Cross-platform operating system upgrade patch automatic deployment method and system

Abstract

The invention discloses a cross-platform operating system upgrade patch automatic deployment method and system, and relates to the technical field of operating system patch management, and the method comprises the following steps: analyzing a patch dependency relationship, and dynamically adjusting a deployment sequence; performing isolation deployment based on a containerization technology; and monitoring the state of each container in real time, and performing real-time monitoring and feedback optimization on a deployment result. By means of the method, accurate control over patch deployment in a complex heterogeneous environment is achieved, the failure rate caused by dependency conflicts is reduced, meanwhile, deployment time is shortened, stability and reliability of system operation are improved, independence between patches is ensured, and the service life of the system is prolonged. According to the method and the system, system crash caused by patch incompatibility or operation failure is avoided, the deployment efficiency is improved, the efficient container isolation and highly compatible patch deployment process is realized, the self-adaptability and long-term stability of the system are improved, and powerful technical support is provided for complex and changeable patch deployment scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of operating system patch management, and in particular to a cross-platform operating system upgrade patch automatic deployment method and system. Background Art

[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 operations. With the increase in operating system complexity and the expansion of deployment scale, this method has obvious limitations in efficiency, accuracy and stability. In recent years, the rise of automated deployment technology has provided new solutions for operating system upgrade management. However, with the diversified development of systems and the widespread application of heterogeneous environments, traditional automated deployment technology faces problems such as cross-platform compatibility, resource conflict management and insufficient dynamic adjustment capabilities. Therefore, how to build a more intelligent, dynamic, cross-platform compatible patch automatic deployment system has become a hot topic in current research and development.

[0003] Although the existing operating system patch automatic deployment technology has improved efficiency to a certain extent, it still has many shortcomings. In terms of patch dependency processing, traditional systems usually use fixed orders or predefined rules for deployment, lacking the ability to dynamically analyze and adjust complex dependencies between patches, which easily leads to deployment failures caused by order conflicts or dependency errors. During the deployment process, traditional methods mostly install patches directly in the system environment without adopting an effective isolation mechanism. This method not only increases the risk of conflicts between patches, but also easily causes system crashes due to patch incompatibility or installation failure. The existing technology lacks real-time monitoring and feedback optimization capabilities, and cannot timely identify and solve potential problems during the deployment process, such as resource conflicts, system overloads or patch errors. Once the deployment fails, the process of recovering to a stable state usually relies on manual intervention, which is inefficient and risky. Therefore, how to achieve cross-platform automatic deployment of operating system upgrade patches while ensuring a 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 problems solved by the present invention are: the existing patch deployment technology has incomplete dependency analysis, lack of isolation mechanism in the deployment process, insufficient real-time monitoring and feedback capabilities, and how to achieve dynamic adjustment, isolated deployment and intelligent optimization of the deployment process.

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

[0007] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment method of the present invention, the patch dependency analysis and dynamic adjustment of the deployment order includes static analysis and dynamic evaluation of the dependency 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), which is expressed as:

[0008]

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

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

[0011] Among them, Order(G) is the sorting result, which indicates the final deployment order of the patches. G=(V,E) is the dependency graph, V indicates the patch set, and E indicates the dependency relationship.

[0012] The system automatically calculates the priority and deployment order of patches based on the type and dependency of each patch. 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 dynamically adjusts the order of patch deployment to deploy basic patches and high-priority security patches.

[0013] The system dynamically adjusts the order of patch deployment according to the hardware resource usage, system load and operating system version, deploys basic patches and high-priority security patches, and adjusts the patch weight W(v) in real time to avoid system conflicts caused by low-priority patches, so that the deployment process can adapt to the current system load. At the same time, the subsequent strategy is optimized in combination with the historical deployment time, and the patch weight W(v) is calculated, which is expressed as:

[0014]

[0015] Where W(v) is the patch weight. The lower the value, the better the deployment.c (v) is the resource change rate, which indicates the change in resource usage per unit time, T d (v) is the deployment time of the patch in seconds, and β is the time sensitivity parameter used to balance the impact of time and weight.

