A method for automating the configuration management of visual workflows based on process components.

Abstract

This invention discloses a configuration automation management method based on process components to realize a visualized workflow, belonging to the field of configuration management technology. It addresses the technical problem of poor autonomous analysis and optimization effects in different aspects of configuration automation management in existing solutions. The method involves monitoring and analyzing passive and active dependencies between different work modules, sorting and combining the results of these analyses, integrating and analyzing the reliability status of configurations corresponding to different work modules using the dependency monitoring set obtained in the pre-processing stage, and adaptively optimizing the configurations of different work modules based on the analysis results. It utilizes the optimization management data of different work module configuration schemes to process and analyze the reliability status of existing work module data calculations, and dynamically updates the standard parameters for all work module data calculations based on the processing and analysis results.

Description

Technical Field

[0001] This invention relates to the field of configuration management technology, and more specifically to a method for automated configuration management based on process components to implement a visual workflow. Background Technology

[0002] Automated configuration management of visualized workflows based on process components is a method to optimize business processes through modular, graphical interface design and automation technology. Its core goal is to reduce technical barriers, improve efficiency, and enable the rapid construction and management of complex processes through flexible configuration.

[0003] Existing configuration automation management solutions based on process components to achieve visualized workflows cannot perform diverse monitoring and analysis of the coupling status between different work modules during implementation, nor can they adaptively and dynamically optimize the configuration of different work modules based on different monitoring and analysis results. This results in poor autonomous analysis and optimization effects in different aspects of configuration automation management. Summary of the Invention

[0004] The purpose of this invention is to provide a configuration automation management method based on process components to realize a visual workflow, which solves the technical problem that the autonomous analysis and optimization effects of different aspects of configuration automation management in existing solutions are not good.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for automating configuration management of visual workflows based on process components includes:

[0007] The passive and active dependencies between different work modules are monitored and analyzed, and the results of the data processing and analysis are sorted and combined to obtain the dependency monitoring set corresponding to different work modules.

[0008] Based on the dependency supervision set, the reliability status of the configuration corresponding to different working modules is integrated and analyzed, and the configuration corresponding to different working modules is dynamically optimized and managed adaptively based on the analysis results.

[0009] The system utilizes optimized management data from different work module configuration schemes to process and analyze the reliability status of data calculations for existing work modules, and dynamically updates the standard parameters for data calculations for all work modules based on the processing and analysis results.

[0010] Preferably, when performing passive dependency monitoring on different work modules in sequence, the total number of first passive dependencies where a work module directly calls other work modules is counted according to the module number, and the total number of second passive dependencies where a work module directly depends on other work modules is counted.

[0011] Based on the module number, the total number of the first passive dependency and the total number of the second passive dependency are calculated using the formula. Calculate the first dependency impact value YY1i for the corresponding working module; where i represents different working modules, i = 1, 2, 3, ..., n; n is a positive integer; N1i and N2i are the total number of first passive dependencies and the total number of second passive dependencies for different working modules, respectively; α1 is the first weight coefficient and is a real number greater than 1; A is the passive dependency impact threshold.

[0012] Preferably, data analysis is performed on the first dependency impact value to determine the passive dependency status corresponding to the relevant work module;

[0013] If the impact value of the first dependency is less than or equal to 1, then the working module to which it belongs will be associated with the normal state of the passive dependency and marked as the first module;

[0014] Conversely, the corresponding working module will be associated with a passive dependency exception state and marked as the second module;

[0015] The first dependency impact value obtained from the corresponding processing of the working module, and the tags of the first or second module are sorted and combined to obtain the first regulatory analysis data.

[0016] Preferably, when performing proactive dependency monitoring on different work modules in sequence, the total number of proactive dependencies of a work module that is directly called by other work modules is counted according to the module number, as well as the failure propagation impact value corresponding to the failure of a work module.

