Enterprise supervision method based on cloud management platform and related equipment

Abstract

The invention discloses an enterprise supervision method based on a cloud management platform and related equipment, and relates to the field of computers. The method comprises the steps that when an employee user logs in a cloud computer account, a work task plan of current login is acquired from the employee user, and the work task plan comprises at least one task type; based on the work task plan, evaluating cloud computer resource configuration data required by the employee user for logging in this time; and selecting a matched core time mode based on the cloud computer resource configuration data, wherein different core time modes have different rates. The problem that a charging mode of a cloud computer is generally based on calculation configuration and use time and is huge in expenditure for enterprises with large demands can be solved.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to an enterprise supervision method based on a cloud management platform and related equipment. Background Art

[0002] Cloud computing is the application of cloud computing technology to personal computing devices. Primarily based on cloud computing, virtualization, and remote desktop protocols, it provides computing resources to users via cloud servers, enabling them to enjoy high-performance computing services without the need for high-performance local devices. Cloud computing is typically billed based on computing configuration and usage time, which can be a significant expense for businesses with high-demand services. Summary of the Invention

[0003] In view of the above problems, the present invention provides an enterprise supervision method and related equipment based on a cloud management platform, the main purpose of which is to solve the problem that the billing model of cloud computers is usually based on computing configuration and usage time, which is a huge expense for enterprises with large demands.

[0004] To solve at least one of the above technical problems, in a first aspect, the present invention provides an enterprise supervision method based on a cloud management platform, the method comprising:

[0005] When an employee user logs into a cloud computer account, obtaining a work task plan for this login from the employee user, wherein the work task plan includes at least one task type;

[0006] Evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan;

[0007] A matching core time mode is selected based on the cloud computer resource configuration data, and different core time modes have different rates.

[0008] Optionally, also include:

[0009] When the current computing resources used by the cloud computer associated with the target employee user are close to the maximum computing capacity of the cloud computer in the current core time mode, obtaining all current process data of the cloud computer;

[0010] Determining theoretical process data associated with the work task plan based on the work task plan logged in this time;

[0011] A first program closing suggestion is generated according to the theoretical process data and all the process data, wherein the first program closing suggestion is used to instruct closing an application corresponding to process data in all the process data that is unrelated to the theoretical process data.

[0012] Optionally, also include:

[0013] When the current computing resources used by the cloud computer associated with the target employee user are close to the maximum computing capacity of the cloud computer in the current core time mode, obtaining all current process data of the cloud computer;

[0014] Analyzing all the process data based on the work task plan logged in this time to divide all the process data into main task process data, task auxiliary process data, and task-irrelevant process data;

[0015] A second program closing suggestion is generated when the main application corresponding to the main task process data includes a target sub-function, and the target sub-function is the same sub-function as the auxiliary function associated with the task auxiliary process data. The second program closing suggestion is used to instruct the closing of the application corresponding to the task-irrelevant process data, as well as the task auxiliary process data in the task auxiliary process data that has the same function as the sub-function of the main application.

[0016] Optionally, the second program closing suggestion also includes function entry prompt information of the sub-function of the main application and / or a usage tutorial of the sub-function.

[0017] Optionally, also include:

[0018] When the login location of the employee user is different from the regular login location, monitoring the combination key input operation of the employee user;

[0019] If the key combination input operation is an invalid shortcut key operation in the current cloud computer system, predicting the function execution requirement of the key combination input operation of the employee user based on all current process data of the cloud computer and the work task plan;

[0020] A shortcut key mapping is established between the combination key input operation and the function execution requirement.

[0021] Optionally, establishing a shortcut key mapping between the combination key input operation and the function execution requirement includes:

[0022] When a confirmation message is received from the employee user in response to the predicted function execution requirement, a shortcut key mapping between the combination key input operation and the function execution requirement is established.

[0023] Optionally, also include:

[0024] Obtaining the employee user's current temporary input device information, wherein the input device information includes key layout information;

[0025] The key of the established shortcut key mapping is used as the reference key, and the position of the key of the established shortcut key mapping is used as the reference key position, and all shortcut key mappings are established in the temporary input device so that the shortcut key mapping in the temporary input device conforms to the blind typing operation habits of the employee user.

[0026] In a second aspect, an embodiment of the present invention further provides an enterprise supervision device based on a cloud management platform, the device comprising:

[0027] an acquiring unit, configured to acquire, from an employee user, a work task plan for this login when the employee user logs into a cloud computer account, the work task plan including at least one task type;

[0028] An evaluation unit, configured to evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan;

[0029] The processing unit is used to select a matching core time mode based on the cloud computer resource configuration data, and different core time modes have different rates.

[0030] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a computer-readable storage medium is provided, and the above-mentioned computer-readable storage medium includes a stored program, wherein when the above-mentioned program is executed by the processor, the above-mentioned enterprise supervision method based on the cloud management platform is implemented.

[0031] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the above-mentioned processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the above-mentioned enterprise supervision method based on the cloud management platform.

