Method for compatible processing of different software version parameters

Abstract

The invention relates to the field of software engineering, and discloses a method for compatible processing of different software version parameters, which comprises the following steps of: establishing a software version database for recording parameter changes among different software versions; constructing a control model based on the software version database; determining parameter configuration of the software target version through the state variable; quantifying the performance loss in the parameter conversion process from the software source version to the software target version based on the calculation target function; and converting the software target version parameter configuration based on the optimal parameter conversion path through the target version parameter configuration and the performance loss of the target version parameter configuration. According to the parameter compatibility processing method based on the software version database and the control model, the software version database is established, default parameters and modification history of each version are recorded, and unified management and automatic tracking of the parameters are achieved. Therefore, human errors are reduced, and the accuracy of parameter adjustment and the efficiency of software version management are improved.

Description

Technical Field

[0001] The present invention relates to the field of software engineering, and in particular to a method for compatibility processing of parameters of different software versions. Background Art

[0002] With the rapid development of information technology, the iteration speed of software systems is accelerating, and version management has become one of the most important links in the software life cycle. In the process of upgrading or downgrading software systems, it is crucial to ensure the compatibility between different versions, especially in the update of parameter configuration and functional modules. How to effectively handle the parameter differences between versions and ensure the stability and reliability of the system has become an important challenge facing current technology.

[0003] However, existing solutions often rely on manually adjusting configuration files to adapt to the parameter differences between the old and new versions. Such manual adjustments are not only time-consuming and labor-intensive, but also prone to human errors, leading to version compatibility issues. In addition, the existing system lacks a unified parameter management mechanism and cannot effectively trace the historical status of parameter changes. During cross-version upgrades or downgrades, there is a lack of effective automation mechanisms to ensure the correctness and consistency of parameters, which may cause software system anomalies or even serious failures such as downtime. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for compatible processing of parameters of different software versions, which solves the problems that manual parameter adjustment is time-consuming and error-prone, the lack of a unified parameter management mechanism makes it difficult to trace the change history, and the correctness and consistency of parameters are difficult to ensure when upgrading or downgrading across versions, which may cause system abnormalities or crashes.

[0005] To achieve the above purpose, the present invention is implemented by the following technical solution: a method for compatibility processing of parameters of different software versions, comprising the following steps: Establishing a software version database, which includes the default parameters and modification history of each version of the software, and is used to record parameter changes between different versions of the software; Building a control model based on the software version database, wherein the control model represents the default parameters and modification history of each version of the software through state variables; Through the state variables, the parameter changes between the software source version and the software target version are analyzed to determine the parameter configuration of the software target version; Evaluate the performance loss of the software target version parameter configuration based on the calculation objective function, which is used to quantify the performance loss during the conversion process from the software source version to the software target version parameter; An optimal parameter conversion path is generated through the target version parameter configuration and the performance loss of the target version parameter configuration, and the software target version parameter configuration is converted based on the optimal parameter conversion path.

[0006] Preferably, the software version database further includes the version number and version parameter type of each version of the software.

[0007] Preferably, the state variables include version number, parameter name, parameter value and modification time, which are used to model parameter changes between different versions of the software.

[0008] Preferably, when the objective function is calculated, the performance loss includes system resource consumption, compatibility difference, crash probability and software performance degradation index.

[0009] Preferably, the optimal parameter conversion path is obtained through the performance loss in the objective function, and the optimal parameter conversion path is calculated by an optimization algorithm.

[0010] Preferably, the performance loss of the software target version parameter configuration is based on a differential evaluation of the differences between the source version and the target version, wherein the evaluation includes the addition, deletion and modification of parameters and the change of parameter types.

[0011] Preferably, the objective function comprises an instantaneous cost function, which is calculated based on the impact on software performance at each time point to dynamically adjust resource usage and performance consumption during parameter conversion.

[0012] Preferably, the optimization algorithm is obtained by solving the Hamilton-Jacobi-Bellman equation and is used to minimize system performance loss to determine the optimal strategy for parameter adjustment.

