Method and system for realizing custom large screen function managed by cloud platform

Abstract

The invention discloses a user-defined large screen function implementation method and system for cloud platform management, and belongs to the technical field of cloud platform management.The following operations are carried out on data sources, formats and display of physical machines and virtual machines in a cloud platform cluster; data acquisition, processing and combination are carried out on components with different specific function requests; dividing the use states of the host and the virtual machine in a data display form; displaying the data combination in the form of a custom large screen according to variable user requirements; wherein the data source comprises proportion data of a physical host, a CPU (Central Processing Unit) of a virtual host, a memory and a disk in the cluster; alarm data of the virtual host; and product data. According to the method, the optimal state of the server during operation can be ensured, so that the information of the whole cloud management platform is cleaner and clearer, and the problem is more conveniently positioned; functions are simple and easy to operate, and data sources are clear.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud platform management, and specifically to a method and system for implementing a custom large screen function for cloud platform management. Background Art

[0002] In the Internet era, all application services are deployed on servers, and the requirements for servers are getting higher and higher. For the running status of applications, and the usage of resources such as the network, memory, and disk of the servers, we need to manage these issues, collect, organize, and process them. How to efficiently manage and run the servers has become the most important issue.

[0003] Secondly, the use of servers has gradually evolved into cloud services, that is, a physical host is evolved into multiple virtualized servers. This makes the running efficiency of physical hosts and virtual hosts more demanding. How to visually manage these virtual services and achieve the maximum amount of visualization is also a problem that needs to be solved currently. Summary of the Invention

[0004] The technical task of the present invention is to address the above deficiencies, and provide a method and system for implementing a custom large screen function for cloud platform management, which can ensure the best state of the server during operation, make the information of the entire cloud management platform cleaner, clearer, and more convenient for problem positioning; the function is simple and easy to operate, and the data sources are clear and distinct.

[0005] The technical solution adopted by the present invention to solve its technical problems is:

[0006] A method for implementing a custom large screen function for cloud platform management performs the following operations on the data sources, formats, and displays of physical machines and virtual machines in a cloud platform cluster:

[0007] Through the acquisition and combination form of data sources, different components are obtained, processed, and combined for specific function requests;

[0008] Through the data display form, the usage status of hosts and virtual machines is divided;

[0009] The variable user requirements are presented in the form of a custom large screen for data combination;

[0010] Among them, the data sources include: the CPU, memory, and disk occupancy data of physical hosts and virtual hosts within the cluster; the alarm data of virtual hosts; and product data.

[0011] The data source is an important basic part for the large screen display of physical machines and hosts under the same cloud platform cluster; the data display is the process of combining external data of the current service; the custom large screen is one of the forms for users to display host data, and other display transformations can be performed according to variable requirements.

[0012] Furthermore, this method is implemented in a front-end and back-end separated form:

[0013] The front end uses the AngularJs scaffolding, utilizes the internally encapsulated HTTP protocol technology, communicates in the RestFul code style, and obtains the operation data in the data format of the custom large screen (mainly in the form of json data);

[0014] The back end is built using springBoot or springCloud technology, mainly in the Java development language, adopts the RestFul code style, obtains the data in the PostgreSQL database through the ORM framework technology spring data JPA, and performs json formatting processing on the data.

[0015] Furthermore, for the front end, based on the JavaScript development language, with the Angular.js (version 8) scaffolding as the project architecture, using npm (version 6.14) as the package management tool, and implementing functions with the NG-ZORRO components;

[0016] For the back end, based on the Java development language, with the springBoot (version 2.4.3) suite as the project architecture, using gradle (version 6.8.3) as the package management tool;

[0017] Data storage: Manage data with PGSQL (PostgreSQL) as the data storage repository;

[0018] Network communication: Conduct external and front-end and back-end communication using the HTTP protocol;

[0019] Communication specification: Operate communication using the RESTFul API interface technology;

[0020] Data template: Create the initial data for project operation using the liqiubase technology;

[0021] Data interaction: Use the spring data JPA technology in the spring suite as the interaction standard.