[0016] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment method described in the present invention, the patch dependency analysis and dynamic adjustment of the deployment order also includes the system determining the mutual dependencies between patches by analyzing the operating system components and functional modules involved in each patch, the dependencies include the prerequisite requirements of the patches and the version conflicts between the patches, the dependency analysis adopts the topological sorting method of graph theory to regard the patches as nodes, the dependencies between the patches as edges, and establish a dependency graph, the system calculates the optimal deployment order according to the dependency graph, and during the patch deployment process, the system will continuously monitor the operating system resources and running status, and dynamically adjust the dependency analysis.

[0017] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment method described in the present invention, the isolated deployment based on containerization technology includes packaging each patch into an independent container and embedding all necessary operating system environments and dependent libraries in 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 life cycle of the patch container through automated tools, including patch creation, startup, stop and version control. During the patch deployment process in the container, when there is a compatibility problem or installation fails, the container automatically restores to the previous stable version. The patch container management tool automatically selects the appropriate container version according to the requirements of different operating system platforms to ensure compatibility during the patch deployment process.

[0018] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment method described in the present invention, the isolated deployment based on containerization technology also includes that before the patch deployment starts, the system automatically selects the most suitable container image according to the type and operating system version of each patch. After determining the deployment order of the patches, each patch needs to select an adapted container image for isolated deployment. The image selection needs to comprehensively consider the compatibility distance between the container and the image, the stability score of the image, and the historical regression record, and calculate the optimal image selection result M(C), which is expressed as:

[0019]

[0020] Among them, M(C) is the optimal mirror selection result, d ci (C,I i ) is container C and image I iThe compatibility distance is in the range [0,1]. The lower the value, the better. S m (I i ) is the stability score of the image, ranging from [0,1]. The higher the value, the more stable the image. r (I i ) is the historical rollback times of the image, which takes a non-negative integer, w1 and ω2 are the weight parameters of stability and rollback times respectively, and I is the set of all optional images.

[0021] The patch container is started in an independent virtual environment and executes the preset installation script. The script will be configured and deployed according to the requirements of the operating system and the characteristics of the patch. Each container has a self-contained environment and depends on the rest of the operating system. There is no mutual interference during the patch deployment process. If it is found that the patch cannot be successfully installed or an error occurs in the container, the system automatically switches to the rollback mechanism and restores to the previous stable state.

[0022] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment method described in the present invention, the real-time monitoring and feedback optimization of the deployment results includes that during the patch deployment process, the system collects the running status data of the patch container in real time through the monitoring platform, including resource usage, container health status, abnormal logs and performance indicators. The real-time monitoring data is analyzed by an intelligent algorithm to predict possible problems in patch deployment. When resources do not conflict, based on the analysis results, the system adjusts the priority or order of the patches during the deployment process. When a patch is monitored to be likely to cause errors or conflicts, the system automatically takes remedial measures, including suspending the deployment of rollback patches or starting backup resources. After the deployment is completed, the system adjusts the next deployment strategy based on historical feedback data to optimize patch selection and order.

[0023] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment method described in the present invention, 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, and evaluating the performance changes and system status during the deployment process. After receiving the feedback data, the system self-learns according to the patch deployment results, trains historical data through a machine learning algorithm, and automatically identifies the patterns of successful deployment and failed deployment. The system optimizes based on the patterns of successful deployment and failed deployment, and adjusts the patch deployment strategy and priority. In terms of hardware configuration, when the patch causes performance degradation or errors, the system automatically adjusts the priority of the patch in future deployment through a feedback mechanism, optimizes the selection of patches based on feedback, and generates an optimization report for reference by operators.

[0024] Another object of the present invention is to provide a cross-platform operating system upgrade patch automatic deployment system, which can monitor the status of each container in real time, perform real-time monitoring and feedback optimization on the deployment results, and solve the problem that the current operating system patch management technology contains a lack of real-time status monitoring and insufficient dynamic adjustment capabilities during the patch deployment process.