[0032] By means of the above technical solution, the enterprise supervision method and related equipment based on the cloud management platform provided by the present invention obtain the work task plan for this login from the employee user when the employee user logs into the cloud computer account, and the work task plan includes at least one task type; based on the work task plan, the cloud computer resource configuration data required for the employee user's login is evaluated; based on the cloud computer resource configuration data, a matching core time mode is selected, and different core time modes have different rates. In this way, over-configuration or insufficient resources caused by the traditional fixed allocation method based on functions is avoided, and the utilization rate of computing resources is improved. Through intelligent billing model matching, the ineffective use of high computing resources is reduced, making enterprise cloud computing expenditures more reasonable. The burden of manual configuration by users is reduced, the intelligence level of cloud computers is improved, and resource scheduling is made more efficient.

[0033] Correspondingly, the enterprise supervision device, electronic device and computer-readable storage medium based on the cloud management platform provided by the embodiments of the present invention also have the above-mentioned technical effects.

[0034] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0036] Figure 1 A schematic diagram of a process of an enterprise supervision method based on a cloud management platform provided by an embodiment of the present invention is shown;

[0037] Figure 1a A schematic diagram of an application scenario of an enterprise supervision method based on a cloud management platform provided by an embodiment of the present invention is shown;

[0038] Figure 2 A schematic block diagram of the composition of an enterprise monitoring device based on a cloud management platform provided by an embodiment of the present invention is shown;

[0039] Figure 3 A schematic block diagram of the composition of an enterprise supervision electronic device based on a cloud management platform provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0041] In order to solve the problem that the billing model of cloud computers is usually based on computing configuration and usage time, which is a huge expense for enterprises with large demands, the embodiment of the present invention provides an enterprise supervision method based on the cloud management platform, such as Figure 1 As shown, the method includes:

[0042] S110. When an employee user logs into a cloud computer account, obtain a work task plan for this login from the employee user, where the work task plan includes at least one task type.

[0043] S110. Evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan.

[0044] S130. Select a matching core time mode based on the cloud computer resource configuration data, where different core time modes have different rates.

[0045] Understandably, in traditional cloud PC allocation models, enterprises typically allocate computing resources based on user roles or fixed configurations. However, this approach carries the risk of redundant computing resources (over-configuration leading to resource waste) or insufficient computing resources (under-configuration leading to slow or failed task execution). This method uses the task plan submitted by the user when logging in to infer the maximum computing resources the user may use during this login, and allocates cloud PC resources accordingly, rather than simply allocating resources based on the user's fixed role. When logging in, the user provides a task plan or extracts task schedules from the enterprise system: the system infers the software, application tools, and computing requirements that the user may use based on the task description, rather than relying directly on the user to manually specify specific computing resources (such as CPU, memory, GPU, etc.). Matching required computing resources based on task analysis: The system predicts the maximum computing power required for this login based on the task content, combined with historical resource usage data and typical workload analysis, and pre-allocates the corresponding cloud PC configuration to ensure sufficient computing power to support the user's entire work cycle. Select the optimal core time mode to optimize costs: Since different tasks have different computing requirements and usage durations, the system will match the most economical core time mode (such as on-demand billing, package mode, reserved instance mode, etc.) based on the task load after computing resources are allocated to optimize enterprise cloud computing costs.

[0046] For example, when a user logs into a cloud computer, the system will ask the user to fill out a work task plan for this login in order to predict their computing resource requirements. Since user task plans typically don't explicitly include technical details (for example, they won't directly submit "8-core CPU required"), but instead provide higher-level task descriptions (such as "demand matching" or "function development"), the system needs to use intelligent task analysis to infer the application tools and computing resource requirements that the user may use. Users can submit task plans in several ways: manually enter a task overview. Users can enter tasks such as "demand matching," "testing the system," or "writing a functional module." The system then infers resource requirements based on task keywords; extract tasks from the enterprise office system. If the enterprise integrates a project management system (such as Outlook calendar), the system can automatically obtain the user's task schedule, such as "10:00 AM requirements review meeting" or "2:00 PM automated testing," and analyze computing requirements based on the task type; and intelligently recommend tasks based on historical data. If a user doesn't fill out a task, the system can make a prediction based on the user's task data from similar time periods. For example, if a developer's tasks on the past few Tuesdays involved code writing and testing, the system can infer that the current login is likely to perform similar tasks and pre-match computing resources. By inferring computing requirements from user task plans, the system avoids the resource waste caused by fixed configurations. For example, traditional methods might default to all developers being assigned an 8-core CPU. However, if a developer only needs to write code that day, the system can identify their actual needs and only requires a 2-core CPU, thus reducing the inefficient allocation of 6 cores. Intelligent recommendations based on historical data reduce the burden of manual user input, improve system automation, and mitigate resource mismatches caused by user unfamiliarity with the system.