[0022] Furthermore, the functions of the custom large screen are divided into a preset template mode and a custom mode:

[0023] Preset template, when initializing the cloud platform system, serves as the basic template for data display for quick display and use;

[0024] Custom mode is created according to specific requirements for the host data under the cluster in different scenarios, different times, different tasks, etc.

[0025] Furthermore, the diversified combination method of the custom large screen for resource data is as follows:

[0026] Initialize the assembled resources, which are divided into hosts and virtual machines, and the corresponding monitoring information includes the assembly of information such as CPU, memory, and disk;

[0027] Correspond to three main data tables: the large screen data table, the large screen icon index table, and the large screen index attribute table. Based on the monitored alarm information as the data basis, the custom large screen function is realized; among them, the creation of the data table can be realized according to industry specifications or business specifications.

[0028] Furthermore, the specific implementation steps of the diversified combination of the custom large screen for resource data are as follows:

[0029] 1) Define the custom large screen classification, including:

[0030] Preset template (default data assembly), which will assemble the resource data information to a certain extent and cannot be modified or deleted;

[0031] Custom template (realize data assembly), users can display resources in the form of pie charts, curves, and tables according to their own usage needs at different times and in different specifications. Because according to their own needs, users can create, edit, and delete the large screen;

[0032] 2) Define the operations of the custom large screen:

[0033] The preset template is the system's proprietary data during data initialization, and only needs to be obtained for display and cannot be modified;

[0034] The custom template is created by users according to their own needs, so operations such as display, creation, preview, editing, and deletion are required;

[0035] 3) Define the detailed resource combination in the large screen, including:

[0036] Charts: tables, pie charts, curves;

[0037] Pie chart: cpu_usage, mem_usage, disk_usage;

[0038] Curve: alarm_trend;

[0039] Table: cpu_usage_top, mem_usage_top, disk_usage_top, alarm_list;

[0040] Auxiliary: text, border, picture, index;

[0041] Indicators: cpu_used, cpu_total, mem_used, mem_total, disk_total, disk_used, ebs_total, ecs_total, etc.;

[0042] Auxiliary combination. Because of its high complexity and strong multiplicity, only the indicators have range regulations, and other definitions are defined by yourself according to requirements;

[0043] Icons: Specific representative information for a certain application or product;

[0044] 4) Define the implementation interface of the large screen:

[0045] Operation interfaces visible on the large screen combination data list display page: Query large screen list, detailed large screen editing interface, large screen deletion interface, large screen preview interface;

[0046] Operation interfaces visible on the page that can be created and edited: Data query interface for the component bar, data saving interface, component resource definition acquisition interface;

[0047] The interfaces are sent and responded in the form of json; The logical complexity of the interface business focuses on the data query interface for the component bar and the component resource definition acquisition interface. It is necessary to judge different component types and return diversified data formats for different component data;

[0048] 5) Customize the core data of the large screen:

[0049] Due to cloud platform management, which is a flat-level application, when obtaining the running data of the server, it is necessary to obtain the total amount, usage rate, quantity of the CPU, memory, and disk of the server and the alarm information of the problems generated by the server through underlying components including cms, horizon, prometheus, etc. in the form of the HTTP protocol;

[0050] Perform calculations, combinations, installations, and formatting operations on the obtained data; And perform compliance processing on the data groups with problems without affecting the operations of other data groups;

[0051] For data calculation, calculate the obtained data in the form of percentages, combine and install arrays in different data groups, and format the data in the communication response for the convenience of the applications calling the interfaces;

[0052] 6) Customize the data table of the large screen, including:

[0053] Large screen data table: Store the data of preset templates and custom templates, where the main data of the large screen is stored in the jsonString data format of the text data type of the database;

[0054] Large screen component table: Stores all data in the component bar. Since the data in the component bar is displayed in the form of pictures, the data stored in the database is parsed and saved in base64 format;

[0055] Large screen component property table: Stores the specific descriptions of the selected components in the component bar, such as: CPU, a certain host node, the size of all CPUs, the actual used size, multiple selection or single selection is available, and all can be selected.

