in the data flow transfer trigger condition node candidate set between the various management process links based on the frequency and impact degree of each trigger condition candidate node between the various management process links to obtain the data flow transfer trigger key degree of each trigger condition candidate node between the various management process links;

[0030] Optimal trigger condition node screening processing is performed on the corresponding data flow transfer trigger condition candidate nodes in the data flow transfer trigger condition node candidate set between the various management process links according to the data flow transfer trigger key degree of each trigger condition candidate node between the various management process links to obtain data flow transfer condition trigger nodes between the various management process links.

[0031] Further, the full life cycle management process modeling module includes the following functions:

[0032] Data flow transfer execution flow direction fusion analysis is performed on the data flow transfer execution rules between the various management process links to obtain a data flow transfer execution flow direction fusion graph between the various management process links;

[0033] Flow transfer execution location positioning processing is performed on the data flow transfer condition trigger nodes between the various management process links to obtain data flow transfer execution positioning locations of the trigger nodes between the various management process links;

[0034] Condition trigger node mapping processing is performed on the data flow transfer execution flow direction fusion graph between the various management process links based on the data flow transfer execution positioning locations of the trigger nodes between the various management process links to obtain a data flow transfer execution trigger node mapping graph between the various management process links;

[0035] The data asset whole life cycle management process model is generated by performing trigger node mapping diagram on each data asset management process link based on data flow between each management process link.

[0036] Further, the process compliance anomaly supervision module comprises the following functions:

[0037] Obtaining data asset management compliance rule standards;

[0038] Mapping relationship between data asset management behavior and compliance rule standards is established by mapping relationship between data asset management behavior and compliance rule standards, so as to obtain rule docking mapping relationship between data asset management behavior and compliance rule standards;

[0039] The rule docking mapping relationship between data asset management behavior and compliance rule standards is detected by constructing a detection engine logic framework, so as to obtain a data asset management behavior compliance detection engine logic framework;

[0040] The data asset management operation behavior compliance detection engine is generated by designing an engine integration test for the data asset management operation behavior compliance detection engine logic framework;

[0041] According to the data asset management operation behavior compliance detection engine, the corresponding data asset management process operation behavior in the data asset whole life cycle management process model is automatically supervised in compliance, and the data asset management process non-compliant abnormal operation behavior is obtained.

[0042] Further, the data asset management operation behavior compliance detection engine automatically supervises the corresponding data asset management process operation behavior in the data asset whole life cycle management process model in compliance, and the data asset management process non-compliant abnormal operation behavior is obtained.

[0043] The management operation behavior of each data asset management process link in the data asset whole life cycle management process model is analyzed, and the data asset whole life cycle management process operation behavior is obtained.

[0044] The data asset management operation behavior dynamic compliance detection mechanism is generated according to the data asset management operation behavior compliance detection engine, and the data asset management operation behavior dynamic compliance detection mechanism is obtained.

[0045] Based on the data asset management operation behavior dynamic compliance detection mechanism, the management operation behavior of the corresponding data asset management process link in the data asset whole life cycle management process operation behavior is automatically supervised in compliance, and the data asset management process non-compliant abnormal operation behavior is obtained.

[0046] Further, the management operation behavior of each data asset management process link in the data asset whole life cycle management process model is analyzed, and the data asset whole life cycle management process operation behavior is obtained.

[0047] extracting and processing the operation content of each data asset management process link in the data asset whole life cycle management process model, to obtain the management process operation content corresponding to each data asset management process link;

[0048] mining and analyzing the operation behavior characteristic attribute of the management process operation content corresponding to each data asset management process link, to obtain the management process operation behavior characteristic attribute data corresponding to each data asset management process link;

[0049] performing operation behavior mode recognition analysis on the operation behavior characteristic attribute data corresponding to each data asset management process link, to obtain the management process operation behavior mode corresponding to each data asset management process link;

[0050] based on the management process operation behavior mode corresponding to each data asset management process link, performing whole life cycle management behavior analysis and summarization on the corresponding each data asset management process link in the data asset whole life cycle management process model, to obtain the data asset whole life cycle management process operation behavior.

[0051] Further, the process exception early warning optimization suggestion module comprises the following functions:

[0052] performing exception early warning response processing on the non-compliant abnormal operation behavior of the data asset management process, to generate data asset management process exception early warning response data;

[0053] based on the data asset management process exception early warning response data, performing management process exception positioning report on the corresponding non-compliant abnormal operation behavior of the data asset management process, to obtain a data asset management process non-compliant abnormal behavior supervision report;

[0054] performing management process exception optimization suggestion on the data asset management process non-compliant abnormal behavior supervision report, to generate a data asset management process exception optimization suggestion scheme, so as to perform corresponding data asset management process exception improvement work.

[0055] The beneficial effects of the present application are as follows:

