A cross-platform operating system upgrade patch automatic deployment method and system

Abstract

This invention discloses a cross-platform operating system upgrade patch automatic deployment method and system, relating to the field of operating system patch management technology. The method includes analyzing patch dependencies and dynamically adjusting the deployment order; performing isolated deployment based on containerization technology; and real-time monitoring of the status of each container, providing real-time monitoring and feedback optimization of the deployment results. The method described in this invention achieves precise control over patch deployment in complex heterogeneous environments, reducing the failure rate caused by dependency conflicts, while also reducing deployment time, improving system stability and reliability, ensuring the independence of patches, and avoiding system crashes due to patch incompatibility or failure. While improving deployment efficiency, it achieves efficient container isolation and a highly compatible patch deployment process, enhancing the system's autonomous adaptability and long-term stability, and providing strong technical support for complex and ever-changing patch deployment scenarios.

Description

Technical Field

[0001] This invention relates to the field of operating system patch management technology, specifically to a cross-platform operating system upgrade patch automatic deployment method and system. Background Technology

[0002] With the rapid development of information technology, the automatic deployment of operating system upgrade patches has gradually become an important part of system management. Traditional patch management methods usually rely on manual operation, which has significant limitations in terms of efficiency, accuracy, and stability as operating system complexity increases and deployment scale expands. In recent years, the rise of automated deployment technology has provided a new solution for operating system upgrade management. However, with the diversification of systems and the widespread application of heterogeneous environments, traditional automated deployment technologies face problems such as insufficient cross-platform compatibility, resource conflict management, and dynamic adjustment capabilities. Therefore, how to build a more intelligent, dynamic, and cross-platform compatible automatic patch deployment system has become a current research and development hotspot.

[0003] While existing automated operating system patch deployment technologies have improved efficiency to some extent, they still have many shortcomings. Regarding patch dependency handling, traditional systems typically deploy patches in a fixed order or according to predefined rules, lacking the ability to dynamically analyze and adjust complex dependencies between patches. This easily leads to deployment failures due to order conflicts or dependency errors. During deployment, traditional methods mostly install patches directly in the system environment without effective isolation mechanisms. This not only increases the risk of patch conflicts but also easily causes system crashes due to patch incompatibility or installation failures. Existing technologies lack real-time monitoring and feedback optimization capabilities, failing to identify and resolve potential problems such as resource conflicts, system overload, or patch errors in a timely manner during deployment. Once deployment fails, the process of restoring to a stable state usually relies on manual intervention, which is inefficient and risky. Therefore, how to achieve automated deployment of cross-platform operating system upgrade patches while ensuring reasonable deployment order, environmental isolation, and real-time monitoring and optimization has become an urgent problem to be solved. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing patch deployment technologies suffer from incomplete dependency analysis, lack of isolation mechanisms in the deployment process, insufficient real-time monitoring and feedback capabilities, and the problem of how to achieve dynamic adjustment, isolated deployment, and intelligent optimization of the deployment process.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a cross-platform operating system upgrade patch automatic deployment method, comprising analyzing patch dependencies and dynamically adjusting the deployment order; performing isolated deployment based on containerization technology; monitoring the status of each container in real time, and performing real-time monitoring and feedback optimization of the deployment results.

[0007] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment method described in this invention, the step of analyzing patch dependencies and dynamically adjusting the deployment order includes statically analyzing and dynamically evaluating the dependencies of each patch to be deployed, using a topological sorting algorithm to calculate the dependency order between patches, and calculating the patch priority P(v), expressed as:

[0008]

[0009] Among them, S s (v) indicates the security level of the patch, ranging from [0, 10], R c (v) represents the real-time resource utilization rate, ranging from [0, 10], L d (v) represents the dependency chain length, taken as a non-negative integer. After the priority is calculated, the topological sorting method TopoSort(V,E,{P(v)}) is used to calculate the sorting result Order(G), which is represented as:

[0010] Order(G)=TopoSort(V,E,{P(v)})

[0011] Where Order(G) is the sorting result, representing the final deployment order of the patches, and G = (V, E) is the dependency graph, where V represents the set of patches and E represents the dependency relationship.