[0013] Preferably, the control model further includes a redundant storage and automatic recovery mechanism, which is used to automatically recover to the last successful parameter configuration when the software crashes and the operation is abnormal due to parameter incompatibility, and the redundant storage includes: Multiple storage nodes: used to distribute and store the parameter history records of each version and perform redundant backup of data; Automatic synchronization mechanism: used to synchronize updates to all storage nodes when software parameters are updated to ensure data consistency.

[0014] Preferably, the automatic recovery mechanism includes: Rollback mechanism: when the current software version cannot be restored, it will automatically roll back to the parameter configuration of the previous software version; Anomaly detection, automatically detects system crashes caused by parameter incompatibility and data corruption, and selects recovery paths in real time.

[0015] The present invention provides a method for compatibility processing of parameters of different software versions. It has the following beneficial effects: 1. The present invention uses a parameter compatibility processing method based on a software version database and a control model, establishes a software version database, records the default parameters and modification history of each version of the software, and realizes unified management and automatic tracking of parameters. Compared with the method of manually adjusting the configuration file in the prior art, this method reduces the errors caused by human operation, improves the accuracy of parameter adjustment and the efficiency of software version management.

[0016] 2. The present invention evaluates the performance loss of the software target version parameter configuration based on the calculation objective function, and generates the optimal parameter conversion path through the optimization algorithm to ensure that the performance loss of the software is minimized during the version switching process and maintain system stability. The prior art lacks a parameter optimization mechanism when upgrading or downgrading across versions, which can easily cause software operation abnormalities due to improper parameter adjustment. The present invention dynamically adjusts the parameter configuration to ensure a smooth transition of the software during the version change process, effectively improving system compatibility.

[0017] 3. The present invention implements dynamic adjustment of cross-version parameters by evaluating the performance loss based on the software target version parameter configuration and solving the optimal parameter conversion path. Compared with the prior art, which cannot effectively track parameter differences when versions are changed, and easily leads to the problem of partial function failure or parameter conflict after upgrading, the present invention ensures the compatibility of parameters between different versions through differentiated evaluation and optimal parameter mapping strategy, and improves the maintainability and adaptability of the software system.

[0018] 4. The present invention distributes and stores the software parameter history records on multiple storage nodes through the redundant storage and automatic recovery mechanism in the control model, and automatically recovers to the last successful parameter configuration when the software crashes or runs abnormally due to parameter incompatibility. Compared with the prior art where parameter management lacks an effective backup and recovery mechanism, the present invention provides automatic rollback and anomaly detection capabilities, improving the reliability and data security of the software system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0021] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments.

[0022] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for compatibility processing of parameters of different software versions, characterized in that it includes the following steps: Establish a software version database, which includes the default parameters and modification history of each version of the software, and is used to record parameter changes between different versions of the software; In this embodiment, in order to effectively manage the parameter compatibility between software versions, a software version database is first established. The database is used to record the default parameters of each version and the modification history of each version, and can track and store the changes in parameters between different versions. It provides basic data support for the automatic conversion and compatibility check of parameters between subsequent versions, and ensures that parameter configurations can be quickly restored and adjusted when upgrading or downgrading. Specifically, the software version database plays a core role in the entire software life cycle, providing complete and accurate parameter change records to ensure the stability and compatibility of the software when switching between different versions.

[0023] Specifically, the software version database includes the following: Version number: Each version has a unique version number, which is used to identify different versions. The version number is the index of the parameter change record, ensuring that the parameter configuration of each version can be accurately located when switching versions.

[0024] Default parameters: The default parameter records for each version include all parameters initially configured when the version is released. Default parameters are basic configurations, and the software system will run according to these default parameters without any modification.

[0025] Parameter modification history: In addition to the default parameters, the software version database also records the parameter modification history of each version. This includes information such as the parameter modification content, modification time, and modification reason in the version. By saving the parameter modification history, the parameter changes between software versions can be traced, which is convenient for subsequent error troubleshooting and version recovery.