[0056] The application relates to a process automation supervision system applied to data asset management, which is composed of a process link data mining module, a whole life cycle management process modeling module, a process compliance anomaly supervision module and a process anomaly early warning optimization suggestion module.Compared with the prior art, the application has the beneficial effects that each data asset management process link in the data asset management process is systematically identified and classified, including data acquisition, data storage, data access, data analysis, data sharing and data destruction and the like.The core of the process lies in building a comprehensive and systematic data management framework, which lays a foundation for subsequent process optimization and problem solving through clear definition and description of each link, so that the role and operation mode of each process link can be understood in detail, which can help the organization to effectively manage and monitor the data life cycle, identify potential risks and bottlenecks in data processing, and ensure the quality and safety of data assets.Through mining and analyzing the data flow execution rules of the data flow between each data asset management process link, the main purpose of the process is to identify and define the rules and standards required to be followed in each link during the data flow process.The key of this step lies in mining and analyzing the data flow execution rules, which can ensure the consistency, standardization and controllability of the data processing process.These execution rules can include data transmission time window, data processing priority, data format and standard, etc.Clearing these rules can help reduce conflicts and errors in data processing, thereby improving the efficiency and accuracy of data flow.Meanwhile, through data flow condition trigger node identification analysis of the data flow between each management process link, the core of the process lies in identifying those key condition trigger points in the data flow process.These nodes will trigger changes in data flow or processing under certain conditions.Identifying these trigger nodes is crucial because it can help to clarify the key control points and decision points in the data processing process, thereby effectively monitoring and managing the data flow process.These trigger nodes involve asset state changes, occurrence of specific events, data quality problems and the like.Through identification and analysis of these nodes, the organization can realize real-time monitoring and automatic response mechanism in the data processing process, reduce the need for manual intervention, and improve the efficiency and accuracy of data flow, which can improve the automation and intelligence level of data management, better realize real-time tracking of data asset flow and use, and enhance the flexibility and reliability of the data asset management process.Secondly, by modeling each data asset management process step based on the data flow execution rules and data flow condition trigger nodes between each management process step, the core of this process is to establish a comprehensive life cycle management model that covers the entire life cycle of data from generation, storage, access, analysis, sharing to destruction. The main benefit of this step is that it can provide a comprehensive and systematic perspective for data asset management. By building a full life cycle management model, we can clearly understand the management requirements and operation specifications at each stage, thereby achieving effective management and control of data assets. This model helps to improve the comprehensiveness and systematicness of data management, ensuring that data meets predetermined business objectives and compliance requirements at each stage of the life cycle, thus providing a systematic framework and tool for data asset management and optimization, supporting scientific and efficient data asset management. Then, by obtaining data asset management compliance rules and standards, these standards provide legal, industry standards and best practices guidance for data management and processing. The core of this step is to ensure that the organization's data asset management process complies with existing compliance requirements and regulations, avoiding legal risks or regulatory penalties due to compliance issues. By obtaining and analyzing these rules and standards, we can gain a deep understanding of the specific compliance requirements in data asset management, including data privacy protection, data security, data quality standards, etc., laying a foundation for subsequent compliance analysis and detection, ensuring that all subsequent steps have a clear compliance framework and reference point. Obtaining compliance rules and standards can help organizations develop internal management strategies and operating procedures to ensure the legality and compliance of data asset processing. By designing a management behavior rule detection engine for data asset management compliance rules and standards, the main advantage of this process is that it integrates test verification and optimization of the function and performance of the compliance detection engine to ensure its effective operation in actual environments. The design of the management behavior rule detection engine can simulate actual operation scenarios and check the performance of the engine in processing data asset management operations, including the accuracy, efficiency and stability of compliance checks. This process helps to identify and solve potential problems and defects, such as logic errors, performance bottlenecks or compatibility issues, ensuring that the engine can reliably perform compliance detection tasks in actual applications. By designing and executing integrated tests, the functionality of the detection engine can be optimized, improving its adaptability and stability in different environments, which not only improves the compliance detection capability of data asset management, but also improves the overall performance and user experience of the system.By using the data asset management operational behavior compliance detection engine to automatically monitor compliance anomalies within the data asset management process model, the primary advantage of this process lies in its ability to achieve real-time, automated monitoring of data management behavior, promptly identifying and reporting non-compliant operational behaviors. Automated compliance detection significantly reduces manual inspection workload, improves detection efficiency and accuracy, and thus reduces compliance risks and management costs. By monitoring anomalies at every stage of the data asset management lifecycle, data management compliance can be tracked in real time, allowing for the timely identification and resolution of potential compliance issues. Automated monitoring also provides detailed audit records and reports to support compliance reviews and rectification efforts, helping to improve data asset management compliance and the efficiency of anomaly monitoring, thereby achieving efficient and secure data asset management. Finally, by implementing anomaly warnings and responses to non-compliant anomalies within the data asset management process, the key to this process lies in the rapid detection and resolution of anomalies. This early warning mechanism can identify signs of improper operation early, thereby reducing potential risks and losses. This timely warning prevents issues from escalating, enabling corrective measures to be taken at the earliest stages and avoiding more complex compliance issues later on. By generating detailed abnormal warning response data, a valuable basis can be provided for subsequent abnormal handling. This data can not only help the team understand the nature, frequency and impact of abnormal behavior, but also provide data support for the analysis of abnormal causes, which helps to improve the overall efficiency of data asset management and ensure that operational behavior is carried out within a framework that complies with regulations, thereby improving the data management level of process non-compliant abnormal behavior and enhancing the security and compliance of data asset management. In addition, by making management process abnormality optimization suggestions based on the abnormal warning response data of the data asset management process, we focus on formulating specific optimization suggestions after discovering and analyzing non-compliant abnormal behavior in the data asset management process. The key to this step is to form targeted optimization plans through in-depth abnormal behavior analysis and reporting, thereby promoting the continuous improvement of the data asset management process. The generation of optimization suggestions means that practical improvement measures can be formulated based on actual conditions. These suggestions include updating management processes, improving operating specifications, introducing new monitoring tools or training personnel, etc. By implementing these recommendations, deficiencies in existing processes can be effectively corrected, and similar abnormal behaviors can be prevented from recurring, thereby improving the stability and compliance of the entire data asset management system. Continuous improvement will help organizations better adapt to changing regulatory requirements and business needs, thereby ensuring that data asset management continues to move forward on the path of efficiency and compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0058] Figure 1 Schematic diagram of the modules of the process automation supervision system applied to data asset management according to the present invention;

[0059] Figure 2 for Figure 1 Functional flow diagram of the data mining module in the middle process;

[0060] Figure 3 for Figure 1 Functional flow diagram of the full life cycle management process modeling module. DETAILED DESCRIPTION

[0061] The following is a clear and complete description of the technical system of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0063] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 The present invention provides a process automation supervision system for data asset management, which includes the following modules:

[0065] The process link data mining module is used to acquire each data asset management process link, wherein each data asset management process link comprises a data acquisition process link, a data storage process link, a data access process link, a data analysis process link, a data sharing process link and a data destruction process link; data flow execution rules and condition triggering analysis are performed on each data asset management process link to obtain data flow execution rules and data flow condition triggering nodes between each management process link;

[0066] The full life cycle management process modeling module is used to model each data asset management process link based on the data flow execution rules and the data flow condition triggering nodes between each management process link to generate a data asset full life cycle management process model.

[0067] The process compliance anomaly supervision module is used to acquire data asset management compliance rule standards and design a management behavior rule detection engine based on the data asset management compliance rule standards to generate a data asset management operation behavior compliance detection engine; the data asset management operation behavior compliance detection engine is used to perform compliance automatic anomaly supervision on corresponding data asset management process operation behaviors in the data asset full life cycle management process model to obtain data asset management process non-compliant abnormal operation behaviors.

[0068] The process anomaly early warning optimization suggestion module is used to perform abnormal early warning response processing on the data asset management process non-compliant abnormal operation behaviors to generate data asset management process anomaly early warning response data; management process anomaly optimization suggestions are performed on the data asset management process anomaly early warning response data to generate data asset management process anomaly optimization suggestion schemes to perform corresponding data asset management process anomaly improvement work.

[0069] In the embodiment of the application, please refer to Figure 1 As shown in the figure, the process self-automation supervision system for data asset management comprises the following modules in the example:

[0070] S1: The process link data mining module is used to acquire each data asset management process link, wherein each data asset management process link comprises a data acquisition process link, a data storage process link, a data access process link, a data analysis process link, a data sharing process link and a data destruction process link; data flow execution rules and condition triggering analysis are performed on each data asset management process link to obtain data flow execution rules and data flow condition triggering nodes between each management process link;

[0071] In the embodiment of the present application, the data of each data asset management process link is obtained by using the data acquisition tool in the data asset management process, specifically including extracting the information of the process link from the data source (such as sensor, log system, etc.) to obtain the data, the database system storing the data to obtain the information of the data storage process link, the API or access control system accessing the data to obtain the information of the data access process link, the analysis platform or tool analyzing the data to obtain the information of the data analysis process link, the file exchange system or data sharing platform sharing the data to obtain the information of the data sharing process link, and the clearing tool destroying the data to obtain the information of the data destruction process link. All data needs to be arranged in a standard format, and the detailed information of each process link is recorded, including operation time, personnel involved, data type, etc., so as to obtain each data asset management process link, including data acquisition process link, data storage process link, data access process link, data analysis process link, data sharing process link and data destruction process link. At the same time, the data flow between each data asset management process link is analyzed by mining the execution rules, so as to analyze the data flow execution rules between each management process link by using the rule mining tool (such as data mining algorithm, rule engine, etc.). Through the mining process, the execution rules under different data flow paths and interaction modes are extracted, including data processing order, condition limitation and operation requirement, so as to obtain the data flow execution rules between each management process link. Then, the data flow between each management process link is analyzed by identifying the condition trigger nodes by combining the data flow execution rules between each management process link obtained by the previous mining analysis, so as to analyze the condition trigger nodes in the data flow process by using the trigger condition identification tool (such as condition judgment algorithm, event-driven model, etc.). These nodes are the key points of triggering specific operations or state changes under specific conditions, and by identifying these trigger nodes, the condition dependency relationship of each link in the data flow can be determined, and finally the data flow condition trigger nodes between each management process link are obtained.

[0072] S2: The full life cycle management process modeling module is used for modeling the full life cycle management process of each data asset management process link based on the data flow execution rules and data flow condition trigger nodes between each management process link, so as to generate a data asset full life cycle management process model.