[0025] As a preferred solution of the cross-platform operating system upgrade patch automatic deployment system described in the present 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 isolated 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 on the deployment results.

[0027] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a method for automatically deploying a cross-platform operating system upgrade patch.

[0028] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for automatically deploying a cross-platform operating system upgrade patch.

[0029] Beneficial effects of the present invention: The cross-platform operating system upgrade patch automatic deployment method provided by the present invention analyzes patch dependencies and dynamically adjusts the deployment order, thereby realizing precise control of patch deployment by the system in a complex heterogeneous environment, reducing the failure rate caused by dependency conflicts, while reducing the deployment time, improving the stability and reliability of system operation, and performing isolated deployment based on containerization technology, thereby ensuring the independence of patches and avoiding system crashes caused by patch incompatibility or operation failure. While improving deployment efficiency, efficient container isolation and highly compatible patch deployment processes are realized, the status of each container is monitored in real time, and deployment results are monitored in real time and feedback is optimized, thereby improving the autonomous adaptability and long-term stability of the system, and providing strong technical support for complex and changeable patch deployment scenarios. The present invention achieves better results in terms of 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

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

[0032] Figure 2 The overall flow chart of a cross-platform operating system upgrade patch automatic deployment system provided by the third embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0034] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a cross-platform operating system upgrade patch automatic deployment method, comprising:

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

[0036] Furthermore, analyzing the 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), which is expressed as:

[0037]

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

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

[0040] Among them, Order(G) is the sorting result, which indicates the final deployment order of the patches. G=(V,E) is the dependency graph, V indicates the patch set, and E indicates the dependency relationship.

[0041] The system automatically calculates the priority and deployment order of patches based on the type and dependency of each patch. 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 dynamically adjusts the order of patch deployment to deploy basic patches and high-priority security patches.

[0042] The system dynamically adjusts the order of patch deployment according to the hardware resource usage, system load and operating system version, deploys basic patches and high-priority security patches, and adjusts the patch weight W(v) in real time to avoid system conflicts caused by low-priority patches, so that the deployment process can adapt to the current system load. At the same time, the subsequent strategy is optimized in combination with the historical deployment time, and the patch weight W(v) is calculated, which is expressed as:

[0043]

[0044] Where W(v) is the patch weight. The lower the value, the better the deployment. c (v) is the resource change rate, which indicates the change in resource usage per unit time, T d (v) is the deployment time of the patch in seconds, and β is the 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 determining the interdependencies between patches by analyzing the operating system components and functional modules involved in each patch. The dependencies include the prerequisite requirements of the patches and the version conflicts between patches. The dependency analysis uses the topological sorting method of graph theory to treat patches as nodes and the dependencies between patches as edges to establish a dependency graph. The system calculates the optimal deployment order based on the dependency graph. During the patch deployment process, the system will continuously monitor the operating system resources and operating status and dynamically adjust the dependency analysis.

[0046] It should also be noted that by analyzing patch dependencies and dynamically adjusting the deployment order, the system achieves accuracy and efficiency in patch deployment, effectively solving the problem that traditional patch deployment technology is prone to conflicts in complex dependency environments. By combining static analysis and dynamic evaluation, the dependency chains involved in each patch are systematically sorted out, and a dependency graph based on graph theory is established. On this basis, a topological sorting algorithm is used to calculate the optimal deployment order of patches, and real-time resource occupancy and dependency chain length are introduced as adjustment weights. The patch deployment order is dynamically adjusted through the priority calculation formula, and basic patches with high security levels and strong criticality are given priority to ensure the stability and security of deployment. The patch deployment order also combines dynamic information such as system resource usage and operating system version, so that the deployment process can flexibly adapt to the current system load and operating status. In an environment with tight resources, the system can dynamically adjust the deployment order and give priority to deploying patches with lower resource occupancy requirements, thereby reducing the risk of system overload. Combined with the historical deployment time optimization strategy, the system further improves the efficiency of subsequent deployments and reduces deployment failure rates and repeated installation time.

[0047] S2: Isolated deployment based on containerization technology.