[0026] In some embodiments, the software version database can be stored in a relational database or a non-relational database, depending on the scale and needs of the application. Generally speaking, a relational database can easily perform association queries between versions, while a non-relational database may provide better performance when processing large amounts of data. As an option, the version information in the database can be stored in a distributed structure, which can improve the reliability and access speed of data storage, especially in a large-scale distributed environment, to ensure that each node can access the required version data.

[0027] Specifically, each version parameter record in the database includes the following fields: version number, parameter name, parameter value, modification time, modifier, modification description, etc. Therefore, it is not only possible to record the parameter configuration of each version, but also to trace the historical change process of the parameters, and provide an important basis for subsequent parameter conversion.

[0028] For example, suppose there is a parameter "timeout" in version v1.0 of the software, and its default value is 30 seconds. As the version iterates, in version v1.1, the value of the parameter is changed to 45 seconds. The record in the database will include the following fields: Version number: v1.1 Parameter name: timeout Parameter value: 45 Modification time: May 1, 2023 Modified by: Zhang San Modification description: Adjust the timeout period to improve response speed.

[0029] In addition, the parameter modification history will also save information such as the reason for parameter modification, the modifier, and the comparison values ​​before and after modification for each version, which provides strong support for compatibility conversion between versions.

[0030] In some embodiments, the software version database may also record cross-version parameter mapping rules, that is, how to map parameter values ​​in one version to corresponding parameters in another version. This mapping rule will play an important role in cross-version upgrades or downgrades. The generation of mapping rules usually requires combining the historical data of the version and the actual functional requirements, and determining the compatibility relationship between versions by analyzing the parameter changes between different versions. For example, a version's "timeout" parameter may change from an integer value to a floating time range. The mapping rule can record this change so that when switching across versions, the value of the old version can be automatically converted to the parameter configuration of the new version.

[0031] Assuming that the parameter values ​​stored in the database need to be mapped to the version when upgrading or downgrading the version, the mapping rule can be expressed by the following formula: ; in, Indicates the source version The value of a parameter in the source version The original parameter value of Represents the mapping coefficient, indicating the source version To target version The parameter change relationship between versions is adjusted according to the difference in parameter types between versions. For example, if the target version If the parameter type in changes, the coefficient will reflect this change; Indicates the target version The values ​​of the parameters in are obtained by applying the mapping coefficients To adjust the source version The target version obtained by the parameter value in Parameter value.

[0032] For the recovery and adaptation of cross-version parameters, the software version database can also be combined with the state variables in the control model for dynamic calculation. By introducing historical modification records and mapping rules, the control model will help automatically complete parameter conversion and ensure compatibility during software version upgrades.

[0033] Therefore, the parameter configuration of each version is recorded through the software version database, and the parameter change history between versions is also covered to ensure that this information can be relied upon for accurate parameter conversion and recovery when the version is upgraded or downgraded.

[0034] Build a control model based on the software version database. The control model uses state variables to represent the default parameters and modification history of each version of the software. In this embodiment, the precise management of software version parameters is achieved by constructing a control model. The control model uses state variables to represent the default parameters of each version of the software and its modification history, ensuring that when switching between versions, the software parameter configuration can be accurately converted in real time and automatically, thereby maintaining the stability and compatibility of the system.

[0035] In the previous steps, a software version database has been established, and the default parameters of each version and its modification history have been recorded. Next, based on this database, the control model will model the parameter changes of each version through state variables to further optimize the parameter management and conversion process. In this way, the control model can dynamically reflect the parameter changes between software versions, providing accurate data support for subsequent cross-version parameter conversion and recovery.

[0036] Therefore, the control model is implemented by representing the parameters of each version and its modification history as state variables.

[0037] Specifically, the state variables include the following: Version Number: Each version has a unique version number that is used to identify and associate changes between versions.

[0038] Default parameters: The initial configuration of each version, including all unmodified default parameter values. Default parameters serve as the standard configuration when the system starts.

[0039] Modification history: Record the modified content, modification time, modifier and modification reason of the parameters in each version to ensure complete traceability of parameter changes.

[0040] These state variables together form the basic framework of the control model, which helps to automatically manage and convert software parameters, especially when switching across versions, and can effectively adapt to the differences between the new and old versions.