[0047] For example, after a task plan is submitted, the system assesses the maximum computing resources that may be used for this login and allocates an appropriate cloud PC configuration to ensure sufficient computing power throughout the user's entire work cycle. The system matches relevant software tools based on the task description and estimates the required computing resources accordingly. For example, "demand matching" may involve video conferencing software, document editing tools, etc., with lower computing requirements, while "running automated tests" may involve testing frameworks, database access, etc., with higher computing requirements. The system analyzes the computing resource consumption of the user when performing the same task in the past. For example, if a user's CPU utilization consistently exceeds 75% when performing the "data analysis" task several times, the system can directly match them with a higher-performance cloud PC during the current login to ensure smooth task execution. To prevent insufficient computing resources from impacting work, the system matches the highest computing requirement in the user's task plan. For example, if a user's task includes "writing code + running automated tests," the system will directly match a configuration that meets the highest computing requirement for running automated tests, rather than initially allocating a lower computing power configuration and then dynamically adjusting. This maximum computing power requirement assessment ensures that the user has sufficient computing resources throughout the entire work cycle, preventing task failures or interruptions due to insufficient computing power. By matching computing resources based on task types and historical data analysis, we can reduce the problem of excessive or insufficient resources caused by the traditional function-based allocation method, thereby improving the efficiency of enterprise computing resource utilization.

[0048] For example, after computing resource evaluation is complete, the system will match the optimal core-time mode to optimize enterprise computing costs, reduce inefficient computing resource usage, and improve cloud server utilization. The system can release unused high-performance computing instances after tasks are completed, preventing cloud computers from being idle for long periods of time and optimizing enterprise computing resource scheduling.

[0049] By means of the above technical solution, the enterprise supervision method based on the cloud management platform provided by the present invention obtains the work task plan for this login from the employee user when the employee user logs into the cloud computer account, and the work task plan includes at least one task type; based on the work task plan, the cloud computer resource configuration data required for the employee user's login is evaluated; based on the cloud computer resource configuration data, a matching core time mode is selected, and different core time modes have different rates. In this way, over-configuration or insufficient resources caused by the traditional fixed allocation method based on functions is avoided, and the utilization rate of computing resources is improved. Through intelligent billing model matching, the ineffective use of high computing resources is reduced, making enterprise cloud computing expenditures more reasonable. The burden of manual configuration by users is reduced, the intelligence level of cloud computers is improved, and resource scheduling is made more efficient.

[0050] In one embodiment, it further includes:

[0051] When the current computing resources used by the cloud computer associated with the target employee user are close to the maximum computing capacity of the cloud computer in the current core time mode, obtaining all current process data of the cloud computer;

[0052] Determining theoretical process data associated with the work task plan based on the work task plan logged in this time;

[0053] A first program closing suggestion is generated according to the theoretical process data and all the process data, wherein the first program closing suggestion is used to instruct closing an application corresponding to process data in all the process data that is unrelated to the theoretical process data.

[0054] It is understandable that users may run application processes on cloud computers that are not related to the current task, causing resource consumption to exceed the planned range, thereby affecting the execution efficiency of key tasks and increasing the enterprise's computing costs. To solve this problem, this method automatically obtains data on all running processes when the computing resources of the cloud computer are close to the maximum computing power of the core time mode, and analyzes the processes that should theoretically run in the task plan based on the user's current task plan. Then, it compares the actual processes, filters out unnecessary processes, and generates program shutdown suggestions, instructing users to close irrelevant processes to free up computing resources and improve the overall task execution efficiency. The core of this method is to combine real-time computing resource monitoring, task plan analysis and process optimization strategies to ensure that computing resources are used efficiently and reasonably, avoid resource occupation caused by irrelevant tasks, and reduce unnecessary expenses of enterprises on cloud computing.

[0055] For example, while a user is logged in and using a cloud computer, the system continuously monitors CPU, memory, and GPU usage. If computing resources are nearing the maximum computing capacity of the current core-time mode—that is, CPU utilization consistently exceeds 90% or memory utilization exceeds 95%—the system triggers a process monitoring mechanism, automatically acquiring all currently running processes on the cloud computer and analyzing each process's resource usage. This process involves collecting processes with high CPU usage (such as compute-intensive applications), high memory usage (such as data processing tasks), high GPU usage (such as graphics rendering software), and irrelevant background processes (such as automatic updaters and non-task-related application processes). The purpose of this step is to gain a comprehensive understanding of system resource usage so that task relevance analysis can be performed in the next step to identify which processes are essential for the user's current task plan and which are irrelevant and may need to be closed. For example, if a user's task plan is to "write code," but the system detects that "video player" and "social media application" are currently running, these processes will be identified as irrelevant processes that may affect computing resources and included in the shutdown recommendations in subsequent steps. By monitoring computing resource utilization in real time, we can detect abnormal resource usage in advance, avoid task failures due to computing resource exhaustion, and ensure that critical tasks always have sufficient computing power support.

