Graphical exquisite bag management method

Abstract

The invention provides a graphical exquisite bag management method, and relates to the technical field of computers, and the method comprises the steps: carrying out the deep analysis of an exquisite bag, and extracting depth features; establishing a dependency graph; configuring a graphic interaction interface of the exquisite bag; generating an exquisite package calling instruction according to the interaction information, performing cooperative operation analysis based on a dependency graph, and configuring a time sequence cooperative calling result; synchronously generating a system resource monitoring instruction according to the exquisite package calling instruction; and calling control optimization of the exquisite package is carried out by using a time sequence collaborative calling result and a system resource state monitoring result, an optimization result is generated, and the exquisite package is managed. The technical problem that in the prior art, due to the fact that the calling sequence and resource allocation of the exquisite packages are unreasonable in a complex scene, execution conflicts are generated, and the management efficiency of the exquisite packages is further affected can be solved, and the management efficiency of the graphical exquisite packages is improved by constructing the relation dependency graph and monitoring system resources.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a graphical Linglong package management method. Background Art

[0002] With the continuous development of software development and system integration, package management, as an important tool, plays a vital role in automated deployment, maintenance and updating. In these systems, Linglong packages (i.e., various dependent packages or modules) are widely used as independent and reusable software components to ensure the collaboration between different functional modules. Graphical Linglong package management tools usually display package information, dependencies and execution status to users through a friendly user interface (GUI), allowing users to quickly understand the usage of each Linglong package and operate it when needed. However, in complex scenarios, due to the complex dependencies between packages, if the call order is not arranged properly, it may lead to execution conflicts between packages. At the same time, the usage of system resources is constantly changing during the execution of different packages. If the allocation of these resources is not fully considered, it may lead to resource conflicts and waste, affecting the normal execution of packages. Especially when multiple packages are executed in parallel, the rationality of resource allocation is crucial. When multiple packages work together, execution conflicts are difficult to avoid, especially when the resource usage is too high or the task execution order is wrong, which leads to reduced efficiency of the Linglong package management process and even errors and exceptions.

[0003] In summary, there are technical problems in the prior art that, due to unreasonable calling order and resource allocation of Linglong Package in complex scenarios, execution conflicts and resource waste occur, which further affects the management efficiency of Linglong Package. Summary of the invention

[0004] The purpose of this application is to provide a graphical Linglong package management method to solve the technical problem in the prior art that in complex scenarios, the call order and resource allocation of Linglong packages are unreasonable, resulting in execution conflicts and resource waste, which further affects the efficiency of Linglong package management.

[0005] In view of the above problems, the present application provides a graphical Linglong package management method, wherein the graphical Linglong package management method includes: performing deep analysis on the Linglong package, and extracting deep features based on the deep analysis results; using the deep features to perform dependency analysis between Linglong packages, and establishing a dependency graph; configuring the graphical interactive interface of the Linglong package, and obtaining interaction information of the graphical interactive interface, wherein the interaction information is the user's operation information based on the graphical interactive interface; generating a Linglong package call instruction based on the interaction information, and performing collaborative operation analysis based on the dependency graph based on the Linglong package call instruction, and configuring a timing collaborative call result; synchronously generating a system resource monitoring instruction based on the Linglong package call instruction, performing real-time monitoring of the system resource status based on the system resource monitoring instruction, and establishing a system resource status monitoring result; using the timing collaborative call result and the system resource status monitoring result to optimize the call control of the Linglong package and generate an optimization result; and managing the Linglong package based on the optimization result.

[0006] The technical solution provided in this application has at least the following technical effects or advantages: Through deep analysis of the Linglong package, deep features are extracted according to the deep analysis results; the deep features are used to perform dependency analysis between Linglong packages and establish a dependency graph; the graphical interactive interface of the Linglong package is configured, and the interaction information of the graphical interactive interface is obtained, and the interaction information is the user's operation information based on the graphical interactive interface; the Linglong package call instruction is generated according to the interaction information, and a collaborative operation analysis based on the dependency graph is performed based on the Linglong package call instruction, and a timing collaborative call result is configured; the system resource monitoring instruction is synchronously generated according to the Linglong package call instruction, and the system resource status is monitored in real time according to the system resource monitoring instruction, and the system resource status monitoring result is established; the Linglong package call control optimization is performed using the timing collaborative call result and the system resource status monitoring result, and the optimization result is generated; the Linglong package is managed according to the optimization result. In other words, deep features are extracted through deep analysis of Linglong packages, a dependency graph is constructed, and the interdependencies between packages are clarified; a graphical interactive interface is configured and interaction information is obtained, and collaborative operation analysis is performed based on the dependency graph and user interaction information. At the same time, system resource usage is monitored, and call control optimization is performed based on the timing collaborative call results and system resource status monitoring results, thereby improving the efficiency of graphical Linglong package management.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0009] Figure 1 A flowchart of a graphical Linglong bag management method for this application; Figure 2 This is a flow chart of generating optimization results in a graphical Linglong package management method of this application. DETAILED DESCRIPTION

[0010] This application provides a graphical Linglong package management method to solve the technical problem in the prior art that the call order and resource allocation of Linglong packages in complex scenarios are unreasonable, resulting in execution conflicts and resource waste, which further affects the efficiency of Linglong package management. Through deep analysis of Linglong packages to extract deep features, build a dependency graph, and clarify the interdependencies between packages; configure a graphical interactive interface and obtain interaction information, perform collaborative operation analysis based on the dependency graph and user interaction information, and at the same time, monitor system resource usage, and perform call control optimization based on the timing collaborative call results and system resource status monitoring results, thereby improving the efficiency of graphical Linglong package management.