[0056] Further, in step 2), the custom template operations include:

[0057] Creation operation. This page is divided into a component bar, a large screen custom editing area, and an operation bar;

[0058] The component bar includes charts, icons, and auxiliary modules:

[0059] Charts: Charts include line charts, pie charts, text, and tables;

[0060] Icons: The scope of display depends on the actual deployment of the actual product to determine which products to display;

[0061] Auxiliary: Includes borders, and the border titles can be edited;

[0062] The custom large screen editing area: The area for custom editing of the large screen, where custom editing operations can be performed on the components within the large screen;

[0063] The operation bar: According to the selection in the component bar, specifically define the resource data and obtain the resource data;

[0064] The implementation process of the custom large screen is as follows:

[0065] Create a custom large screen. The creation page is divided into three parts, from left to right are the component bar, the large screen editing area or position arrangement, and the operation bar;

[0066] The component bar is the basis of the custom large screen, mainly in the form of images to facilitate confirmation of whether the components are the ones needed;

[0067] The large screen editing area is the area for storing component planning;

[0068] The operation bar, according to the selected components, selects different attributes, and responds the processing results of the backend to the selected components for display;

[0069] The present invention also claims to protect a custom large screen implementation system for cloud platform management, including:

[0070] The data source acquisition module obtains and combines data sources, and acquires, processes, and combines data from different components for specific function requests;

[0071] The data display module is used to divide the usage status of the host and virtual machines;

[0072] The custom large screen module is used to display data combinations in the form of a custom large screen;

[0073] The system realizes a custom large screen through the above method.

[0074] The present invention also claims a device for realizing a custom large screen in cloud platform management, including: at least one memory and at least one processor;

[0075] The at least one memory is used to store machine-readable programs;

[0076] The at least one processor is used to call the machine-readable program to implement the above method.

[0077] The present invention also claims a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the above method is implemented.

[0078] Compared with the prior art, the method and system for realizing the custom large screen function in cloud platform management of the present invention have the following beneficial effects:

[0079] The logic and technical implementation idea of the custom large screen function are to realize the diversified needs of users, form an excellent structure system, and facilitate the management platform to access the host and use it safely, and quickly locate the platform function; in the form of assembled variations, the information of the entire cloud management platform is tidier, clearer, and more convenient to locate problems. Such a method determines that the function is simple and easy to operate, and the data source is clear and distinct. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a flowchart of the custom large screen creation process provided by an embodiment of the present invention;

[0081] Figure 2 It is a flowchart of the custom large screen component attribute data processing process provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0082] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0083] An embodiment of the present invention provides a method for realizing the custom large screen function in cloud platform management, and the following operations are performed on the data sources, formats, and displays of physical machines and virtual machines in the cloud platform cluster:

[0084] Based on the acquisition and combination form of data sources, different components for specific function requests acquire, process, and combine data;

[0085] Based on the data display form, divide the usage status of the host and virtual machine;

[0086] Present the combined data in the form of a customized large screen for diverse user requirements;

[0087] Data source is an important basic part for the large screen display of physical machines and hosts in the same cluster of the cloud platform; data display is the process of combining external data of the current service; the customized large screen is one of the forms to realize the display of host data by users and can be transformed into other displays according to diverse requirements.

[0088] Among them, the data sources include: the proportion data of CPU, memory, and disk of physical hosts and virtual hosts within the cluster; the alarm data of virtual hosts; product data.

[0089] This method is implemented in a form of separating the front end and the back end:

[0090] The front end uses the AngularJs scaffolding, uses the internally encapsulated HTTP protocol technology, communicates in the RestFul code style, and obtains the operation data in the data format of the customized large screen (mainly in the form of json data);

[0091] The back end is built using springBoot or springCloud technology, mainly in the Java development language, communicates in the RestFul code style, obtains the data in the PostgreSQL database through the ORM framework technology spring data JPA, and performs json formatting processing on the data.

[0092] In the implementation of the function, the function of the customized large screen is divided into a preset template mode and a custom mode:

[0093] The preset template serves as the basic template for data display during the initialization of the cloud platform system for quick display;

[0094] The custom mode is created according to specific requirements for the host data under the cluster in different scenarios such as different scenarios, different times, and different tasks. The host is a general term for physical servers and virtual servers.

[0095] The technology of the customized large screen proposed by this method realizes the visual assembly of the resource data of the host accessing the cloud platform environment at the front end, and realizes operations such as saving, querying, data acquisition, and combination of the customized large screen data at the back end.

