Flow self-supervision platform applied to data asset management

Abstract

The invention relates to the technical field of data asset management, in particular to a process self-supervision platform applied to data asset management. The platform comprises a process link data mining module, a full-life-cycle management process modeling module, a process compliance abnormity supervision module and a process abnormity early warning optimization suggestion module, can obtain each data asset management process link to perform data circulation execution rule and condition trigger analysis, and performs full-life-cycle management process modeling. Generating a data asset full life cycle management process model; obtaining a data asset management compliance rule standard, carrying out management behavior rule detection engine design and compliance automatic exception supervision, carrying out exception early warning response processing and management process exception optimization suggestion at the same time, and generating a data asset management process exception optimization suggestion scheme; and executing corresponding data asset management process abnormity improvement work. According to the invention, the efficiency and reliability of data asset management can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset management, and in particular to a process automation supervision platform applied to data asset management. Background Art

[0002] In the field of data asset management, with the surge in data volume and the increase in management complexity, how to efficiently and accurately supervise and manage data assets has become an important challenge. By building an automated process based on data asset management needs, including data collection, storage, analysis and use, and defining process rules and automated tasks, the management and monitoring of the entire life cycle of data assets can be achieved. Advanced data monitoring technology is used to track the flow and use of data assets in real time, and by deploying real-time data collection systems and data flow analysis tools, the real-time grasp of the status of data assets is ensured. In order to introduce an intelligent rule engine, management rules are automatically generated and adjusted 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. These methods are prone to loopholes when dealing with complex data asset processes, and often lack real-time tracking of data flow and use, resulting in low compliance and regulatory efficiency of data asset management. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a process automation supervision platform applied to data asset management to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a process automation supervision platform for data asset management is provided, which includes the following modules:

[0005] The process link data mining module is used to obtain each data asset management process link, where each data asset management process link includes 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; perform data flow execution rules and condition trigger analysis on each data asset management process link to obtain the data flow execution rules and data flow condition trigger nodes between each management process link;

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

[0007] The process compliance exception supervision module is used to obtain 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; based on the data asset management operation behavior compliance detection engine, the corresponding data asset management process operation behavior in the data asset full life cycle management process model is subject to compliance automated exception supervision to obtain non-compliant abnormal operation behaviors of the data asset management process;

[0008] The process exception warning optimization suggestion module is used to perform exception warning response processing on non-compliant abnormal operation behaviors in the data asset management process to generate data asset management process exception warning response data; to make management process exception optimization suggestions on the data asset management process exception warning response data, and generate data asset management process exception optimization suggestion plans to execute corresponding data asset management process exception improvement work.

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

[0010] By obtaining various data asset management process links from the data asset management process, wherein each data asset management process link includes 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;

[0011] 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;

[0012] Based on the actual data flow routes between each management process link, the data flow interaction pattern recognition and analysis is performed on each data asset management process link to generate a data flow transmission interaction pattern map between each management process link;

[0013] Conduct data flow execution rule mining and analysis on the data flow transmission interaction pattern graph between each management process link to obtain the data flow execution rules between each management process link;

[0014] 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.

[0015] Furthermore, the data flow interaction pattern recognition and analysis of each data asset management process link based on the actual data flow route between each management process link includes:

[0016] Based on the actual data flow routes between various management process links, the corresponding data asset management process links are mapped for data flow transmission paths to obtain a data flow transmission path mapping connection diagram between various management process links;

[0017] The actual routes of data flow between various management process links are mined and analyzed to obtain the interactive relationships between data flow between various management process links;

[0018] Based on the data flow interaction relationship between each management process link, the data flow transmission path mapping connection diagram between each management process link is analyzed for the flow path interaction characteristics, so as to obtain the data flow transmission path interaction characteristic data between each management process link;

[0019] Data flow interaction pattern recognition and analysis are performed on the interactive feature data of the data flow transmission paths between various management process links to generate a data flow transmission interaction pattern map between various management process links.

[0020] Furthermore, the data flow execution rule mining and analysis of the data flow transmission interaction pattern graph between each management process link includes:

[0021] Perform detailed interactive behavior logic analysis on each data flow transmission path in the data flow transmission interactive pattern map between each management process link to obtain the data flow transmission interactive behavior logic between each management process link;

[0022] Conduct interactive behavior logic constraint analysis on the interactive behavior logic of data transfer between various management process links to obtain the interactive behavior logic constraint conditions of data transfer between various management process links;

[0023] Based on the logical constraints of the data flow interaction behavior between each management process link, the data flow operation standard is determined for each data flow transmission path in the data flow transmission interaction pattern map between each management process link, and the data flow interaction behavior operation standard between each management process link is obtained;

[0024] The data flow execution rules are mined and analyzed on the data flow interaction behavior operation standards between each management process link to obtain the data flow execution rules between each management process link.

[0025] Furthermore, the data flow condition triggering node identification and analysis of the actual data flow route between each management process link based on the data flow execution rules between each management process link includes:

[0026] Perform data flow event identification and analysis on the data flow execution rules between various management process links to obtain the data flow process execution event relationship between various management process links;

[0027] Based on the data flow process execution event relationship between each management process link, the actual route of data flow between each management process link is processed by trigger condition node list selection, and the candidate set of data flow trigger condition nodes between each management process link is obtained;

[0028] Perform statistical analysis on the occurrence frequency and impact degree of data flow for each candidate data flow trigger condition node in the candidate set of data flow trigger condition nodes between each management process link, and obtain the occurrence frequency and impact degree of each candidate trigger condition node between each management process link;

[0029] Based on the occurrence frequency and influence degree of each trigger condition candidate node between each management process link, the trigger criticality of each data flow trigger condition candidate node in the data flow trigger condition node candidate set between each management process link is quantitatively calculated to obtain the data flow trigger criticality of each trigger condition candidate node between each management process link;

[0030] According to the data flow trigger criticality of each trigger condition candidate node between each management process link, the corresponding data flow trigger condition candidate nodes in the data flow trigger condition node candidate set between each management process link are screened for the optimal trigger condition node to obtain the data flow condition trigger nodes between each management process link.

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

[0032] Conduct data flow execution direction fusion analysis on the data flow execution rules between various management process links to obtain the data flow execution direction fusion map between various management process links;

[0033] Perform 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;

[0034] 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 by conditional trigger node mapping to obtain the data flow execution trigger node mapping map between each management process link;

[0035] 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.

[0036] Furthermore, the process compliance exception supervision module includes the following functions:

[0037] Obtain data asset management compliance rules and standards;

[0038] Establish a mapping relationship between data asset management behavior and compliance rules and standards for data asset management to obtain a mapping relationship between data asset management behavior and compliance rules and standards;

[0039] The detection engine logic framework is constructed for the rule docking mapping relationship between data asset management behavior and compliance rule standards to obtain the data asset management behavior compliance detection engine logic framework;

[0040] Conduct engine integration test design for the data asset management behavior compliance detection engine logic framework to generate a data asset management operation behavior compliance detection engine;

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

[0042] Furthermore, the data asset management operation behavior compliance detection engine performs compliance automated abnormal supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model, including:

[0043] Conduct management operation behavior analysis on each data asset management process link within the data asset full life cycle management process model to obtain the data asset full life cycle management process operation behavior;

[0044] Generate a dynamic compliance detection mechanism based on the data asset management operation behavior compliance detection engine to obtain a dynamic compliance detection mechanism for data asset management operation behavior;

[0045] Based on the dynamic compliance detection mechanism of data asset management operation behavior, the compliance automated exception supervision is carried out on the management operation behavior of the corresponding data asset management process link within the data asset full life cycle management process operation behavior to obtain the non-compliant abnormal operation behavior of the data asset management process.