[0041] Specifically, the control model ensures that parameter changes in each version can be accurately recorded and managed through the definition of state variables. For example, when a parameter "timeout" in a version is modified, the state variable will record the new value of the parameter and the reason for the modification. In the next version, if the parameter is redefined, the control model can automatically identify the change of "timeout" and calculate the best parameter mapping rule based on the state variable to ensure the compatibility of parameter values.

[0042] In a possible implementation, the control model can also model the parameter changes between versions as a dynamic process by introducing a dynamic system model. By modeling parameter changes, the dynamic model can help the system predict and automatically adjust parameter configurations, thereby achieving seamless switching when the software version is upgraded or downgraded.

[0043] In some embodiments, the state variables of the control model include not only basic parameter values ​​and version information, but also more parameter configuration items can be added as needed, such as parameter type, function description, and functional differences between versions. Through these extensions, the control model can more comprehensively cover all key parameter changes between versions, making parameter compatibility processing more accurate and intelligent.

[0044] Moreover, the control model can be further optimized by introducing the optimal control theory, using the objective function in the formula to minimize the system performance loss and ensure the efficiency and compatibility of the software parameter conversion process.

[0045] As an option, the control model can also dynamically calculate the optimal parameter mapping path when upgrading across versions, and use historical modification data to automatically derive parameter conversion rules between versions. Therefore, the control model not only provides parameter conversion based on state variables, but also ensures compatibility and correctness between versions.

[0046] Specifically, by introducing historical parameter records and mapping rules, the control model can automatically complete parameter adaptation when converting between versions, eliminating the complexity and error risks brought by manual adjustments, and further improving the automation level and maintainability of software version management.

[0047] Therefore, by establishing a control model based on the software version database, it is possible to accurately manage and convert parameters between software versions, ensuring that the system remains stable and reliable when switching versions, and avoiding system crashes or anomalies caused by parameter incompatibility.

[0048] Through the state variables, the parameter changes between the software source version and the software target version are analyzed to determine the parameter configuration of the software target version; In this embodiment, in order to ensure the compatibility of parameters during software version upgrade or downgrade, it is necessary to analyze the parameter changes of the source version and the target version, and determine the parameter configuration of the software target version based on the analysis. The present invention uses state variables to represent the default parameters and modification history of each version, thereby constructing a comprehensive parameter analysis system to accurately determine the parameter changes between versions. This analysis can not only identify the addition, deletion and modification of parameters, but also evaluate the compatibility of different parameters, and finally generate a parameter configuration that meets the requirements of the target version.

[0049] Specifically, by introducing state variables, the present invention can analyze software parameter changes from multiple dimensions and dynamically adjust the parameter configuration of the target version. In a possible implementation, the present invention compares the source version and target version The state variables of the target version are compared to analyze the parameter differences between the two versions. The comparison process mainly involves the following situations: New parameter: A new parameter is added in the target version, but it does not exist in the source version. In this case, you need to assign a default value to the new parameter or calculate a reasonable value based on historical records.

[0050] Parameter deletion: A parameter is removed from the target version, but it exists in the source version. At this point, the system needs to determine whether the parameter affects the function of the target version and decide whether compatibility processing is required.

[0051] Parameter modification: When the value or type of the same parameter in the source version and the target version changes, the system needs to evaluate the impact of the change and decide whether to use mapping rules for conversion.

[0052] Specifically, the present invention uses a mapping function to calculate the parameter change relationship between the source version and the target version, and determines the parameter configuration of the target version accordingly.

[0053] In some embodiments, the parameter mapping relationship It can be adjusted dynamically according to the parameter type change rules. For example: When the source version parameter is an integer and the target version is a floating point type, the mapping relationship may be converted as follows: ; When the unit of a parameter changes, such as a time parameter from seconds to milliseconds, the conversion rule can be defined as: ; This ensures the correct conversion of parameter units and keeps the configuration of the target version consistent.