[0096] As a cloud management platform with front-end and back-end separation, there is a standard specification combination for the implemented technologies. The technical standards for the entire project are as follows:

[0097] Front-end technology: Based on the JavaScript programming language, with the Angular.js (version 8) scaffolding as the project architecture, npm (version 6.14) as the package management tool, and NG-ZORRO components to implement functions;

[0098] Back-end technology: Based on the Java programming language, with the springBoot (version 2.4.3) suite as the project architecture, and gradle (version 6.8.3) as the package management tool;

[0099] Data storage: Use PGSQL (PostgreSQL) as the data storage repository to manage data;

[0100] Network communication: Use the HTTP protocol for external and front-end to back-end communication;

[0101] Communication specification: Use RESTFul API interface technology to operate communication;

[0102] Data template: Use the liqiubase technology to create the initial data for the project to run;

[0103] Data interaction: Use the spring data JPA technology in the spring suite as the interaction standard.

[0104] Through the technical standards of the above cloud platform, the steps to implement the custom large screen function are described. Here, taking the diversified assembly of resource data for the custom large screen as the core, the implementation of the function is described:

[0105] First, initialize the assembled resources, which are divided into hosts and virtual machines, and assemble the monitoring information such as CPU, memory, and disk corresponding to them. They respectively correspond to three main data tables, the large screen data table, the large screen icon index table, and the large screen index attribute table. Based on the monitored alarm information as the data basis, the custom large screen function is implemented. The creation of the data table can be achieved according to industry specifications or business specifications.

[0106] The main implementation methods are as follows:

[0107] 1. Define the custom large screen classification.

[0108] Preset template (default data assembly), which will assemble the resource data information to a certain extent and cannot be modified or deleted.

[0109] Custom template (for data assembly). Users can display resources in the form of pie charts, curves, and tables according to their usage requirements at different times and in different specifications. Since it is based on their own needs, users can create, edit, and delete the large screen.

[0110] 2. Define the operations for custom large screens.

[0111] The preset template is the system's proprietary data during data initialization. It only needs to be retrieved for display and cannot be modified.

[0112] The custom template is created by users according to their own needs, so operations such as display, creation, preview, editing, and deletion are required.

[0113] For the creation operation, this page is divided into a component bar, a large screen custom editing area, and an operation bar;

[0114] The component bar includes charts, icons, and auxiliary modules:

[0115] Charts: The charts include line charts, pie charts, text, and tables;

[0116] Icons: The scope of display depends on the actual deployment of the actual product to determine which products to display;

[0117] Auxiliary: Includes borders, and the border titles can be edited.

[0118] The custom large screen editing area: The area for custom editing of the large screen, where custom editing operations can be performed on the components within the large screen.

[0119] The operation bar: According to the selection in the component bar, specifically define the resource data and obtain the resource data.

[0120] 3. Define the detailed resource combinations in the large screen.

[0121] Charts: Tables, pie charts, curves;

[0122] Pie charts: cpu_usage, mem_usage, disk_usage;

[0123] Curves: alarm_trend;

[0124] Tables: cpu_usage_top, mem_usage_top, disk_usage_top, alarm_list;

[0125] Auxiliary: Text, borders, pictures, metrics;

[0126] Indicators: cpu_used, cpu_total, mem_used, mem_total, disk_total, disk_used, ebs_total, ecs_total, etc.;

[0127] Auxiliary combination. Because of its high complexity and strong multi-sidedness, only the indicators have range regulations, and other definitions are defined by yourself according to requirements;

[0128] Icons: Specific representative information for a certain application or product;

[0129] 4. Define the implementation interface of the large screen.

[0130] Operation interfaces visible on the large screen combination data list display page: query large screen list, detailed large screen editing interface, large screen deletion interface, large screen preview interface;

[0131] Operation interfaces visible on the page that can be created and edited: data query interface for the component bar, data saving interface, component resource definition acquisition interface;

[0132] The interfaces are sent and responded in the form of json; The logical complexity of the interface business focuses on the data query interface for the component bar and the component resource definition acquisition interface. It is necessary to judge different component types and return diverse data formats for different component data.