[0017] Based on the module number, the total number of active dependencies and the impact value of fault propagation are calculated using the formula. Calculate the second dependency impact value YY2i for the corresponding working module; where N3i and N4i are the total number of active dependencies and the failure propagation impact value for different working modules, respectively; α2 is the second weighting coefficient and is a real number greater than 1; B is the active dependency impact threshold.

[0018] Preferably, data analysis is performed on the second dependency impact value to determine the active dependency status corresponding to the working module;

[0019] If the impact value of the second dependency is less than or equal to 1, then the working module to which it belongs will be associated with the normal state of the active dependency and marked as the third module;

[0020] Conversely, the corresponding working module will be associated with an active dependency exception state and marked as the fourth module;

[0021] The second dependency impact value and the tags of the third or fourth module obtained from the corresponding processing of the working module are sorted and combined to obtain the second regulatory analysis data;

[0022] The first and second regulatory analysis data corresponding to the working modules are sorted and combined to obtain the dependent regulatory set.

[0023] Preferably, the dependency monitoring set corresponding to the working module is obtained, and the second dependency impact value and the second dependency impact value in the dependency monitoring set are expressed by the formula. Calculate the dependency integration degree ai corresponding to the working module;

[0024] And, obtain the cohesion bi corresponding to the working module;

[0025] The dependency integration degree and cohesion degree obtained by the corresponding processing of the work module are analyzed together, and the configuration reliability status of the work module is determined based on the analysis results, and targeted optimization management is implemented.

[0026] Preferably, if ai≤0 and bi≥C, then the configuration status of the corresponding working module is reliable, and its existing configuration scheme is maintained; C is the cohesion threshold.

[0027] If ai > 0 or bi < C, it indicates that the configuration status of the relevant working module is locally reliable, and performs local optimization management on its existing configuration scheme.

[0028] If ai > 0 and bi < C, then the configuration status of the module to which it belongs is unreliable, and its existing configuration scheme will be optimized and managed as a whole.

[0029] Preferably, the first processing value for fault handling and recovery of all working modules before the optimization and management of the working module configuration scheme is obtained, and the second processing value for fault handling and recovery of all working modules after the optimization and management of the working module configuration scheme is obtained, and then processed using the formula... Calculate the implementation validity p corresponding to the optimization management of the formula scheme for all working modules; where x1 and x2 are the first treatment value and the second treatment value, respectively; and x0 is the treatment standard deviation.

[0030] Preferably, if the implementation validity is greater than or equal to 1, it indicates that the data calculation status of the existing working module is reliable, and the standard parameters for the data calculation of all working modules are not updated;

[0031] Conversely, it will indicate that the data calculation status of the existing working module is unreliable and update the standard parameters for data calculation of all working modules.

[0032] Preferably, the standard parameters for data calculation include a first weighting coefficient, a second weighting coefficient, and a cohesion threshold.

[0033] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0034] This invention monitors and analyzes passive and active dependencies between different work modules, and sorts and combines the results of these analyses to obtain dependency monitoring sets for different work modules. This enables digital processing and analysis of the dependency status of different work modules from various perspectives, improving the diversity and comprehensiveness of work module configuration monitoring based on process components to achieve visualized workflow.

[0035] This invention integrates and analyzes the reliability status of configurations corresponding to different work modules by combining the dependency monitoring set obtained in the early stage, and adaptively optimizes and manages the configurations corresponding to different work modules based on the analysis results. This enables the expansion and utilization of monitoring data corresponding to different aspects of different work modules to perform overall dimension-based configuration status processing analysis and optimization management, thereby improving the autonomous analysis and optimization effect of different aspects of configuration automation management.

[0036] This invention utilizes optimized management data from different work module configuration schemes to process and analyze the reliability status of existing work module corresponding data calculations. Based on the processing and analysis results, it dynamically updates the standard parameters for all work module corresponding data calculations. This enables the mining and analysis of optimized management data from different work module configuration schemes in the early stages, determines the overall implementation effect of optimized management for different configuration schemes, and proactively processes the data. This can effectively improve the accuracy and reliability of optimized management for different work module configuration schemes. Attached Figure Description

[0037] The invention will now be further described with reference to the accompanying drawings.