[0073] In the embodiments of the present application, by collecting all data flow execution rules related to the management of the links between the process, these rules describe the data transmission relationship between the management process links, data format, processing logic and their mutual dependence, and by combining the data flow execution rules between the management process links and the specific location of the data flow condition trigger node, the corresponding management process links of each data asset are modeled for the whole life cycle management process, so as to import the previously analyzed data flow execution rules and data flow condition trigger nodes into the modeling tool by using a system modeling tool (such as a SysML modeling tool or a UML modeling tool), and according to the trigger nodes and their conditions identified in the modeling tool, a comprehensive management process model is constructed, which includes the whole process of data assets from acquisition, storage, access, analysis, sharing and destruction, and the specific operations include defining the input and output of each link, data processing rules, trigger conditions and node behavior, establishing the connection between the links and the data flow path, and finally constructing and generating the data asset whole life cycle management process model.

[0074] S3: Process compliance anomaly supervision module, used for acquiring data asset management compliance rule standards, and designing management behavior rule detection engine according to the data asset management compliance rule standards, to generate a data asset management operation behavior compliance detection engine; and performing compliance automatic anomaly supervision on the corresponding data asset management process operation behavior in the data asset whole life cycle management process model according to the data asset management operation behavior compliance detection engine, to obtain data asset management process non-compliant abnormal operation behavior;

[0075] In the embodiments of the present application, the compliance rules standards of data asset management are collected from industry standard organizations, regulatory documents and company internal compliance guidelines, including accessing industry standard databases (such as ISO, NIST or standards provided by local regulatory authorities), downloading and organizing compliance requirements of data asset management, for example, by referring to data protection requirements in ISO / IEC27001 standard, collecting specific compliance clauses, and converting them into easy-to-resolve document formats, while organizing the obtained compliance rule standards into a systematic list, including specific requirements of data asset management, operation specifications, permission control and record keeping requirements, etc., to obtain data asset management compliance rule standards. At the same time, rule mapping tools (such as IBM ODM or Drools) are used to create rule mapping to import the obtained compliance rule standards into the mapping tool, and to classify rules according to the actual data asset management process definition and operation behavior, and to map each requirement of the compliance rule standard to the specific operation step in the data asset management behavior, this process involves matching each compliance clause with the management behavior, generating a rule mapping relationship diagram, and building a detection engine framework based on the rule mapping relationship diagram obtained by the previous analysis, to define the detection logic and framework architecture, this process uses design tools (such as UML modeling tools or architecture design software) to build the logical framework of the detection engine, the framework includes data input module (for receiving operation behavior data), rule matching module (for detection according to the previously established rule mapping relationship) and report generation module (for generating compliance detection results), and the integrated test tool (such as JUnit or Selenium) is used to verify the design of the integrated test of the constructed detection engine logical framework, in the integrated test process, the data asset management behavior data and the compliance rule standards are input into the test environment, and test cases are designed to cover all expected operation behaviors and compliance scenarios, including normal operation, boundary conditions and abnormal situations, after running the test, the response time, detection accuracy and compliance report generation of the detection engine are recorded, to ensure the reliability and accuracy of the finally generated operation behavior compliance detection engine in actual application, so as to verify the generated data asset management operation behavior compliance detection engine.Then, the data asset management operation behavior compliance detection engine is used to automatically monitor the compliance of the corresponding data asset management process operation behavior in the data asset full life cycle management process model, and the generated detection engine is integrated into the management process monitoring system (such as Elasticsearch or Logstash) to monitor the operation behavior of the data asset full life cycle in real time. The detection engine automatically applies compliance rules to check each data asset management process operation behavior in the data asset full life cycle management process model. If an operation that does not meet the compliance standards is found, it is determined as a corresponding non-compliant abnormal operation behavior, and finally the data asset management process non-compliant abnormal operation behavior is obtained.

[0076] S4: The process abnormality early warning optimization suggestion module is used to perform abnormality early warning response processing on the data asset management process non-compliant abnormal operation behavior to generate data asset management process abnormality early warning response data; perform management process abnormality optimization suggestion on the data asset management process abnormality early warning response data to generate data asset management process abnormality optimization suggestion scheme to perform corresponding data asset management process abnormality improvement work.

[0077] In the embodiments of the present application, by giving early warning rules based on the set standards and historical data, the operation records in the data asset management process are monitored in real time through a data analysis platform (such as Splunk or ELK Stack), the operations include configuring data monitoring system, real-time analysis of operation behavior, discovery of behavior inconsistent with standard operation, such as illegal data access, unauthorized operation request, etc., and automatic recording of detailed information of related operation, including timestamp, operator, operation content, etc. The corresponding abnormal operation behavior in the data asset management process is given an abnormal early warning response through an automatic early warning mechanism, to obtain records including abnormal early warning signal, abnormal type, frequency and specific operation behavior, so as to generate data asset management process abnormal early warning response data. At the same time, the corresponding data asset management process abnormal operation behavior is given a management process abnormal specific location positioning report by combining the data asset management process abnormal early warning response data generated by the previous abnormal response, so as to import the early warning response data into a data analysis tool (such as Tableau or Power BI) and perform visual processing, identify the concentration area or specific location of abnormal operation through the statistics and analysis of abnormal behavior, and generate a detailed management process abnormal positioning report by combining the process log and operation record. Then, the management process abnormal positioning report obtained by the previous analysis is given a management process abnormal optimization suggestion analysis, so as to identify the root cause of the abnormality through an analysis tool (such as root cause analysis tool or process improvement model), and make targeted optimization suggestions according to the abnormal behavior and influence identified in the report, the optimization suggestions including adjusting operation process, strengthening permission management, increasing data audit and monitoring, etc., so as to generate a data asset management process abnormal optimization suggestion scheme, and finally implement the data asset management process abnormal optimization suggestion scheme in the data asset management process to solve the abnormal behavior in the data asset management process, so as to perform corresponding data asset management process abnormal improvement work.

[0078] Further, the process link data mining module includes the following functions:

[0079] The various data asset management process links are obtained from the data asset management process, wherein the various data asset management process links include data acquisition process link, data storage process link, data access process link, data analysis process link, data sharing process link and data destruction process link;

[0080] The data flow conversion tracking analysis is performed between the various data asset management process links to obtain the actual data flow conversion route between the various management process links;

[0081] Based on the actual data flow routes between each management process link, the data flow interaction pattern recognition and analysis of each data asset management process link is carried out to generate a data flow transmission interaction pattern map between each management process link;

[0082] Conduct data flow execution rule mining and analysis on the data flow and transmission interaction pattern graph between each management process link to obtain the data flow execution rules between each management process link;

[0083] Based on the data flow execution rules between each management process link, the actual data flow route between each management process link is analyzed to identify the data flow condition trigger node to obtain the data flow condition trigger node between each management process link.

[0084] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 The functional flow diagram of the data mining module in the process link is as follows. In this embodiment, the data mining module in the process link includes the following functions:

[0085] S11: Obtaining various data asset management process links from the data asset management process, wherein the various data asset management process links include data acquisition process link, data storage process link, data access process link, data analysis process link, data sharing process link, and data destruction process link;

[0086] In an embodiment of the present invention, data of each data asset management process link is obtained by using a data acquisition tool during the data asset management process, specifically including extracting information of the data acquisition process link from a data source (such as a sensor, a log system, etc.), obtaining information of the data storage process link from a database system that stores data, obtaining information of the data access process link from an API or access control system that accesses data, obtaining information of the data analysis process link from an analysis platform or tool that analyzes data, obtaining information of the data sharing process link from a file exchange system or data sharing platform that shares data, and obtaining information of the data destruction process link from a cleaning tool that destroys data. All data must be organized in a standard format, and detailed information of each process link must be recorded, including operation time, participants, data type, etc., to ultimately obtain each data asset management process link, including data acquisition process link, data storage process link, data access process link, data analysis process link, data sharing process link, and data destruction process link.