[0133] Example of the data query interface for the component bar:

[0134]

[0135]

[0136]

[0137] Example of the component resource definition acquisition interface:

[0138]

[0139] 5. Customize the core data of the large screen.

[0140] Since cloud platform management is a flat-level application, when obtaining the running data of the server, it is necessary to obtain the total amount, usage rate, quantity of the CPU, memory, and disk of the server and the alarm information of the problems generated by the server through underlying components including cms, horizon, prometheus, etc. in the form of the HTTP protocol.

[0141] Perform calculations, combinations, installations, and formatting operations on the obtained data; And perform compliance processing on the data groups with problems without affecting the operations of other data groups.

[0142] Calculation of data: The obtained data is calculated in the form of percentages, combined and installed in different data groups as arrays, and the communication response data is formatted for the convenience of the application calling the interface.

[0143] 6. Customize the data table of the large screen.

[0144] Large screen data table: Stores the data of preset templates and custom templates, and the main data of the large screen is stored in the jsonString data format of the text data type in the database.

[0145] Large screen component table: Stores all the data in the component bar. The data in the component bar is displayed in the form of pictures, so the data stored in the database is parsed and saved in the base64 format.

[0146] Large screen component property table: Stores the specific descriptions of the selected components in the component bar, such as: CPU, a certain host node, the size of all CPUs, the actual used size, multiple selection and single selection are available, and all can be selected.

[0147] As Figure 1 shown, the implementation process of customizing the large screen is as follows:

[0148] Create a custom large screen. The created page is divided into three parts, from left to right are the component bar, the large screen editing area or position arrangement, and the operation bar;

[0149] The component bar is the basis of the custom large screen, mainly composed of images for easy confirmation of whether the components are those needed.

[0150] The large screen editing area is the area for storing component planning.

[0151] The operation bar selects different attributes according to the selected components, and responds the processing results of the backend to the selected components for display.

[0152] As Figure 2 shown, the data assembly process analysis of the custom large screen is as follows:

[0153] When an external request enters the backend, the backend judges the obtained data through the index information;

[0154] The obtained arrays are calculated, combined, installed, and formatted according to the index information;

[0155] The result of the data is responded to the outside.

[0156] Note: Taking the memory usage as the standard, the example is as follows:

[0157]

[0158] Taking the memory usage ranking of multiple virtual machines under the cluster as the standard, for example:

[0159]

[0160]

[0161]

[0162]

[0163] An embodiment of the present invention further provides a custom large screen implementation system for cloud platform management. The system includes:

[0164] A data source acquisition module, which obtains and combines data sources, and acquires, processes, and combines data from different components for specific function requests;

[0165] A data display module, which is used to divide the usage status of hosts and virtual machines;

[0166] A custom large screen module, which is used to display data combinations in the form of a custom large screen;

[0167] The data sources include: the CPU, memory, and disk occupancy data of physical hosts and virtual hosts within the cluster; the alarm data of virtual hosts; and product data.

[0168] This system is implemented in a front-end and back-end separated form:

[0169] The front end uses the AngularJs scaffolding, uses the internally encapsulated HTTP protocol technology, communicates in the RestFul code style, and obtains the operation data in the data format of the custom large screen (mainly in the form of json data);

[0170] The back end is built using springBoot or springCloud technology, mainly in the Java development language, communicates in the RestFul code style, obtains the data in the PostgreSQL database through the ORM framework technology spring data JPA, and performs json formatting processing on the data.

[0171] In the implementation of the function, the function of the custom large screen is divided into a preset template mode and a custom mode:

[0172] The preset template, when the cloud platform system is initialized, serves as the basic template for data display for quick display;

[0173] The custom mode is created according to specific requirements for the host data under the cluster in different scenarios such as different scenes, different times, and different tasks. Hosts refer to the general term of physical servers and virtual servers.

[0174] The system realizes the custom large screen through the method for realizing the custom large screen managed by the cloud platform in the above embodiments.