[0046] Furthermore, the management operation behavior analysis of each data asset management process link in the data asset full life cycle management process model includes:

[0047] Extract and process the process link operation content of each data asset management process link in the data asset full life cycle management process model to obtain the management process operation content corresponding to each data asset management process link;

[0048] Conducting mining and analysis of the operation behavior characteristic attributes of the management process operation content corresponding to each data asset management process link, and obtaining the management process operation behavior characteristic attribute data corresponding to each data asset management process link;

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

[0050] Based on the management process operation behavior pattern corresponding to each data asset management process link, the full life cycle management behavior of each data asset management process link corresponding to the data asset full life cycle management process model is analyzed and summarized to obtain the data asset full life cycle management process operation behavior.

[0051] Furthermore, the process abnormality warning optimization suggestion module includes the following functions:

[0052] Conduct abnormal warning response processing for non-compliant abnormal operation behaviors in the data asset management process to generate abnormal warning response data for the data asset management process;

[0053] Based on the data asset management process abnormal warning response data, the corresponding data asset management process non-compliant abnormal operation behavior is reported on the management process abnormality, and a supervision report on the non-compliant abnormal behavior of the data asset management process is obtained;

[0054] Provide management process anomaly optimization suggestions for the supervision report on non-compliant abnormal behavior in the data asset management process, generate data asset management process anomaly optimization suggestion plans, and execute corresponding data asset management process anomaly improvement work.

[0055] Beneficial effects of the present invention:

[0056] The process automation supervision platform applied to data asset management proposed in the present invention is composed of a process link data mining module, a full life cycle management process modeling module, a process compliance exception supervision module, and a process exception warning optimization suggestion module. Compared with the prior art, the beneficial effect of the present application lies in the systematic identification and classification of various data asset management process links in the data asset management process, including data acquisition, data storage, data access, data analysis, data sharing, and data destruction. The core of this process is to build a comprehensive and systematic data management framework, and lay the foundation for subsequent process optimization and problem solving by clearly defining and describing each link. In this way, the role and operation mode of each process link can be understood in detail, which can help organizations effectively manage and monitor the data life cycle, identify potential risks and bottlenecks in data processing, and ensure the quality and security of data assets. By mining and analyzing the data flow execution rules between the various data asset management process links, the main purpose of this process is to identify and define the rules and specifications that need to be followed in each link in the data flow process. The key to this step is to ensure the consistency, standardization and controllability of the data processing process by mining and analyzing the execution rules of data flow. These execution rules can include the time window of data transmission, the priority of data processing, the data format and standard, etc. Clarifying these rules helps to reduce conflicts and errors in data processing, thereby improving the efficiency and accuracy of data flow. At the same time, by identifying and analyzing the data flow condition trigger nodes between the various management process links, the core of this process is to identify the key condition trigger points in the data flow process. These nodes will trigger changes in data flow or processing under certain conditions. The key to identifying these trigger nodes is that it can help clarify the key control points and decision points in the data processing process, so as to effectively monitor and manage the data flow process. These trigger nodes involve changes in the status of assets, the occurrence of specific events, and the emergence of data quality problems. By identifying and analyzing these nodes, organizations can implement real-time monitoring and automated response mechanisms during data processing, reduce the need for human intervention, and improve the efficiency and accuracy of data flow. This can enhance the automation and intelligence levels of data management, and better achieve real-time tracking of data asset flow and usage, thereby enhancing the flexibility and reliability of the data asset management process.Secondly, by modeling the full life cycle management process of each data asset management process link based on the data flow execution rules between each management process link and the data flow condition trigger node, the core of this process is to establish a comprehensive life cycle management model to fully cover 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 the management of data assets. By building a full life cycle management model, we can clearly understand the management requirements and operating specifications of data at each stage, so as to achieve effective management and control of data assets. This model helps to improve the comprehensiveness and systematicness of data management and ensure that data meets the predetermined business goals and compliance requirements at each stage of the life cycle. This can provide a systematic framework and tools for the management and optimization of data assets, thereby supporting the realization of scientific and efficient data asset management. Then, by obtaining data asset management compliance rules and standards, these standards provide legal, industry norms and best practices for data management and processing. The core of this step is to ensure that the organization's data asset management process complies with current compliance requirements and regulations, and avoid legal risks or regulatory penalties due to compliance issues. By obtaining and analyzing these rules and standards, we can gain an in-depth understanding of the specific compliance requirements in data asset management, including data privacy protection, data security, data quality standards, etc., laying the foundation for the subsequent compliance analysis and detection, and ensuring that all subsequent steps have a clear compliance framework and reference point. Obtaining compliance rule standards can help organizations formulate internal management policies and operating procedures to ensure the legality and compliance of the data asset processing process. The main advantage of designing a management behavior rule detection engine based on data asset management compliance rule standards is that it verifies and optimizes the functions and performance of the compliance detection engine through integrated testing to ensure that it can operate effectively in the actual environment. The design of the management behavior rule detection engine can simulate actual operation scenarios and check the performance of the engine when processing data asset management operations, including the accuracy, efficiency and stability of compliance checks. This process helps to discover and solve potential problems and defects, such as logical errors, performance bottlenecks or compatibility issues, and ensure that the engine can reliably perform compliance detection tasks in actual applications. By designing and executing integrated tests, the functions of the detection engine can be optimized and its adaptability and stability in different environments can be improved, which can not only improve the compliance detection capabilities of data asset management, but also improve the overall performance and user experience of the system.By using the compliance detection engine of data asset management operation behavior to conduct compliance automated exception supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model, the main advantage of this process is that it can realize real-time and automated monitoring of data management behavior, timely discover and report non-compliant operation behavior, and automated compliance detection can greatly reduce the workload of manual inspection, improve detection efficiency and accuracy, thereby reducing compliance risks and management costs. By conducting abnormal supervision on each link in the data asset full life cycle management process, it is possible to track the compliance status of data management in real time, identify and handle potential compliance issues in a timely manner, and automated supervision can also provide detailed audit records and reports to provide support for compliance review and rectification, which is conducive to improving the compliance of data asset management and the efficiency of abnormal supervision, thereby achieving efficient and secure data asset management. Finally, by conducting abnormal warning response processing on non-compliant abnormal operation behaviors in the data asset management process, the key to this process is the rapid detection and processing of abnormal behaviors. Through the early warning mechanism, signs of improper operation can be identified early, thereby reducing potential risks and losses. This timely warning can prevent the expansion of problems, so that corrective measures can be taken at the early stage of the problem to avoid more complex compliance issues in the future. By generating detailed abnormal warning response data, it can provide valuable basis 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 the operation behavior is carried out within the framework of compliance with the regulations, thereby improving the data management level of non-compliant abnormal behavior in the process 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 behaviors 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 the actual situation. These suggestions include updating management processes, improving operating specifications, introducing new monitoring tools or training personnel, etc. By implementing these suggestions, deficiencies in existing processes can be effectively corrected and similar abnormal behaviors can be prevented from happening again, 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 from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0058] Figure 1 It is a module diagram of the process automation supervision platform applied to data asset management of the present invention;

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

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

[0061] The technical platform of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor platforms and / or microcontroller platforms.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" 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 platform for data asset management, the platform includes the following modules:

[0065] The process link data mining module is used to obtain each data asset management process link, where each data asset management process link includes 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; perform data flow execution rules and condition trigger analysis on each data asset management process link to obtain the data flow execution rules and data flow condition trigger nodes between each management process link;

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

[0067] The process compliance exception supervision module is used to obtain 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; based on the data asset management operation behavior compliance detection engine, the corresponding data asset management process operation behavior in the data asset full life cycle management process model is subject to compliance automated exception supervision to obtain non-compliant abnormal operation behaviors of the data asset management process;

[0068] The process exception warning optimization suggestion module is used to perform exception warning response processing on non-compliant abnormal operation behaviors in the data asset management process to generate data asset management process exception warning response data; to make management process exception optimization suggestions on the data asset management process exception warning response data, and generate data asset management process exception optimization suggestion plans to execute corresponding data asset management process exception improvement work.