[0087] S12: Track and analyze the data flow between each data asset management process link to obtain the actual data flow route between each management process link;

[0088] In the embodiment of the present application, the data flow path between each data asset management process link is tracked and analyzed, the data flow of each link is collected by using a data flow monitoring tool (such as a data flow management platform, a log analysis tool, etc.), and a data flow path diagram is generated, which shows the whole process of data from the acquisition link to the destruction link. The analysis process needs to mark the data flow path between each link, the data transfer frequency and the data volume, so as to identify the actual route of data flow and its characteristics, and finally obtain the actual route of data flow between each management process link.

[0089] S13: Based on the actual route of data flow between each management process link, data flow interaction mode identification analysis is performed on each data asset management process link to generate a data flow transfer interaction mode diagram between each management process link.

[0090] In the embodiment of the present application, the data flow interaction mode between each data asset management process link is identified and analyzed by combining the actual route of data flow between each management process link obtained by the previous tracking analysis, so as to identify the data interaction mode between each management process link by using a data interaction analysis tool (such as a pattern recognition algorithm, a machine learning model, etc.), analyze the common characteristics of the data flow mode by training the identification model, and generate a data flow transfer interaction mode diagram, which shows the interaction mode and pattern of data between different links, and finally generates a data flow transfer interaction mode diagram between each management process link.

[0091] S14: Data flow execution rule mining analysis is performed on the data flow transfer interaction mode diagram between each management process link to obtain the data flow execution rule between each management process link.

[0092] In the embodiment of the present application, the data flow transfer interaction mode diagram between each management process link obtained by the previous identification analysis is subjected to execution rule mining analysis, so as to analyze the data flow execution rule between each management process link by using a rule mining tool (such as a data mining algorithm, a rule engine, etc.), extract the execution rule under different data flow paths and interaction modes through the mining process, and finally obtain the data flow execution rule between each management process link, which specifically includes the processing order of data, the condition limitation and the operation requirement.

[0093] S15: Based on the data flow execution rule between each management process link, data flow condition trigger node identification analysis is performed on the actual route of data flow between each management process link to obtain the data flow condition trigger node between each management process link.

[0094] In the embodiment of the present application, the data flow actual route between each management process link is analyzed by combining the data flow execution rules between each management process link obtained by previous mining analysis, to analyze the conditional trigger nodes in the data flow path by using trigger condition identification tools (such as conditional judgment algorithm, event-driven model, etc.), which are the key points of triggering specific operations or state changes under specific conditions. By identifying these trigger nodes, the conditional dependency relationship of each link in the data flow can be determined, and the data flow conditional trigger nodes between each management process link are obtained.

[0095] Further, the data flow interaction mode identification analysis of each data asset management process link based on the data flow actual route between each management process link includes:

[0096] The data flow transmission path mapping connection diagram between each management process link is obtained by mapping the data flow path of each data asset management process link based on the data flow actual route between each management process link.

[0097] In the embodiment of the present application, the data flow path of each data asset management process link is mapped based on the data flow actual route between each management process link obtained by previous analysis, to complete the mapping by deploying data flow diagram tools or process modeling software. Using these tools, the data input and output in each link are mapped one by one, and a detailed path transmission diagram is established. The specific operation includes collecting data transmission records from each link, generating a data flow diagram, and marking the starting and ending points of each flow path. It is ensured that each data transmission path is accurately recorded in the diagram. These records will form the data flow path connection diagram between each management process link, provide a clear overview of the data flow route, and finally obtain the data flow transmission path mapping connection diagram between each management process link.

[0098] Preferably, the data flow actual route between each management process link is analyzed to obtain the data flow interaction relationship between each management process link.

[0099] In the embodiment of the present application, the data flow actual route between each management process link is analyzed in depth to analyze the data exchange mode between each link by using data analysis tools such as graph database or network analysis software, and the interaction relationship between the flow links is mined by using algorithms (such as node centrality analysis), to identify the frequent nodes and main transmission paths of data transmission, and finally obtain the data flow interaction relationship between each management process link.

[0100] Preferably, the data flow transfer path interaction feature analysis is performed on the data flow transfer path mapping connection graph between the management process links based on the data flow transfer interaction relationship between the management process links to obtain data flow transfer path interaction feature data between the management process links.

[0101] In the embodiment of the present application, the interaction feature analysis of the data flow transfer path is performed on the data flow transfer path mapping connection graph between the corresponding management process links by combining the data flow transfer interaction relationship between the management process links obtained by the previous analysis, so as to analyze the interaction feature of each path in the data flow transfer interaction relationship by using the transfer path analysis tool. The operation process includes inputting the interaction relationship graph into the feature analysis software, calculating and extracting the interaction feature data of each path such as the transfer frequency, delay time and data loss rate, and finally obtaining the data flow transfer path interaction feature data between the management process links.

[0102] Preferably, the data flow transfer interaction mode recognition analysis is performed on the data flow transfer path interaction feature data between the management process links to generate a data flow transfer interaction mode graph between the management process links.

[0103] In the embodiment of the present application, the data flow transfer interaction mode recognition analysis is performed on the data flow transfer path interaction feature data between the management process links obtained by the previous analysis by using data mode recognition techniques such as clustering analysis and pattern matching algorithms, so as to recognize the typical interaction mode in the data flow transfer. The operation steps include inputting the interaction feature data into the mode recognition software, applying a machine learning algorithm (for example, K-means clustering) to classify different interaction modes, and thus generating a graph showing the data flow transfer interaction mode between the management process links. This graph will show the common mode and abnormal situation in the data flow, and finally generate a data flow transfer interaction mode graph between the management process links.

[0104] Further, the data flow transfer execution rule mining analysis on the data flow transfer interaction mode graph between the management process links includes:

[0105] The interaction behavior logic refinement analysis is performed on each data flow transfer path in the data flow transfer interaction mode graph between the management process links to obtain the data flow transfer interaction behavior logic between the management process links.

[0106] In the embodiment of the present application, each data flow path in the data flow transmission interaction mode atlas between the management process links is analyzed by interaction behavior logic refinement, which involves operations including using modeling tools (such as Visio or UML modeling tools) to identify each node and connection in the data flow path atlas one by one, and by analyzing the starting point, termination point and transmission mode of each data transmission in detail, the interaction behavior logic is determined, which can include data processing rules, data conversion operations and data transmission protocols, and the input, processing and output of each step need to be recorded, and finally the data flow transmission interaction behavior logic between the management process links is obtained.

[0107] Preferably, the data flow transmission interaction behavior logic between the management process links is analyzed by interaction behavior logic constraint analysis, and the data flow transmission interaction behavior logic constraint conditions between the management process links are obtained.

[0108] In the embodiment of the present application, the data flow transmission interaction behavior logic between the management process links obtained by the previous analysis is analyzed by interaction behavior logic constraint analysis, and the data flow limitation conditions in each link are identified, which include data validity, real-time requirement and error handling mechanism, so as to check each data flow in detail by using constraint analysis tools (such as Constraint Logic Programming or other analysis tools), and ensure that the behavior in each link meets the preset constraint conditions, for example, in the welding robot system, it is necessary to ensure that the welding parameter data does not exceed the preset range in the transmission process, and conforms to the industrial standard in data conversion, and finally the data flow transmission interaction behavior logic constraint conditions between the management process links are obtained.

[0109] Preferably, the data flow transmission interaction mode atlas between the management process links is analyzed by interaction behavior logic constraint analysis, and the data flow transmission interaction behavior logic constraint conditions between the management process links are obtained.