[0011] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0012] For examples, please see the attached Figure 1The present application provides a graphical Linglong package management method, wherein the graphical Linglong package management method specifically includes the following steps: S100: Perform deep analysis on the Linglong bag and extract deep features based on the deep analysis results.

[0013] Furthermore, the present application S100 includes: Obtain the call records of Linglong packages, perform Linglong package association analysis based on the call records, and establish a first association; perform data analysis on each Linglong package, use the data analysis results to perform similarity calculation, and establish a second association, wherein the similarity calculation includes feature similarity calculation, function similarity calculation, and behavior similarity calculation; perform dependency depth and dependency breadth analysis between Linglong packages based on the first association and the second association, and extract deep features.

[0014] Specifically, Linglong Package refers to graphical operations and Linglong Package in software development, which are mainly used to solve the complexity and compatibility issues of traditional software package formats under Linux systems. In Linux systems, especially deepin operating systems, Linglong Package is a new software package format that aims to simplify the software installation and management process. By using a graphical interface, users can more conveniently install, update and uninstall software without relying on command line operations. Deep analysis refers to a more detailed analysis of Linglong Package, usually including a comprehensive analysis of its source code, metadata, file structure, dependencies, functional characteristics, etc. Deep analysis is not limited to the extraction of surface data, but also goes deep into the specific implementation of the package, dependencies, potential conflicts and problems, including but not limited to file structure analysis, metadata analysis, source code or binary analysis, behavior analysis, etc.

[0015] Collect the call records of each Linglong package. These data can be obtained through tools such as calltree, gprof, kprof and gdb. Usually automatically generated through log files or monitoring systems, they record each call event of the package, including the package name, call time, execution results, and other packages called. Call records refer to the logs or data of each Linglong package being called, executed or interacted, containing information such as timestamps, executed packages, called packages, and execution results, which are used to track the usage and interaction history of each Linglong package. The collected call records are organized into a connectivity matrix, where the rows of the matrix represent the addresses of the called functions and the columns represent the addresses of the calling functions. The intersections in the matrix represent the call relationships between the two functions, and the accumulated values ​​represent the number of calls. Linglong package association analysis is performed based on the call records to find the associations between Linglong packages and establish the first association. Association analysis is an analytical technique used to discover the regularities and patterns of the simultaneous occurrence of certain attributes in a large number of data sets.

[0016] Perform data analysis on each Linglong package, including analysis of package attributes, functions, behaviors, and other information. Extract relevant information by performing in-depth analysis of the source code or configuration files of the Linglong package. Use Python's json and yaml libraries to parse description files (manifest or metadata), package names, version numbers, dependency lists, etc. Use system monitoring tools to capture run logs and analyze the behavioral characteristics of the package at runtime, such as the functions called and the outputs generated. Perform feature similarity calculations on the data analysis results and perform feature comparisons based on the basic properties of the package (such as the operating system it depends on and resource requirements). Calculate the similarity between two packages by comparing the feature vectors of each package using cosine similarity.

[0017] Perform functional similarity calculation on the data analysis results, extract the functional description of the package, convert it into a functional vector, and evaluate the degree of functional similarity by comparing the similarity of the functional vectors. For example, use TF-IDF (term frequency-inverse document frequency) to extract keywords in the functional description; use cosine similarity to calculate the similarity of the functional description. Perform behavioral similarity calculation on the data analysis results to analyze the behavior of the package at runtime, such as system calls, memory usage patterns, network communications, etc. Calculate behavioral similarity by comparing behavioral logs. For example, extract the runtime behavioral features of the package (such as the number of system calls, network access frequency); use the dynamic time warping algorithm (DTW) to match time series behavioral data.

[0018] The results of the feature, function and behavior similarity calculations are combined to generate the second association matrix of the package, which reflects the similarity between Linglong packages. Through similarity calculations, the second association is established, which not only identifies the direct dependencies between packages, but also further associates those packages with similar functions and behaviors through similarities. Combining the first association and the second association, the dependency depth and dependency breadth analysis between packages is performed. Dependency depth refers to analyzing the hierarchical structure of the package through the package dependency graph to determine how long the dependency chain of a certain package is; dependency breadth refers to analyzing the number of other packages that a certain package directly depends on, which helps identify key packages in the system.

[0019] Based on the dependency analysis results, deep features are extracted, which describe the importance, complexity, and interdependence of packages in the system. Deep features refer to the key attributes or characteristics of packages extracted from the dependency depth and breadth analysis, which describe key attributes such as the package's functions, behavior patterns, and resource requirements. Through the first association and the second association, the system can clearly identify the direct dependencies and functional similarities between packages, avoiding potential errors in the package management process. The extraction of deep features helps to identify key packages and core functions in the system, thereby more accurately scheduling and optimizing the installation, update, and uninstallation operations of packages.

[0020] S200: Utilize the deep features to perform dependency analysis between Linglong packages and establish a dependency graph.

[0021] Specifically, the extracted deep features are sorted to ensure that the data is accurate. The dependencies of Linglong packages can be identified through their internal dependency fields. Each package in Linglong packages has a clear dependency list, including basic dependencies, runtime dependencies, and transitive dependencies, which can be obtained by parsing the metadata file of the package. Dependency analysis is performed using the dependency data extracted from the deep features. By comparing the dependency fields of each package, the dependency relationship between packages is identified. A directed graph can be used to represent the dependency relationship between packages. Each package is a node. If package A depends on package B, a directed edge is drawn from A to B in the graph. Based on the metadata of each package, the dependency fields are extracted and the relationship is established.