[0048] Furthermore, isolated deployment based on containerization technology includes packaging each patch into an independent container and embedding all necessary operating system environments and dependent libraries in 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 life cycle of patch containers through automated tools, including patch creation, startup, stop and version control. During the patch deployment process in the container, when there are compatibility issues or installation fails, the container automatically restores to the previous stable version. The patch container management tool automatically selects the appropriate container version according to the requirements of different operating system platforms to ensure compatibility during the patch deployment process.

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

[0050]

[0051] Among them, M(C) is the optimal mirror selection result, d ci (C,I i ) is container C and image I iThe compatibility distance is in the range [0,1]. The lower the value, the better. S m (I i ) is the stability score of the image, ranging from [0,1]. The higher the value, the more stable the image. r (I i ) is the historical rollback times of the image, which takes a non-negative integer, w1 and ω2 are the weight parameters of stability and rollback times respectively, and I is the set of all optional images.

[0052] The patch container is started in an independent virtual environment and executes the preset installation script. The script will be configured and deployed according to the requirements of the operating system and the characteristics of the patch. Each container has a self-contained environment and depends on the rest of the operating system. There is no mutual interference during the patch deployment process. If it is found that the patch cannot be successfully installed or an error occurs in the container, the system automatically switches to the rollback mechanism and restores to the previous stable state.

[0053] It should also be noted that through isolated deployment based on containerization technology, the system effectively solves the problem of system instability caused by dependency conflicts or compatibility issues during patch deployment, improves the security and reliability of deployment, and encapsulates each patch into an independent container, which contains all the operating system environment and dependency libraries required by the patch, thereby realizing sandboxed independent deployment, ensuring that patches do not interfere with each other, and avoiding deployment conflicts or system crashes caused by mutual influence between patches in traditional methods. Containerized deployment not only provides isolation between patches, but also optimizes the adaptability of deployment through intelligent image selection algorithms. The system combines patch type and operating system version to comprehensively consider Considering the compatibility distance between the container and the image, the stability score of the image, and the historical rollback record, the optimal container image is selected for deployment. In a multi-platform environment, the system can automatically adjust the selection of container images according to the 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 the rollback mechanism to restore the container to the previous stable version. Through this mechanism, the system can quickly respond to deployment failures and avoid affecting the normal operation of the system. It realizes the isolation and automation of patch deployment on a variety of platforms, and improves the security, flexibility and reliability of the deployment process.

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

[0055] Furthermore, real-time monitoring and feedback optimization of deployment results include: during the patch deployment process, the system collects the running status data of the patch container in real time through the monitoring platform, including resource usage, container health status, abnormal logs and performance indicators; the real-time monitoring data is analyzed by intelligent algorithms to predict possible problems in patch deployment; when resources do not conflict, based on the analysis results, the system adjusts the priority or order of patches during deployment; when a patch is monitored to cause errors or conflicts, the system automatically takes remedial measures, including suspending deployment, rolling back the patch or starting backup resources; after the deployment is completed, the system adjusts the next deployment strategy based on historical feedback data to optimize patch selection and order.

[0056] It should be noted that real-time monitoring and feedback optimization of deployment results also include the system collecting real-time deployment data, analyzing the deployment effect of each patch, and evaluating performance changes and system status during the deployment process. After receiving feedback data, the system conducts self-learning based on the patch deployment results, trains historical data through machine learning algorithms, and automatically identifies patterns of successful and failed deployments. The system optimizes based on the patterns of successful and failed deployments and adjusts the patch deployment strategy and priority. In terms of hardware configuration, when a patch causes performance degradation or errors, the system automatically adjusts the priority of the patch in future deployments through a feedback mechanism, optimizes the selection of patches based on feedback, and generates optimization reports for operators' reference.