[0038] Figure 1 This is a flowchart of the configuration automation management method for realizing a visual workflow based on process components, as described in this invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] like Figure 1 As shown, this invention provides a method for automated configuration management of visualized workflows based on process components, including:

[0041] The system monitors and analyzes passive and active dependencies between different work modules, and sorts and combines the results of these analyses to obtain dependency monitoring sets for each work module; including:

[0042] When performing passive dependency monitoring on different work modules in sequence, the total number of first passive dependencies that a work module directly calls and depends on other work modules is counted according to the module number, and the total number of second passive dependencies that a work module directly depends on other work modules is also counted.

[0043] Among them, the process component that realizes the visualization workflow can be Vue-Super-Flow. As a draggable and configurable flowchart component based on Vue.js, Vue-Super-Flow can provide an intuitive and powerful tool for creating and managing visualization workflows.

[0044] The module number of the working module can be determined based on the preliminary design data, or it can be customized according to the application requirements of the actual application scenario;

[0045] Directly invoke dependencies; specifically, working module A directly invokes working module B via IP / port.

[0046] Direct state dependency, specifically, the execution logic of working module a depends on the runtime state of working module b;

[0047] Based on the module number, the total number of the first passive dependency and the total number of the second passive dependency are calculated using the formula. Calculate the first dependency impact value YY1i for the corresponding work module; where i represents different work modules, i = 1, 2, 3, ..., n; n is a positive integer, representing the total number of all work modules; N1i and N2i are the total number of first passive dependencies and the total number of second passive dependencies for different work modules, respectively; α1 is the first weight coefficient, which is a real number greater than 1, and the specific value is not limited and can be customized according to the application requirements of the actual application scenario; A is the passive dependency impact threshold, which is determined based on the design requirements data of the work module in the early stage.

[0048] It should be noted that the first dependency impact value is used to process and calculate different data of the passive aspect of the working module to digitally represent its corresponding passive dependency state.

[0049] It is understood that the passive dependency monitoring of different working modules in this embodiment of the invention only involves two indicators: direct call dependency and direct state dependency. The corresponding indicators can be added, deleted, or modified according to the application requirements of the actual application scenario.

[0050] Perform data analysis on the first dependency impact value to determine the passive dependency status corresponding to the relevant work module;

[0051] If the impact value of the first dependency is less than or equal to 1, then the working module to which it belongs will be associated with the normal state of the passive dependency and marked as the first module;

[0052] Conversely, the corresponding working module will be associated with a passive dependency exception state and marked as the second module;

[0053] The first dependency impact value obtained from the corresponding processing of the working module and the tag of the first or second module are sorted and combined to obtain the first regulatory analysis data;

[0054] In this embodiment of the invention, by performing passive dependency monitoring on different working modules, it is possible to obtain digital data on the passive dependency status of different working modules, and also to provide reliable passive dependency monitoring data support for subsequent data processing and analysis of the reliable configuration status of the corresponding working modules.

[0055] When performing proactive dependency monitoring on different work modules in sequence, the total number of proactive dependencies of a work module that is directly called by other work modules is counted according to the module number, as well as the failure propagation impact value when a work module fails.

[0056] Among them, the fault propagation impact value is specifically the number of other working modules that become inoperable due to the failure of a working module.

[0057] Based on the module number, the total number of active dependencies and the impact value of fault propagation are calculated using the formula. Calculate the second dependency impact value YY2i for the corresponding working module; where N3i and N4i are the total number of active dependencies and the fault propagation impact value for different working modules, respectively; α2 is the second weight coefficient, which is a real number greater than 1. The specific value is not limited and can be customized according to the application requirements of the actual application scenario; B is the active dependency impact threshold, which can also be determined according to the design requirements data of the working module in the early stage.

[0058] It should be noted that the second dependency impact value is used to process and calculate different data of the active aspect of the working module to digitally represent its corresponding active dependency state.