[0054] In another implementation, the present invention can also introduce historical modification records to analyze parameter change trends to optimize the final configuration of the target version parameters. For example, if a parameter shows an increasing trend in the modification history of multiple versions, when determining the target version parameter configuration, a reasonable parameter value can be automatically predicted based on the historical trend without having to rely entirely on default values ​​or fixed conversion rules.

[0055] As an option, in a large-scale distributed software system, the present invention can be combined with a cluster management component to ensure that the target version parameter configuration of all nodes is consistent. When performing version upgrades across multiple nodes, the state variables of each node may be different, so a global parameter synchronization mechanism is required to ensure that the final target version parameter configuration is consistent throughout the system.

[0056] In general, the target version parameter determination process of the present invention can be combined with optimal control theory to optimize the parameter conversion path. For example, by introducing the Hamilton-Jacobi-Bellman equation, it can be ensured that the adjustment process of the target version parameters has the least impact on the system performance, thereby achieving the best parameter configuration solution. The optimization model can be expressed as: ; in, Represents the overall optimization target value of the target version parameter conversion; Indicates that the system is at time The state variables at the time of , that is, the current parameter configuration; Represents the control input of the parameters, that is, the operation of parameter adjustment; represents the instantaneous cost function, which measures the impact of parameter adjustment on system performance; Represents the terminal cost function, which measures the quality of the final target version parameter configuration.

[0057] By solving the optimization equation, the present invention can find the optimal parameter conversion path between different versions, ensuring the minimum cost of parameter adjustment while maintaining the stability and compatibility of the system.

[0058] Therefore, through the parameter change analysis method based on state variables, the parameter difference between the software source version and the target version can be accurately determined, and the final parameter configuration of the target version can be generated accordingly. By introducing parameter mapping rules, historical modification records and optimal control theory, the present invention ensures the rationality of cross-version parameter adjustment and improves the stability and compatibility of the software system during version upgrade or downgrade.

[0059] Evaluate the performance loss of the software target version parameter configuration based on the calculation objective function, which is used to quantify the performance loss during the conversion process from the software source version to the software target version parameter; In this embodiment, during the software version management process, changes in parameter configuration may affect the overall performance, compatibility, and operational stability of the system. In order to accurately evaluate the performance loss caused by parameter adjustment when switching software versions, the present invention introduces an objective function for quantitative analysis to quantify the performance loss during the parameter conversion process from the software source version to the software target version. By calculating the objective function, the present invention can dynamically evaluate the compatibility of parameters of different versions, and optimize the adjustment strategy during the parameter conversion process to ensure the stability of software operation and resource utilization.

[0060] In the above steps, the default parameters of each version and their modification history have been stored in the software version database, and the state variables have been established through the control model to represent the parameter changes between software versions. On this basis, the present invention further calculates the objective function to accurately evaluate the performance loss of parameter configuration, and then provides the optimal parameter conversion solution.

[0061] Specifically, the objective function in the previous steps is It is mainly used to measure the deviation of the software target version parameter configuration compared with the source version, and to quantify the system performance loss caused by it. The calculation of the objective function takes into account multiple factors, including but not limited to system resource consumption, compatibility differences, crash probability, software performance degradation index, and additional computing costs caused by parameter adjustment.

[0062] In one possible implementation, the instantaneous cost function of the objective function is It can be further refined into: ; in, Indicates the CPU computing resource consumption caused by parameter adjustment; Indicates the memory usage during parameter conversion; Indicates the impact of parameter changes on disk I / O load, such as logging, database updates, etc. Indicates the probability of system crash due to parameter incompatibility, which is calculated based on historical data and software stability model; , , , : is the weight coefficient of different performance loss items, indicating the importance of each loss on the overall system.

[0063] As an option, when the parameters of the target version differ greatly from the source version, the present invention can dynamically adjust the calculation method of the objective function. For example, when the parameter type changes, the system can introduce a compatibility cost function: ; in, Indicates the compatibility cost caused by changes in parameter types or values; Indicates the source version The value of a parameter in Indicates the target version The value of the parameter in; represents the parameter compatibility determination function; Indicates the total number of parameters, that is, the number of parameters that need to be compared in the software.