[0110] In the embodiment of the present application, the operation standard of each data flow transfer path is determined by marking the data flow transfer operation standard of each corresponding data flow transfer and interaction mode graph between the data flow transfer interaction behavior logical constraint conditions of each management process link obtained by previous analysis, which includes detailed operation standard definition of each data flow transfer path, including data format, transfer frequency, processing time and error handling mechanism, and specific operation specification is formulated by using standardized tools (such as ISO or IEC standard), for example, in the control system of the welding process, the transmission time of the data packet, the structure of the data packet, the error detection and correction method need to be determined, to ensure that the operation of data assets in each link meets the specification, and finally the data flow transfer interaction behavior operation standard between each management process link is obtained.

[0111] Preferably, the data flow transfer execution rule between each management process link is obtained by mining and analyzing the data flow transfer execution rule of the data flow transfer interaction behavior operation standard between each management process link.

[0112] In the embodiment of the present application, the data flow transfer execution rule between each management process link is obtained by mining and analyzing the data flow transfer execution rule of the data flow transfer interaction behavior operation standard between each management process link obtained by previous analysis, which includes data flow transfer execution steps, processing logic, abnormal situation processing and feedback mechanism, and in the implementation, the data mining tool can extract patterns and rules from historical data, for example, common data errors and their processing methods in the welding process are analyzed to formulate specific data flow transfer execution rules, to ensure the stability and reliability of the system, and finally the data flow transfer execution rule between each management process link is obtained.

[0113] Further, the data flow transfer condition trigger node identification analysis of the actual route of the data flow transfer between each management process link based on the data flow transfer execution rule between each management process link includes:

[0114] The data flow transfer execution rule between each management process link is analyzed to obtain the data flow transfer process execution event relationship between each management process link.

[0115] In the embodiment of the present application, the data flow transfer event is identified and analyzed by performing the data flow transfer rule between each management process link obtained by previous mining, so as to collect and arrange the data flow transfer record of each management link, and create a data flow transfer event model by using a data analysis tool, and identify the data flow transfer event between different management links. This process includes classifying the data flow transfer event, establishing the relationship diagram between events, and identifying the time sequence and order of data transmission. In the specific operation, the process modeling tool (such as Visio or BPMN tool) can be used to visualize the data flow transfer process, and the data analysis software (such as Excel, Python or R) can be used for data statistics and event correlation analysis, and finally the data flow transfer process execution event relationship between each management process link is obtained.

[0116] Preferably, the data flow transfer actual route between each management process link is subjected to trigger condition node candidate set processing based on the data flow transfer process execution event relationship between each management process link, and the data flow transfer trigger condition node candidate set between each management process link is obtained.

[0117] In the embodiment of the present application, the trigger condition node of the data flow transfer actual route between the corresponding each management process link is further selected by combining the data flow transfer process execution event relationship between each management process link obtained by previous analysis, so as to list all possible trigger conditions by using the trigger condition node selection algorithm (such as decision tree algorithm), and process and analyze the trigger condition data by using the data mining tool (such as RapidMiner, WEKA), and list all trigger condition nodes, and finally obtain the data flow transfer trigger condition node candidate set between each management process link.

[0118] Preferably, the data flow transfer trigger condition node candidate set in each data flow transfer trigger condition candidate node between each management process link is subjected to data flow transfer frequency and influence degree statistical analysis, and the appearance frequency and influence degree of each trigger condition candidate node between each management process link are obtained.

[0119] In the embodiment of the present application, the data flow transfer frequency and influence degree of each data flow transfer trigger condition candidate node in the data flow transfer trigger condition node candidate set between each management process link obtained by previous analysis is subjected to statistical analysis, so as to calculate the frequency of each trigger condition node appearing in the data flow transfer process by using the data statistical tool (such as Pandas library in Python), and evaluate the influence degree of each trigger condition node on the data flow transfer process by applying the influence analysis method (such as regression analysis or causal inference), and finally the appearance frequency and influence degree of each trigger condition candidate node between each management process link are obtained by statistical calculation.

[0120] Preferably, the trigger key degree of each data flow triggering condition candidate node in the data flow triggering condition node candidate set between the management process links is quantitatively calculated based on the occurrence frequency and the influence degree of each trigger condition candidate node between the management process links, to obtain the data flow triggering key degree of each trigger condition candidate node between the management process links.

[0121] In the embodiment of the present application, the trigger key degree of each data flow triggering condition candidate node in the data flow triggering condition node candidate set between the management process links is quantitatively calculated by combining the occurrence frequency and the influence degree of each trigger condition candidate node between the management process links obtained by the previous quantitative calculation, to evaluate the key degree of each trigger condition candidate node by using a weighted scoring model (such as weighted average method or analytic hierarchy process), which multiplies the occurrence frequency and the influence degree and combines the weight to quantitatively calculate the key degree score of each node, to finally obtain the data flow triggering key degree of each trigger condition candidate node between the management process links.

[0122] Preferably, the optimal trigger condition node in the data flow triggering condition node candidate set between the management process links is screened according to the data flow triggering key degree of each trigger condition candidate node between the management process links, to obtain the data flow triggering condition node between the management process links.

[0123] In the embodiment of the present application, the optimal node in the data flow triggering condition node candidate set between the management process links is screened according to the data flow triggering key degree of each trigger condition candidate node between the management process links obtained by the previous quantitative calculation, to sort the trigger condition nodes by applying an optimization algorithm (such as genetic algorithm or linear programming), and select the node with the highest key degree as the final data flow triggering condition node, which utilizes an optimization software (such as LINDO or Gurobi) to implement the screening process, to ensure that the selected node can effectively trigger the data flow between the management process links, to finally obtain the data flow triggering condition node between the management process links.

[0124] Further, the whole life cycle management process modeling module includes the following functions:

[0125] The data flow execution flow direction fusion analysis is performed on the data flow execution rules between the management process links, to obtain the data flow execution flow direction fusion graph between the management process links;

[0126] Perform data flow execution position positioning processing on the data flow condition triggering nodes between each management process link to obtain the data flow execution positioning position of the triggering nodes between each management process link;

[0127] Based on the data flow execution positioning position of the trigger node between each management process link, the data flow execution flow fusion map between each management process link is processed with conditional trigger node mapping to obtain the data flow execution trigger node mapping between each management process link;

[0128] Based on the data flow execution trigger node mapping diagram between each management process link, the full life cycle management process modeling of each data asset management process link is carried out to generate a data asset full life cycle management process model.

[0129] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 The functional flow diagram of the full life cycle management process modeling module in this embodiment includes the following functions:

[0130] S21: Perform data flow execution direction fusion analysis on the data flow execution rules between each management process link to obtain a data flow execution direction fusion map between each management process link;

[0131] In an embodiment of the present invention, by collecting all data flow execution rules involved in management process links, these rules describe the data transfer relationship, data format, processing logic and their mutual dependence between each management process link, and by using a process modeling tool (such as a BPMN modeling tool or similar process modeling software) to input all management process links and their corresponding data flow rules, the tool will generate a preliminary flow chart based on the input data rules, showing the path and direction of data flow from one link to another. At the same time, by applying a traffic analysis algorithm (such as the shortest path algorithm in graph theory), the main paths and bottlenecks of data flow are identified and analyzed, and the data flow relationship and execution path of all management process links will be displayed in a graphical form, and finally a data flow execution flow fusion map between each management process link is obtained.

[0132] S22: performing flow execution position positioning processing on the data flow condition triggering nodes between each management process link to obtain the data flow execution positioning position of the triggering nodes between each management process link;

[0133] In the embodiment of the present application, by identifying all conditional trigger nodes in the positioning management process link, these nodes are the key decision points in the data flow, usually dependent on specific conditions or events, using process mining technology (such as event log-based process mining tools) to analyze each management process link in detail, extract the trigger nodes and their corresponding conditions, these tools identify trigger conditions and their positions by analyzing historical data and event logs, and map the trigger node positions to the data flow execution flow fusion graph, the specific operation includes aligning the position information of the trigger node with the corresponding node in the process graph, and clearly defining the actual position of each trigger node in the overall data flow, thereby forming a data flow execution positioning situation marked with trigger node positions, showing the specific position of the trigger node in each management process link, and finally obtaining the data flow execution positioning position of the trigger node between each management process link.