[0069] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of a module of a process automation supervision platform for data asset management according to the present invention. In this example, the process automation supervision platform for data asset management includes the following modules:

[0070] S1: Process link data mining module, used to obtain each data asset management process link, where each data asset management process link includes 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; perform data flow execution rules and condition trigger analysis on each data asset management process link to obtain data flow execution rules and data flow condition trigger nodes between each management process link;

[0071] 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 storing data, obtaining information of the data access process link from an API or access control system for accessing data, obtaining information of the data analysis process link from an analysis platform or tool for analyzing data, obtaining information of the data sharing process link from a file exchange system or data sharing platform for sharing data, and obtaining information of the data destruction process link from a cleaning tool for destroying data. All data need to be organized in a standard format, and detailed information of each process link, including operation time, participants, data type, etc., is recorded, 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, by mining and analyzing the execution rules of data flow between various data asset management process links, the data flow execution rules between various management process links are analyzed by using rule mining tools (such as data mining algorithms, rule engines, etc.). Through the mining process, the execution rules under different data flow paths and interaction modes are extracted, including the processing order, conditional restrictions and operation requirements of the data, so as to obtain the data flow execution rules between various management process links. Then, by combining the data flow execution rules between various management process links obtained by previous mining and analysis, the conditional trigger nodes of the data flow between the corresponding management process links are identified and analyzed, so as to analyze the conditional trigger nodes in the data flow process by using trigger condition identification tools (such as condition judgment algorithms, event-driven models, etc.). These nodes are the key points for data to trigger 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 clarified, and finally the data flow conditional trigger nodes between various management process links are obtained.

[0072] S2: Full life cycle management process modeling module, used to perform full life cycle management process modeling for each data asset management process link based on the data flow execution rules between each management process link and the data flow condition trigger node, so as to generate a data asset full life cycle management process model;

[0073] 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 combining the data flow execution rules between each management process link and the specific position of the data flow condition trigger node, the corresponding data asset management process link is modeled for the full life cycle management process, so that by adopting a system modeling tool (such as a SysML modeling tool or a UML modeling tool), the data flow execution rules and data flow condition trigger nodes obtained by the previous analysis are imported into the 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 entire process of data asset acquisition, storage, access, analysis, sharing and destruction. The specific operations include defining the input and output, data processing rules, trigger conditions and node behaviors of each link, establishing the connection between the links and the data flow path, and finally constructing a data asset full life cycle management process model.

[0074] S3: Process compliance exception supervision module, which is used to obtain 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; according to the data asset management operation behavior compliance detection engine, the corresponding data asset management process operation behavior in the data asset full life cycle management process model is subject to compliance automated exception supervision to obtain non-compliant abnormal operation behaviors of the data asset management process;

[0075] In an embodiment of the present invention, compliance rules and standards for data asset management are collected from industry standard organizations, regulatory documents, and internal company compliance guidelines, including accessing industry standard databases (such as standards provided by ISO, NIST, or local regulatory agencies), downloading and organizing compliance requirements for 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, and organizing the obtained compliance rules and standards into a systematic list, including specific requirements for data asset management, operating specifications, authority control, and record keeping requirements, so as to obtain compliance rules and standards for data asset management. At the same time, by using rule mapping tools (such as IBM ODM or Drools) to create rule mapping to import the obtained compliance rule standards into the mapping tool, and classify the rules according to the actual data asset management process definition and operation behavior. When establishing the mapping relationship, each requirement of the compliance rule standard is mapped to the specific operation steps in the data asset management behavior. This process involves matching each compliance clause with the management behavior, generating a rule docking mapping relationship diagram, and constructing the framework of the detection engine by using the rule docking 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 a data input module (for receiving operation behavior data), a rule matching module (for In addition to the test module (for detection based on previously established rule mapping relationships) and the report generation module (for generating compliance detection results), the built detection engine logic framework is also verified and designed for integration testing by using integrated testing tools (such as JUnit or Selenium). During the integration 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 operational behaviors and compliance scenarios, including normal operations, boundary conditions, and abnormal situations. After running the test, the response time of the detection engine, detection accuracy, and generation of compliance reports are recorded to ensure the reliability and accuracy of the final generated operational behavior compliance detection engine in actual applications, thereby verifying the generated data asset management operational behavior compliance detection engine.Then, by using the data asset management operation behavior compliance detection engine obtained after previous integration test verification, the corresponding data asset management process operation behavior in the data asset full life cycle management process model is automatically supervised for compliance exceptions. By integrating the generated detection engine into the management process monitoring system (such as Elasticsearch or Logstash), the operation behavior of the data asset full life cycle is monitored 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 will be supervised and identified as the corresponding non-compliant abnormal operation behavior, and finally the non-compliant abnormal operation behavior of the data asset management process is obtained.

[0076] S4: Process abnormality warning optimization suggestion module is used to perform abnormal warning response processing on non-compliant abnormal operation behaviors in the data asset management process to generate data asset management process abnormality warning response data; to make management process abnormality optimization suggestions on the data asset management process abnormality warning response data, and generate a data asset management process abnormality optimization suggestion plan to execute the corresponding data asset management process abnormality improvement work.

[0077] In an embodiment of the present invention, by performing early warning response processing on non-compliant abnormal operation behaviors occurring in the data asset management process, early warning rules need to be set. These rules are based on set standards and historical data, and 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 operation includes configuring a data monitoring system, performing real-time analysis on the operation behavior, discovering behaviors that do not conform to standard operations, such as illegal data access, unauthorized operation requests, etc., and automatically recording detailed information on related operations, including timestamps, operators, operation content, etc., and performing abnormal early warning response processing on the corresponding non-compliant abnormal operation behaviors occurring in the data asset management process through an automated early warning mechanism to obtain records including abnormal early warning signals, abnormal types, frequencies, and specific operation behaviors, thereby generating abnormal early warning response data for the data asset management process. At the same time, by combining the data asset management process abnormal warning response data generated by the previous abnormal response, the corresponding data asset management process non-compliant abnormal operation behavior is reported on the specific location of the management process abnormality, so as to use data analysis tools (such as Tableau or Power BI) to import the warning response data and perform visualization processing, identify the concentrated area or specific location of the abnormal operation through statistics and analysis of abnormal behavior, and generate a detailed management process abnormality location report by combining process logs and operation records. Then, the management process abnormality optimization recommendation analysis is performed on the management process abnormality location report obtained by the previous analysis, so as to use analysis tools (such as root cause analysis tools or process improvement models) to identify the root cause of the abnormality, and formulate targeted optimization suggestions based on the abnormal behavior and impact identified in the report. The optimization suggestions include adjusting the operation process, strengthening authority management, increasing data auditing and monitoring, etc., so as to recommend the generation of data asset management process abnormality optimization suggestion plan, and finally implement the data asset management process abnormality optimization suggestion plan in the data asset management process to solve the abnormal behavior in the data asset management process, so as to perform the corresponding data asset management process abnormality improvement work.