[0022] According to the results of dependency analysis, packages and their dependencies are organized into a graph structure. The nodes of the graph represent Linglong packages, and the edges represent dependencies. Take each Linglong package as a node, and build directed edges in the graph according to the dependencies between Linglong packages. Connect all Linglong packages with dependencies to form a complete dependency graph. The dependency graph is a graph structure built through dependency analysis. The nodes represent Linglong packages, and the edges represent the dependency of one package on another. The dependency graph can clearly show the dependency hierarchy and interaction between packages. Through the visualization of the graph, users can intuitively see which packages depend on other packages and which packages are key dependencies of the system. The dependency graph provides intuitive package dependency information to help users understand the relationship between packages. For complex systems, it can clearly show which packages are key dependencies and which packages are optional dependencies.

[0023] S300: configuring the graphical interaction interface of Linglongbao, and obtaining interaction information of the graphical interaction interface, where the interaction information is operation information of the user based on the graphical interaction interface.

[0024] Specifically, configure the graphical interactive interface of Linglong Package, that is, the interface that interacts with the user through graphical elements (such as buttons, menus, text boxes, icons, etc.). The graphical interactive interface (GUI) is the medium for users to interact with the system. When configuring the GUI of Linglong Package, you first need to design the interface elements to ensure that users can complete the management of Linglong Package through simple operations. The design of the interface must meet user needs and ensure its ease of use and intuitiveness. The interface element design includes the display of Linglong Package list, operation buttons (providing installation, uninstallation, update and other operation buttons, users can choose to operate a certain package by clicking the corresponding button), search box (allowing users to search Linglong Package by package name or other keywords), status bar, etc.

[0025] For example, suppose a simple GUI interface is designed. The package list displays currently installed packages, and buttons are provided for users to choose to install, update, or uninstall a package. Users can search for Linglong packages based on package names or other keywords in the search box, and the system will display matching packages. After clicking a package, the interface displays detailed package information and provides operation buttons.

[0026] When users operate through the graphical interactive interface, it is necessary to capture the user's operation information and pass it to the backend for processing. The interactive information is the content of the user's operation in the graphical interactive interface, including the specific operation the user wants to perform, the target package, the selected version, the operation confirmation, etc. For example, an event-driven programming model is used to capture the user's clicks, selections, inputs and other operations on the interface, and the interactive information is passed to the system through the background data processing mechanism (such as using APIs, message queues, etc.) for corresponding operations. Through the graphical interactive interface, users can manage Linglong packages more intuitively and conveniently without having to enter complex commands through the command line, which reduces the threshold for operation.

[0027] S400: Generate a Linglong package calling instruction according to the interactive information, and perform collaborative operation analysis based on the dependency graph based on the Linglong package calling instruction to configure a timing collaborative calling result.

[0028] Specifically, based on the interactive information, that is, the operation information input by the user through the graphical interactive interface, including the selected target Linglong package, operation type (such as installation, uninstallation, update) and other relevant parameters, the operation type, target package, version and other information are parsed from the interactive information input by the user through the graphical interface, and a standardized Linglong package call instruction is generated based on the parsed result. The Linglong package call instruction refers to a system instruction generated by the analysis of the interactive information, which indicates the operation (such as installation, update, uninstallation) that the system needs to perform on the target Linglong package and its dependency analysis. Collaborative operation analysis refers to determining the operation order and operation mode between multiple packages and whether they can be executed in parallel or step by step based on the analysis of the dependency graph.

[0029] Parse the interactive information entered by the user through the graphical interface, extract the operation target (i.e., the target Linglong package) and the operation type (such as installation or uninstallation). Combine the target package and the operation type to form a standardized call instruction. Determine the target package based on the call instruction, and analyze the collaborative relationship between packages through the dependency graph. Find the location of the target package in the dependency graph and determine the direct and indirect dependencies of the target package. Determine whether the installation, update, or uninstallation of the package requires the support of other packages, and check whether multiple operations can be performed in parallel. Usually traverse the dependency graph through depth-first search (DFS) to determine the complete dependency chain of the target package.

[0030] According to the dependency depth of the complete dependency chain of the target package, the layers are divided to determine which packages belong to the same layer. According to the layer division, a timing call plan is generated, including which packages are executed first, which packages can be executed in parallel, and which packages are executed last. The timing call plan is executed and verified to determine the final timing coordination call result. The timing coordination call result refers to the call plan generated after the coordination operation analysis, which defines the order and timing of the package operations to ensure that the dependencies are correct and the operations are efficient. The timing coordination call result refers to the call instruction execution plan configured according to the dependency and execution order of the call instruction, including the execution order and parallel execution strategy. By combining the call instruction with the dependency graph, the dependency chain of the package can be quickly analyzed and the optimal call plan can be generated. The dependency depth analysis and timing plan configuration ensure that the operation order of the package is correct, while optimizing efficiency by using parallel operations.

[0031] S500: Synchronously generate a system resource monitoring instruction according to the Linglong package calling instruction, perform real-time monitoring of the system resource status according to the system resource monitoring instruction, and establish a system resource status monitoring result.

[0032] Specifically, system resource monitoring instructions are generated based on the call instructions of the Linglong package to monitor the resource usage of the system, such as the occupancy status of CPU, memory, disk, network and other resources. According to the requirements of the Linglong package call instructions, corresponding system resource monitoring instructions are generated to ensure that system resources are effectively monitored when executing Linglong package operations. System resource monitoring instructions track the usage of system resources during the call process, such as CPU usage, memory occupancy, disk I / O, network traffic, etc.

[0033] First, the package management operation type (such as installation, uninstallation, and update) is determined by parsing the call instructions of Linglong package. Then, according to the nature of the operation (for example, installing a large package may require more CPU and memory resources), the corresponding system resource monitoring requirements are determined. For example, if the installation package needs to download a large amount of data, the system needs to monitor network bandwidth and disk space.