[0064] In general, the objective function calculation results can be used to guide the optimization of the software parameter conversion path. For example, when an evaluation finds that a certain parameter adjustment scheme will cause a larger CPU load or a higher probability of system crash, the system can choose another adjustment strategy, such as introducing transition parameters or adjusting conversion rules to reduce performance loss. Specifically, in the cross-version parameter compatibility processing, the present invention combines the Hamilton-Jacobi-Bellman equation to optimize the solution process of the objective function to ensure that the parameter adjustment scheme is optimal: ; in, represents the Hamilton function under optimal control; Represents the instantaneous cost function, which is used to measure the system state at each time point and control input Immediate impact on system performance; Representation value function Status The derivative of reflects the system state The impact of changes in on the objective function; Represents the dynamic equation of the system, describing the state of the system and control input How to influence the evolution of the system.

[0065] In some embodiments, the calculation result of the objective function can be used to adjust the optimal parameter conversion path to ensure that the parameter adjustment has the least impact on system performance and ensure the rationality of the parameter configuration of the target version. For example, during the version migration process, if it is found that some parameter adjustments may lead to an unstable system state, the system can iteratively calculate the objective function and select the adjustment path with the lowest cost to reduce system risk.

[0066] As an option, the objective function can also be combined with historical parameter adjustment data to optimize the calculation through a machine learning model. For example, the system can train a prediction model based on previous version upgrade data, assess the risks of certain parameter adjustments in advance, and make optimization adjustments before the conversion to reduce performance loss.

[0067] Therefore, the performance loss of the software target version parameter configuration is evaluated by calculating the objective function to ensure that during the version conversion process, the system resource consumption is minimized, the compatibility is optimal, and the crash probability is lowest. By combining the instant cost function, the compatibility cost function and the optimal control optimization method, the present invention can achieve accurate adjustment of software version parameters and ensure the stability and efficiency of the system in cross-version switching.

[0068] An optimal parameter conversion path is generated through the target version parameter configuration and the performance loss of the target version parameter configuration, and the software target version parameter configuration is converted based on the optimal parameter conversion path.

[0069] In this embodiment, in order to ensure a smooth transition between software versions, especially in terms of changes in software parameters, the performance loss of the target version parameter configuration is calculated to further generate an optimal parameter conversion path. The main goal of this process is to generate an optimal path to adjust the parameter configuration of the target version through a comprehensive evaluation of parameter changes, thereby minimizing the performance loss of the system and maintaining the stability and compatibility of the system during the version switching process.

[0070] In the above steps, the parameter changes of the source version and the target version have been analyzed through the software version database and control model, and the parameter configuration of the target version has been determined. On this basis, the performance loss of the parameter configuration of the target version is calculated to quantify the performance loss that may be caused by the parameter conversion process from the source version to the target version. This performance loss mainly includes but is not limited to system resource consumption (such as CPU, memory, disk I / O, etc.), compatibility differences (such as missing or inconsistent functions), crash probability and software performance degradation. Through a comprehensive evaluation of these factors, the system can accurately calculate the impact of parameter conversion.

[0071] In this embodiment, when generating the optimal parameter conversion path, the objective function seeks the optimal parameter adjustment strategy by comprehensively evaluating the parameter configuration and performance loss of the target version. This strategy ensures a smooth transition of the software system by minimizing the system's resource consumption, crash risk, and other performance indicators. During the calculation process, the system performs a differentiated evaluation based on the differences between the source version and the target version, including the addition, deletion, modification of parameters, and changes in parameter types, to derive the most appropriate parameter mapping rules.

[0072] Generally speaking, when determining the optimal path, considering that some key parameters have a greater impact on system performance, the system can assign higher weights to the parameters through weighted averaging to ensure that the parameters with the greatest impact on the system are given priority during the conversion process, thereby optimizing resource usage, improving performance, and reducing the risk of system failure or incompatibility.