[0056] For example, after the system obtains all the running process data of the cloud computer, the next step is to determine which processes are directly related to the user's current task and which processes may be irrelevant. To this end, the system will infer the processes that should be run theoretically based on the user's task plan and compare them with the actual process list. The system determines the theoretical process data in three ways: (1) Based on keyword matching of the task plan, that is, parsing the task description and inferring the application software that the task may use. For example, if the task plan is "demand docking", it may involve software such as Zoom, Teams, and Word, while if the task is "automated testing", it may involve tools such as Selenium, JMeter, and database clients. (2) Based on historical task execution record analysis, that is, the system uses big data analysis to find common processes in the historical execution process of similar tasks, and based on this, infer the processes required for the user's current task. For example, if a user has always run Python and JupyterNotebook in the past few "data analysis" tasks, then when executing similar tasks this time, these processes will also be matched as theoretical processes. (3) Based on enterprise policy and process whitelist matching, that is, if the enterprise has fixed software usage specifications, the system will automatically compare the list of applications allowed by the enterprise. For example, some enterprises may restrict entertainment applications (such as video players) from running on cloud computers, and thus automatically identify them as irrelevant processes. By intelligently analyzing the user's task plan and historical task data, the system can accurately identify the processes required for the task and filter out irrelevant processes in the next step to optimize computing resource allocation and improve the efficiency of cloud computer use.

[0057] For example, after obtaining all the process data of the current cloud computer and the theoretical process data related to the task, the system will compare the two sets of data and identify the processes that are not related to the task, and then generate the first program shutdown suggestion, instructing the user to close the unnecessary processes to free up computing resources and ensure the stable operation of the core task. The specific shutdown suggestion generation rules are as follows: (1) If the process is completely irrelevant to the task, it is strongly recommended to close it. For example, the user's task plan is to "test the system", but the system finds that the "music player" or "web video playback" process is running. These processes are not related to the current task, occupy computing resources but do not help the task execution, so the system will include them in the forced shutdown suggestion list. (2) If the process may affect computing resources but is not completely irrelevant, it is moderately recommended to close it. For example, the user's task is to "compile code", but multiple large IDEs (such as VS Code, Eclipse, PyCharm, etc.) are running at the same time, resulting in excessive CPU and memory consumption. The system will prompt the user to close unnecessary IDE instances to reduce resource waste. (3) If a task is completed but processes are still running, it is recommended to release resources. For example, if a user completes a test task in the morning, but JMeter or Postman is still occupying CPU resources, the system will recommend that the user close these processes to free up computing resources and avoid unnecessary enterprise cloud computing expenses. The shutdown suggestion will be prompted through system notifications or pop-up windows, and a one-click shutdown function will be provided, allowing users to quickly release computing resources and prevent tasks from experiencing performance degradation or crashes due to insufficient resources. By intelligently filtering and closing unnecessary processes, the execution efficiency of tasks can be improved, task delays or failures caused by resource waste can be avoided, and enterprise cloud computing costs can be reduced.

[0058] It is understandable that by real-time monitoring of CPU, memory, and GPU usage, the system can detect resource depletion issues in advance and take measures to optimize processes to avoid impacting critical tasks. Secondly, through task plan analysis and historical data matching, the system can accurately identify the computing processes required for the user's current task, ensuring that computing resources are concentrated on task-related applications, avoiding resource waste due to the running of irrelevant background processes. Finally, through intelligent shutdown suggestions and one-click optimization functions, users can quickly release computing resources, reduce cloud computing expenses, and improve the utilization of enterprise computing resources.

[0059] According to some embodiments, further comprising:

[0060] When the current computing resources used by the cloud computer associated with the target employee user are close to the maximum computing capacity of the cloud computer in the current core time mode, obtaining all current process data of the cloud computer;

[0061] Analyzing all the process data based on the work task plan logged in this time to divide all the process data into main task process data, task auxiliary process data, and task-irrelevant process data;

[0062] A second program closing suggestion is generated when the main application corresponding to the main task process data includes a target sub-function, and the target sub-function is the same sub-function as the auxiliary function associated with the task auxiliary process data. The second program closing suggestion is used to instruct the closing of the application corresponding to the task-irrelevant process data, as well as the task auxiliary process data in the task auxiliary process data that has the same function as the sub-function of the main application.

[0063] Understandably, when performing tasks, users may simultaneously run multiple applications with duplicate functionality or unrelated processes, resulting in wasted computing resources. For example, a user might run a primary application (such as Photoshop) while also running a standalone application that provides the same auxiliary functionality (such as the Snipping Tool for screenshots). This results in unnecessary usage of computing resources. To address these issues, this method automatically retrieves data from all running processes when it detects that the cloud computer's computing resources are approaching the maximum computing capacity of the current core-time mode. Based on the currently logged-in work task plan, it categorizes the processes into primary task processes, auxiliary task processes, and unrelated task processes. It then further analyzes whether the primary application already includes the functionality provided by the auxiliary task processes. If the primary application already has the same functionality, the system generates a secondary program shutdown suggestion, instructing the user to close unrelated processes and redundant auxiliary task processes, thereby freeing up computing resources and optimizing cloud computer performance. The core of this method lies in combining task plan analysis, function matching, and dynamic process optimization strategies to ensure that computing resources are focused on the most important tasks while reducing the resource usage of redundant processes and lowering enterprise computing costs.