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

[0079] By obtaining various data asset management process links from the data asset management process, wherein each data asset management process link includes 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] 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;

[0081] Based on the actual data flow routes between each management process link, the data flow interaction pattern recognition and analysis is performed on each data asset management process link 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 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: by obtaining various data asset management process links from the 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;

[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 storing data, obtaining information of the data access process link from an API or access control system for accessing data, obtaining information of the data analysis process link from an analysis platform or tool for analyzing data, obtaining information of the data sharing process link from a file exchange system or data sharing platform for sharing data, and obtaining information of the data destruction process link from a cleaning tool for destroying data. All data need to be organized in a standard format, and detailed information of each process link, including operation time, participants, data type, etc., is recorded, and finally each data asset management process link is obtained, including 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.

[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 an embodiment of the present invention, the data flow paths between various data asset management process links are tracked and analyzed, so as to collect the data flow conditions of each link by using data flow monitoring tools (such as data flow management platforms, log analysis tools, etc.), and generate a data flow path diagram, which shows the entire process from data acquisition to destruction. The analysis process needs to mark the data flow path between each link, the frequency of data transfer and the amount of data, so as to identify the actual route of data flow and its characteristics, and finally obtain the actual route of data flow between various management process links.

[0089] S13: Based on the actual data flow routes between each management process link, data flow interaction pattern recognition and analysis are performed on each data asset management process link to generate a data flow transmission interaction pattern map between each management process link;

[0090] In an embodiment of the present invention, the data flow interaction pattern is identified and analyzed between each corresponding data asset management process link by combining the actual data flow route between each management process link obtained by previous tracking and analysis, so as to identify the data interaction pattern between each management process link by using data interaction analysis tools (such as pattern recognition algorithms, machine learning models, etc.), and by training the recognition model, the common characteristics of the data flow pattern are analyzed, and a data flow transmission interaction pattern map is generated. The map shows the interaction method and pattern of data between different links, and finally a data flow transmission interaction pattern map between each management process link is generated.

[0091] S14: performing data flow execution rule mining and analysis on the data flow transmission interaction pattern graph between each management process link to obtain the data flow execution rules between each management process link;

[0092] In an embodiment of the present invention, an execution rule mining analysis is performed on the data flow transmission interaction pattern map between each management process link obtained by the previous identification and analysis, so as to analyze the data flow execution rules between each management process link by utilizing rule mining tools (such as data mining algorithms, rule engines, etc.), and through the mining process, the execution rules under different data flow paths and interaction modes are extracted, specifically including the data processing order, conditional restrictions and operation requirements, and finally the data flow execution rules between each management process link are obtained.

[0093] S15: 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, so as to obtain the data flow condition trigger node between each management process link.

[0094] In an embodiment of the present invention, by combining the data flow execution rules between the various management process links obtained by previous mining and analysis, the actual routes of data flow between the corresponding management process links are identified and analyzed for conditional trigger nodes, so as to analyze the conditional trigger nodes in the data flow path by using trigger condition identification tools (such as condition judgment algorithms, event-driven models, etc.). These nodes are the key points where data triggers 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 clarified, and finally the data flow conditional trigger nodes between the various management process links are obtained.

[0095] Furthermore, the data flow interaction pattern recognition and analysis of each data asset management process link based on the actual data flow route between each management process link includes:

[0096] Based on the actual data flow routes between various management process links, the corresponding data asset management process links are mapped for data flow transmission paths to obtain a data flow transmission path mapping connection diagram between various management process links;

[0097] In an embodiment of the present invention, the data flow paths of the corresponding data asset management process links are mapped and connected by combining the actual data flow routes between the various management process links obtained by previous analysis, and this is completed by deploying data flow diagram tools or process modeling software. These tools are used to map the data input and output in each link one by one, and establish a detailed path transfer diagram. The specific operations include collecting data transmission records from each link, generating a data flow diagram, and marking the starting and ending points of each flow path to ensure that each data transfer path is accurately recorded in the diagram. These records will form a data flow path connection diagram between the various management process links, provide a clear overview of the data flow routes, and ultimately obtain a data flow transfer path mapping connection diagram between the various management process links.

[0098] Preferably, the actual routes of data flow between various management process links are mined and analyzed to obtain the interactive relationship between data flow between various management process links;

[0099] In an embodiment of the present invention, an in-depth interactive relationship analysis is performed on the actual routes of data flow between various management process links, so as to analyze the data exchange pattern between each link by using data analysis tools, such as graph database or network analysis software, and use algorithms (such as node centrality analysis) to mine the interactive relationship between flow links, identify frequent nodes and main transmission paths of data transmission, and finally obtain the data flow interactive relationship between various management process links.

[0100] Preferably, based on the data flow interaction relationship between each management process link, the data flow transmission path mapping connection diagram between each management process link is analyzed for flow path interaction characteristics, so as to obtain data flow transmission path interaction characteristic data between each management process link;

[0101] In an embodiment of the present invention, the interactive characteristics of the flow paths are analyzed by combining the data flow interactive relationship between each management process link obtained by the previous analysis with the data flow transmission path mapping connection diagram between the corresponding management process links, so as to use the flow path analysis tool to analyze the interactive characteristics of each path in the data flow interactive relationship. The operation process includes inputting the interactive relationship diagram into the characteristic analysis software, calculating and extracting the interactive characteristic data of each path, such as flow frequency, delay time and data loss rate, etc., and finally obtaining the interactive characteristic data of the data flow transmission path between each management process link.

[0102] Preferably, data flow interaction pattern recognition analysis is performed on the data flow transmission path interaction feature data between various management process links to generate a data flow transmission interaction pattern map between various management process links.

[0103] In an embodiment of the present invention, data pattern recognition techniques such as cluster analysis and pattern matching algorithms are used to identify and analyze the data flow interaction patterns of the data flow transmission path interaction feature data between each management process link previously analyzed to identify typical interaction patterns in data flow. The operation steps include inputting the interaction feature data into the pattern recognition software, applying a machine learning algorithm (such as K-means clustering) to classify different interaction patterns, thereby generating a graph showing the data flow transmission interaction patterns between each management process link. This graph will show common patterns and abnormal situations in data flow, and ultimately generate a data flow transmission interaction pattern graph between each management process link.

[0104] Furthermore, the data flow execution rule mining and analysis of the data flow transmission interaction pattern graph between each management process link includes:

[0105] Perform detailed interactive behavior logic analysis on each data flow transmission path in the data flow transmission interactive pattern map between each management process link to obtain the data flow transmission interactive behavior logic between each management process link;

[0106] In an embodiment of the present invention, by performing a detailed analysis of the interactive behavior logic of each data flow path in the data flow and transmission interaction pattern map between each management process link, the operations involved include using a modeling tool (such as Visio or UML modeling tool) to identify each node and connection in the data flow path map one by one, and determining its interactive behavior logic by analyzing the starting point, end point and transmission method of each data transmission in detail. These behavior logics may include data processing rules, data conversion operations and data transmission protocols, and the input, processing and output of each step must be recorded to finally obtain the data flow and transmission interactive behavior logic between each management process link.