[0059] Similarly, in this embodiment of the invention, the monitoring of active dependencies of different working modules only involves two indicators: the total number of active dependencies of a working module that is directly called by other working modules, and the failure propagation impact value when a working module fails. The corresponding indicators can also be added, deleted, or modified according to the application requirements of the actual application scenario.

[0060] Perform data analysis on the second dependency impact value to determine the active dependency status corresponding to the relevant work module;

[0061] If the impact value of the second dependency is less than or equal to 1, then the working module to which it belongs will be associated with the normal state of the active dependency and marked as the third module;

[0062] Conversely, the corresponding working module will be associated with an active dependency exception state and marked as the fourth module;

[0063] The second dependency impact value and the tags of the third or fourth module obtained from the corresponding processing of the working module are sorted and combined to obtain the second regulatory analysis data;

[0064] By performing proactive dependency monitoring on different work modules, we can obtain digital data on the proactive dependency status of different work modules, and provide reliable proactive dependency monitoring data support for subsequent data processing and analysis of the reliable status of the corresponding work modules.

[0065] The first and second regulatory analysis data corresponding to the working modules are sorted and combined to obtain the dependent regulatory set;

[0066] In this embodiment of the invention, by monitoring and analyzing passive and active dependencies between different work module processes, and sorting and combining the results of data processing and analysis from different aspects, a dependency monitoring set corresponding to different work modules is obtained. This enables digital processing and analysis of the dependency status of different work modules from different perspectives, and improves the diversity and comprehensiveness of work module configuration monitoring based on process components to realize visualized workflow.

[0067] Based on the dependency monitoring set, data integration and analysis are performed on the configuration reliability status of different working modules, and the configuration of different working modules is dynamically optimized and managed adaptively based on the analysis results; including:

[0068] Obtain the dependency supervision set corresponding to the working module, and then use the formula to compare the second dependency impact value and the second dependency impact value in the dependency supervision set. Calculate the dependency integration degree ai corresponding to the working module;

[0069] And, obtain the cohesion bi corresponding to the working module;

[0070] Cohesion is used to digitally represent the degree of association between functions (such as functions, classes, and code blocks) within a working module; the higher the cohesion, the better.

[0071] The specific value of cohesion can be determined based on existing quantitative standards for the cohesion of working modules. For example, cohesion can be measured by the ratio of shared fields among methods in a statistical class. The specific implementation steps will not be elaborated here.

[0072] Perform a joint analysis on the dependency integration degree and cohesion degree obtained from the corresponding processing of the work modules;

[0073] If ai≤0 and bi≥C, it indicates that the configuration status of the working module is reliable and maintains its existing configuration scheme; C is the cohesion threshold, and the specific value is not limited. It can be customized according to the actual application needs of the actual application scenario and the quantification standard of cohesion.

[0074] If ai > 0 or bi < C, it indicates that the configuration status of the relevant working module is locally reliable, and performs local optimization management on its existing configuration scheme.

[0075] If ai > 0 and bi < C, then the configuration status of the module to which it belongs is unreliable, and its existing configuration scheme will be optimized and managed as a whole.

[0076] Among them, local optimization management specifically allows for the addition, deletion, and modification of certain configuration indicators in the existing configuration scheme;

[0077] Overall optimization management allows for the addition, deletion, and modification of overall configuration indicators in existing configuration schemes;

[0078] It is worth noting that by performing a joint analysis of the dependency integration degree and cohesion obtained from the processing of different aspects of the working modules, the reliability and comprehensiveness of the analysis of the configuration state processing of the working modules can be effectively improved.

[0079] In this embodiment of the invention, the dependency monitoring set obtained through preprocessing is combined to perform data integration and analysis on the configuration reliability status corresponding to different working modules. Based on the analysis results, the configuration corresponding to different working modules is dynamically optimized and managed adaptively. This enables the expansion and utilization of monitoring data corresponding to different aspects of different working modules to perform overall dimension-based configuration status processing analysis and optimization management, thereby improving the autonomous analysis and optimization effect of different aspects of configuration automation management.