[0064] For example, when a user uses a cloud computer, the system will continuously monitor the CPU, memory, and GPU usage. If the computing resources are close to the maximum computing capacity of the cloud computer's current core time mode, that is, the CPU utilization rate continues to exceed 90%, or the memory utilization rate exceeds 95%, the system will trigger the process monitoring mechanism, automatically obtain all process data currently running on the cloud computer, and analyze its computing resource usage. The acquired process data includes: (1) processes with the highest CPU usage, such as large compilation tasks, data calculation tasks, video rendering tasks, etc.; (2) processes with the largest memory consumption, such as database queries, large Excel file processing, etc.; (3) GPU computing task processes, such as processes involved in tasks such as 3D modeling and AI training; (4) background processes, such as automatic update programs, social media applications running in the background, etc. After the system collects all process data, it will proceed to the next step to analyze the correlation between these processes and user tasks. In this way, it can ensure that when resources are close to the upper limit, the system can actively detect and analyze process data instead of waiting for the task to crash before intervening, thereby ensuring task stability and reducing performance degradation caused by resource exhaustion.

[0065] For example, after obtaining all the currently running process data of the cloud computer, the system needs to classify the processes based on the user's task plan to determine which processes are necessary for the task, which are auxiliary, and which are irrelevant. The classification method is as follows: (1) Main task process data: refers to the core application of the current task. For example, if the user's task is "video editing", then Adobe Premiere Pro is the main task process; (2) Task auxiliary process data: refers to the process that provides additional functions for the main task. For example, Adobe Premiere Pro may call Adobe Media Encoder to export videos. This process belongs to the task auxiliary process; (3) Task irrelevant process data: refers to processes that are completely irrelevant to the current task. For example, if the user is running video editing software, but at the same time opens Spotify to play music, or runs the Chrome browser to play online videos, these processes will be marked as task irrelevant processes. Therefore, through task plan parsing and application process matching, the system can intelligently identify the core processes and auxiliary processes required for the task, and at the same time distinguish irrelevant processes for subsequent optimization, thereby ensuring that computing resources are reasonably allocated and improving the computing efficiency of the cloud computer.

[0066] Exemplarily, after completing the process classification, the system will further check whether the function of the main task process is repeated with the function of the task auxiliary process. If the main task process already has the same function provided by the task auxiliary process, the task auxiliary process can be closed to release computing resources. For example, a user's task is "image editing" and its main task process is Photoshop, but the system detects that the user is running Windows Snipping Tool to take screenshots at the same time, and Photoshop itself already provides a screenshot function, so the system will recommend closing the Snipping Tool to reduce resource consumption. Similarly, if the user's task is "code writing" and the main task process is Visual Studio Code, but multiple code debugging tools (such as PyCharm, Eclipse, etc.) are running at the same time, and VS Code itself has built-in debugging functions, the system will recommend closing other debugging tools. In addition, the system will combine the first program closing suggestion (the closing suggestion for task-irrelevant processes) to generate a second program closing suggestion, and simultaneously close the task-irrelevant processes and the task auxiliary processes with duplicate functions, and provide the user with a one-click closing option to quickly optimize computing resources. Therefore, by intelligently detecting and shutting down auxiliary processes with duplicate functions, the system can reduce redundant use of computing resources and ensure that computing resources are concentrated on key applications of the task, thereby improving the execution efficiency of the task.

[0067] It is understandable that by real-time monitoring of CPU, memory, and GPU usage, the system can detect computing resource shortages in advance and take measures to optimize processes to avoid affecting critical tasks. Secondly, through task planning analysis and application process matching, the system can accurately identify the core processes and auxiliary processes required for the task, while distinguishing irrelevant processes and functionally duplicated processes, thereby reducing unnecessary resource usage. Finally, through intelligent shutdown suggestions and one-click optimization functions, users can quickly release computing resources, reduce cloud computing expenses, and improve the utilization of enterprise computing resources.

[0068] According to some embodiments, the second program closing suggestion further includes function entry prompt information of the sub-function of the main application and / or a usage tutorial of the sub-function.

[0069] It is understandable that when the system generates a suggestion to close the second program, in order to help the user smoothly switch to the sub-function of the main application, the system will also provide function entry prompt information and / or a tutorial on how to use the function. For example, when the user is using Photoshop for image editing and running the Snipping Tool for screenshots, and Photoshop itself already has a screenshot function, the system will show the user the shortcut key information of the Photoshop screenshot tool (such as "Press Ctrl+Shift+4 to take a screenshot") while suggesting to close the Snipping Tool, and provide a guide link to help the user quickly understand how to take a screenshot in Photoshop. If the user uses Excel to process data and runs an independent data filtering tool at the same time, and Excel itself already has a similar filtering function, the system will provide an entry location prompt for the Excel data filtering function and attach a brief tutorial on the Excel filtering function to help the user master the relevant functions. The technical effect of this step is that it not only helps users optimize the use of computing resources, but also improves the user's familiarity with the main application, enabling them to use existing functions more efficiently and improve productivity.

[0070] According to some embodiments, further comprising:

[0071] When the login location of the employee user is different from the regular login location, monitoring the combination key input operation of the employee user;

[0072] If the key combination input operation is an invalid shortcut key operation in the current cloud computer system, predicting the function execution requirement of the key combination input operation of the employee user based on all current process data of the cloud computer and the work task plan;

[0073] A shortcut key mapping is established between the combination key input operation and the function execution requirement.