[0107] Preferably, the interactive behavior logic constraint analysis is performed on the interactive behavior logic of data flow transmission between various management process links to obtain the interactive behavior logic constraint conditions of data flow between various management process links;

[0108] In an embodiment of the present invention, by performing interactive behavior logic constraint analysis on the interactive behavior logic of data flow transmission between various management process links obtained by previous analysis, it is necessary to identify the data flow restriction conditions in each link. These restriction conditions include data validity, real-time requirements and error handling mechanisms, so as to use constraint analysis tools (such as Constraint Logic Programming or other analysis tools) to perform a detailed inspection of each data flow to ensure that its behavior in each link meets the preset constraints. For example, in a welding robot system, it is necessary to ensure that the welding parameter data does not exceed the preset range during the transmission process and meets industrial standards during data conversion, so as to finally obtain the data flow interactive behavior logic constraint conditions between various management process links.

[0109] Preferably, based on the data flow interaction behavior logic constraints between each management process link, the data flow operation standard is determined for each data flow transmission path in the data flow transmission interaction pattern map between each management process link, so as to obtain the data flow interaction behavior operation standard between each management process link;

[0110] In an embodiment of the present invention, by combining the logical constraints of the data flow interaction behavior between each management process link obtained by previous analysis, the data flow operation standard of each data flow transmission path corresponding to the data flow transmission interaction pattern map between each management process link is calibrated to determine the operation standard of each data flow path. This process includes a detailed operation standard definition for each data flow path, including data format, transmission frequency, processing time and error handling mechanism, and specific operation specifications are formulated by using standardized tools (such as ISO or IEC standards). For example, in the control system of the welding process, it is necessary to determine the transmission time of the data packet, the structure of the data packet, and the method of error detection and correction to ensure that the operation of data assets in each link complies with the specifications, and finally obtain the data flow interaction behavior operation standard between each management process link.

[0111] Preferably, data flow execution rule mining and analysis is performed on the data flow interaction behavior operation standards between various management process links to obtain the data flow execution rules between various management process links.

[0112] In an embodiment of the present invention, by mining and analyzing the data flow execution rules of the data flow interaction behavior operation standards between various management process links obtained by previous analysis, it is required to use data mining technology (such as decision tree analysis or rule mining algorithm) to extract execution rules. These rules include the execution steps, processing logic, abnormal situation processing and feedback mechanism of data flow. During implementation, data mining tools can be used to extract patterns and rules from historical data. For example, common data errors that occur in the welding process and their processing methods are analyzed to formulate specific execution rules for data flow, ensure the stability and reliability of the system, and finally obtain the data flow execution rules between various management process links.

[0113] Furthermore, the data flow condition triggering node identification and analysis of the actual data flow route between each management process link based on the data flow execution rules between each management process link includes:

[0114] Perform data flow event identification and analysis on the data flow execution rules between various management process links to obtain the data flow process execution event relationship between various management process links;

[0115] In an embodiment of the present invention, data flow events are identified and analyzed by using the data flow execution rules between various management process links previously mined, so as to collect and organize the data flow records of each management link, and use data analysis tools to create a data flow event model to identify data flow events between different management links. This process includes classifying data flow events, establishing a relationship diagram between events, and identifying the time series and order of data transmission. In specific operations, process modeling tools (such as Visio or BPMN tools) can be used to visualize the data flow process, and data analysis software (such as Excel, Python or R) can be used to perform data statistics and event correlation analysis, and finally the data flow process execution event relationship between various management process links is obtained.

[0116] Preferably, based on the data flow process execution event relationship between each management process link, the actual route of data flow between each management process link is processed by trigger condition node list selection to obtain a candidate set of data flow trigger condition nodes between each management process link;

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

[0118] Preferably, a statistical analysis of the data flow occurrence frequency and influence degree of each data flow trigger condition candidate node in the data flow trigger condition node candidate set between each management process link is performed to obtain the occurrence frequency and influence degree of each trigger condition candidate node between each management process link;

[0119] In an embodiment of the present invention, a statistical analysis of the frequency of occurrence and the degree of influence of data flow is performed on each data flow trigger condition candidate node in the data flow trigger condition node candidate set between each management process link obtained by previous analysis, so as to calculate the frequency of occurrence of each trigger condition node in the data flow process by utilizing data statistical tools (such as the Pandas library in Python), and to evaluate the degree of influence of each trigger condition node on the data flow process by applying impact analysis methods (such as regression analysis or causal inference), and finally the frequency of occurrence and the degree of influence of each trigger condition candidate node between each management process link are obtained by statistical calculation.

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

[0121] In an embodiment of the present invention, the trigger criticality of the corresponding data flow trigger condition candidate nodes in the data flow trigger condition node candidate set between each management process link is quantitatively calculated by combining the occurrence frequency and influence degree of each trigger condition candidate node between each management process link obtained by previous quantitative calculation, so as to evaluate the criticality of each trigger condition candidate node by using a weighted scoring model (such as weighted average method or hierarchical analysis method). The model multiplies the occurrence frequency and influence degree and combines the weight to quantitatively calculate the criticality score of each node, and finally obtains the data flow trigger criticality of each trigger condition candidate node between each management process link.

[0122] Preferably, according to the data flow triggering criticality of each trigger condition candidate node between each management process link, the corresponding data flow trigger condition candidate nodes in the data flow trigger condition node candidate set between each management process link are screened for the optimal trigger condition node to obtain the data flow condition trigger nodes between each management process link.

[0123] In an embodiment of the present invention, the data flow triggering criticality of each trigger condition candidate node between each management process link obtained by previous quantitative calculation is combined to perform optimal node screening on the corresponding data flow trigger condition candidate nodes in the data flow trigger condition node candidate set between each management process link, so as 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 criticality as the final data flow trigger condition node. This step uses optimization software (such as LINDO or Gurobi) to implement screening processing to ensure that the selected node can effectively trigger data flow between each management process link, and finally obtain the data flow condition trigger node between each management process link.

[0124] Furthermore, the full life cycle management process modeling module includes the following functions:

[0125] Conduct data flow execution direction fusion analysis on the data flow execution rules between various management process links to obtain the data flow execution direction fusion map between various management process links;

[0126] Perform 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 by conditional trigger node mapping to obtain the data flow execution trigger node mapping map 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 flow fusion analysis on the data flow execution rules between various management process links to obtain a data flow execution flow fusion map between various management process links;

[0131] In an embodiment of the present invention, by collecting all data flow execution rules involved in the management process links, these rules describe the data transfer relationship, data format, processing logic and their mutual dependence between the various management process links, 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 flowing 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 are displayed in a graphical form, and finally a fusion map of data flow execution direction between the various management process links 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 an embodiment of the present invention, by identifying and locating all conditional trigger nodes in the management process links, these nodes are key decision points in data flow, and usually rely on specific conditions or events. Process mining technology (such as process mining tools based on event logs) is used to perform a detailed analysis of each management process link to extract the trigger nodes and their corresponding conditions. These tools identify the trigger conditions and their locations by analyzing historical data and event logs, and map the locations of the trigger nodes to the data flow execution flow fusion map. The specific operation includes aligning the location information of the trigger nodes with the corresponding nodes in the flowchart, clarifying the actual position of each trigger node in the overall data flow, thereby forming a data flow execution positioning situation marked with the trigger node position, 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 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;

[0135] In an embodiment of the present invention, by combining the data flow execution positioning position of the trigger node between each management process link obtained by previous positioning, the data flow execution flow direction fusion map corresponding to each management process link is mapped to the node, so that the previously marked trigger node position information is mapped one by one to the generated fusion map, and the trigger node is positioned and marked by using a map mapping tool (such as GIS data mapping software or a flowchart editing tool), and the conditions and positions of the trigger nodes are directly displayed on the map. This process includes precise coordinate positioning of each trigger node and adding detailed trigger condition descriptions to the fusion map. These mapping results will generate an execution trigger node mapping diagram containing conditional trigger nodes, which can clearly display the conditions of each trigger node and its specific mapping relationship in the data flow, and finally obtain a data flow execution trigger node mapping diagram between each management process link.