[0175] Front-end technology: Based on the JavaScript development language, with the scaffolding of Angular.js (version 8) as the project architecture, npm (version 6.14) as the package management tool, and NG-ZORRO components to implement functions;

[0176] Back-end technology: Based on the Java development language, with the springBoot (version 2.4.3) suite as the project architecture and gradle (version 6.8.3) as the package management tool;

[0177] Data storage: Manage data with PGSQL (PostgreSQL) as the data storage repository;

[0178] Network communication: Use the HTTP protocol for external and front-end and back-end communication;

[0179] Communication specification: Operate communication with RESTFul API interface technology;

[0180] Data template: Create the initial data for project operation with the liqiubase technology;

[0181] Data interaction: Use the spring data JPA technology in the spring suite as the interaction standard.

[0182] Through the technical standards of the above cloud platform, the custom large screen function is realized. Here, taking the diversified assembly of resource data for the custom large screen as the core, the implementation of the function is described:

[0183] First, initialize the assembled resources, which are divided into hosts and virtual machines, and assemble the monitoring information such as CPU, memory, and disk corresponding to them. They respectively correspond to three main data tables, the large screen data table, the large screen icon index table, and the large screen index attribute table, and realize the custom large screen function based on the monitored alarm information. The creation of the data table can be realized according to industry specifications or business specifications.

[0184] The main implementation methods are as follows:

[0185] 1. Define the custom large screen classification.

[0186] The preset template (default data assembly) will assemble the resource data information to a certain extent and cannot be modified or deleted.

[0187] The custom template (realize data assembly). Users can display resources in the form of pie charts, curves, and tables according to their own usage requirements at different times and in different specifications. Since it is according to their own needs, users can create, edit, and delete the large screen.

[0188] 2. Define the operations of the custom large screen.

[0189] The preset template is the system's proprietary data during data initialization. It only needs to be obtained for display and cannot be modified.

[0190] The custom template is created by users according to their own needs, so operations such as display, creation, preview, editing, and deletion are required.

[0191] Create operation. This page is divided into a component bar, a large screen custom editing area, and an operation bar;

[0192] The component bar includes charts, icons, and auxiliary modules:

[0193] Charts: The charts include line charts, pie charts, text, and tables;

[0194] Icons: The scope of display depends on the actual deployment of the actual product to determine which products to display;

[0195] Auxiliary: Includes borders, and the border titles can be edited.

[0196] The custom large screen editing area: The area for custom editing of the large screen, where custom editing operations can be performed on the components within the large screen.

[0197] The operation bar: According to the selection of the component bar, specifically define the resource data and obtain the resource data.

[0198] 3. Define the detailed resource combinations in the large screen.

[0199] Charts: Tables, pie charts, curves;

[0200] Pie charts: cpu_usage, mem_usage, disk_usage;

[0201] Curves: alarm_trend;

[0202] Tables: cpu_usage_top, mem_usage_top, disk_usage_top, alarm_list;

[0203] Auxiliary: Text, borders, pictures, metrics;

[0204] Indicators: cpu_used, cpu_total, mem_used, mem_total, disk_total, disk_used, ebs_total, ecs_total, etc.;

[0205] Auxiliary combination. Due to its high complexity and strong multiplicity, only the indicators have range regulations, and other definitions are defined by yourself according to requirements;

[0206] Icons: Specific representative information for a certain application or product;

[0207] 4. Define the implementation interface of the large screen.

[0208] Visible operation interfaces on the large screen combination data list display page: query large screen list, detailed large screen editing interface, large screen deletion interface, large screen preview interface;

[0209] Visible operation interfaces that can be created and edited: data query interface in the component bar, data saving interface, component resource definition acquisition interface;

[0210] The interfaces are sent and responded in the form of json; The logical complexity of the interface business focuses on the data query interface in the component bar and the component resource definition acquisition interface. It is necessary to judge different component types and return diverse data formats for different component data.

[0211] 5. Customize the core data of the large screen.

[0212] Since cloud platform management is a flat-level application, when obtaining the running data of the server, it is necessary to pass through underlying components including cms, horizon, prometheus, etc., and obtain the total amount, usage rate, quantity of the server's CPU, memory, disk, and the alarm information of the problems generated by the server through the HTTP protocol form.

[0213] Perform calculations, combinations, installations, and formatting operations on the obtained data; And perform compliance processing on the data groups with problems without affecting the operations of other data groups.

