Software self-healing supervision processing method

Abstract

The invention discloses a software self-healing supervision processing method, and belongs to the technical field of software supervision. The technical problem that in an existing scheme, software self-healing supervision processing is poor in diversity and reliability is solved. Different self-healing schemes executed in the software self-healing implementation process are supervised, processed and analyzed, single execution supervision data of the different self-healing schemes are dynamically updated according to analysis results, and overall scheme optimization processing prompts are dynamically implemented. A plurality of single execution supervision state data of different self-healing schemes are utilized to carry out supervision analysis on the self-healing reliability aspect and the self-healing stability aspect on the different self-healing schemes respectively, and according to analysis results, optimization processing prompting on the whole level and execution optimization processing prompting on the local level are carried out on the different self-healing schemes. Diversified expansion analysis and utilization of all single execution supervision data in the earlier stage of different self-healing schemes are realized, and the diversity and reliability of software self-healing supervision processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software supervision, and in particular to a software self-healing supervision processing method. Background Art

[0002] Software self-healing refers to the ability of a software system to self-detect, diagnose and repair errors or faults, aiming to improve system reliability and availability. This technology attempts to mimic the self-healing mechanism of organisms and automatically restore the normal operation of the system without human intervention.

[0003] When implementing existing software self-healing supervision and processing solutions, most of them still remain at the stage of supervising and processing a number of preset self-healing solutions. They are unable to conduct supervision and analysis on the implementation effects and implementation defects of different self-healing solutions from different aspects, and proactively implement targeted autonomous optimization management of different self-healing solutions based on the analysis results. As a result, the diversity of software self-healing supervision and processing is poor and the reliability is poor. Summary of the Invention

[0004] The purpose of the present invention is to provide a software self-healing supervision processing method for solving the technical problems of diversity and poor reliability of software self-healing supervision processing in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A software self-healing supervision processing method, comprising:

[0007] Monitor and analyze different self-healing solutions executed during the software self-healing implementation process, obtain the single execution supervision status corresponding to different self-healing solutions, and dynamically update the single execution supervision data;

[0008] The supervision status data of several single executions of different self-healing schemes are used to conduct supervision analysis on the self-healing reliability and self-healing stability of different self-healing schemes. Based on the analysis results, optimization processing prompts at the overall level and execution optimization processing prompts at the local level are provided for different self-healing schemes.

[0009] Preferably, a first timestamp of when the self-healing solution starts executing and a second timestamp of when the self-healing solution ends executing are obtained according to the software log, and a self-test result after the self-healing solution is executed is obtained;

[0010] Conduct data analysis on the self-test results after the self-healing solution is executed;

[0011] If the self-test result is 0, the total number of normal self-healing processes corresponding to the self-healing scheme is increased by one, and the difference between the first and second timestamps of the self-healing scheme is calculated, as well as the total amount of resources consumed during the self-healing process between the first and second timestamps, is calculated using the formula Calculate and obtain the single execution value DZ of the self-healing solution. Where TC and ZX are the processing difference and total resource consumption of the self-healing solution, respectively. A and B are the standard processing difference and standard total resource consumption of the self-healing solution, respectively. is the floor function.

[0012] Preferably, if the single execution value is less than or equal to 0, the single execution supervision state when the self-healing solution is executed normally is determined to be normal, and the corresponding single execution supervision normal total is increased by one;

[0013] Otherwise, it is determined that the single execution supervision status is abnormal when the self-healing plan is executed normally, and the corresponding total number of single execution supervision anomalies is increased by one, and the corresponding total number of single execution duration anomalies is increased by one and / or the total number of single execution efficiency anomalies is increased by one.

[0014] Preferably, if the self-check result is 1, the self-healing solution is marked as an abnormal self-healing solution, and an overall solution optimization processing prompt is actively implemented.

[0015] Preferably, when performing a self-healing reliability supervision analysis on different self-healing schemes using a plurality of single execution supervision status data, the total number of times the self-healing schemes are marked as abnormal self-healing schemes is counted and analyzed;

[0016] If the total number of times an abnormal self-healing solution is marked is 0, the self-healing solution is associated with the self-healing reliability label;

[0017] If the total number of marks of the abnormal self-healing solution is not 0, then the formula Calculate the self-healing reliability ZK of the self-healing scheme. Where N and M are the total number of times the self-healing scheme is marked and the total number of times self-healing is executed, respectively. C is the self-healing reliability threshold of the self-healing scheme.

[0018] If the self-healing reliability is less than or equal to 0, the self-healing solution is associated with a self-healing partial reliability label and partial solution optimization is performed.