[0136] S24: 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 performed to generate a data asset full life cycle management process model.

[0137] In an embodiment of the present invention, a trigger node mapping diagram is executed to model the full life cycle management process of each corresponding data asset management process link by combining the data flow between each management process link obtained by previous mapping analysis, so as to import the trigger node mapping diagram obtained by previous analysis into the modeling tool by adopting a system modeling tool (such as a SysML modeling tool or a UML modeling tool), and build a comprehensive management process model based on the trigger nodes and their conditions identified in the diagram. The model includes the entire process of data asset acquisition, storage, access, analysis, sharing and destruction. The specific operations include defining the input and output, data processing rules, trigger conditions and node behaviors of each link, establishing the connection between the links and the data flow path, and finally building a data asset full life cycle management process model to fully display the management strategies and processes of data assets throughout the life cycle, so as to realize the systematic management and optimization of data assets.

[0138] Furthermore, the process compliance exception supervision module includes the following functions:

[0139] Obtain data asset management compliance rules and standards;

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

[0141] Preferably, a data asset management behavior mapping relationship is established for the data asset management compliance rule standard to obtain a rule docking mapping relationship between the data asset management behavior and the compliance rule standard;

[0142] In an embodiment of the present invention, a rule mapping is created by using a rule mapping tool (such as IBM ODM or Drools) in the process of establishing a data asset management behavior mapping relationship for data asset management compliance rule standards, so as to import the acquired compliance rule standards into the mapping tool, and classify the rules according to the actual data asset management process definition and operation behavior. When establishing the mapping relationship, each requirement of the compliance rule standard is mapped to the specific operation steps in the data asset management behavior. This process involves matching each compliance clause with the management behavior, generating a rule docking mapping relationship diagram, clarifying how the data operation behavior meets various compliance requirements, and forming a complete mapping relationship document between the data asset management behavior and the compliance rule standard, and finally obtaining the rule docking mapping relationship between the data asset management behavior and the compliance rule standard.

[0143] Preferably, a detection engine logic framework is constructed for the rule docking mapping relationship between data asset management behavior and compliance rule standards to obtain a data asset management behavior compliance detection engine logic framework;

[0144] In an embodiment of the present invention, the framework of the detection engine is constructed by mapping the rule docking relationship between the data asset management behavior and the compliance rule standard obtained by the previous analysis, so as to define the detection logic and framework architecture. This process uses design tools (such as UML modeling tools or architecture design software) to construct the logical framework of the detection engine. The framework includes a data input module (for receiving operation behavior data), a rule matching module (for detection according to the previously established rule mapping relationship) and a report generation module (for generating compliance detection results). In specific implementation, it is necessary to import the rule mapping relationship into the detection engine framework, configure the logical rules to ensure that the detection engine can automatically apply the rules and generate detection results, which involves writing the framework logic code, setting the logical process of rule execution, and ensuring that the logical framework can effectively support compliance detection tasks, and finally constructing the data asset management behavior compliance detection engine logical framework.

[0145] Preferably, an engine integration test design is performed on the data asset management behavior compliance detection engine logic framework to generate a data asset management operation behavior compliance detection engine;

[0146] In an embodiment of the present invention, an integrated test tool (such as JUnit or Selenium) is used to perform integrated test verification design on the constructed data asset management behavior compliance detection engine logic framework. During the integrated test 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 operational behaviors and compliance scenarios, including normal operations, boundary conditions and abnormal situations. After running the test, the response time, detection accuracy and generation of the compliance report of the detection engine 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 final generated operational behavior compliance detection engine in actual applications, and finally generate a data asset management operational behavior compliance detection engine.

[0147] Preferably, the data asset management operation behavior compliance detection engine performs compliance automated exception supervision on the corresponding data asset management process operation behavior within the data asset full life cycle management process model to obtain non-compliant abnormal operation behaviors of the data asset management process.

[0148] In an embodiment of the present invention, by using a data asset management operation behavior compliance detection engine previously obtained after integration test verification, the corresponding data asset management process operation behavior in the data asset full life cycle management process model is automatically supervised for compliance exceptions, so as to integrate the generated data asset management operation behavior compliance detection engine 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 will be supervised and determined as a corresponding non-compliant abnormal operation behavior, and detailed exception information will be recorded to finally obtain non-compliant abnormal operation behavior of the data asset management process.

[0149] Furthermore, the data asset management operation behavior compliance detection engine performs compliance automated abnormal supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model, including:

[0150] Conduct management operation behavior analysis on each data asset management process link within the data asset full life cycle management process model to obtain the data asset full life cycle management process operation behavior;

[0151] In an embodiment of the present invention, a statistical analysis of the management operation behavior is performed on each data asset management process link in the data asset full life cycle management process model to import the process model data into a process analysis tool (such as Visio or Lucidchart), and the operation steps, roles involved, required resources and operation sequence of each data asset management process link are analyzed to extract the detailed operation behavior of each data asset management process link. At the same time, the operation behavior is refined by using a process modeling tool to record the operation type, operation frequency, interaction point and operation result of each step. This process involves a comprehensive analysis of the task description, execution status and process efficiency of each data asset management process link, thereby generating a complete view of all management process operation behaviors of data assets, and ultimately obtaining the data asset full life cycle management process operation behavior.

[0152] Preferably, a dynamic compliance detection mechanism is generated according to a compliance detection engine for data asset management operation behavior to obtain a dynamic compliance detection mechanism for data asset management operation behavior;

[0153] In an embodiment of the present invention, a dynamic compliance detection mechanism is generated by using a previously designed and generated 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 compliance standards and detection rules, such as data access rights, operation frequency restrictions, etc., the compliance detection platform performs real-time monitoring and dynamic analysis of 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, to ensure that when the operation behavior deviates from the compliance standard, the relevant abnormal operation behavior data can be automatically identified and recorded, and finally a dynamic compliance detection mechanism for data asset management operation behavior is obtained.

[0154] Preferably, based on the dynamic compliance detection mechanism of data asset management operation behavior, the compliance automated exception supervision is performed on the management operation behavior of the corresponding data asset management process link within the data asset full life cycle management process operation behavior to obtain the non-compliant abnormal operation behavior of the data asset management process.