[0057] It should also be noted that the real-time monitoring platform collects container operation status data, including resource usage, container health status, abnormal logs and performance indicators, and combines intelligent algorithms to analyze and process the data in real time. When an abnormality is found in the patch deployment process (such as resource conflict or performance degradation), the system can immediately take remedial measures, including suspending deployment, rolling back patches or allocating spare resources to ensure the stability of the deployment process. By collecting historical deployment data and using machine learning algorithms for self-learning and optimization, the system optimizes the next round of deployment strategies based on historical patterns of deployment success and failure. In specific hardware configurations or operating system versions, the system automatically adjusts the priority and deployment order of patches through feedback data to avoid efficiency reduction due to repeated errors. The optimization report generated by the system can provide operation and maintenance personnel with a reference for improving deployment strategies, further improving the accuracy and stability of patch deployment. In a multi-platform environment, the system ensures that the deployment process of different platforms is highly stable and adaptable through real-time optimization feedback mechanisms. Through intelligent monitoring and feedback optimization, the risk of system failures caused by deployment errors is reduced, 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 experiments.

[0059] First, six typical cross-platform deployment environments were selected, including different operating system versions and hardware configurations, and patch analysis, isolated deployment, and monitoring optimization tests were performed respectively. The experimental environment included high-load servers, low-resource embedded devices, and cloud virtual machines to ensure that the test results were universal. In the early stage of the experiment, the system constructed a patch dependency graph through static analysis and dynamic evaluation. Using the topological sorting algorithm, the system generated the optimal deployment order, and adjusted the weights of real-time resource occupancy and dependency chain length, giving priority to the deployment of critical patches with high security levels. In the deployment stage, each patch was packaged into an independent container, which embedded the adapted operating system environment and dependency library. The system dynamically selected the optimal image version based on the hardware configuration, and monitored resource occupancy and container health during the deployment process. For containers that failed to deploy, the system triggered an automatic rollback mechanism to restore to a stable version. At the same time, the real-time monitoring module records resource usage, exception logs and deployment efficiency data; after the deployment is completed, the system analyzes all experimental data, generates an optimization report and updates the next round of deployment strategies; according to the experimental results, the present invention not only improves the efficiency and success rate of patch deployment, but also improves the stability and adaptability in a cross-platform environment, and is innovative and practical.

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

[0061] The patch analysis module is used to analyze patch dependencies and dynamically adjust the deployment order; the isolated 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.

[0062] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0064] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0065] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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; Isolated deployment based on containerization technology; Monitor the status of each container in real time, and perform real-time monitoring and feedback optimization on the deployment results.

2. The cross-platform operating system upgrade patch automatic deployment method according to claim 1, characterized in that: The patch dependency analysis and dynamic adjustment of the deployment order includes statically analyzing and dynamically evaluating the dependency 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), which is expressed as: Among them, S s (v) represents the security level of the patch, ranging from [0,10], R c (v) is the real-time resource occupancy rate, ranging from [0,10], L d (v) is the length of the dependency chain, which is a non-negative integer. After the priority calculation is completed, the topological sorting method TopoSort(V, E, {P(v)}) is used to calculate the sorting result Order(G), which is expressed as: Order(G)=TopoSort(V,E,{P(v)}) Among them, Order(G) is the sorting result, which indicates the final deployment order of the patches. G = (V, E) is the dependency graph, V represents the patch set, and E represents the dependency relationship. The system automatically calculates the priority and deployment order of patches based on the type and dependency of each patch. 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 dynamically adjusts the order of patch deployment to deploy basic patches and high-priority security patches. The system dynamically adjusts the order of patch deployment according to the hardware resource usage, system load and operating system version, deploys basic patches and high-priority security patches, and adjusts the patch weight W(v) in real time to avoid system conflicts caused by low-priority patches, so that the deployment process can adapt to the current system load. At the same time, the subsequent strategy is optimized in combination with the historical deployment time, and the patch weight W(v) is calculated, which is expressed as: Where W(v) is the patch weight. The lower the value, the better the deployment. c (v) is the resource change rate, which indicates the change in resource usage per unit time, T d (v) is the deployment time of the patch in seconds, and β is the time sensitivity parameter used to balance the impact of time and weight.