[0074] Understandably, when users use cloud computers from unconventional login locations (such as remote work or business trips), some key combinations and shortcuts may become ineffective due to differences in keyboard layout, software configuration, or system environment, thus affecting user productivity. Furthermore, because different enterprise applications may use different shortcut combinations in different environments, users may attempt to use invalid shortcuts, resulting in operational failures or the need to frequently adjust their habits.

[0075] Exemplarily, when a user logs in to a cloud computer, the system will detect the current login location and compare it with the user's historical regular login location. If the user's login location is different from the past, for example, the user usually uses the cloud computer in the office, but this time the login location is remote work (such as at home or away); or the user's regular login device is a Windows computer, but this time a Mac or Linux terminal is used to access the cloud computer; or the user has always used a fixed keyboard layout, but this time due to device replacement, a different keyboard mapping scheme is used (such as American keyboard vs. British keyboard). If the system detects that the user's login location is abnormal, it will activate the shortcut key monitoring mode and continuously monitor the user's combination key input operations to analyze their input behavior. For example, a user may try to use a shortcut key (such as Ctrl+Shift+S) to perform an operation, but if the shortcut key is invalid in the current cloud computer environment, the system will record the operation and enter the next analysis stage. The technical effect of this step is to ensure that when the user's shortcut key is unavailable due to environmental changes, the system can capture the input behavior in time so as to optimize its operating experience later.

[0076] For example, when a user attempts to use an invalid shortcut key on a cloud computer (i.e., the shortcut key is not registered in the current system environment or does not trigger any function), the system does not directly discard the input. Instead, it intelligently predicts the user's shortcut key intention based on the user's current task and running applications. Specific prediction methods may include: analyzing the user's work task plan. For example, if the user's task plan involves "document editing" but the user attempts to use Ctrl+Shift+S, the system may infer that the user's intention is to "save as a new file" and further confirm the application they are currently using (such as Word or Google Docs). Or, based on matching all current process data on the cloud computer, the system will check the currently running application and its default shortcut key configuration. For example, a user may be using Photoshop and try to use Ctrl+Alt+D, but Photoshop does not have such a shortcut key. The system may infer that the user intended to "adjust brightness" or "switch layer mode." Another method is to model historical shortcut key usage data. For example, if the user has used the shortcut key for a certain action in a normal environment in the past (such as using Alt+T to switch tabs in the office), but the shortcut key is invalid this time, the system can infer the functional requirement of the shortcut key and recommend remapping. Alternatively, it can be based on a database of industry-standard shortcut keys. For example, if the system finds that a shortcut key is commonly used in certain industry-specific software (such as Figma, AutoCAD, and Visual Studio Code), it will further confirm whether it can be adapted to the corresponding function. Therefore, even if the shortcut key is invalid in the current environment, the system can still infer the user's actual functional needs by analyzing the user's tasks and software environment, ensuring that the user's operation is not affected by the invalid shortcut key.

[0077] For example, after successfully inferring the user's intention to use an invalid shortcut key, the system automatically establishes a shortcut key mapping relationship to ensure that the user can continue to use the same operating habits without having to manually adapt to the new environment. If the system finds that the user's shortcut key operation has the same function as an existing shortcut key, it will directly map it. For example, in some software, Ctrl+Shift+S may correspond to "Save As", but if Ctrl+Shift+S is invalid in the current environment and F12 provides the same function, the system will automatically remap the shortcut key to F12. If there is no equivalent shortcut key in the current environment, but the system has clearly identified the user's operation requirements, it will automatically create a new shortcut key. For example, if a user tries to press Ctrl+Alt+D to execute "Adjust Brightness", but the current software does not have a default shortcut key, the system will create a new shortcut key mapping for the user (such as Ctrl+L) and prompt the user to the existence of the new shortcut key. If the system detects that the shortcut key used by the user may conflict with an existing shortcut key, the system will provide modification suggestions. For example, a user may be accustomed to using Ctrl+Space to switch input methods, but in some environments, this shortcut key may trigger speech recognition. The system will suggest that the user use Shift+Space instead and provide shortcut modification guidance. In addition, after creating a shortcut mapping, the system will also provide a shortcut change notification, letting users know the newly mapped shortcut key combination and allowing them to choose whether to accept the new shortcut setting. The technical effect of this step is that by automatically adapting the shortcut mapping, the system can ensure that users can maintain consistent operating methods in different work environments, improving work efficiency and reducing work interruptions caused by shortcut key failures.

[0078] According to some embodiments, establishing a shortcut key mapping between the combination key input operation and the function execution requirement includes:

[0079] When a confirmation message is received from the employee user in response to the predicted function execution requirement, a shortcut key mapping between the combination key input operation and the function execution requirement is established.

[0080] According to some embodiments, further comprising:

[0081] Obtaining the employee user's current temporary input device information, wherein the input device information includes key layout information;

[0082] The key of the established shortcut key mapping is used as the reference key, and the position of the key of the established shortcut key mapping is used as the reference key position, and all shortcut key mappings are established in the temporary input device so that the shortcut key mapping in the temporary input device conforms to the blind typing operation habits of the employee user.