[0214] For data calculation, calculate the obtained data in the form of percentages, combine and install arrays with different data groups, and format the data in the communication response for the convenience of the applications calling the interfaces.

[0215] 6. Customize the data table of the large screen.

[0216] Large screen data table: Store the data of preset templates and custom templates, where the main data of the large screen is stored in the jsonString data format in the text data type of the database.

[0217] Large screen component table: Stores all the data of the component bar. Since the data in the component bar is displayed in the form of pictures, the data stored in the database is parsed and saved in base64 format.

[0218] Large screen component attribute table: Stores the specific descriptions of the selected components in the component bar, such as: CPU, a certain host node, the size of all CPUs, the actual used size, multiple selection and single selection are available, and all can be selected.

[0219] An embodiment of the present invention further provides a custom large screen implementation device for cloud platform management, including: at least one memory and at least one processor;

[0220] The at least one memory is used to store machine-readable programs;

[0221] The at least one processor is used to call the machine-readable program to implement the custom large screen implementation method described in the above embodiment.

[0222] An embodiment of the present invention further provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the custom large screen implementation method described in the above embodiment is implemented. Specifically, a system or device equipped with a storage medium can be provided, on which software program code for implementing the functions of any one of the above embodiments is stored, and the computer (or CPU or MPU) of the system or device is made to read and execute the program code stored in the storage medium.

[0223] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0224] Embodiments of the storage medium for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer through a communication network.

[0225] In addition, it should be clear that not only can the actual operations be completed in part or in whole by executing the program code read by the computer, but also by instructions based on the program code to cause the operating system and the like operating on the computer, so as to implement the functions of any one of the above embodiments.

[0226] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU or the like installed on the expansion board or the expansion unit is made to execute part or all of the actual operations, thereby implementing the functions of any one of the above embodiments.

[0227] The present invention has been described in detail above with reference to the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above-mentioned multiple embodiments, those skilled in the art can know that more embodiments of the present invention can be obtained by combining the code review means in the above different embodiments, and these embodiments are also within the protection scope of the present invention.

Claims

1. A method for implementing a custom large-screen function managed by a cloud platform, characterized in that: Perform the following operations on the data source, format, and display of physical and virtual machines in the cloud platform cluster: By acquiring and combining data sources, different components are requested to acquire data, process and combine data for specific functions; The usage status of the host and virtual machine is divided through data display; Display data combinations in the form of customized large screens according to changing user needs; The data sources include: CPU, memory, and disk usage data of physical hosts and virtual hosts in the cluster; alarm data of virtual hosts; and product data.

2. According to claim 1, a method for implementing a custom large-screen function managed by a cloud platform is characterized in that: This method is implemented in the form of front-end and back-end separation: The front end uses AngularJs scaffolding, uses the internally encapsulated HTTP protocol technology, and adopts the RestFul code style for communication to obtain the operation data in the data format of the custom large screen; The backend is built using springBoot or springCloud technology, mainly using the Java development language and the RestFul code style. The ORM framework technology spring data JPA is used to obtain data from the PostgreSQL database and format the data in json.

3. A method for implementing a custom large-screen function managed by a cloud platform according to claim 1 or 2, characterized in that: The front-end is based on JavaScript development language, with Angular.js scaffolding as the project architecture, npm as the package management tool, and NG-ZORRO components to implement functions; The backend is based on the Java development language, with the springBoot family bucket as the project architecture and gradle as the package management tool; Data storage: Use PGSQL as the data repository to manage data; Network communication: Use HTTP protocol for external and front-end and back-end communication; Communication specifications: Use RESTFul API interface technology to operate communications; Data template: Use liqiubase technology to create initial data for project operation; Data interaction: The spring data JPA technology in the spring family bucket is used as the interaction standard.

4. The method for implementing a custom large-screen function managed by a cloud platform according to claim 1, characterized in that: The functions of the custom large screen are divided into preset template mode and custom mode: Preset templates are used as the basic templates for data display when the cloud platform system is initialized, so as to facilitate quick display and use; Custom mode: Create host data in the cluster according to specific needs in different scenarios, different times, and different task scenarios.