[0073] As an option, in the specific implementation, the calculation of the objective function can also be flexibly adjusted according to the needs of the actual application scenario. The system can optimize the parameter configuration of the target version through historical modification records and automatically adjust the conversion strategy. For example, the change trend of certain parameters between multiple versions may show regularity. The system can automatically predict the appropriate parameter value based on these historical trends to reduce manual intervention.

[0074] In a possible implementation, the present invention combines optimal control theory to optimize the parameter conversion path of the target version. By introducing the optimal control method, the present invention can ensure that when upgrading or downgrading across versions, the system can find the optimal parameter conversion path and ensure stable operation of the system while minimizing performance loss.

[0075] Specifically, by quantifying the performance loss of the target version parameter configuration, the present invention can generate the optimal parameter conversion path, so that the software can effectively adapt to the parameter changes of the target version when switching versions, avoiding system anomalies or crashes caused by incompatibility. Through this process, the automation of software version management is greatly improved, the risk of human operation is reduced, and the stability and reliability of the system are improved.

[0076] Therefore, by calculating the performance loss of the target version parameter configuration and generating the optimal parameter conversion path based on this, an accurate automated solution is provided that can ensure the efficiency and compatibility of software parameter conversion during the software version switching process, minimize system performance loss, and improve software stability and reliability.

[0077] And by introducing redundant storage and automatic recovery mechanisms, we can deal with possible crashes or abnormal situations caused by parameter incompatibility during the operation of the software system. Redundant storage and automatic recovery mechanisms can ensure that the system can quickly recover to a stable state when a failure occurs, and ensure the correctness and consistency of parameters, thereby avoiding system crashes or functional failures caused by parameter configuration problems during software version updates or downgrades.

[0078] Specifically, in this embodiment, the redundant storage function not only ensures the reliability of historical parameter data, but also enables distributed backup of data between multiple storage nodes, ensuring that even if some nodes fail, the system can still restore complete data from other nodes. Redundant storage includes the following: Multiple storage nodes: Each node is used to store the parameter history of each version of the software, and redundant backup of data is achieved through distributed storage. In some embodiments, the number of storage nodes can be adjusted according to the scale of the system to ensure high availability and fault tolerance of data. For example, the version history is not only stored on the master node, but also on multiple backup nodes, so that even if a storage node fails, the system can still obtain the required historical data through other nodes.

[0079] Automatic synchronization mechanism: This mechanism automatically synchronizes the data of all storage nodes when the software parameters are updated to ensure data consistency between different storage nodes. Generally, the automatic synchronization mechanism will synchronize the change records to all storage nodes immediately after the parameters are updated, ensuring that the system always maintains the latest parameter status in a multi-node environment. If a storage node cannot be synchronized and updated in time, the system will detect and resynchronize to ensure that all nodes store the latest and correct parameter configuration.

[0080] The combination of redundant storage and automatic synchronization mechanism can greatly improve the reliability of data storage and the recovery ability of the system. Through redundant storage, any single point of failure will not affect the normal operation of the software, thus ensuring that the software can continue to run when an exception occurs.

[0081] The automatic recovery mechanism includes the following two main functions: Rollback mechanism: When the software cannot run normally due to parameter incompatibility or other problems, the rollback mechanism can be automatically triggered to restore the software to the parameter configuration of the previous version. The rollback mechanism automatically rolls back to a stable parameter configuration by comparing the parameter records of the current version and the previous version, ensuring the continuity and stability of the system during version switching or downgrade. This mechanism reduces human intervention and avoids the risk of system crashes caused by manual configuration errors.

[0082] Anomaly detection: The system has a built-in anomaly detection function that automatically detects system crashes or performance degradation caused by parameter incompatibility or data corruption during software operation. When an anomaly is detected, the system immediately selects the appropriate recovery path and starts the recovery process. Anomaly detection can not only determine whether there is incompatibility by matching parameter values ​​and types, but also detect potential problems in real time based on changes in system performance, such as memory leaks and response delays. Through this mechanism, the system can predict and take preventive measures before a crash occurs, effectively improving the system's fault tolerance and reliability.