[0134] S23: Based on the data flow execution positioning position of the trigger node between each management process link, the data flow execution flow fusion graph between each management process link is mapped and processed to obtain a data flow execution trigger node mapping graph between each management process link.

[0135] In the embodiment of the present application, by combining the previously positioned data flow execution positioning position of the trigger node between each management process link, the mapping processing of the nodes between the corresponding data flow execution flow fusion graph between each management process link is performed to map the previously marked trigger node position information to the generated fusion graph one by one, and the trigger node is positioned and marked by using a graph mapping tool (such as GIS data mapping software or process graph editing tool), and the conditions and positions of the trigger nodes are directly displayed on the graph, this process includes accurate coordinate positioning of each trigger node, and adding detailed trigger condition description on the fusion graph, these mapping results will generate an execution trigger node mapping graph containing conditional trigger nodes, which can clearly display the condition of each trigger node and its specific mapping relationship in the data flow, and finally obtain the data flow execution trigger node mapping graph between each management process link.

[0136] S24: Based on the data flow execution trigger node mapping graph between each management process link, the whole life cycle management process modeling is performed on each data asset management process link to generate a data asset whole life cycle management process model.

[0137] In the embodiment of the present application, the modeling of the full life cycle management process of each data asset management process link is performed by combining the data flow between each management process link obtained by the previous mapping analysis and the trigger node mapping diagram, so as to import the trigger node mapping diagram obtained by the previous analysis into a system modeling tool (such as a SysML modeling tool or a UML modeling tool) by using the system modeling tool, and construct a comprehensive management process model according to the trigger nodes and their conditions identified in the graph, which includes the full process of data assets from acquisition, storage, access, analysis, sharing and destruction. The specific operation includes defining the input and output of each link, data processing rules, trigger conditions and node behavior, establishing the connection between the links and the data flow path, and finally constructing and generating the data asset full life cycle management process model, so as to completely show the management strategy and process of the data asset in the whole life cycle, and realize the systematic management and optimization of the data asset.

[0138] Further, the process compliance anomaly supervision module includes the following functions:

[0139] Obtaining data asset management compliance rule standards;

[0140] In the embodiment of the present application, the compliance rule standards of data asset management are collected from industry standard organizations, regulatory documents and company internal compliance guidelines, including accessing industry standard databases (such as standards provided by ISO, NIST or local regulatory agencies), downloading and sorting the compliance requirements of data asset management, for example, by referring to the data protection requirements in the ISO / IEC27001 standard, collecting specific compliance clauses, and converting them into an easy-to-parse document format. At the same time, the obtained compliance rule standards are sorted into a systematic list, including specific requirements of data asset management, operation specifications, permission control and record keeping requirements, etc., and finally the data asset management compliance rule standards are obtained.

[0141] Preferably, the data asset management behavior mapping relationship is established for the data asset management compliance rule standards, so as to obtain the rule docking mapping relationship between the data asset management behavior and the compliance rule standards.

[0142] In the embodiment of the present application, the rule mapping is created by using a rule mapping tool (such as IBM ODM or Drools) in the process of establishing the data asset management behavior mapping relationship of the data asset management compliance rule standard, to import the obtained compliance rule standard into the mapping tool, and to classify the rules according to the actual data asset management process definition and operation behavior, and to map each requirement of the compliance rule standard to a specific operation step in the data asset management behavior, which involves matching each compliance clause with the management behavior, generating a rule mapping relationship diagram, explicitly showing how the data operation behavior meets each compliance requirement, and forming a complete mapping relationship document between the data asset management behavior and the compliance rule standard, and finally obtaining the rule mapping relationship between the data asset management behavior and the compliance rule standard.

[0143] Preferably, the rule mapping relationship between the data asset management behavior and the compliance rule standard is detected by constructing an engine logic framework, and a data asset management behavior compliance detection engine logic framework is obtained.

[0144] In the embodiment of the present application, the rule mapping relationship between the data asset management behavior and the compliance rule standard obtained by the previous analysis is constructed into a detection engine framework, to define the detection logic and framework architecture, which uses a design tool (such as a UML modeling tool or an architecture design software) to construct the logic framework of the detection engine, the framework including a data input module (for receiving operation behavior data), a rule matching module (for detecting according to the previously established rule mapping relationship), and a report generation module (for generating compliance detection results), and in the specific implementation, the rule mapping relationship needs to be imported into the detection engine framework, and the logic rules need to be configured to ensure that the detection engine can automatically apply the rules and generate the detection results, which involves writing framework logic code, setting the logic flow of rule execution, and ensuring that the logic framework can effectively support the compliance detection task, and finally a data asset management behavior compliance detection engine logic framework is constructed.

[0145] Preferably, the data asset management behavior compliance detection engine logic framework is designed for engine integration testing, to generate a data asset management operation behavior compliance detection engine.

[0146] In the embodiment of the present application, the verification design of the integrated test tool (such as JUnit or Selenium) is used to perform integrated testing on the built data asset management behavior compliance detection engine logic framework. In the integrated testing process, the data asset management behavior data and compliance rule standards are input into the test environment, and test cases are designed to cover all expected operation behaviors and compliance scenarios, including normal operation, boundary conditions and abnormal conditions. After running the test, the response time of the detection engine, the detection accuracy and the generation of the compliance report are recorded. Through the analysis of the test results, the problems in the detection engine logic framework are identified and corrected, and the optimization design is performed to ensure the reliability and accuracy of the finally generated operation behavior compliance detection engine in actual application. Finally, the data asset management operation behavior compliance detection engine is generated.

[0147] Preferably, the data asset management operation behavior compliance detection engine is used to perform compliance automatic abnormal supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model, and data asset management process non-compliant abnormal operation behavior is obtained.

[0148] In the embodiment of the present application, the data asset management operation behavior compliance detection engine obtained through the integrated testing verification is used to perform compliance automatic abnormal supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model. The generated data asset management operation behavior compliance detection engine is integrated into the management process monitoring system (such as Elasticsearch or Logstash) to monitor the operation behavior of the data asset full life cycle in real time. The data asset management operation behavior compliance detection engine automatically applies compliance rules to check each data asset management process operation behavior in the data asset full life cycle management process model. If an operation that does not meet the compliance standards is found, it is determined as the corresponding non-compliant abnormal operation behavior, and detailed abnormal information is recorded. Finally, the data asset management process non-compliant abnormal operation behavior is obtained.

[0149] Further, the compliance automatic abnormal supervision of the data asset management operation behavior compliance detection engine on the corresponding data asset management process operation behavior in the data asset full life cycle management process model comprises:

[0150] The management operation behavior analysis is performed on each data asset management process link in the data asset full life cycle management process model to obtain the data asset full life cycle management process operation behavior.

[0151] In the embodiment of the present application, by statistically analyzing the management operation behavior of each data asset management process link in the data asset whole life cycle management process model, the process model data is imported into the process analysis tool (such as Visio or Lucidchart), and by analyzing the operation steps, involved roles, required resources and operation sequence of each data asset management process link, the detailed operation behavior of each data asset management process link is extracted, at the same time, by using the process modeling tool to refine the operation behavior, the operation type, operation frequency, interaction point and operation result of each step are recorded, this process involves comprehensive analysis of the task description, execution and process efficiency of each data asset management process link, thereby generating a complete view of the operation behavior of all data asset management processes, and finally obtaining the operation behavior of the data asset whole life cycle management process.