[0019] Otherwise, the self-healing solution will be associated with the self-healing unreliable label, and the overall solution optimization processing prompt will be implemented.

[0020] Preferably, when implementing the self-healing stability supervision analysis for different self-healing schemes, the total number of normal and abnormal single-execution supervisions corresponding to the self-healing schemes are counted, and the formula Calculate the self-healing stability ZWk of the self-healing solution. In the formula, k is 1 and 2, representing the execution time and efficiency of the self-healing solution, respectively. n1 and n2 are the total number of single execution time anomalies and the total number of single execution efficiency anomalies of the self-healing solution, respectively. Dk is D1 and D2, representing the first and second self-healing stability thresholds of the self-healing solution, respectively.

[0021] If the self-healing stability is greater than or equal to 0, the self-healing plan is associated with the execution duration stability label and the execution efficiency stability label.

[0022] Otherwise, the self-healing solution is associated with the unstable execution duration label and the unstable execution efficiency label.

[0023] Preferably, targeted execution optimization processing prompts are implemented for the self-healing solution according to the unstable execution time label or the unstable execution efficiency label;

[0024] In addition, based on the unstable execution time label and the unstable execution efficiency label, some optimization processing prompts are implemented for the corresponding self-healing plan.

[0025] Preferably, the first total number of optimization processing prompts for the corresponding implementation of the self-healing solution history and the second total number of all optimization processing prompts are counted, and the formula Calculate and obtain the partial processing value BC corresponding to the self-healing solution; where m1 and m2 are the first total number and the second total number corresponding to the self-healing solution, respectively; and b is the partial processing threshold.

[0026] If the partial processing value is less than or equal to 0, it is determined that all partial optimization processing prompts of the self-healing solution history are valid as a whole;

[0027] Otherwise, it is determined that all partial solution optimization processing prompts of the self-healing solution history are invalid as a whole, and the overall solution optimization processing prompt is implemented.

[0028] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0029] The present invention monitors and analyzes different self-healing schemes executed during the software self-healing implementation process, uses the analysis results to dynamically update the single execution supervision data of different self-healing schemes, and dynamically implements overall scheme optimization processing prompts, thereby improving the diversity and reliability of the single execution supervision processing of different self-healing schemes.

[0030] The present invention utilizes several single execution supervision status data of different self-healing schemes to perform supervision analysis on the self-healing reliability and self-healing stability of different self-healing schemes, and provides overall optimization processing prompts and local execution optimization processing prompts for different self-healing schemes based on the analysis results. This realizes diversified expansion analysis and utilization of all single execution supervision data of different self-healing schemes in the early stage, thereby improving the diversity and reliability of software self-healing supervision processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below with reference to the accompanying drawings.

[0032] Figure 1 The present invention is a flowchart of a software self-healing monitoring processing method. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, the present invention is a software self-healing supervision processing method, comprising:

[0035] Monitor and analyze different self-healing solutions executed during the software self-healing implementation process, obtain the single execution supervision status corresponding to different self-healing solutions, and dynamically update the single execution supervision data; including:

[0036] Obtain the first timestamp of the start of the self-healing solution execution and the second timestamp of the end of the self-healing solution execution according to the software log, and obtain the self-test result after the self-healing solution execution;

[0037] The units of different timestamps are all accurate to the second. The self-test result contains a value of 0 or 1. A value of 0 indicates that the execution effect of the self-healing solution is effective; a value of 1 indicates that the execution effect of the self-healing solution is partially effective or ineffective.

[0038] In addition, each self-healing solution has a corresponding self-checking solution. The self-checking solution can be determined based on the self-healing object and self-healing rules corresponding to the self-healing solution. The specific implementation steps and content are not detailed here.

[0039] Conduct data analysis on the self-test results after the self-healing solution is executed;

[0040] If the self-test result is 0, the total number of normal self-healing processes corresponding to the self-healing scheme is increased by one, and the difference between the first and second timestamps of the self-healing scheme is calculated, as well as the total amount of resources consumed during the self-healing process between the first and second timestamps, is calculated using the formula Calculate and obtain the single execution value DZ of the self-healing solution. Where TC and ZX are the processing difference and total resource consumption of the self-healing solution, respectively. A and B are the standard processing difference and total resource consumption corresponding to the self-healing solution, respectively. The specific values ​​are not limited and can be determined based on the operational design data corresponding to the self-healing solution. is the floor function;

[0041] It should be noted that the single execution value is used to process and calculate different aspects of the execution data when the self-healing solution is executed normally, so as to digitally represent the corresponding single execution status;

[0042] It is understandable that although the self-test results after the self-healing solution is executed are normal, some execution behaviors may not meet the design requirements. Therefore, by calculating the data of single execution values, we can not only further expand the analysis of the normal single execution of different self-healing solutions, but also provide reliable single execution supervision data support for subsequent data expansion analysis of different aspects of the self-healing solution.