[0012] The system automatically calculates the priority and deployment order of patches based on their type and dependencies. The adjustment of the patch order takes into account the real-time system status, including hardware resource usage, system load, and operating system version. The system will dynamically adjust the deployment order of patches, deploying basic patches and high-priority security patches.

[0013] The system dynamically adjusts the order of patch deployment based on hardware resource usage, system load, and operating system version, deploying basic patches and high-priority security patches. To avoid system conflicts caused by low-priority patches, the patch weight W(v) is adjusted in real time to adapt the deployment process to the current system load. Simultaneously, it optimizes subsequent strategies by incorporating historical deployment times. The patch weight W(v) is calculated and expressed as:

[0014]

[0015] Where W(v) is the patch weight, the lower the value, the higher the priority for deployment, ΔRc (v) is the resource change rate, representing the change in resource usage per unit time, T d (v) represents the deployment time of the patch in seconds, and β is a time sensitivity parameter used to balance the impact of time and weight.

[0016] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment method described in this invention, the step of analyzing patch dependencies and dynamically adjusting the deployment order further includes the system analyzing the operating system components and functional modules involved in each patch to determine the mutual dependencies between patches. The dependencies include the pre-requirements of the patches and version conflicts between patches. The dependency analysis uses the graph theory topological sorting method to treat patches as nodes and the dependencies between patches as edges, thus establishing a dependency graph. The system calculates the optimal deployment order based on the dependency graph. During the patch deployment process, the system continuously monitors operating system resources and running status and dynamically adjusts the dependency analysis.

[0017] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment method described in this invention, the isolated deployment based on containerization technology includes packaging each patch into an independent container and embedding all necessary operating system environments and dependency libraries within the container. Each patch container is deployed in an independent sandbox environment and is completely isolated from the remaining containers to avoid mutual interference between patches. Containerized deployment manages the lifecycle of patch containers through automated tools, including patch creation, startup, shutdown, and version control. During patch deployment within a container, if compatibility issues or installation failures occur, the container automatically reverts to the previous stable version. The patch container management tool automatically selects a suitable container version according to the needs of different operating system platforms to ensure compatibility during patch deployment.

[0018] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment method described in this invention, the isolated deployment based on containerization technology further includes, before patch deployment begins, the system automatically selects the most suitable container image based on the type and operating system version of each patch. After determining the deployment order of the patches, each patch needs to select an appropriate container image for isolated deployment. The image selection needs to comprehensively consider the compatibility distance between the container and the image, the image's stability score, and historical regression records, and calculate the optimal image selection result M(C), expressed as:

[0019]

[0020] Where M(C) is the optimal mirror selection result, d ci (C,I i ) represents container C and image I iThe compatibility distance, ranging from [0,1], is better with lower values. S m (I i H represents the stability score of the image, ranging from [0,1]. A higher value indicates a more stable image. r (I i ) represents the historical rollback count of the image, taken as a non-negative integer, w1 and ω2 are the weight parameters for stability and rollback count, respectively, and I is the set of all available images.

[0021] The patch container starts in an independent virtual environment and executes a preset installation script. The script will be configured and deployed according to the operating system requirements and the characteristics of the patch. Each container has a self-contained environment and depends on the rest of the operating system. The patch deployment process does not interfere with each other. If a patch fails to install or an error occurs in the container, the system automatically switches to the rollback mechanism to restore the previous stable state.

[0022] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment method described in this invention, the real-time monitoring and feedback optimization of the deployment results includes the following: during the patch deployment process, the system collects real-time running status data of the patch container through a monitoring platform, including resource usage, container health status, exception logs, and performance indicators. The real-time monitoring data is analyzed by intelligent algorithms to predict potential problems in patch deployment. When there are no resource conflicts, the system adjusts the priority or order of patches during deployment based on the analysis results. When a patch is detected as potentially causing errors or conflicts, the system automatically takes remedial measures, including pausing the deployment of a rollback patch or activating backup resources. After deployment, the system adjusts the next deployment strategy based on historical feedback data, optimizing patch selection and order.