[0034] According to the generated system resource monitoring instructions, the system resource status is monitored in real time to ensure that the system does not overload when executing package operations. The usage of CPU, memory, disk, network and other resources is collected regularly; the monitoring results are updated dynamically, and the resource status can be displayed on the console or presented in real time through the graphical interface; if the resource usage exceeds a certain threshold (such as CPU usage exceeding 90%), an alarm is triggered or the package management operation is suspended.

[0035] Based on the summary and record of system resource usage collected during the real-time monitoring of system resource status, the real-time monitored system resource data is stored and analyzed to establish system resource status monitoring results, which can be log files, database records, or real-time charts on the monitoring interface. System resource status monitoring results should include historical data on resource usage, current status, and alerts for any abnormal conditions, such as CPU utilization, memory usage, disk I / O, etc. By monitoring system resource usage in real time, resource bottlenecks can be discovered in a timely manner during package management operations to avoid system crashes or overloads.

[0036] S600: Utilize the timing coordination call result and the system resource status monitoring result to perform call control optimization of Linglong package and generate optimization result.

[0037] S700: Perform Linglong package management according to the optimization result.

[0038] Specifically, the timing coordination call result refers to the call instruction execution plan configured according to the dependency and execution order of the call instructions, including the execution order and parallel execution strategy. The system resource status monitoring result should include the historical data of resource usage, the current status, and any abnormal situation alerts, such as CPU utilization, memory usage, disk I / O, etc.

[0039] In the dependency graph, the whole is divided into several nodes according to a specific algorithm to facilitate local optimization and management. Each node represents a subtask or module. According to the results of the timing collaborative call, the tasks are divided into different nodes, and a prediction window is created to divide the time interval of the task. When executing the nodes of each prediction window, the timing change trend is predicted in combination with the resource status monitoring results. The conflict risk factor of each window is calculated in combination with the prediction data (such as the possibility of memory overload, CPU contention risk, etc.), and the risk is judged according to the conflict risk factor to determine whether optimization is needed. If the conflict risk factor is too high, an optimization instruction is generated to call optimization.

[0040] The execution plan is adjusted through the optimization algorithm to find the optimal call control strategy to ensure the highest efficiency and the least conflict in task completion. Each execution task is evaluated for execution stability, speed, and system resource adaptation, and the fitness is calculated based on this. The execution task with the highest fitness is taken as the final optimization result. The optimization result is the best call execution plan adjusted by the optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.), including the specific task execution order, timing arrangement, and resource allocation strategy, to ensure that the resource conflicts between tasks are minimized and the execution efficiency is maximized.

[0041] Extract the specific content of the execution plan from the optimization results, such as the execution order of tasks, timing arrangement (such as start time and end time), parallel execution task groups, resource allocation strategies, etc. For example, use the plan parsing module (which can be implemented based on Python) to read and decode the optimization result file and generate the corresponding task execution instructions. Execute each task (that is, the function of each Linglong package) according to the parsed optimization plan. For each task, execute the installation or update command, and monitor the resource status synchronously to ensure that the execution process does not cause conflicts or resource overloads. Strictly execute the timing and resource plan according to the optimization results to ensure that the tasks are completed under ideal conditions. Through real-time monitoring and feedback mechanisms, discover and solve problems in resource allocation and reduce the waste of system resources.

[0042] Further, the present application S400 includes: The target package is determined by using the Linglong package calling instruction, and the dependency graph is traversed through depth-first search to generate a complete dependency chain of the target package; the dependency depth of each package in the complete dependency chain is calculated, and the hierarchy is divided according to the dependency depth calculation result to generate a timing calling plan; parallel call verification of the timing calling plan is executed, and the timing collaborative call result is configured according to the parallel call verification result and the target package.

[0043] Specifically, according to the Linglong package call instruction, that is, the operation (such as installation, update, uninstallation) that the system needs to perform on the target Linglong package, the target Linglong package, that is, the target package, is determined. Through the dependency graph, the depth-first search algorithm is used to start from the target package and traverse all the dependent packages related to it to generate a complete dependency chain. Depth-first search is a graph traversal algorithm that preferentially goes as far as possible along a certain path of the graph until it cannot continue, and then backtracks to the previous node to continue exploring unvisited branches. This algorithm is suitable for finding paths or chain structures, such as dependency chains. The specific process is as follows: Initialize a set to store visited nodes to avoid repeated visits. Define a recursive function, initialize the starting node, and mark it as visited; traverse the direct dependent packages (child nodes) of the current node; recursively call the recursive function to process the child nodes; add the current node to the complete dependency chain. Call the recursive function to generate a dependency chain starting from the target package.

[0044] For example, a dependency graph is A→B→D; A→C→E; the target package is A, and the recursive function is called from A, traversing A's child nodes B and C, calling the recursive function on B, finding that B depends on D, and recursively processing D; calling the recursive function on C, finding that C depends on E, and recursively processing E. Return from the deepest D and E to B and C, and finally return to the target package A, and the final generated complete dependency chain is A→B→D→C→E.

[0045] Calculate the depth of the complete dependency chain, stratify all packages according to the depth, and generate a timing call plan. Dependency depth refers to the level of a package in the dependency chain, indicating the degree of indirect dependency of the package compared to the target package. The depth of the target package itself is 0, the depth of the directly dependent package is 1, and the depth of the dependent package is 2. According to the dependency depth of each package, the packages are assigned to different levels. Packages at different levels will be executed in sequence to ensure that the dependent package is executed before the package that depends on it. Define an initial depth for each package, and the target package depth is 0; traverse the dependency graph, for each dependent package, the dependency depth is equal to the target package depth plus 1; recursively calculate the depth of each dependent package.