[0083] It is understandable that in an enterprise cloud office environment, users may use cloud computers on different devices. Users may switch from a standard PC keyboard to a Mac keyboard, or from a full-size keyboard to a compact keyboard (such as a laptop keyboard or a wireless keyboard), resulting in changes in the key layout. In addition, users may use keyboard layouts from different regions (such as the US QWERTY keyboard, the UK QWERTY keyboard, the AZERTY keyboard, or the DVORAK keyboard), with different physical positions of keys, affecting the user's shortcut key usage habits. In addition, users may use cloud computers through remote desktops or external devices, such as using an external keyboard, virtual keyboard, or touch screen input device for an iPad, causing some shortcut key mappings to become invalid. In the above cases, users may still be accustomed to using the original keyboard shortcut layout for blind typing (i.e., operating without looking at the keyboard), but due to changes in the input device, some shortcut keys may be in different positions than before, thereby affecting the user's operating efficiency. To solve this problem, this method automatically detects the key layout information of the temporary input device when the user changes the input device, and adjusts the physical position of the shortcut keys based on the shortcut key mapping established by the user so that it conforms to the user's blind typing habits on the new device. The core of this method lies in input device adaptation, shortcut key mapping conversion and blind typing habit learning, ensuring that users can maintain a consistent shortcut key operation experience on different devices and improving the operational smoothness of cloud computers.

[0084] For example, when a user logs in to a cloud computer or changes an input device, the system will automatically detect the currently connected input device information, which may include: input device type, such as a standard PC keyboard, Mac keyboard, compact keyboard, mechanical keyboard, virtual keyboard, or touch screen keyboard. Key layout information, detecting the layout of the current keyboard, such as American QWERTY, British QWERTY, AZERTY, DVORAK, etc. The number of physical keys of the input device can be identified (such as whether it has an Fn key, whether there are independent function keys, whether there is a numeric keypad, etc.). It can check whether the current input device supports the key combination that the user is accustomed to. For example, some compact keyboards may lack independent F1-F12 keys, or the position of the Ctrl key is different from the user's habits. The system can automatically detect the key layout information when the user changes the input device, so that the shortcut key mapping can be adjusted later to make it conform to the user's usage habits.

[0085] Exemplarily, after detecting the key layout of the input device, the system will use the shortcut key mapping established by the user as a reference and readjust the position of the shortcut keys on the new input device to maintain the user's blind typing habits. The specific mapping adjustment method can be key logic matching, that is, if the user's original shortcut key is Ctrl+C (copy), but the position of the Ctrl key on the new keyboard has changed (such as the Command key on the Mac keyboard replaces the Ctrl key), the system will automatically adjust the shortcut key so that the user can still use the same operation method to copy on the new device. It can also be matched according to physical key positions, that is, if the user is accustomed to using Alt+Tab to switch windows, but the Alt key position of the new keyboard is different from that of a standard PC keyboard (such as some compact keyboards may use Fn+Tab), the system will remap the shortcut key so that it is still in the physical position familiar to the user. In this way, the system can automatically adjust the shortcut key mapping on different input devices to make it consistent with the user's muscle memory and blind typing habits, thereby reducing operational errors caused by device replacement and improving user work efficiency.

[0086] Furthermore, as a response to the above Figure 1 In addition to the implementation of the method shown in the figure, the embodiment of the present invention also provides an enterprise monitoring device based on the cloud management platform for Figure 1 This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 2 As shown, the device includes:

[0087] An acquiring unit 21 is configured to acquire a work task plan for this login from an employee user when the employee user logs into a cloud computer account, wherein the work task plan includes at least one task type;

[0088] An evaluation unit 22, configured to evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan;

[0089] The processing unit 23 is configured to select a matching core time mode based on the cloud computer resource configuration data, where different core time modes have different rates.

[0090] By means of the above technical solution, the enterprise supervision device based on the cloud management platform provided by the present invention obtains the work task plan for this login from the employee user when the employee user logs into the cloud computer account, and the work task plan includes at least one task type; based on the work task plan, the cloud computer resource configuration data required for the employee user's login is evaluated; based on the cloud computer resource configuration data, a matching core time mode is selected, and different core time modes have different rates. In this way, over-configuration or insufficient resources caused by the traditional fixed allocation method based on functions is avoided, and the utilization rate of computing resources is improved. Through intelligent billing model matching, the ineffective use of high computing resources is reduced, making enterprise cloud computing expenditures more reasonable. The burden of manual configuration by users is reduced, the intelligence level of cloud computers is improved, and resource scheduling is made more efficient.

[0091] The processor contains a core, which retrieves the corresponding program unit from the memory. One or more cores can be configured, and by adjusting the core parameters, a cloud-based enterprise management method can be implemented.

[0092] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed by a processor, it implements the above-mentioned enterprise supervision method based on the cloud management platform.

[0093] An embodiment of the present invention provides a processor configured to run a program, wherein when the program is run, the enterprise supervision method based on the cloud management platform is executed:

[0094] When an employee user logs into a cloud computer account, obtaining a work task plan for this login from the employee user, wherein the work task plan includes at least one task type;

[0095] Evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan;

[0096] A matching core time mode is selected based on the cloud computer resource configuration data, and different core time modes have different rates.