[0080] The system utilizes optimized management data from different work module configuration schemes to analyze the reliability of data calculations for existing work modules, and dynamically updates the standard parameters for data calculations for all work modules based on the analysis results; including:

[0081] Obtain the first processing value for fault handling and recovery for all working modules before the optimization and management of the working module configuration scheme, and obtain the second processing value for fault handling and recovery for all working modules after the optimization and management of the working module configuration scheme. Both the first and second processing values ​​are average recovery times, i.e., the average time required for the system to recover from a fault, in minutes. This is then calculated using the formula... Calculate the implementation validity p corresponding to the optimization management of the formula schemes for all work modules; where x1 and x2 are the first treatment value and the second treatment value, respectively; and x0 is the treatment standard deviation.

[0082] The steps for obtaining the standard deviation x0 include:

[0083] Count the total number of units managed by the first module (which performs local optimization management) and the total number of units managed by the second module (which performs overall optimization management) separately, and then use the formula... Calculate the optimization implementation rate c for all working modules; where m1 and m2 are the total number of the first module and the total number of the second module, respectively.

[0084] The calculated optimization implementation rate is matched with several optimization implementation impact ranges in the pre-built optimization implementation impact table to obtain the optimization implementation impact range to which the optimization implementation rate belongs and mark it as the target optimization implementation impact range. The optimization implementation impact identifier associated with the target optimization implementation impact range is set as the processing standard deviation.

[0085] The specific values ​​of the impact range of several optimization implementations can be determined based on the configuration design requirements data in the early stage, or based on the configuration test data; each optimization implementation impact range is preset with a corresponding optimization implementation impact identifier, which can be the median value of the corresponding optimization implementation impact range;

[0086] If the validity is greater than or equal to 1, it indicates that the data calculation status of the existing working module is reliable, and the standard parameters for the data calculation of all working modules will not be updated.

[0087] Conversely, it will indicate that the current data calculation status of the corresponding working module is unreliable and update the standard parameters for data calculation of all working modules.

[0088] The standard parameters for data calculation include a first weighting coefficient, a second weighting coefficient, and a cohesion threshold. Furthermore, the standard parameters can be updated by increasing or decreasing the corresponding values ​​of the first weighting coefficient, the second weighting coefficient, and the cohesion threshold to improve the reliability of the corresponding data analysis. The specific values ​​to be increased or decreased can be determined based on the configured test data, and there are no specific limitations.

[0089] In this embodiment of the invention, the reliability status of the data calculation corresponding to the existing work modules is processed and analyzed using the optimized management data of different work module configuration schemes. Based on the processing and analysis results, the standard parameters of the data calculation corresponding to all work modules are dynamically updated. This realizes the mining and analysis of the optimized management data of different work module configuration schemes in the early stage, determines the overall implementation effect of the optimized management of different configuration schemes, and actively processes it, which can effectively improve the accuracy and reliability of the optimized management of different work module configuration schemes.

[0090] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0091] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0093] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for automating the configuration management of visualized workflows based on process components, characterized in that: include: The passive and active dependencies between different work modules are monitored and analyzed, and the results of the data processing and analysis are sorted and combined to obtain the dependency monitoring set corresponding to different work modules. Among them, when performing active dependency monitoring on different work modules in sequence, the total number of active dependencies of a work module that is directly called by other work modules is counted according to the module number, as well as the failure propagation impact value when a work module fails. Based on the module number, the total number of active dependencies and the impact value of fault propagation are calculated using the formula. Calculate the second dependency impact value YY2i for the corresponding working module; where N3i and N4i are the total number of active dependencies and the failure propagation impact value for different working modules, respectively; α2 is the second weighting coefficient, which is a real number greater than 1; and B is the active dependency impact threshold. Perform data analysis on the second dependency impact value to determine the active dependency status of the corresponding work module; If the impact value of the second dependency is less than or equal to 1, then the working module to which it belongs will be associated with the normal state of the active dependency and marked as the third module; Conversely, the corresponding working module will be associated with an active dependency exception state and marked as the fourth module; The second dependency impact value and the tags of the third or fourth module obtained from the corresponding processing of the working module are sorted and combined to obtain the second regulatory analysis data; Based on the dependency supervision set, the reliability status of the configuration corresponding to different working modules is integrated and analyzed, and the configuration corresponding to different working modules is dynamically optimized and managed adaptively based on the analysis results. The system utilizes optimized management data from different work module configuration schemes to process and analyze the reliability status of data calculations for existing work modules, and dynamically updates the standard parameters for data calculations for all work modules based on the processing and analysis results.