3. The cross-platform operating system upgrade patch automatic deployment method as claimed in claim 2, characterized in that: The method of analyzing patch dependencies and dynamically adjusting the deployment sequence also includes the system analyzing the operating system components and functional modules involved in each patch to determine the interdependencies between patches, where the dependencies include the prerequisite requirements of the patches and the version conflicts between the patches. The dependency analysis uses a topological sorting method of graph theory to treat the patches as nodes and the dependencies between the patches as edges to establish a dependency graph. The system calculates the optimal deployment sequence based on the dependency graph. During the patch deployment process, the system continuously monitors the operating system resources and operating status and dynamically adjusts the dependency analysis.

4. The cross-platform operating system upgrade patch automatic deployment method as claimed in claim 3, characterized in that: The isolated deployment based on containerization technology includes packaging each patch into an independent container and embedding all necessary operating system environments and dependent libraries in 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 life cycle of patch containers through automated tools, including patch creation, startup, stop and version control. During the patch deployment process in the container, when there is a compatibility problem or installation fails, the container automatically restores to the previous stable version. The patch container management tool automatically selects the appropriate container version according to the requirements of different operating system platforms to ensure compatibility during patch deployment.

5. The cross-platform operating system upgrade patch automatic deployment method according to claim 4, characterized in that: The isolated deployment based on containerization technology also includes that before the patch deployment begins, the system automatically selects the most suitable container image according to the type and operating system version of each patch. After determining the deployment order of the patches, each patch needs to select an adapted container image for isolated deployment. The image selection needs to comprehensively consider the compatibility distance between the container and the image, the stability score of the image, and the historical regression record, and calculate the optimal image selection result M(C), which is expressed as: Among them, M(C) is the optimal mirror selection result, d ci (C,I i ) is container C and image I i The compatibility distance is in the range [0,1]. The lower the value, the better. S m (I i ) is the stability score of the image, ranging from [0,1]. The higher the value, the more stable the image. r (I i ) is the historical rollback times of the image, which takes a non-negative integer, w1 and ω2 are weight parameters of stability and rollback times respectively, and I is the set of all optional images; The patch container is started in an independent virtual environment and executes the preset installation script. The script will be configured and deployed according to the requirements of the operating system and the characteristics of the patch. Each container has a self-contained environment and depends on the rest of the operating system. There is no mutual interference during the patch deployment process. If it is found that the patch cannot be successfully installed or an error occurs in the container, the system automatically switches to the rollback mechanism and restores to the previous stable state.

6. The cross-platform operating system upgrade patch automatic deployment method as claimed in claim 5, characterized in that: The real-time monitoring and feedback optimization of deployment results include that during the patch deployment process, the system collects the running status data of the patch container in real time through the monitoring platform, including resource usage, container health status, abnormal logs and performance indicators, and analyzes the real-time monitoring data through an intelligent algorithm to predict possible problems in patch deployment. When resources do not conflict, based on the analysis results, the system adjusts the priority or order of patches during the deployment process. When a patch is monitored to be likely to cause an error or conflict, the system automatically takes remedial measures, including suspending deployment to roll back the patch or starting backup resources. After the deployment is completed, the system adjusts the next deployment strategy based on historical feedback data to optimize patch selection and order.

7. The cross-platform operating system upgrade patch automatic deployment method according to claim 6, 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. After receiving feedback data, the system self-learns according to the patch deployment results, trains historical data through machine learning algorithms, and automatically identifies patterns of successful and failed deployments. The system optimizes based on the patterns of successful and failed deployments and adjusts the strategy and priority of patch deployment. In terms of hardware configuration, when a patch causes performance degradation or errors, the system automatically adjusts the priority of the patch in future deployments through a feedback mechanism, optimizes the selection of patches based on feedback, and generates an optimization report for reference by operators.

8. A system using the cross-platform operating system upgrade patch automatic deployment method as claimed in any one of claims 1 to 7, characterized in that: Including patch analysis module, isolation deployment module, 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 to perform 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 perform real-time monitoring and feedback optimization on the deployment results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the cross-platform operating system upgrade patch automatic deployment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cross-platform operating system upgrade patch automatic deployment method according to any one of claims 1 to 7 are implemented.

Images