[0023] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment method described in this invention, the real-time monitoring and feedback optimization of deployment results further includes the system collecting real-time deployment data, analyzing the deployment effect of each patch, evaluating performance changes and system status during the deployment process, and, upon receiving feedback data, performing self-learning based on the patch deployment results. This involves training historical data using machine learning algorithms to automatically identify successful and failed deployment patterns. The system then optimizes based on these patterns, adjusting patch deployment strategies and priorities. In terms of hardware configuration, when a patch causes performance degradation or errors, the system automatically adjusts the priority of patches in future deployments through a feedback mechanism, optimizes patch selection based on feedback, and generates an optimization report for operator reference.

[0024] Another objective of this invention is to provide a cross-platform operating system upgrade patch automatic deployment system that can monitor and optimize deployment results in real time by monitoring the status of each container, thereby solving the problem that current operating system patch management technologies lack real-time status monitoring and dynamic adjustment capabilities during the patch deployment process.

[0025] As a preferred embodiment of the cross-platform operating system upgrade patch automatic deployment system described in this invention, it includes a patch analysis module, an isolation deployment module, and a monitoring and optimization module.

[0026] The patch analysis module is used to analyze patch dependencies and dynamically adjust the deployment order; the isolation deployment module is used to perform isolated deployment based on containerization technology; the monitoring and optimization module is used to monitor the status of each container in real time and perform real-time monitoring and feedback optimization of the deployment results.

[0027] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program being steps of an automatic deployment method for cross-platform operating system upgrade patches.

[0028] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement steps of a method for automatically deploying cross-platform operating system upgrade patches.

[0029] The beneficial effects of this invention are as follows: The cross-platform operating system upgrade patch automatic deployment method provided by this invention analyzes patch dependencies and dynamically adjusts the deployment order, achieving precise control over patch deployment in complex heterogeneous environments. This reduces the failure rate caused by dependency conflicts, while also reducing deployment time and improving system stability and reliability. Based on containerization technology, isolated deployment ensures the independence of patches, avoiding system crashes caused by patch incompatibility or failure. While improving deployment efficiency, it achieves efficient container isolation and a highly compatible patch deployment process. Real-time monitoring of the status of each container and real-time monitoring and feedback optimization of deployment results enhance the system's autonomous adaptability and long-term stability. This provides strong technical support for complex and ever-changing patch deployment scenarios. This invention achieves better results in dynamic analysis and adjustment of patch dependencies, compatibility and stability of containerized isolated deployment, and intelligence and adaptability of real-time monitoring and feedback optimization. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 The first embodiment of the present invention provides an overall flowchart of a cross-platform operating system upgrade patch automatic deployment method.

[0032] Figure 2 The following is an overall flowchart of a cross-platform operating system upgrade patch automatic deployment system provided for the third embodiment of the present invention. Detailed Implementation

[0033] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0034] Example 1, referring to Figure 1 As an embodiment of the present invention, a cross-platform operating system upgrade patch automatic deployment method is provided, comprising:

[0035] S1: Analyze patch dependencies and dynamically adjust the deployment order.

[0036] Furthermore, analyzing patch dependencies and dynamically adjusting the deployment order involves static analysis and dynamic evaluation of the dependencies of each patch to be deployed, using a topology sorting algorithm to calculate the dependency order between patches, and calculating the patch priority P(v), expressed as:

[0037]

[0038] Where Ss(v) represents the security level of the patch, ranging from [0,10], R c (v) represents the real-time resource utilization rate, ranging from [0, 10], L d (v) represents the dependency chain length, taken as a non-negative integer. After the priority is calculated, the topological sorting method TopoSort(V,E,{P(v)}) is used to calculate the sorting result Order(G), which is represented as:

[0039] Order(G)=TopoSort(V,E,{P(v)})

[0040] Where Order(G) is the sorting result, representing the final deployment order of the patches, and G = (V, E) is the dependency graph, where V represents the set of patches and E represents the dependency relationship.