[0046] After the dependency depth is calculated, each package is grouped according to its depth value to form different levels. Linglong packages will be called in order of the levels, and packages with the same depth value can be operated in parallel. Based on the hierarchical division, a timing call plan is generated according to the order of the levels. Packages within each level can be executed in parallel, while packages between different levels need to be executed in depth order to ensure the correct dependencies. Starting from the deepest level, calls are executed layer by layer; packages within each level can be executed in parallel without depending on the execution order of other packages in the level. The timing call plan refers to the generation of a call sequence plan based on dependencies and depth after calculating the dependency depth of each package and dividing the levels, ensuring that packages are installed or uninstalled in order to avoid conflicts and dependency errors.

[0047] According to the hierarchy of the sequential call plan, starting from the deepest layer, the call operation is executed layer by layer. Parallel operations are used for packages at the same layer, and their calls are verified to be successful. Parallel call verification refers to the simultaneous execution of multiple call instructions to verify their correctness and efficiency. The sequential collaborative call results refer to the coordination and integration according to the execution order and results of the call instructions. The complete dependency chain is generated using depth-first search to ensure that the installation, update, and uninstall operations of the package can correctly handle complex dependencies. Through dependency depth calculation and hierarchical division, the calling sequence of the package is ensured to be logical to avoid call failures caused by sequence errors. Packages at the same level can be operated in parallel, which significantly shortens the execution time and improves efficiency. Verification is performed after each call step to ensure the correctness of each package operation and reduce human intervention and errors.

[0048] Further, as attached Figure 2 As shown, the present application S600 includes: Perform node segmentation of the timing collaborative call result based on the segmentation model to establish a node segmentation result; use the node segmentation result to create a prediction window, use the prediction window to predict the timing state change based on the resource state monitoring result, and establish a timing state change prediction result; calculate the execution conflict risk factor according to the timing state change prediction result, perform optimal fitting based on the execution conflict risk factor, and establish the optimization result.

[0049] Specifically, the results of timing collaborative calls usually contain a series of package operation tasks with complex dependencies, which are coordinated and executed in chronological order. In order to manage and optimize these tasks more effectively, it is necessary to use a segmentation model to divide the tasks and convert them into nodes that can be processed independently. First, it is necessary to define how to divide the results of timing collaborative calls. The segmentation rules are usually determined based on the dependencies and resource requirements between packages. According to the defined rules, the segmentation model divides the operation tasks in the timing collaborative call results into multiple time nodes according to the time sequence and resource requirements. Each node will contain a set of related operation tasks. During the segmentation process, it is also necessary to ensure that the operations of each node do not occupy excessive system resources. For example, when dividing nodes, if multiple operations in a node need to use a large amount of memory or CPU at the same time, these operations need to be divided into different nodes to avoid resource conflicts.

[0050] The segmentation model is an algorithm or method used to divide complex tasks or data into multiple smaller parts (nodes) according to certain rules. It is used to segment the results of the timing coordination call into multiple time nodes. Each node represents a specific package operation task, and reasonably distributes large-scale operation tasks to multiple time periods to avoid overloading system resources. The node segmentation result is a set of operation nodes obtained after being processed by the segmentation model. These nodes are divided into different time periods according to the timing and resource requirements. The operations within each node are coordinated and do not conflict with each other. After the timing coordination call results are segmented, the system will generate a set of nodes, each of which contains multiple operation tasks. Each node needs to be analyzed to ensure that the tasks within the node do not conflict with each other during execution.

[0051] Node segmentation results refer to the segmentation results, which represent each segmented timing operation node. These nodes are executed independently of other nodes, and the operation of each node is usually associated with a specific time period or resource usage. The specific information of each node includes the required resources (CPU, memory, disk, etc.), execution time, involved packages, etc.

[0052] Based on the node segmentation results, a period of time is selected as the prediction window, focusing on the operation and resource status changes within a specific period of time, helping the system predict the change trend of system resources in the future period of time. Use historical system resource monitoring data, combined with the current operation plan, to build a prediction window and analyze the resource usage pattern. The data in the window will help calculate the change trend of resources and predict the future resource status by fitting historical data.

[0053] Based on the created forecast window, you can predict resource status changes in the future. Based on the forecast window and historical resource status monitoring data, use the time series analysis model to predict the change trend of resource status in the future time period. According to the characteristics of the resource status, select a suitable time series forecast model, such as the long short-term memory network or the ARIMA model. Input the historical resource status data in the forecast window into the forecast model to calculate the resource usage in the future time period. Generate the resource status change trend in the forecast window, including the usage of resources such as CPU and memory at each time point.

[0054] According to the prediction results of the time series state change, evaluate the risks of resource competition, task dependency conflict, etc., and determine the execution conflict risk factor of each node. The prediction results of the end time series state change of each prediction window are used as the initial system state of the next prediction window. Based on the initial system state of the current prediction window and the conflict risk factor of the end time series state change result, superposition calculation is performed, and the superposition calculation result is used as the conflict risk factor of the current prediction window. The above steps are performed for all prediction windows, and the conflict risk factor of each prediction window is risk-judged to determine whether optimization is required. The conflict risk factor represents the value of the resource conflict risk, which is used to evaluate the degree of system resource bottleneck that may be caused during task execution.

[0055] If optimization is required, local optimization instructions are generated to reduce the conflict risk factor, and optimize resource allocation and execution efficiency as the objective function for optimization. Optimize resource allocation for conflicting nodes (such as reducing CPU resource allocation); reorder the execution order of nodes to avoid resource competition; use optimization algorithms (such as genetic algorithms or particle swarm algorithms) to perform overall fitting of the states of all windows to find the global optimal solution. Determine the optimal execution plan based on the optimized fitness function value.