[0097] An embodiment of the present invention provides an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to call program instructions in the memory to execute the above-mentioned cloud management platform-based enterprise supervision method:

[0098] When an employee user logs into a cloud computer account, obtaining a work task plan for this login from the employee user, wherein the work task plan includes at least one task type;

[0099] Evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan;

[0100] A matching core time mode is selected based on the cloud computer resource configuration data, and different core time modes have different rates.

[0101] An embodiment of the present invention provides an electronic device 30, such as Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned enterprise supervision method based on the cloud management platform.

[0102] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0103] The present application also provides a computer program product, which, when executed on a process management electronic device, is adapted to execute a program for initializing the following method steps:

[0104] Obtaining packaging type information of e-commerce logistics products before loading, transshipment or delivery. The packaging type information is obtained based on packaging image information recognition, and the packaging type information includes the material, size and shape of the packaging;

[0105] Predicting the load-bearing boundary information of each commodity package to be loaded based on the package type information;

[0106] The optimal product stacking strategy is calculated using a three-dimensional loading algorithm based on the weight information, load-bearing boundary information, packaging type information, and three-dimensional modeling information of the vehicle storage space of different products, so as to generate product stacking prompt information based on the stacking strategy.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of the methods, electronic devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable process management electronic device to produce a machine, so that the instructions executed by the processor of the computer or other programmable process management electronic device generate instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] In a typical configuration, an electronic device includes one or more processors (CPUs), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, and the like.

[0109] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip. Memory is an example of a computer-readable medium.

[0110] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage electronic devices or any other non-transmission media that can be used to store information that can be accessed by computing electronic devices. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0111] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or electronic device that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or electronic device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not preclude the presence of additional identical elements in the process, method, commodity, or electronic device that includes the element.

[0112] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable computer-readable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. An enterprise supervision method based on a cloud management platform, characterized in that: include: When an employee user logs into a cloud computer account, obtaining a work task plan for this login from the employee user, wherein the work task plan includes at least one task type; Evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan; A matching core time mode is selected based on the cloud computer resource configuration data, and different core time modes have different rates.

2. The method according to claim 1, characterized in that Also includes: When the current computing resources used by the cloud computer associated with the target employee user are close to the maximum computing capacity of the cloud computer in the current core time mode, obtaining all current process data of the cloud computer; Determining theoretical process data associated with the work task plan based on the work task plan logged in this time; A first program closing suggestion is generated according to the theoretical process data and all the process data, wherein the first program closing suggestion is used to instruct closing an application corresponding to process data in all the process data that is unrelated to the theoretical process data.

3. The method according to claim 1, characterized in that Also includes: When the current computing resources used by the cloud computer associated with the target employee user are close to the maximum computing capacity of the cloud computer in the current core time mode, obtaining all current process data of the cloud computer; Analyzing all the process data based on the work task plan logged in this time to divide all the process data into main task process data, task auxiliary process data, and task-irrelevant process data; A second program closing suggestion is generated when the main application corresponding to the main task process data includes a target sub-function, and the target sub-function is the same sub-function as the auxiliary function associated with the task auxiliary process data. The second program closing suggestion is used to instruct the closing of the application corresponding to the task-irrelevant process data, as well as the task auxiliary process data in the task auxiliary process data that has the same function as the sub-function of the main application.

4. The method according to claim 3, characterized in that The second program closing suggestion also includes function entry prompt information of the sub-function of the main application and / or a usage tutorial of the sub-function.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: When the login location of the employee user is different from the regular login location, monitoring the combination key input operation of the employee user; If the key combination input operation is an invalid shortcut key operation in the current cloud computer system, predicting the function execution requirement of the key combination input operation of the employee user based on all current process data of the cloud computer and the work task plan; A shortcut key mapping is established between the combination key input operation and the function execution requirement.

6. The method according to claim 5, characterized in that The step of establishing a shortcut key mapping between the combination key input operation and the function execution requirement includes: When a confirmation message is received from the employee user in response to the predicted function execution requirement, a shortcut key mapping between the combination key input operation and the function execution requirement is established.

7. The method according to claim 6, characterized in that Also includes: Obtaining the employee user's current temporary input device information, wherein the input device information includes key layout information; The key of the established shortcut key mapping is used as the reference key, and the position of the key of the established shortcut key mapping is used as the reference key position, and all shortcut key mappings are established in the temporary input device so that the shortcut key mapping in the temporary input device conforms to the blind typing operation habits of the employee user.

8. An enterprise monitoring device based on a cloud management platform, characterized in that: The device comprises: an acquiring unit, configured to acquire, from an employee user, a work task plan for this login when the employee user logs into a cloud computer account, the work task plan including at least one task type; An evaluation unit, configured to evaluate the cloud computer resource configuration data required for the employee user's current login based on the work task plan; The processing unit is used to select a matching core time mode based on the cloud computer resource configuration data, and different core time modes have different rates.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the enterprise supervision method based on the cloud management platform according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the enterprise supervision method based on the cloud management platform as described in any one of claims 1 to claim 7.

Images