5. The method for implementing a custom large-screen function managed by a cloud platform according to claim 1, characterized in that: The diversified combination of resource data in the custom large screen is as follows: Initialize the assembly resources, divide them into host and virtual machine, and assemble the corresponding monitoring information including CPU, memory and disk information; They correspond to three main data tables: large-screen data table, large-screen icon indicator table, and large-screen indicator attribute table. The monitored alarm information is used as the data basis to realize the customized large-screen function. Among them, the creation of the data table is implemented according to industry specifications or business specifications.

6. A method for implementing a custom large-screen function managed by a cloud platform according to claim 1 or 5, characterized in that: The specific steps to implement the diversified combination of resource data on the custom large screen are as follows: 1) Define custom large screen categories, including: The preset template will assemble the resource data information and cannot be modified or deleted; Customized templates allow users to display resources in the form of charts, curves, and tables according to their own needs, time, and specifications. Users can create, edit, and delete large screens. 2) Define the operation of custom large screen: The preset template is the system-specific data when the data is initialized. It only needs to be obtained for display and cannot be modified; Custom templates are created by users according to their own needs, so they need to be displayed, created, previewed, edited, and deleted; 3) Define the detailed resource combination in the large screen, including: Charts: tables, pie charts, curves; Auxiliary: text, border, picture, indicator; Icon: represents specific information of a specific application or product; 4) Define the implementation interface of the large screen: The visible operation interfaces on the large-screen combination data list display page: query large-screen list, large-screen editing detailed explanation interface, large-screen deletion interface, and large-screen preview interface; The visible operation interfaces of the pages that can be created and edited include: data query interface of the component bar, data saving interface, and component resource definition acquisition interface; The interfaces are all sent and responded in the form of JSON; the logical complexity of the interface business depends on the data query interface of the component bar and the component resource definition acquisition interface. These two interfaces need to judge different component types and return different component data in a variety of data formats; 5) Customize the core data of the large screen: When you need to obtain the running data of the server, you can use the underlying components including cms, horizon, and prometheus to obtain the total amount, usage rate, and number of the server's CPU, memory, and disk, as well as the alarm information of server problems through the HTTP protocol; Calculate, combine, install, and format the acquired data; and process problematic data groups in compliance without affecting operations on other data groups; Data calculation: calculate the acquired data in percentage form, combine and install arrays in different data groups, and format the communication response data to facilitate the use of the application calling the interface; 6) Customize the data table of the large screen, including: Large screen data table: stores the data of preset templates and custom templates. The main data of the large screen is stored in the jsonString data format using the text data type of the database. Large screen component table: stores all data in the component column. The data stored in the database is parsed and saved in base64 format; Large screen component property table: stores the detailed description of the component selected in the component bar.

7. A method for implementing a custom large-screen function managed by a cloud platform according to claim 6, characterized in that: In step 2), the custom template operation includes: Create an operation. This page is divided into the component bar, large screen custom editing area, and operation bar; The component bar contains charts, icons, and auxiliary modules: Charts: Charts include line graphs, pie charts, texts, and tables; Icons: The range determines which products are displayed based on the actual deployment of the products; Auxiliary: Contains borders, and border titles can be edited; The custom large screen editing area: the area for custom editing of the large screen, where custom editing operations can be performed on the components in the large screen; The operation bar: defines resource data specifically and obtains resource data according to the selection of the component bar; The implementation process of customizing the large screen is as follows: Create a custom large screen. The creation page is divided into three parts: from left to right, the component bar, the large screen editing area or position layout, and the operation bar; The component bar is mainly composed of images, so that you can easily confirm whether it is the component you need; The large screen editing area is an area for storing component planning; The operation bar selects different attributes according to the selected component, responds the backend processing result to the selected component, and displays it.

8. A cloud platform-managed custom large-screen function implementation system, characterized in that: include: The data source acquisition module obtains and processes and combines data from different components for specific functions through the acquisition and combination of data sources; Data display module, used to divide the usage status of hosts and virtual machines; Customized large screen module, used to display data combination in the form of customized large screen; The system realizes a customized large screen through the method described in any one of claims 1 to 7.

9. A device for implementing a custom large-screen function managed by a cloud platform, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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