[0056] Optimal fitting refers to optimizing and adjusting according to the evaluation results of the execution conflict risk factors, and reducing the conflict risk and ensuring the efficient use of resources by adjusting the execution timing, resource allocation and other parameters. By combining prediction and optimization, resource bottlenecks can be avoided and system stability can be improved. Through optimized task scheduling, execution time can be reduced, task completion efficiency can be improved, task resource allocation can be dynamically adjusted, and system resources can be maximized to reduce idleness or overload.

[0057] Furthermore, the present application also includes the following steps: The terminal time series state change prediction result of each prediction window is used as the initial system state of the next prediction window, and the conflict risk factor of the current prediction window is superimposed and calculated based on the conflict risk factor of the initial system state and the terminal time series state change result, and the superimposed calculation result is used as the conflict risk factor of the current prediction window; after traversing all the prediction windows, all the calculated conflict risk factors are used to perform risk identification factor by factor, and the risk identification result is used to perform call optimization of the time series collaborative call result to establish the optimization result.

[0058] Specifically, for each prediction window, extract the prediction results of the terminal time series state change, that is, the resource usage status at the end of the window, including CPU occupancy, memory usage, etc. The resource status at the end of the prediction window is used as the initial state of the next window to ensure temporal coherence and consistency, and to ensure that the changes in resource status are continuous during the calculation of multiple consecutive prediction windows. The conflict risk factor is an indicator used to measure the probability or impact of system resource overload, conflict, or impact within a certain time window, usually considering factors such as the overlap of multiple resource requirements and concurrent execution of tasks.

[0059] Based on the conflict risk factors of the initial system state (i.e., the end state of the previous window) and the end time series state change results, the conflict risk factor of the current window is superimposed and calculated. In other words, the conflict risk factor of the current window is combined with the conflict risk factor of the end of the previous window for cumulative analysis. Through weighted superposition, if the resource conflicts of some windows have a greater impact on the system, these windows can be given higher weights. Superposition calculation refers to combining the conflict risk factor of each forecast window with the results of the previous window for cumulative analysis to form a risk assessment within a continuous time period. Repeat the above steps for each forecast window to calculate the conflict risk factor of each window.

[0060] Perform risk identification on each conflict risk factor, and determine whether the resources may be overloaded or conflict based on the conflict risk factor. Set a factor threshold in advance based on the system's resource capacity, usage scenarios, and expected load conditions. Compare all conflict risk factors obtained from all prediction windows with the factor threshold to obtain the risk identification result. If the risk identification result is lower than the factor threshold, it is considered that the system resource allocation is within a safe range and no adjustment is required. The original timing coordination call result is retained, that is, execution continues according to the current resource scheduling. Conversely, if the conflict risk factor of a window is greater than or equal to the threshold, the optimization requirement is triggered. Based on the local optimization instructions, analyze which tasks need to be adjusted and which resources need to be reallocated to reduce resource conflicts.

[0061] The optimization is called through the local optimization instruction execution, and the fitness of each solution obtained by the optimization is evaluated, and finally the one with the highest fitness is selected as the optimization result. The optimized result is the optimization result, including the adjusted task execution plan and resource allocation plan, which can effectively reduce the probability of resource conflicts and improve the system resource utilization and stability. Through the superposition calculation and risk identification of the conflict risk factors of each prediction window, an optimization plan for the timing coordination call result is finally generated, which helps the system identify and resolve potential resource conflicts in advance, avoid system overload, and thus optimize resource allocation and improve overall system performance and reliability.

[0062] Furthermore, the present application also includes the following steps: Configure factor thresholds, use the factor thresholds to trigger and identify all conflicting risk factors, and establish the risk identification result; if the risk identification result is lower than the factor threshold, retain the timing coordination call result of the corresponding position; if the risk identification result is higher than or equal to the factor threshold, generate a local optimization instruction, execute the call optimization according to the local optimization instruction, and generate the optimization result.

[0063] Specifically, factor thresholds are configured based on the system's resource capacity, usage scenarios, and expected load conditions. The factor threshold is a preset standard value used to assess the severity of system resource conflicts. When the conflict risk factor is higher than or equal to the threshold, it indicates that the system is in a potential overload or risk state, and measures need to be taken to optimize or adjust it. The conflict risk factor of each forecast window is compared with the pre-set factor threshold to determine the risk status of each forecast window. Trigger identification is the process of evaluating the conflict risk factor. By comparing it with the factor threshold, it identifies which time windows' risk factors require measures (such as adjusting the system configuration, performing optimization, etc.). The risk identification result refers to the result generated by comparing the conflict risk factor with the factor threshold, including being lower than the factor threshold or higher than or equal to the factor threshold.

[0064] If the conflict risk factor is lower than the factor threshold, that is, the risk judgment result is lower than the factor threshold, it is considered that the system resource allocation is within a safe range and no adjustment is required. At this time, the original timing coordination call result is retained, that is, execution continues according to the current resource scheduling.

[0065] If the conflict risk factor of a window is greater than or equal to the threshold, that is, the risk judgment result is higher than or equal to the factor threshold, the optimization demand is triggered. At this time, the system will generate a local optimization instruction, execute the optimization process, adjust task scheduling or resource allocation, and optimize system operation. A local optimization instruction is an optimization instruction generated according to specific conditions, which performs local optimization for a specific time window or task. Local optimization usually aims to relieve system pressure and adjust task scheduling to avoid resource conflicts.