2. The method for automated configuration management of visualized workflow based on process components according to claim 1, characterized in that, When performing passive dependency monitoring on different work modules in sequence, the total number of first passive dependencies that a work module directly calls and depends on other work modules is counted according to the module number, and the total number of second passive dependencies that a work module directly depends on other work modules is also counted. Based on the module number, the total number of the first passive dependency and the total number of the second passive dependency are calculated using the formula. Calculate the first dependency impact value YY1i for the corresponding working module; where i represents different working modules, i=1,2,3,...,n; n is a positive integer; N1i and N2i are the total number of first passive dependencies and the total number of second passive dependencies for different working modules, respectively; α1 is the first weight coefficient and is a real number greater than 1; A is the passive dependency impact threshold.

3. The method for automated configuration management of visualized workflows based on process components according to claim 2, characterized in that, Perform data analysis on the first dependency impact value to determine the passive dependency status corresponding to the relevant work module; If the impact value of the first dependency is less than or equal to 1, then the working module to which it belongs will be associated with the normal state of the passive dependency and marked as the first module; Conversely, the corresponding working module will be associated with a passive dependency exception state and marked as the second module; The first dependency impact value obtained from the corresponding processing of the working module, and the tags of the first or second module are sorted and combined to obtain the first regulatory analysis data.

4. The method for automated configuration management of visualized workflows based on process components according to claim 3, characterized in that, The first and second regulatory analysis data corresponding to the working modules are sorted and combined to obtain the dependent regulatory set.

5. The method for automated configuration management of visual workflow based on process components according to claim 4, characterized in that, The dependency integration degree (ai) for each working module is obtained using the formula. Calculated; And, obtain the cohesion bi corresponding to the working module; The dependency integration degree and cohesion degree obtained by the corresponding processing of the work module are analyzed together, and the configuration reliability status of the work module is determined based on the analysis results, and targeted optimization management is implemented.

6. The method for automated configuration management of a visual workflow based on process components according to claim 5, characterized in that, If ai≤0 and bi≥C, then the configuration status of the corresponding working module is reliable, and its existing configuration scheme is maintained; C is the cohesion threshold. If ai > 0 or bi < C, it indicates that the configuration status of the relevant working module is locally reliable, and performs local optimization management on its existing configuration scheme. If ai > 0 and bi < C, then the configuration status of the module to which it belongs is unreliable, and its existing configuration scheme will be optimized and managed as a whole.

7. The method for automated configuration management of visual workflow based on process components according to claim 6, characterized in that, Obtain the first processing value for fault handling and recovery for all work modules before the work module configuration scheme optimization and management, and obtain the second processing value for fault handling and recovery for all work modules after the work module configuration scheme optimization and management, and use the formula... Calculate the implementation validity p corresponding to the optimization management of the formula scheme for all working modules; where x1 and x2 are the first treatment value and the second treatment value, respectively; and x0 is the treatment standard deviation.

8. The method for automated configuration management of visual workflow based on process components according to claim 7, characterized in that, If the validity is greater than or equal to 1, it indicates that the data calculation status of the existing working module is reliable, and the standard parameters for the data calculation of all working modules will not be updated. Conversely, it will indicate that the data calculation status of the existing working module is unreliable and update the standard parameters for data calculation of all working modules.

9. The method for automated configuration management of a visual workflow based on process components according to claim 7, characterized in that, The standard parameters for data calculation include a first weighting coefficient, a second weighting coefficient, and a cohesion threshold.

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