[0083] As an option, in some complex systems, the rollback mechanism and anomaly detection can be used together. When the system detects an operational anomaly due to parameter incompatibility, the automatic recovery mechanism will not only roll back to the previous version of the configuration, but also automatically select a recovery path based on the current system status to minimize the recovery time and system downtime, ensuring that the system can resume normal operation as soon as possible.

[0084] Specifically, the combination of anomaly detection and rollback mechanism can effectively improve the self-recovery ability of the system. The system can autonomously determine when to start the recovery mechanism during operation, avoiding the tediousness and potential risks of manual intervention. For example, during a cross-version upgrade, if the system detects that the parameter value of a certain version causes incompatibility, the rollback mechanism will automatically restore the parameter to a stable state, thus avoiding the situation where the system cannot be restored to normal for a long time.

[0085] Therefore, through the comprehensive use of redundant storage, automatic synchronization mechanism and automatic recovery mechanism, the present invention significantly improves the software system's ability to handle exceptions during version switching, minimizes the risk of system crashes caused by version incompatibility or parameter configuration errors, and enhances the system's robustness and high availability.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for compatibility processing of parameters of different software versions, characterized in that: The following steps are involved: Establishing a software version database, which includes the default parameters and modification history of each version of the software, and is used to record parameter changes between different versions of the software; Building a control model based on the software version database, wherein the control model represents the default parameters and modification history of each version of the software through state variables; Through the state variables, the parameter changes between the software source version and the software target version are analyzed to determine the parameter configuration of the software target version; Evaluate the performance loss of the software target version parameter configuration based on the calculation objective function, which is used to quantify the performance loss during the conversion process from the software source version to the software target version parameter; An optimal parameter conversion path is generated through the target version parameter configuration and the performance loss of the target version parameter configuration, and the software target version parameter configuration is converted based on the optimal parameter conversion path.

2. A method for compatibility processing of parameters of different software versions according to claim 1, characterized in that: The software version database further includes the version number and version parameter type of each version of the software.

3. The method for compatibility processing of parameters of different software versions according to claim 1, characterized in that: The state variables include version number, parameter name, parameter value and modification time, which are used to model parameter changes between different versions of the software.

4. The method for compatibility processing of parameters of different software versions according to claim 1, characterized in that: When the objective function is calculated, the performance loss includes system resource consumption, compatibility difference, crash probability and software performance degradation index.

5. The method for compatibility processing of parameters of different software versions according to claim 1, characterized in that: The optimal parameter conversion path is obtained through the performance loss in the objective function, and the optimal parameter conversion path is calculated by an optimization algorithm.

6. The method for compatibility processing of parameters of different software versions according to claim 1, characterized in that: The performance loss of the software target version parameter configuration is based on a differential evaluation of the differences between the source version and the target version, wherein the evaluation includes the addition, deletion and modification of parameters and the change of parameter types.

7. The method for compatibility processing of parameters of different software versions according to claim 5, characterized in that: The objective function includes an instant cost function, which is calculated based on the impact of each time point on software performance to dynamically adjust resource usage and performance consumption during parameter conversion.

8. The method for compatibility processing of parameters of different software versions according to claim 5, characterized in that: The optimization algorithm is obtained by solving the Hamilton-Jacobi-Bellman equation and is used to minimize system performance loss and determine the optimal strategy for parameter adjustment.

9. The method for compatibility processing of parameters of different software versions according to claim 1, characterized in that: The control model further includes a redundant storage and automatic recovery mechanism for automatically recovering to the last successful parameter configuration when the software crashes or when an operation abnormality occurs due to parameter incompatibility, and the redundant storage includes: Multiple storage nodes: used to distribute and store the parameter history records of each version and perform redundant backup of data; Automatic synchronization mechanism: used to synchronize updates to all storage nodes when software parameters are updated to ensure data consistency.

10. The method for compatibility processing of parameters of different software versions according to claim 9, characterized in that: The automatic recovery mechanism includes: Rollback mechanism: when the current software version cannot be restored, it will automatically roll back to the parameter configuration of the previous software version; Anomaly detection, automatically detects system crashes caused by parameter incompatibility and data corruption, and selects recovery paths in real time.

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