[0066] Construct optimization goals, such as ensuring the execution efficiency of tasks while reducing conflict risk factors. Multi-objective optimization can be used, with risk factors, task execution resource costs, task completion stability, maximum allowable system resource consumption, and minimum task stability requirements as objective functions. Determine the list of tasks that need to be optimized. Reallocate resources in the task execution environment to reduce the priority of tasks with high conflict risks or reduce their resource usage. Adjust the order of task execution to avoid high-risk tasks being executed at the same time as other resource-intensive tasks. Optimize the specific operations of task execution, such as changing algorithms, optimizing execution paths, etc., and finally obtain multiple task execution plans. Use the fitness function to evaluate each task execution plan and select the best plan.

[0067] According to the local optimization instructions, analyze which tasks need to be adjusted and which resources need to be reallocated to reduce resource conflicts. Call optimization refers to the optimization process of adjusting the task execution order, resource allocation, etc. according to the results of the fitness function to improve the efficiency, stability and resource utilization of task execution. By comparing with the factor threshold, high-risk and low-risk windows can be accurately identified and distinguished to ensure the pertinence of optimization measures, timely adjust task scheduling and resource allocation, avoid system crashes or performance bottlenecks under high load, and enhance system stability and reliability.

[0068] Furthermore, the present application also includes the following steps: Configure the execution stability feature and use the execution stability feature as the first evaluation feature; configure the speed evaluation feature and use the speed evaluation feature as the second evaluation feature; configure the system resource adaptation evaluation feature and use the system resource adaptation evaluation feature as the third evaluation feature; construct a fitness function according to the first evaluation feature, the second evaluation feature and the third evaluation feature, and use the fitness function to complete the call optimization.

[0069] Specifically, the execution stability feature is configured, that is, the reliability and stability features of the system when executing tasks, which usually include task failure rate, execution error frequency, task running time fluctuation range, etc., reflecting the robustness of system operation. The execution stability feature needs to extract stability indicators from the task execution log, such as task failure rate, error occurrence rate, stability of task execution time, etc. The execution stability feature is used as the first evaluation feature.

[0070] Configure the speed evaluation feature, i.e. the speed or efficiency feature of task execution, including the response time, completion time or average execution time of the task, which is used to measure the efficiency of the system in completing tasks. Select and set indicators that reflect the execution speed, such as execution time, response time, etc., and use the speed evaluation feature as the second evaluation feature.

[0071] Configuration system resource adaptation evaluation features, that is, the rationality and efficiency of resource allocation during task execution, including the utilization and adaptation of CPU, memory, I / O and other resources, reflecting whether the system effectively utilizes available resources. By monitoring the utilization of CPU, memory, and network bandwidth, combined with resource configuration, the adaptability of resource utilization is calculated, and the system resource adaptation evaluation feature is used as the third evaluation feature.

[0072] The fitness function is a function model that integrates the above three features and is used to evaluate the overall performance. The fitness function can be in the form of a weighted sum model, a linear regression model, or a machine learning model. For example, according to the importance of the execution stability feature, the speed evaluation feature, and the system resource adaptation evaluation feature, weights are assigned to determine the weight of each evaluation feature. According to the relationship between the weight of each evaluation feature and the corresponding evaluation feature, a fitness function is constructed. The fitness function can be called and optimized using a genetic algorithm, a particle swarm optimization algorithm, or a greedy algorithm to select the task execution plan with the highest fitness.

[0073] Each execution task is evaluated for execution stability, speed, and system resource adaptation, and the corresponding evaluation characteristic values ​​are obtained. The fitness value of each task is calculated according to the fitness function, and the fitness values ​​are sorted from high to low. According to the calculation results of the fitness function, the task execution order or resource allocation scheme with a higher fitness value is selected to optimize the overall system performance. Through the comprehensive evaluation of execution stability, speed, and resource adaptability, the system can achieve multi-objective optimization and improve task execution efficiency.

[0074] Furthermore, the present application also includes the following steps: The optimization result is used to control the execution of Linglong package and generate execution monitoring; execution verification is performed according to the execution monitoring and the optimization result, and execution exceptions are established; and the execution exceptions are reported as exceptions.

[0075] Specifically, according to the task sequence and resource allocation plan in the optimization result, the corresponding Linglong package is executed step by step. The execution status of the task is recorded in real time, including the task start time, end time, resource utilization (such as CPU, memory, disk I / O, etc.) and any possible abnormal events (such as timeouts). The execution monitoring data is compared with the optimization result plan one by one to calculate the execution deviation (such as time delay, resource overrun, etc.). For example, assuming that the execution time of task A in the plan is 2 seconds, but the actual time is 3 seconds, it is judged as a timeout. Among the deviations found by comparison and verification, the part that exceeds the predefined threshold is judged as abnormal, and the abnormal record is sent to the management system or user end. For example, if the CPU utilization exceeds the plan by more than 10% or the task timeout exceeds 2 seconds, an alarm is triggered.

[0076] Execution anomalies are notified to relevant personnel through the alarm system via email, SMS, sound or color, so that they can be handled in a timely manner. At the same time, an abnormality report is generated and stored for problem diagnosis and resolution. The optimization results are used as the basis for execution to ensure that tasks are executed according to the planned sequence and resource allocation, reduce errors caused by human intervention, and through the monitoring module, the task execution status and resource usage are grasped in real time, and problems in the execution process are identified and located in a timely manner, shortening the problem response time.