[0152] Preferably, the data asset management operation behavior compliance detection mechanism is generated according to the data asset management operation behavior compliance detection engine, and the data asset management operation behavior dynamic compliance detection mechanism is obtained.

[0153] In the embodiment of the present application, the generation of the dynamic compliance detection mechanism is performed by using the previously designed data asset management operation behavior compliance detection engine, so as to build a compliance detection platform (such as IBM OpenPages or MetricStream) through the data asset management operation behavior compliance detection engine, and by inputting the operation behavior data into the compliance detection platform, setting the compliance standards and detection rules, such as data access permission, operation frequency limit, etc., the compliance detection platform performs real-time monitoring and dynamic analysis on the operation behavior according to the defined rules, thereby generating a dynamic compliance detection mechanism, which includes a real-time data monitoring module, a rule engine and an abnormal alarm system, ensuring that when the operation behavior deviates from the compliance standard, the related abnormal operation behavior data can be automatically identified and recorded, and finally the data asset management operation behavior dynamic compliance detection mechanism is obtained.

[0154] Preferably, based on the data asset management operation behavior dynamic compliance detection mechanism, the management operation behavior of the corresponding data asset management process link in the data asset whole life cycle management process operation behavior is automatically supervised for compliance, and the data asset management process non-compliant abnormal operation behavior is obtained.

[0155] In the embodiment of the present application, the management operation behavior compliance of the corresponding data asset management process link in the operation behavior of the data asset whole life cycle management process is automatically monitored by using the previously generated data asset management operation behavior dynamic compliance detection mechanism, the generated detection mechanism is deployed to the operation behavior monitoring system (such as Splunk or Elastic Stack), the system receives and analyzes the operation behavior data of each management process link in real time, and automatically monitors the operation behavior data according to the set compliance detection rules, and automatically identifies and marks the non-compliant operation behavior by comparing the real-time operation behavior with the compliance standard, which includes the detailed information of the abnormal operation, the violation type and the potential impact on the process, and finally obtains the non-compliant abnormal operation behavior of the data asset management process.

[0156] Further, the management operation behavior analysis of each data asset management process link in the data asset whole life cycle management process model includes:

[0157] The operation content of each data asset management process link in the data asset whole life cycle management process model is extracted and processed to obtain the management process operation content corresponding to each data asset management process link.

[0158] In the embodiment of the present application, the operation content of each data asset management process link in the data asset whole life cycle management process model is extracted and processed to extract the operation content of each link from the existing data asset whole life cycle management process model by detailed document review of the whole management process and using process modeling tools (such as Bizagi or ARIS), the specific operation includes importing process diagram or process document, identifying operation steps, participating roles and required resources of each process link one by one, the extracted operation content should include specific task description, operation requirement and interaction with other links, and finally the management process operation content corresponding to each data asset management process link is obtained.

[0159] Preferably, the operation behavior characteristic attribute of each data asset management process link corresponding to the management process operation content is mined and analyzed to obtain the management process operation behavior characteristic attribute data corresponding to each data asset management process link.

[0160] In the embodiment of the present application, the operation behavior characteristic attribute of the management process operation content corresponding to each data asset management process link is analyzed by using a data mining tool (such as RapidMiner or WEKA), the extracted operation content data is imported into the mining tool, and relevant attributes and indexes are set, such as operation frequency, involved roles, data input and output, etc., through feature engineering technology, the key characteristic attributes of each operation behavior are identified, including common operation mode, abnormal behavior identification and operation complexity, etc., which are used to describe the specific attributes of each link operation behavior and the influence on the whole process, and finally the management process operation behavior characteristic attribute data corresponding to each data asset management process link is obtained.

[0161] Preferably, the operation behavior mode recognition analysis is performed on the management process operation behavior characteristic attribute data corresponding to each data asset management process link, and the management process operation behavior mode corresponding to each data asset management process link is obtained.

[0162] In the embodiment of the present application, the operation behavior mode recognition analysis is performed on the management process operation behavior characteristic attribute data corresponding to each data asset management process link, and the management process operation behavior mode corresponding to each data asset management process link is obtained.

[0163] Preferably, based on the management process operation behavior mode corresponding to each data asset management process link, the whole life cycle management behavior analysis and summary of each data asset management process link corresponding to each data asset management process link in the data asset whole life cycle management process model is performed, and the data asset whole life cycle management process operation behavior is obtained.

[0164] In the embodiment of the present application, the operation behavior of each data asset management process link in the data asset whole life cycle management process model is analyzed and summarized by combining the operation behavior mode of each data asset management process link corresponding to the previously analyzed management process operation behavior mode, to integrate the operation behavior mode identified in the previous step, and to use data summarization tools (such as Microsoft Excel or Tableau) to summarize the operation behavior mode of all links, and to classify and analyze according to the process link. The summary analysis should include the comprehensive evaluation of the operation behavior mode of each management link, the influence analysis of the process, and the recommended improvement measures, and finally the data asset whole life cycle management process operation behavior is obtained.

[0165] Further, the process exception early warning optimization suggestion module includes the following functions:

[0166] The abnormal operation behavior of the data asset management process is processed for abnormal early warning response to generate data asset management process abnormal early warning response data;

[0167] In the embodiment of the present application, by processing the abnormal operation behavior in the data asset management process for early warning response, the early warning rules need to be set. These rules are based on the set standards and historical data, and the operation records in the data asset management process are monitored in real time through the data analysis platform (such as Splunk or ELK Stack). The operation includes configuring the data monitoring system, analyzing the operation behavior in real time, finding the behavior inconsistent with the standard operation, such as illegal data access, unauthorized operation request, etc., then an abnormal early warning event is generated, and the detailed information of the related operation is automatically recorded, including timestamp, operator, operation content, etc. Through the automatic early warning mechanism, the abnormal operation behavior in the data asset management process is processed for abnormal early warning response, thereby generating corresponding abnormal early warning response data, including abnormal early warning signal, abnormal type, frequency and specific operation behavior record, and finally generating data asset management process abnormal early warning response data.

[0168] Preferably, the data asset management process abnormal positioning report of the corresponding data asset management process is generated based on the data asset management process abnormal early warning response data, and the data asset management process abnormal behavior supervision report is obtained;

[0169] In the embodiment of the present application, the data asset management process abnormality early warning response data generated by combining the previously abnormal response is used to generate a management process abnormality positioning report of the corresponding data asset management process non-compliant abnormal operation behavior, so as to import and visualize the early warning response data by using a data analysis tool (such as Tableau or Power BI), identify the concentrated area or specific location of the abnormal operation by statistical analysis and analysis of the abnormal behavior, and generate a detailed management process abnormality positioning report by combining the process log and operation record, which includes detailed description of the abnormal operation behavior, influence range, occurrence frequency and related operation process node, and finally obtains a data asset management process non-compliant abnormal behavior supervision report.

[0170] Preferably, the data asset management process non-compliant abnormal behavior supervision report is subjected to management process abnormality optimization suggestion, and a data asset management process abnormality optimization suggestion scheme is generated to perform corresponding data asset management process abnormality improvement work.

[0171] In the embodiment of the present application, the data asset management process non-compliant abnormal behavior supervision report obtained by previous analysis is subjected to management process abnormality optimization suggestion analysis, so as to identify the root cause of the abnormality by using an analysis tool (such as a root cause analysis tool or a process improvement model), and make targeted optimization suggestions according to the abnormal behavior and influence identified in the report, the optimization suggestions including measures such as adjusting operation process, strengthening permission management, increasing data audit and monitoring, and the optimization suggestion scheme generated describes the implementation steps, expected effect and execution plan of each improvement measure, so as to generate a data asset management process abnormality optimization suggestion scheme, and finally solve the abnormal behavior in the data asset management process by implementing the data asset management process abnormality optimization suggestion scheme in the data asset management process, so as to improve the process compliance and the security of data asset management.