[0041] The system automatically calculates the priority and deployment order of patches based on their type and dependencies. The adjustment of the patch order takes into account the real-time system status, including hardware resource usage, system load, and operating system version. The system will dynamically adjust the deployment order of patches, deploying basic patches and high-priority security patches.

[0042] The system dynamically adjusts the order of patch deployment based on hardware resource usage, system load, and operating system version, deploying basic patches and high-priority security patches. To avoid system conflicts caused by low-priority patches, the patch weight W(v) is adjusted in real time to adapt the deployment process to the current system load. Simultaneously, it optimizes subsequent strategies by incorporating historical deployment times. The patch weight W(v) is calculated and expressed as:

[0043]

[0044] Where W(v) is the patch weight, the lower the value, the higher the priority for deployment, ΔR c (v) is the resource change rate, representing the change in resource usage per unit time, T d (v) represents the deployment time of the patch in seconds, and β is a time sensitivity parameter used to balance the impact of time and weight.

[0045] It should be noted that analyzing patch dependencies and dynamically adjusting the deployment order also includes the system analyzing the operating system components and functional modules involved in each patch to determine the inter-patch dependencies. Dependencies include the pre-requirements of patches and version conflicts between patches. The dependency analysis uses graph theory's topological sorting method to treat patches as nodes and the dependencies between patches as edges, thus establishing a dependency graph. The system calculates the optimal deployment order based on the dependency graph. During the patch deployment process, the system continuously monitors operating system resources and running status, and dynamically adjusts the dependency analysis accordingly.

[0046] It should also be noted that by analyzing patch dependencies and dynamically adjusting the deployment order, the system achieves precision and efficiency in patch deployment, effectively solving the problem of conflicts that easily occur in complex dependency environments using traditional patch deployment techniques. Through a combination of static analysis and dynamic evaluation, the system systematically sorts out the dependency chains involved in each patch and establishes a dependency graph based on graph theory. On this basis, a topological sorting algorithm is used to calculate the optimal deployment order of patches. Real-time resource utilization and dependency chain length are introduced as adjustment weights, and the patch deployment order is dynamically adjusted through a priority calculation formula, prioritizing high-security and critical basic patches to ensure deployment stability and security. The patch deployment order also incorporates dynamic information such as system resource usage and operating system version, enabling the deployment process to flexibly adapt to the current system load and operating status. In resource-constrained environments, the system can dynamically adjust the deployment order, prioritizing the deployment of patches with lower resource utilization requirements, thereby reducing the risk of system overload. Combined with historical deployment time optimization strategies, the system further improves the efficiency of subsequent deployments and reduces deployment failure rates and reinstallation time.

[0047] S2: Deployment is based on containerization technology for isolation.

[0048] Furthermore, containerized deployment involves packaging each patch into an independent container and embedding all necessary operating system environments and dependency libraries within the container. Each patch container is deployed in an independent sandbox environment and is completely isolated from the remaining containers, avoiding interference between patches. Containerized deployment manages the lifecycle of patch containers through automated tools, including patch creation, startup, shutdown, and version control. During patch deployment within a container, if compatibility issues or installation failures occur, the container automatically reverts to the previous stable version. The patch container management tool automatically selects the appropriate container version based on the requirements of different operating system platforms, ensuring compatibility during patch deployment.

[0049] It should be noted that isolated deployment based on containerization technology also includes automatically selecting the most suitable container image based on the type and operating system version of each patch before patch deployment begins. After determining the deployment order of patches, each patch needs to select an appropriate container image for isolated deployment. Image selection needs to comprehensively consider the compatibility distance between the container and the image, the image's stability score, and historical regression records, and calculate the optimal image selection result M(C), expressed as:

[0050]

[0051] Where M(C) is the optimal mirror selection result, d ci (C,I i ) represents container C and image I iThe compatibility distance, ranging from [0,1], is better with lower values. S m (I i H represents the stability score of the image, ranging from [0,1]. A higher value indicates a more stable image. r (I i ) represents the historical rollback count of the image, taken as a non-negative integer, w1 and ω2 are the weight parameters for stability and rollback count, respectively, and I is the set of all available images.