[0043] In addition, the formula calculations involved in the embodiments of the present invention are all standardized before the formula calculations are performed. The standardization includes but is not limited to extracting numerical values ​​from the calculation data to achieve de-unitization and dimensionalization of the calculation data.

[0044] At the same time, the formula calculation of the single execution value in the embodiment of the present invention is only implemented by a technical means, and can also be implemented according to existing technical means, such as using a pre-trained recognition model to perform data analysis on the processing difference and the total amount of consumed resources, and setting the output value as the single execution value; the training of the recognition model and the data analysis can be implemented according to existing technical means, and the specific implementation steps are not repeated here;

[0045] If the single execution value is less than or equal to 0, the single execution supervision status when the self-healing plan is executed normally is determined to be normal, and the corresponding single execution supervision normal total is increased by 1;

[0046] Otherwise, it is determined that the single execution supervision state is abnormal when the self-healing solution is executed normally, and the corresponding total number of single execution supervision anomalies is increased by one, and the corresponding total number of single execution duration anomalies is increased by one and / or the total number of single execution efficiency anomalies is increased by one;

[0047] Among them, when When , the total number of abnormal single execution durations of the self-healing solution is increased by one;

[0048] when When , the total number of single execution efficiency anomalies of the self-healing solution is increased by one;

[0049] If the self-test result is 1, the self-healing solution will be marked as an abnormal self-healing solution, and the overall solution optimization processing prompt will be proactively implemented.

[0050] In an embodiment of the present invention, by monitoring and analyzing the different self-healing solutions executed during the software self-healing implementation process, dynamically updating the single execution supervision data of different self-healing solutions using the analysis results, and dynamically implementing overall solution optimization processing prompts, the diversity and reliability of the single execution supervision processing of different self-healing solutions are improved.

[0051] Utilize the single execution supervision status data of different self-healing solutions to conduct supervision analysis on the self-healing reliability and self-healing stability of different self-healing solutions. Based on the analysis results, provide optimization processing tips at the overall level and execution optimization processing tips at the local level for different self-healing solutions. This includes:

[0052] When using a number of single execution supervision status data to conduct supervision analysis on the self-healing reliability of different self-healing schemes, the total number of times the self-healing scheme is marked as abnormal is counted and analyzed;

[0053] If the total number of times an abnormal self-healing solution is marked is 0, the self-healing solution is associated with the self-healing reliability label;

[0054] If the total number of marks of the abnormal self-healing solution is not 0, then the formula Calculate the self-healing reliability ZK of the self-healing solution. Where N and M are the total number of times the self-healing solution is marked and the total number of times self-healing is executed, respectively. C is the self-healing reliability threshold of the self-healing solution. The specific value is not limited and can be determined based on the operational design data corresponding to the self-healing solution, or customized according to the application requirements of the actual application scenario.

[0055] It should be explained that the self-healing reliability is used to process and calculate the marking data of the abnormal self-healing solution to digitally represent its corresponding self-healing reliability;

[0056] It is understandable that by analyzing the data of the self-healing reliability, the self-healing reliability corresponding to the self-healing solution is determined, so as to implement targeted optimization processing prompts for part of the solution or the overall solution;

[0057] If the self-healing reliability is less than or equal to 0, the self-healing solution is associated with a self-healing partial reliability label and partial solution optimization is performed.

[0058] Otherwise, the self-healing solution will be associated with the self-healing unreliable label, and the overall solution optimization process will be implemented;

[0059] Among them, some solutions provide optimization processing prompts, specifically prompts for the execution rules or execution content of the self-healing solution;

[0060] Prompts for overall solution optimization, specifically prompts for the execution rules and content of the self-healing solution;

[0061] In an embodiment of the present invention, by utilizing a number of single-execution supervision status data to implement supervision analysis on the self-healing reliability of different self-healing schemes, it is possible to expand the use of the marking data of different self-healing schemes in the early stage, realize active supervision analysis of the self-healing reliability of different self-healing schemes, and improve the active expansion analysis effect of the single supervision processing of software self-healing.