[0077] In summary, the graphical Linglong package management method provided by this application has the following technical effects: Through deep analysis of the Linglong package, deep features are extracted according to the deep analysis results; the deep features are used to perform dependency analysis between Linglong packages and establish a dependency graph; the graphical interactive interface of the Linglong package is configured, and the interaction information of the graphical interactive interface is obtained, and the interaction information is the user's operation information based on the graphical interactive interface; the Linglong package call instruction is generated according to the interaction information, and a collaborative operation analysis based on the dependency graph is performed based on the Linglong package call instruction, and a timing collaborative call result is configured; the system resource monitoring instruction is synchronously generated according to the Linglong package call instruction, and the system resource status is monitored in real time according to the system resource monitoring instruction, and the system resource status monitoring result is established; the Linglong package call control optimization is performed using the timing collaborative call result and the system resource status monitoring result, and the optimization result is generated; the Linglong package is managed according to the optimization result. In other words, deep features are extracted through deep analysis of Linglong packages, a dependency graph is constructed, and the interdependencies between packages are clarified; a graphical interactive interface is configured and interaction information is obtained, and collaborative operation analysis is performed based on the dependency graph and user interaction information. At the same time, system resource usage is monitored, and call control optimization is performed based on the timing collaborative call results and system resource status monitoring results, thereby improving the efficiency of graphical Linglong package management.

[0078] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A graphical Linglong package management method, characterized in that: include: Perform deep analysis on Linglong bag and extract deep features based on the deep analysis results; Using the deep features to perform dependency analysis on Linglong Baolou, and establish a dependency graph; Configure the graphical interaction interface of Linglongbao, and obtain interaction information of the graphical interaction interface, wherein the interaction information is operation information of the user based on the graphical interaction interface; Generate a Linglong package call instruction according to the interaction information, and perform collaborative operation analysis based on the dependency graph based on the Linglong package call instruction to configure a timing collaborative call result; Synchronously generate a system resource monitoring instruction according to the Linglong package call instruction, perform real-time monitoring of the system resource status according to the system resource monitoring instruction, and establish a system resource status monitoring result; Utilizing the timing coordination call result and the system resource status monitoring result to optimize the call control of Linglong package and generate an optimization result; Linglong package management is performed according to the optimization results.

2. A graphical Linglong package management method as claimed in claim 1, characterized in that: The deep analysis of the Linglong bag and the extraction of deep features according to the deep analysis result include: Obtaining a call record of Linglong Package, performing Linglong Package association analysis according to the call record, and establishing a first association; Performing data analysis on each Linglong bag, performing similarity calculation using the data analysis result, and establishing a second association, wherein the similarity calculation includes feature similarity calculation, function similarity calculation, and behavior similarity calculation; The dependency depth and dependency breadth of Linglong Baojian are analyzed based on the first association and the second association to extract deep features.

3. A graphical Linglong package management method as claimed in claim 1, characterized in that: The step of performing collaborative operation analysis based on the dependency graph based on the Linglong package call instruction and configuring the timing collaborative call result includes: Determine the target package by using the Linglong package call instruction, and traverse the dependency graph by depth-first search to generate a complete dependency chain of the target package; Calculate the dependency depth of each package in the complete dependency chain, divide the layers according to the dependency depth calculation result, and generate a timing call plan; Execute parallel call verification of the timing call plan, and configure the timing coordination call result according to the parallel call verification result and the target package.

4. A graphical Linglong package management method as claimed in claim 1, characterized in that: The step of optimizing the call control of Linglong package by using the timing coordination call result and the system resource status monitoring result to generate the optimization result includes: Perform node segmentation of the time series collaborative call result based on the segmentation model, and establish a node segmentation result; Using the node segmentation result to create a prediction window, using the prediction window to predict the time series state change based on the resource state monitoring result, and establishing a time series state change prediction result; An execution conflict risk factor is calculated according to the prediction result of the time series state change, and an optimization fitting is performed based on the execution conflict risk factor to establish the optimization result.

5. A graphical Linglong package management method as claimed in claim 4, characterized in that: The step of calculating the execution conflict risk factor according to the prediction result of the time series state change, performing optimal fitting based on the execution conflict risk factor, and establishing the optimal fitting result includes: The prediction result of the terminal time series state change of each prediction window is used as the initial system state of the next prediction window, and the conflict risk factor of the current prediction window is superimposed and calculated based on the conflict risk factors of the initial system state and the terminal time series state change result, and the superimposed calculation result is used as the conflict risk factor of the current prediction window; After traversing all the prediction windows, all the calculated conflicting risk factors are used to perform risk identification factor by factor, and the risk identification results are used to perform call optimization of the time series collaborative call results to establish the optimization results.

6. A graphical Linglong package management method as claimed in claim 5, characterized in that: The step of using all calculated conflict risk factors to perform risk identification factor by factor, using the risk identification results to perform call optimization of the time series collaborative call results, and establishing the optimization results includes: Configure factor thresholds, perform trigger identification of all conflict risk factors through the factor thresholds, and establish the risk identification result; If the risk identification result is a result lower than the factor threshold, retaining the time series collaborative call result of the corresponding position; If the risk identification result is higher than or equal to the factor threshold, a local optimization instruction is generated, and the optimization call is executed according to the local optimization instruction to generate the optimization result.

7. A graphical Linglong package management method as claimed in claim 6, characterized in that: The step of executing the call optimization according to the local optimization instruction and generating the optimization result includes: configuring an execution stability feature, and using the execution stability feature as a first evaluation feature; configuring a speed evaluation feature, and using the speed evaluation feature as a second evaluation feature; Configuring a system resource adaptation evaluation feature, and using the system resource adaptation evaluation feature as a third evaluation feature; A fitness function is constructed according to the first evaluation feature, the second evaluation feature, and the third evaluation feature, and the call optimization is completed using the fitness function.

8. A graphical Linglong package management method as claimed in claim 1, characterized in that: The graphical Linglong package management method also includes: Using the optimization result to control the execution of Linglong package and generate execution monitoring; Perform execution verification according to the execution monitoring and the optimization result, and establish execution exceptions; The execution exception is reported as an exception.

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