[0052] The patch container starts in an independent virtual environment and executes a preset installation script. The script will be configured and deployed according to the operating system requirements and the characteristics of the patch. Each container has a self-contained environment and depends on the rest of the operating system. The patch deployment process does not interfere with each other. If a patch fails to install or an error occurs in the container, the system automatically switches to the rollback mechanism to restore the previous stable state.

[0053] It should also be noted that, through isolated deployment based on containerization technology, the system effectively solves the system instability issues caused by dependency conflicts or compatibility problems during patch deployment, improving deployment security and reliability. Each patch is encapsulated as an independent container, containing all the necessary operating system environment and dependency libraries, thus achieving sandboxed independent deployment. This ensures that patches do not interfere with each other, avoiding deployment conflicts or system crashes caused by mutual interference between patches in traditional methods. Containerized deployment not only provides isolation between patches but also optimizes deployment adaptability through intelligent image selection algorithms. The system comprehensively considers patch type and operating system version. Considering the compatibility distance between containers and images, the stability score of images, and historical rollback records, the system selects the optimal container image for deployment. In multi-platform environments, the system can automatically adjust the selection of container images based on platform characteristics, thereby improving the compatibility of cross-platform patch deployment. When problems are encountered during container deployment (such as compatibility conflicts or installation failures), the system will automatically trigger a rollback mechanism to restore the container to a previous stable version. Through this mechanism, the system can quickly respond to deployment failures and avoid affecting the normal operation of the system. This achieves the isolation and automation of patch deployment on diverse platforms, improving the security, flexibility, and reliability of the deployment process.

[0054] S3: Monitors the status of each container in real time, and provides real-time monitoring and feedback optimization of deployment results.

[0055] Furthermore, real-time monitoring and feedback optimization of deployment results include the system collecting real-time operational status data of patch containers through a monitoring platform during patch deployment, including resource usage, container health status, exception logs, and performance metrics. The real-time monitoring data is analyzed by intelligent algorithms to predict potential problems during patch deployment. When resources are not conflicting, the system adjusts the priority or order of patches during deployment based on the analysis results. When a patch is detected as potentially causing errors or conflicts, the system automatically takes remedial measures, including pausing deployment and rolling back the patch or activating backup resources. After deployment, the system adjusts the next deployment strategy based on historical feedback data, optimizing patch selection and order.

[0056] It should be noted that real-time monitoring and feedback optimization of deployment results also includes the system collecting real-time deployment data, analyzing the deployment effect of each patch, evaluating performance changes and system status during the deployment process, and, upon receiving feedback data, learning from the patch deployment results by training historical data using machine learning algorithms to automatically identify successful and failed deployment patterns. The system then optimizes based on these patterns, adjusting patch deployment strategies and priorities. In terms of hardware configuration, when a patch causes performance degradation or errors, the system automatically adjusts the priority of patches in future deployments through a feedback mechanism, optimizes patch selection based on feedback, and generates optimization reports for operators' reference.

[0057] It should also be noted that by collecting container runtime status data through a real-time monitoring platform, including resource usage, container health status, anomaly logs, and performance metrics, and combining this data with intelligent algorithms for real-time analysis and processing, the system can immediately take remedial measures when anomalies (such as resource conflicts or performance degradation) are detected during patch deployment. These measures include pausing deployment, rolling back patches, or allocating spare resources to ensure the stability of the deployment process. By collecting historical deployment data and utilizing machine learning algorithms for self-learning and optimization, the system optimizes the next round of deployment strategies based on historical patterns of successful and failed deployments. In specific hardware configurations or operating system versions, the system automatically adjusts the priority and deployment order of patches based on feedback data, thereby avoiding efficiency degradation caused by repeated errors. The optimization reports generated by the system can provide operators with references for improving deployment strategies, further enhancing the accuracy and stability of patch deployment. In multi-platform environments, the system ensures high stability and adaptability of the deployment process across different platforms through a real-time optimization feedback mechanism. Through intelligent monitoring and feedback optimization, the system reduces the risk of system failures caused by deployment errors, providing reliable technical support for complex and diverse patch deployment scenarios.