[0155] In an embodiment of the present invention, by using a previously generated dynamic compliance detection mechanism for data asset management operation behaviors, automated exception supervision is performed on the compliance of management operation behaviors of corresponding data asset management process links within the data asset full life cycle management process operation behaviors, so that the generated detection mechanism is deployed to an 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 its operation behavior data according to the set compliance detection rules. By comparing real-time operation behaviors with compliance standards, the system automatically identifies and marks non-compliant operation behaviors, including detailed information on abnormal operations, violation types and their potential impact on the process, and finally obtains non-compliant abnormal operation behaviors in the data asset management process.

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

[0157] Extract and process the process link operation content of each data asset management process link in the data asset full life cycle management process model to obtain the management process operation content corresponding to each data asset management process link;

[0158] In an embodiment of the present invention, the operation content of each data asset management process link in the data asset full life cycle management process model is extracted and processed, so as to conduct a detailed document review of the entire management process and use a process modeling tool (such as Bizagi or ARIS) to extract the operation content of each link from the existing data asset full life cycle management process model. The specific operations include importing a flowchart or a process document, identifying the operation steps, participating roles and required resources of each process link one by one, and the extracted operation content should include specific task descriptions, operation requirements, and interactive relationships with other links, so as to finally obtain the management process operation content corresponding to each data asset management process link.

[0159] Preferably, the operation behavior characteristic attribute mining and analysis is performed on 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;

[0160] In an embodiment of the present invention, a data mining tool (such as RapidMiner or WEKA) is used to mine and analyze the characteristic attributes of operation behavior of the management process operation content corresponding to each data asset management process link, so as to import the extracted operation content data into the mining tool, and set relevant attributes and indicators, such as operation frequency, roles involved, data input and output, etc., and identify the key characteristic attributes of each operation behavior through feature engineering technology, including common operation modes, abnormal behavior identification and operation complexity, etc., to describe in detail the specific attributes of the operation behavior of each link and its impact on the overall process, and finally obtain the management process operation behavior characteristic attribute data corresponding to each data asset management process link.

[0161] Preferably, the management process operation behavior characteristic attribute data corresponding to each data asset management process link is subjected to operation behavior pattern recognition analysis to obtain the management process operation behavior pattern corresponding to each data asset management process link;

[0162] In an embodiment of the present invention, a pattern recognition algorithm (such as cluster analysis or classification algorithm) is used to identify and analyze the operation behavior patterns of the management process operation behavior characteristic attribute data corresponding to each data asset management process link previously mined and analyzed, so that the characteristic attribute data previously mined and generated is input into the pattern recognition algorithm (such as Python's Scikit-learn library), and through algorithm analysis, the common patterns of each operation behavior, such as high-frequency operation patterns, abnormal operation patterns, etc., are identified, which are used to list in detail the operation patterns of each data asset management process link, the frequency of occurrence of the patterns and their impact on the process, so as to help further understand and optimize the management process, and finally obtain the management process operation behavior patterns corresponding to each data asset management process link.

[0163] Preferably, based on the management process operation behavior pattern corresponding to each data asset management process link, the full life cycle management behavior analysis and summary are performed on each data asset management process link corresponding to the data asset full life cycle management process model to obtain the data asset full life cycle management process operation behavior.

[0164] In an embodiment of the present invention, the full life cycle management behavior of each data asset management process link corresponding to each data asset management process link obtained in the previous analysis is analyzed and summarized to integrate the operation behavior patterns identified in the previous steps, and the operation behavior patterns of all links are summarized by using data summary tools (such as Microsoft Excel or Tableau), and classified and analyzed according to the process links. The summary analysis should include a comprehensive evaluation of the operation behavior pattern of each management link, an analysis of the impact on the process, and suggested improvement measures, so as to finally obtain the operation behavior of the data asset full life cycle management process.

[0165] Furthermore, the process abnormality warning optimization suggestion module includes the following functions:

[0166] Conduct abnormal warning response processing for non-compliant abnormal operation behaviors in the data asset management process to generate abnormal warning response data for the data asset management process;

[0167] In an embodiment of the present invention, by performing early warning response processing on abnormal operation behaviors occurring in the data asset management process, early warning rules need to be set. These rules are based on set standards and historical data, and 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 operation includes configuring a data monitoring system, performing real-time analysis on the operation behavior, and generating an abnormal early warning event if any behavior that does not conform to the standard operation is found, such as illegal data access, unauthorized operation requests, etc., and automatically recording detailed information of the relevant operation, including timestamp, operator, operation content, etc., and performing abnormal early warning response processing on the corresponding abnormal operation behaviors occurring in the data asset management process through an automated early warning mechanism, thereby generating corresponding abnormal early warning response data in response, including abnormal early warning signals, abnormal types, frequencies, and records of specific operation behaviors, and finally generating abnormal early warning response data for the data asset management process.

[0168] Preferably, based on the data asset management process abnormal warning response data, a management process abnormal location report is performed on the corresponding data asset management process non-compliant abnormal operation behavior to obtain a data asset management process non-compliant abnormal behavior supervision report;

[0169] In an embodiment of the present invention, by combining the data asset management process abnormal warning response data generated by the previous abnormal response, a location report of the specific location of the management process abnormality of the corresponding data asset management process non-compliant abnormal operation behavior is generated, so that the warning response data can be imported and visualized using data analysis tools (such as Tableau or Power BI), and the concentrated area or specific location of the abnormal operation is identified through statistics and analysis of the abnormal behavior, and by combining the process log and operation record, a detailed management process abnormality location report is generated, which includes a detailed description of the abnormal operation behavior, the scope of impact, the frequency of occurrence and related operation process nodes, and finally a data asset management process non-compliant abnormal behavior supervision report is obtained.

[0170] Preferably, management process anomaly optimization suggestions are made for the supervision report on non-compliant abnormal behavior in the data asset management process, and a data asset management process anomaly optimization suggestion plan is generated to execute the corresponding data asset management process anomaly improvement work.

[0171] In an embodiment of the present invention, a recommendation analysis for optimizing the management process anomaly is performed on the supervision report on non-compliant abnormal behavior of the data asset management process obtained by previous analysis, so as to identify the root cause of the anomaly through an analysis tool (such as a root cause analysis tool or a process improvement model), and formulate targeted optimization suggestions based on the abnormal behavior and impact identified in the report. The optimization suggestions include measures such as adjusting operating procedures, strengthening authority management, and increasing data auditing and monitoring. The generated optimization suggestion plan describes in detail the implementation steps, expected effects and execution plans of each improvement measure, thereby recommending the generation of a data asset management process anomaly optimization suggestion plan. Finally, the data asset management process anomaly optimization suggestion plan is implemented in the data asset management process to resolve the abnormal behavior in the data asset management process, thereby improving process compliance and the security of data asset management.

[0172] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

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

Claims

1. A process automation supervision platform for data asset management, characterized in that: Includes the following modules: The process link data mining module is used to obtain each data asset management process link, where each data asset management process link includes 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; perform data flow execution rules and condition trigger analysis on each data asset management process link to obtain the data flow execution rules and data flow condition trigger nodes between each management process link; The full life cycle management process modeling module is used to perform full life cycle management process modeling for each data asset management process link based on the data flow execution rules between each management process link and the data flow condition triggering nodes to generate a data asset full life cycle management process model; The process compliance anomaly supervision module is used to obtain the data asset management compliance rule standards and design the management behavior rule detection engine according to the data asset management compliance rule standards to generate the 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 full life cycle management process model is automatically supervised for compliance exceptions, and the non-compliant abnormal operation behavior of the data asset management process is obtained; The process exception warning optimization suggestion module is used to perform exception warning response processing on non-compliant abnormal operation behaviors in the data asset management process to generate data asset management process exception warning response data; to make management process exception optimization suggestions on the data asset management process exception warning response data, and generate data asset management process exception optimization suggestion plans to execute corresponding data asset management process exception improvement work.