[0062] In addition, when analyzing the self-healing stability of different self-healing solutions, the total number of normal and abnormal single-execution supervisions corresponding to the self-healing solution is counted, and the total number of normal and abnormal single-execution supervisions is calculated using the formula Calculate and obtain the self-healing stability ZWk of the self-healing solution. In the formula, k is 1 and 2, representing the execution duration and execution efficiency of the self-healing solution, respectively. n1 and n2 are the total number of single execution duration anomalies and the total number of single execution efficiency anomalies of the self-healing solution, respectively. Dk is D1 and D2, representing the first and second self-healing stability thresholds of the self-healing solution, respectively. The specific values ​​are not limited and can be determined based on the operational design data corresponding to the self-healing solution, or customized according to the application requirements of the actual application scenario.

[0063] It should be explained that self-healing stability is used to process and calculate data from different execution aspects to digitally represent the local execution stability status of different execution aspects. This serves the purpose of digitally representing the local execution stability status of the self-healing solution in terms of execution duration and efficiency, and also provides reliable data support for different execution optimization processing prompts corresponding to the self-healing solution, as well as optimization processing prompts for some solutions.

[0064] If the self-healing stability is greater than or equal to 0, the self-healing plan is associated with the execution duration stability label and the execution efficiency stability label.

[0065] Otherwise, the self-healing solution will be associated with the unstable execution time label and the unstable execution efficiency label;

[0066] Implement targeted execution optimization prompts for the self-healing solution based on the unstable execution time label or the unstable execution efficiency label.

[0067] In the embodiment of the present invention, by performing data analysis on different self-healing stabilities obtained by calculation and implementing targeted optimization prompts for different execution aspects of the self-healing solution based on the analysis results, the diversity of the expanded utilization of the single execution supervision data of the early self-healing solution is improved.

[0068] Furthermore, based on the unstable execution time label and the unstable execution efficiency label, some optimization processing prompts are implemented for the self-healing plan.

[0069] It is worth noting that, unlike existing technical solutions that can only perform data analysis in a single dimension through a single technical means, in the embodiment of the present invention, by performing data analysis on self-healing stability, it is possible to implement local execution optimization prompts in the self-healing execution dimension, and to implement partial solution optimization processing prompts in the self-healing solution dimension, thereby improving the diversity and scalability of regulatory analysis in terms of self-healing stability.

[0070] In addition, the first total number of optimization processing prompts for the corresponding implementation of the self-healing solution history and the second total number of all optimization processing prompts are counted, and the formula is used Calculate and obtain the partial processing value BC corresponding to the self-healing solution. Where m1 and m2 are the first and second totals corresponding to the self-healing solution, respectively. b is the partial processing threshold. The specific value is not limited and can be determined based on the operational design data corresponding to the self-healing solution or customized according to the application requirements of the actual application scenario.

[0071] It should be noted that the partial processing value is used to calculate the processing data from the optimization processing dimension of the self-healing solution to digitally represent the corresponding partial processing status;

[0072] If the partial processing value is less than or equal to 0, it is determined that all partial optimization processing prompts of the self-healing solution history are valid as a whole;

[0073] Otherwise, it is determined that all partial optimization processing prompts of the self-healing plan are invalid as a whole, and the overall optimization processing prompt is implemented;

[0074] It is worth noting that, unlike the separate supervision and analysis of self-healing reliability and self-healing stability for self-healing solutions, the embodiments of the present invention implement extended analysis from the dimension of processing optimization prompts by performing extended calculations of partial processing values ​​for different self-healing solutions. This allows for active supervision and analysis prompts for the optimization processing of all partial solutions of the self-healing solution, further increasing the diversity of the analysis prompts for the optimization processing of the self-healing solution corresponding to the overall solution.

[0075] In an embodiment of the present invention, by utilizing several single execution supervision status data of different self-healing schemes, supervision analysis of self-healing reliability and self-healing stability is performed on different self-healing schemes respectively, and based on the analysis results, optimization processing prompts at the overall level and execution optimization processing prompts at the local level are provided to different self-healing schemes, thereby realizing diversified expansion analysis and utilization of all single execution supervision data of different self-healing schemes in the early stage, and improving the diversity and reliability of software self-healing supervision processing.

[0076] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0077] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0078] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or hardware plus software functional modules.

[0079] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, but that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A software self-healing supervision and processing method, characterized in that: include: Monitor and analyze different self-healing solutions executed during the software self-healing implementation process, obtain the single execution supervision status corresponding to different self-healing solutions, and dynamically update the single execution supervision data; The supervision status data of several single executions of different self-healing schemes are used to conduct supervision analysis on the self-healing reliability and self-healing stability of different self-healing schemes. Based on the analysis results, optimization processing prompts at the overall level and execution optimization processing prompts at the local level are provided for different self-healing schemes.