[0058] Example 2 is an embodiment of the present invention, which provides a cross-platform operating system upgrade patch automatic deployment method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0059] First, six typical cross-platform deployment environments (including different operating system versions and hardware configurations) were selected for patch analysis, isolated deployment, and monitoring optimization testing. Experimental environments included high-load servers, low-resource embedded devices, and cloud virtual machines to ensure the universality of the test results. In the initial stage of the experiment, the system constructed a patch dependency graph through static analysis and dynamic evaluation. Using a topology sorting algorithm, the system generated the optimal deployment order and adjusted the weights of real-time resource utilization and dependency chain length, prioritizing the deployment of high-security-level critical patches. During the deployment phase, each patch was packaged into an independent container, which embedded the appropriate operating system environment and dependency libraries. The system dynamically selected the optimal image version based on the hardware configuration and monitored resource utilization and container health during deployment. For containers that failed to deploy, the system triggered an automatic rollback mechanism to restore to a stable version. Meanwhile, the real-time monitoring module records resource usage, anomaly logs, and deployment efficiency data. After deployment, the system analyzes all experimental data, generates an optimization report, and updates the deployment strategy for the next round. According to the experimental results, this invention not only improves the efficiency and success rate of patch deployment but also enhances stability and adaptability in cross-platform environments, demonstrating innovation and practicality.

[0060] Example 3, referring to Figure 2 As an embodiment of the present invention, a cross-platform operating system upgrade patch automatic deployment system is provided, including a patch analysis module, an isolation deployment module, and a monitoring and optimization module.

[0061] The patch analysis module analyzes patch dependencies and dynamically adjusts the deployment order; the isolated deployment module performs isolated deployment based on containerization technology; and the monitoring and optimization module monitors the status of each container in real time and provides real-time monitoring and feedback optimization of the deployment results.

[0062] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0064] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0065] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0066] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A cross-platform operating system upgrade patch automatic deployment method, characterized in that, include: Analyze patch dependencies and dynamically adjust deployment order; Isolation deployment based on containerization technology; Monitor the status of each container in real time, and perform real-time monitoring and feedback optimization of deployment results; The process of analyzing patch dependencies and dynamically adjusting deployment order includes statically analyzing and dynamically evaluating the dependencies of each patch to be deployed, using a topology sorting algorithm to calculate the dependency order between patches, and calculating patch priorities. , is represented as: in, Indicates the security level of the patch, ranging from [0,10]. This represents real-time resource utilization, ranging from [0,10]. The length of the dependency chain is a non-negative integer. After priority calculation, the topological sorting method TopoSort is used. Calculate the sorting results , is represented as: in, The sorting result indicates the final deployment order of the patches. For dependency graphs, Represents a set of patches. Indicates dependency relationships; The system automatically calculates the priority and deployment order of patches based on their type and dependencies. The adjustment of the patch order takes into account the real-time system status, including hardware resource usage, system load and operating system version. The system will dynamically adjust the deployment order of patches, deploying basic patches and high-priority security patches. The system dynamically adjusts the order of patch deployment based on hardware resource usage, system load, and operating system version, deploying basic patches and high-priority security patches. To avoid system conflicts caused by low-priority patches, patch weights are assigned. Real-time adjustments are made to adapt the deployment process to the current system load, while optimizing subsequent strategies based on historical deployment times and calculating patch weights. , is represented as: in, As a patch weight, the lower the value, the higher the priority for deployment. The resource change rate represents the change in resource usage per unit of time. This is the deployment time of the patch, in seconds. This is a time sensitivity parameter used to balance the effects of time and weight. The real-time monitoring and feedback optimization of deployment results includes the system collecting real-time operational status data of patch containers through a monitoring platform during patch deployment, including resource usage, container health status, exception logs, and performance indicators. The real-time monitoring data is analyzed by intelligent algorithms to predict potential problems in patch deployment. When resources are not conflicting, the system adjusts the priority or order of patches during deployment based on the analysis results. When a patch is detected as potentially causing errors or conflicts, the system automatically takes remedial measures, including pausing deployment and rolling back the patch or activating backup resources. After deployment, the system adjusts the next deployment strategy based on historical feedback data, optimizing patch selection and order.