2. The process automation supervision platform for data asset management according to claim 1 is characterized in that: The process link data mining module includes the following functions: By obtaining various data asset management process links from the data asset management process, wherein each data asset management process link includes 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; 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; Based on the actual data flow routes between each management process link, the data flow interaction pattern recognition and analysis is performed on each data asset management process link to generate a data flow transmission interaction pattern map between each management process link; Conduct data flow execution rule mining and analysis on the data flow transmission interaction pattern graph between each management process link to obtain the data flow execution rules between each management process link; 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.

3. The process automation supervision platform for data asset management according to claim 2 is characterized in that: The data flow interaction pattern recognition and analysis of each data asset management process link based on the actual data flow route between each management process link includes: Based on the actual data flow routes between various management process links, the corresponding data asset management process links are mapped for data flow transmission paths to obtain a data flow transmission path mapping connection diagram between various management process links; The actual routes of data flow between various management process links are mined and analyzed to obtain the interactive relationships between data flow between various management process links; Based on the data flow interaction relationship between each management process link, the data flow transmission path mapping connection diagram between each management process link is analyzed for the flow path interaction characteristics, so as to obtain the data flow transmission path interaction characteristic data between each management process link; Data flow interaction pattern recognition and analysis are performed on the interactive feature data of the data flow transmission paths between various management process links to generate a data flow transmission interaction pattern map between various management process links.

4. The process automation supervision platform for data asset management according to claim 2 is characterized in that: The data flow execution rule mining and analysis of the data flow transmission interaction pattern graph between each management process link includes: Perform detailed interactive behavior logic analysis on each data flow transmission path in the data flow transmission interactive pattern map between each management process link to obtain the data flow transmission interactive behavior logic between each management process link; Conduct interactive behavior logic constraint analysis on the interactive behavior logic of data transfer between various management process links to obtain the interactive behavior logic constraint conditions of data transfer between various management process links; Based on the logical constraints of the data flow interaction behavior between each management process link, the data flow operation standard is determined for each data flow transmission path in the data flow transmission interaction pattern map between each management process link, and the data flow interaction behavior operation standard between each management process link is obtained; The data flow execution rules are mined and analyzed on the data flow interaction behavior operation standards between each management process link to obtain the data flow execution rules between each management process link.

5. The process automation supervision platform for data asset management according to claim 2 is characterized in that: The data flow condition triggering node identification and analysis of the actual data flow route between each management process link based on the data flow execution rules between each management process link includes: Perform data flow event identification and analysis on the data flow execution rules between various management process links to obtain the data flow process execution event relationship between various management process links; Based on the data flow process execution event relationship between each management process link, the actual route of data flow between each management process link is processed by trigger condition node list selection, and the candidate set of data flow trigger condition nodes between each management process link is obtained; Perform statistical analysis on the occurrence frequency and impact degree of data flow for each candidate data flow trigger condition node in the candidate set of data flow trigger condition nodes between each management process link, and obtain the occurrence frequency and impact degree of each candidate trigger condition node between each management process link; Based on the occurrence frequency and influence degree of each trigger condition candidate node between each management process link, the trigger criticality of each data flow trigger condition candidate node in the data flow trigger condition node candidate set between each management process link is quantitatively calculated to obtain the data flow trigger criticality of each trigger condition candidate node between each management process link; According to the data flow trigger criticality of each trigger condition candidate node between each management process link, the corresponding data flow trigger condition candidate nodes in the data flow trigger condition node candidate set between each management process link are screened for the optimal trigger condition node to obtain the data flow condition trigger nodes between each management process link.

6. The process automation supervision platform for data asset management according to claim 1 is characterized in that: The full life cycle management process modeling module includes the following functions: Conduct data flow execution direction fusion analysis on the data flow execution rules between various management process links to obtain the data flow execution direction fusion map between various management process links; Perform 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; 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 by conditional trigger node mapping to obtain the data flow execution trigger node mapping map between each management process link; 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.

7. The process automation supervision platform for data asset management according to claim 1 is characterized in that: The process compliance exception supervision module includes the following functions: Obtain data asset management compliance rules and standards; Establish a mapping relationship between data asset management behavior and compliance rules and standards for data asset management to obtain a mapping relationship between data asset management behavior and compliance rules and standards; The detection engine logic framework is constructed for the rule docking mapping relationship between data asset management behavior and compliance rule standards to obtain the data asset management behavior compliance detection engine logic framework; Conduct engine integration test design for the data asset management behavior compliance detection engine logic framework to generate a 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 full life cycle management process model is automatically supervised for compliance exceptions to obtain non-compliant abnormal operation behaviors of the data asset management process.

8. The process automation supervision platform for data asset management according to claim 7 is characterized in that: The data asset management operation behavior compliance detection engine performs compliance automated abnormal supervision on the corresponding data asset management process operation behavior in the data asset full life cycle management process model, including: Conduct management operation behavior analysis on each data asset management process link within the data asset full life cycle management process model to obtain the data asset full life cycle management process operation behavior; Generate a dynamic compliance detection mechanism based on the data asset management operation behavior compliance detection engine to obtain a dynamic compliance detection mechanism for data asset management operation behavior; Based on the dynamic compliance detection mechanism of data asset management operation behavior, the compliance automated exception supervision is carried out on the management operation behavior of the corresponding data asset management process link within the data asset full life cycle management process operation behavior to obtain the non-compliant abnormal operation behavior of the data asset management process.

9. The process automation supervision platform for data asset management according to claim 8 is characterized in that: The management operation behavior analysis of each data asset management process link in the data asset full life cycle management process model includes: Extract and process the process link operation content of each data asset management process link in the data asset full life cycle management process model to obtain the management process operation content corresponding to each data asset management process link; Conducting mining and analysis of the operation behavior characteristic attributes of the management process operation content corresponding to each data asset management process link, and obtaining the management process operation behavior characteristic attribute data corresponding to each data asset management process link; Perform operation behavior pattern recognition and analysis on the management process operation behavior characteristic attribute data corresponding to each data asset management process link, and obtain the management process operation behavior pattern corresponding to each data asset management process link; Based on the management process operation behavior pattern corresponding to each data asset management process link, the full life cycle management behavior of each data asset management process link corresponding to the data asset full life cycle management process model is analyzed and summarized to obtain the data asset full life cycle management process operation behavior.

10. The process automation supervision platform for data asset management according to claim 1 is characterized in that: The process abnormality warning optimization suggestion module includes the following functions: Conduct abnormal warning response processing for non-compliant abnormal operation behaviors in the data asset management process to generate abnormal warning response data for the data asset management process; Based on the data asset management process abnormal warning response data, the corresponding data asset management process non-compliant abnormal operation behavior is reported on the management process abnormality, and a supervision report on the non-compliant abnormal behavior of the data asset management process is obtained; Provide management process anomaly optimization suggestions for the supervision report on non-compliant abnormal behavior in the data asset management process, generate data asset management process anomaly optimization suggestion plans, and execute corresponding data asset management process anomaly improvement work.

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