2. A software self-healing supervision processing method according to claim 1, characterized in that: Obtain the first timestamp of the start of the self-healing solution execution and the second timestamp of the end of the self-healing solution execution according to the software log, and obtain the self-test result after the self-healing solution execution; Conduct data analysis on the self-test results after the self-healing solution is executed; If the self-test result is 0, the total number of normal self-healing processes corresponding to the self-healing scheme is increased by one, and the difference between the first and second timestamps of the self-healing scheme is calculated, as well as the total amount of resources consumed during the self-healing process between the first and second timestamps, is calculated using the formula Calculate and obtain the single execution value DZ of the self-healing solution. Where TC and ZX are the processing difference and total resource consumption of the self-healing solution, respectively. A and B are the standard processing difference and standard total resource consumption of the self-healing solution, respectively. is the floor function.

3. A software self-healing supervision processing method according to claim 2, characterized in that: If the single execution value is less than or equal to 0, the single execution supervision status when the self-healing plan is executed normally is determined to be normal, and the corresponding single execution supervision normal total is increased by 1; Otherwise, it is determined that the single execution supervision status is abnormal when the self-healing plan is executed normally, and the corresponding total number of single execution supervision anomalies is increased by one, and the corresponding total number of single execution duration anomalies is increased by one and / or the total number of single execution efficiency anomalies is increased by one.

4. A software self-healing supervision processing method according to claim 2, characterized in that: If the self-test result is 1, the self-healing solution will be marked as an abnormal self-healing solution, and the overall solution optimization processing prompt will be proactively implemented.

5. A software self-healing supervision processing method according to claim 4, characterized in that: When using a number of single execution supervision status data to conduct supervision analysis on the self-healing reliability of different self-healing schemes, the total number of times the self-healing scheme is marked as abnormal is counted and analyzed; If the total number of times an abnormal self-healing solution is marked is 0, the self-healing solution is associated with the self-healing reliability label; If the total number of marks of the abnormal self-healing solution is not 0, then the formula Calculate the self-healing reliability ZK of the self-healing scheme. Where N and M are the total number of times the self-healing scheme is marked and the total number of times self-healing is executed, respectively. C is the self-healing reliability threshold of the self-healing scheme. If the self-healing reliability is less than or equal to 0, the self-healing solution is associated with a self-healing partial reliability label and partial solution optimization is performed. Otherwise, the self-healing solution will be associated with the self-healing unreliable label, and the overall solution optimization processing prompt will be implemented.

6. A software self-healing monitoring and processing method according to claim 5, characterized in that: When analyzing the self-healing stability of different self-healing solutions, the total number of normal and abnormal single-execution supervisions corresponding to the self-healing solution is counted, and the total number of normal and abnormal single-execution supervisions is calculated using the formula Calculate the self-healing stability ZWk of the self-healing solution. In the formula, k is 1 and 2, representing the execution time and efficiency of the self-healing solution, respectively. n1 and n2 are the total number of single execution time anomalies and the total number of single execution efficiency anomalies of the self-healing solution, respectively. Dk is D1 and D2, representing the first and second self-healing stability thresholds of the self-healing solution, respectively. If the self-healing stability is greater than or equal to 0, the self-healing plan is associated with the execution duration stability label and the execution efficiency stability label. Otherwise, the self-healing solution is associated with the unstable execution duration label and the unstable execution efficiency label.

7. A software self-healing monitoring and processing method according to claim 6, characterized in that: Implement targeted execution optimization prompts for the self-healing solution based on the unstable execution time label or the unstable execution efficiency label. In addition, based on the unstable execution time label and the unstable execution efficiency label, some optimization processing prompts are implemented for the corresponding self-healing plan.

8. A software self-healing monitoring and processing method according to claim 7, characterized in that: Statistics of the first total number of optimization tips for the corresponding implementation of the self-healing plan history and the second total number of all optimization tips, and the formula Calculate and obtain the partial processing value BC corresponding to the self-healing solution; where m1 and m2 are the first total number and the second total number corresponding to the self-healing solution, respectively; and b is the partial processing threshold. If the partial processing value is less than or equal to 0, it is determined that all partial optimization processing prompts of the self-healing solution history are valid as a whole; Otherwise, it is determined that all partial solution optimization processing prompts of the self-healing solution history are invalid as a whole, and the overall solution optimization processing prompt is implemented.

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