2. The cross-platform operating system upgrade patch automatic deployment method as described in claim 1, characterized in that: The analysis of patch dependencies and dynamic adjustment of deployment order also includes the system analyzing the operating system components and functional modules involved in each patch to determine the inter-patch dependencies. Dependencies include the pre-requirements of patches and version conflicts between patches. The dependency analysis uses graph theory's topological sorting method to treat patches as nodes and the dependencies between patches as edges, thus establishing a dependency graph. The system calculates the optimal deployment order based on the dependency graph. During the patch deployment process, the system continuously monitors operating system resources and running status, and dynamically adjusts the dependency analysis accordingly.

3. The cross-platform operating system upgrade patch automatic deployment method as described in claim 2, characterized in that: The containerization-based isolated deployment involves packaging each patch into an independent container and embedding all necessary operating system environments and dependency libraries within the container. Each patch container is deployed in an independent sandbox environment and is completely isolated from the remaining containers to avoid mutual interference between patches. Containerized deployment manages the lifecycle of patch containers through automated tools, including patch creation, startup, shutdown, and version control. During patch deployment within a container, if compatibility issues or installation failures occur, the container automatically reverts to the previous stable version. The patch container management tool automatically selects the appropriate container version based on the requirements of different operating system platforms to ensure compatibility during patch deployment.

4. The cross-platform operating system upgrade patch automatic deployment method as described in claim 3, characterized in that: The containerization-based isolated deployment also includes automatically selecting the most suitable container image based on the type and operating system version of each patch before patch deployment begins. After determining the deployment order of patches, each patch needs to select an appropriate container image for isolated deployment. Image selection needs to comprehensively consider the compatibility distance between the container and the image, the image's stability score, and historical regression records to calculate the optimal image selection result. , is represented as: in, Select the best mirror image. For containers With mirror The compatibility distance, ranging from [0,1], is determined by the value; a lower value is better. This represents the stability score of the image, ranging from [0,1]. A higher value indicates a more stable image. This represents the number of historical rollbacks for the mirror image, taken as a non-negative integer. and These are the weighting parameters for stability and rollback count, respectively. The set of all possible images; The patch container starts in an independent virtual environment and executes a preset installation script. The script will be configured and deployed according to the operating system requirements and the characteristics of the patch. Each container has a self-contained environment and depends on the rest of the operating system. The patch deployment process does not interfere with each other. If a patch fails to install or an error occurs in the container, the system automatically switches to the rollback mechanism to restore the previous stable state.

5. The cross-platform operating system upgrade patch automatic deployment method as described in claim 4, characterized in that: The real-time monitoring and feedback optimization of deployment results also includes the system collecting real-time deployment data, analyzing the deployment effect of each patch, evaluating performance changes and system status during the deployment process, and, upon receiving feedback data, learning from the patch deployment results by training on historical data using machine learning algorithms to automatically identify successful and failed deployment patterns. The system optimizes based on these patterns, adjusting patch deployment strategies and priorities. In terms of hardware configuration, when a patch causes performance degradation or errors, the system automatically adjusts the priority of patches in future deployments through a feedback mechanism, optimizes patch selection based on feedback, and generates an optimization report for operators' reference.

6. A system employing the cross-platform operating system upgrade patch automatic deployment method as described in any one of claims 1 to 5, characterized in that: Includes a patch analysis module, an isolation deployment module, and a monitoring and optimization module; The patch analysis module is used to analyze patch dependencies and dynamically adjust the deployment order; The isolation deployment module is used for isolation deployment based on containerization technology; The monitoring and optimization module is used to monitor the status of each container in real time and to monitor and optimize the deployment results in real time.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the cross-platform operating system upgrade patch automatic deployment method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cross-platform operating system upgrade patch automatic deployment method according to any one of claims 1 to 5.

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