[0172] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0173] The above description is only a specific embodiment of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications of these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A process automation monitoring system applied to data asset management, characterized in that, Comprise the following modules: Process link data mining module, for obtaining each data asset management process link, wherein each data asset management process link comprises data acquisition process link, data storage process link, data access process link, data analysis process link, data sharing process link and data destruction process link; data flow execution rule and condition triggering analysis are carried out on each data asset management process link, so as to obtain the data flow execution rule and the data flow condition triggering node between each management process link; Whole life cycle management process modeling module, for whole life cycle management process modeling of each data asset management process link based on the data flow execution rule and the data flow condition triggering node between each management process link, to generate data asset whole life cycle management process model; Among them Comprise the following functions: Data flow execution flow fusion analysis is carried out on the data flow execution rule between each management process link, and the data flow execution flow fusion graph between each management process link is obtained; The data flow execution positioning location of the triggering node between each management process link is obtained by flow transfer execution location positioning processing of the data flow condition triggering node between each management process link; Based on the data flow execution positioning location of the triggering node between each management process link, the data flow execution flow fusion graph between each management process link is subjected to condition triggering node mapping processing, and the data flow execution triggering node mapping graph between each management process link is obtained; Based on the data flow execution triggering node mapping graph between each management process link, whole life cycle management process modeling is carried out on each data asset management process link, to generate data asset whole life cycle management process model; Process compliance exception supervision module, for obtaining data asset management compliance rule standard, and designing management behavior rule detection engine according to data asset management compliance rule standard, to generate data asset management operation behavior compliance detection engine; According to the data asset management operation behavior compliance detection engine, the corresponding data asset management process operation behavior in the data asset whole life cycle management process model is subjected to compliance automatic exception supervision, and the data asset management process non-compliance exception operation behavior is obtained; Process exception early warning optimization suggestion module, for abnormal early warning response processing of data asset management process non-compliance exception operation behavior, to generate data asset management process exception early warning response data; management process exception optimization suggestion is carried out on data asset management process exception early warning response data, and data asset management process exception optimization suggestion scheme is generated, to execute corresponding data asset management process exception improvement work.

2. The process automation monitoring system for data asset management according to claim 1, wherein, The process link data mining module comprises the following functions: Each data asset management process link is obtained from the data asset management process, wherein each data asset management process link comprises data acquisition process link, data storage process link, data access process link, data analysis process link, data sharing process link and data destruction process link; Tracking and analyzing data flow between each data asset management process link to obtain actual data flow routes between each management process link; Based on the actual data flow routes between each management process link, data flow interaction mode recognition analysis is performed on each data asset management process link to generate data flow transmission interaction mode graphs between each management process link; Based on the actual data flow routes between each management process link, data flow interaction mode recognition analysis is performed on each data asset management process link to generate data flow transmission interaction mode graphs between each management process link; Based on the actual data flow routes between each management process link, data flow interaction mode recognition analysis is performed on each data asset management process link to generate data flow transmission interaction mode graphs between each management process link; 3. The process automation monitoring system for data asset management of claim 2, wherein, The data flow interaction mode recognition analysis based on the actual data flow routes between each management process link includes: Based on the actual data flow routes between each management process link, data flow transmission path mapping processing is performed on the corresponding data asset management process link to obtain data flow transmission path mapping connection graphs between each management process link; Based on the actual data flow routes between each management process link, data flow transmission path mapping processing is performed on the corresponding data asset management process link to obtain data flow transmission path mapping connection graphs between each management process link; Based on the actual data flow routes between each management process link, data flow transmission path mapping processing is performed on the corresponding data asset management process link to obtain data flow transmission path mapping connection graphs between each management process link; The data flow interaction mode recognition analysis based on the actual data flow routes between each management process link includes:

4. 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The process automation monitoring system for data asset management of claim 2, wherein, The data flow transfer condition trigger node identification analysis on the actual data flow transfer route between the management process links based on the data flow transfer execution rules between the management process links comprises: The data flow transfer event identification analysis on the data flow transfer execution rules between the management process links is performed to obtain the data flow transfer process execution event relationship between the management process links; The trigger condition node list selection processing on the actual data flow transfer route between the management process links based on the data flow transfer process execution event relationship between the management process links is performed to obtain the data flow transfer trigger condition node candidate set between the management process links; The data flow transfer occurrence frequency and influence degree statistical analysis on each data flow transfer trigger condition candidate node in the data flow transfer trigger condition node candidate set between the management process links is performed to obtain the occurrence frequency and influence degree of each trigger condition candidate node between the management process links; The trigger key degree quantitative calculation on each data flow transfer trigger condition candidate node in the data flow transfer trigger condition node candidate set between the management process links based on the occurrence frequency and influence degree of each trigger condition candidate node between the management process links is performed to obtain the data flow transfer trigger key degree of each trigger condition candidate node between the management process links; The optimal trigger condition node screening processing on the corresponding data flow transfer trigger condition candidate node in the data flow transfer trigger condition node candidate set between the management process links according to the data flow transfer trigger key degree of each trigger condition candidate node between the management process links is performed to obtain the data flow transfer condition trigger node between the management process links.

6. The process automation monitoring system for data asset management of claim 1, wherein, The process compliance anomaly supervision module comprises the following functions: Obtain data asset management compliance rule standards; Establish a data asset management behavior mapping relationship for the data asset management compliance rule standards to obtain a rule docking mapping relationship between the data asset management behavior and the compliance rule standards; Build a data asset management behavior compliance detection engine logic framework for the rule docking mapping relationship between the data asset management behavior and the compliance rule standards to obtain a data asset management behavior compliance detection engine logic framework; Design an engine integration test for the data asset management behavior compliance detection engine logic framework to generate a data asset management operation behavior compliance detection engine; Perform compliance automatic anomaly supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model according to the data asset management operation behavior compliance detection engine to obtain a data asset management process non-compliant abnormal operation behavior.

7. The process automation monitoring system for data asset management of claim 6, wherein, The compliance automatic anomaly supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model according to the data asset management operation behavior compliance detection engine comprises: Perform management operation behavior analysis on each data asset management process link in the data asset full life cycle management process model to obtain a data asset full life cycle management process operation behavior; According to the data asset management operation behavior compliance detection engine, a dynamic compliance detection mechanism is generated, and a data asset management operation behavior dynamic compliance detection mechanism is obtained. Based on the data asset management operation behavior dynamic compliance detection mechanism, the management operation behavior corresponding to the data asset management process link in the data asset whole life cycle management process operation behavior is automatically supervised for compliance, and a data asset management process non-compliant abnormal operation behavior is obtained.

8. The process automation monitoring system for data asset management of claim 7, wherein, The management operation behavior analysis of each data asset management process link in the data asset whole life cycle management process model includes: The management operation behavior analysis of each data asset management process link in the data asset whole life cycle management process model includes: The management operation behavior analysis of each data asset management process link in the data asset whole life cycle management process model includes: The management operation behavior analysis of each data asset management process link in the data asset whole life cycle management process model includes: The management operation behavior analysis of each data asset management process link in the data asset whole life cycle management process model includes:

9. The process automation monitoring system for data asset management of claim 1, wherein, Based on the management operation behavior mode of each data asset management process link, the whole life cycle management behavior analysis of each corresponding data asset management process link in the data asset whole life cycle management process model is summarized, and the data asset whole life cycle management process operation behavior is obtained. The process exception early warning optimization suggestion module includes the following functions: The process exception early warning optimization suggestion module includes the following functions: The process exception early warning optimization suggestion module includes the following functions: The process exception early warning optimization suggestion module includes the following functions:

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