Cloud platform interface generation method and apparatus

Abstract

The disclosure provides a cloud platform interface generation method, which can be applied to the technical field of cloud computing. The method comprises the following steps: obtaining cloud platform data through a web crawler technology; classifying the cloud platform data to determine first target data and second target data, wherein the first target data comprises environmental information data and application information data, and the second target data comprises application node information and container information; establishing a target model for the first target data and the second target data, wherein the target model comprises an application layer, a template layer and a container layer; and encapsulating the target model to generate interface information, wherein the interface information comprises an interface calling method and an interface document. The disclosure also provides a cloud platform interface generation device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] This disclosure relates to the field of cloud computing technology, specifically to the field of distributed technology, and more specifically to a cloud platform interface generation method, apparatus, device, storage medium, and program product. Background Technology

[0002] As Docker technology matures, it has spurred the development of various leading distributed architecture solutions based on container technology, including Kubernetes. With the accelerated development of on-premises platforms and the increasing scale of applications migrating to the cloud, various customized, visual cloud platform management systems based on open-source distributed architecture frameworks have emerged. These systems provide application operations personnel with convenient visual interfaces by encapsulating the interfaces of open-source distributed architecture frameworks.

[0003] Due to limitations in the cloud platform management interface, it is impossible to directly provide convenient, fast, and effective (API) call interfaces to the outside world. However, if the underlying framework logic is used directly without accessing the cloud platform management interface, on the one hand, the inconsistency between the underlying and encapsulated logic makes it impossible to provide a convenient solution, and on the other hand, bypassing the cloud platform management system to directly call the underlying layer increases the risk of use.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] In view of the above problems, this disclosure provides cloud platform interface generation methods, apparatus, devices, media and program products.

[0006] According to the first aspect of this disclosure, a method for generating cloud platform interfaces is provided, comprising:

[0007] Obtain data from cloud platforms using web crawling technology;

[0008] The cloud platform data is classified to determine first target data and second target data, wherein the first target data includes environmental information data and application information data, and the second target data includes application node information and container information;

[0009] A target model is established for the first target data and the second target data, the target model including an application layer, a template layer, and a container layer; and

[0010] The target model is encapsulated to generate interface information, wherein the interface information includes interface calling methods and interface documentation.

[0011] According to embodiments of this disclosure, classifying the cloud platform data to determine the first target data and the second target data includes:

[0012] Determine the data acquisition speed information and data timeliness information of the cloud platform; and

[0013] Based on the acquisition speed information and the data timeliness information, the cloud platform data classification first target data and second target data are determined.

[0014] According to embodiments of this disclosure, determining the first target data and the second target data for cloud platform data classification based on the acquisition speed information and the data timeliness information includes:

[0015] The method for acquiring cloud platform data is determined based on the acquisition speed information and the data timeliness information; and

[0016] The cloud platform data is classified into first target data and second target data according to the acquisition method of the cloud platform data. The first target data is acquired through real-time access and retrieval, while the second target data is acquired through data persistence.

[0017] According to embodiments of this disclosure, establishing a target model for the first target data and the second target data includes:

[0018] An application layer is established based on the first target data, wherein the application layer includes application information, version information, environment information, basic partition information, and basic parameter information; and

[0019] A template layer and a container layer are established based on the second target data, wherein the template layer includes the application node information and the container layer includes the container information.

[0020] According to embodiments of this disclosure, the application node information includes application node data information and application node template information. The application node data information includes the application node name, application node ID, host IP, and container information contained in the node. The application node template information includes the application node template tag, application node template version information, and application node template hardware and software limitation information.

[0021] According to embodiments of this disclosure, the container information includes container ID, container name, container image information, mapping port information, and storage directory configuration.

[0022] According to embodiments of this disclosure, the method further includes:

[0023] Receive user request information; and

[0024] Based on the required information, the target model is encapsulated to generate a customized interface.

[0025] A second aspect of this disclosure provides a cloud platform interface generation apparatus, comprising: an acquisition module for acquiring cloud platform data through web crawler technology;

[0026] The data classification module is used to classify the cloud platform data to determine the first target data and the second target data, wherein the first target data includes environmental information data and application information data, and the second target data includes application node information and container information;

[0027] A data modeling module is used to establish a target model for the first target data and the second target data, wherein the target model includes an application layer, a template layer, and a container layer; and

[0028] The interface generation module is used to encapsulate the target model to generate interface information, wherein the interface information includes interface calling methods and interface documentation.

[0029] According to embodiments of this disclosure, the data classification module includes:

[0030] The first determining submodule is used to determine the data acquisition speed information and data timeliness information of the cloud platform; and

[0031] The second determining submodule is used to determine the first target data and the second target data of the cloud platform data classification based on the acquisition speed information and the data timeliness information.

[0032] According to embodiments of this disclosure, the second determining submodule includes:

[0033] The determining unit is configured to determine the acquisition method of the cloud platform data based on the acquisition speed information and the data timeliness information; and

[0034] The classification unit is used to classify the cloud platform data into first target data and second target data according to the acquisition method of the cloud platform data, wherein the first target data is acquired through real-time access and retrieval, and the second target data is acquired through data persistence.

[0035] According to embodiments of this disclosure, the data modeling module includes:

[0036] The first data modeling submodule is used to establish an application layer based on the first target data, wherein the application layer includes application information, version information, environment information, basic partition information, and basic parameter information; and

[0037] The second data modeling submodule is used to establish a template layer and a container layer based on the second target data, wherein the template layer includes the application node information and the container layer includes the container information.

[0038] According to embodiments of this disclosure, the apparatus further includes:

[0039] The receiving module is used to receive user request information; and

[0040] The interface customization module encapsulates the target model to generate a customized interface based on the required information.

[0041] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the cloud platform interface generation method described above.

[0042] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the cloud platform interface generation method described above.

[0043] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned cloud platform interface generation method.

[0044] The cloud platform interface generation method provided by this disclosure simulates logging into the cloud platform using web crawler technology to obtain cloud platform data. The cloud platform data is then categorized according to different dimensions to generate first target data and second target data, thereby improving data access efficiency. Target models are established for the first and second target data across three dimensions: application layer, template layer, and container layer. These target models are then encapsulated to generate interface information. Data extraction and integration are performed using web crawler data extraction methods, and the data information is abstracted, modeled, and encapsulated into interfaces. This achieves seamless and secure access to the cloud platform while ensuring the usability of interface encapsulations across different dimensions. It provides developers with an intuitive, convenient, secure, and effective method for calling interfaces, facilitating secondary development and improving development efficiency. Attached Figure Description

[0045] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0046] Figure 1 The illustrations depict application scenarios of cloud platform interface generation methods, apparatus, devices, media, and program products according to embodiments of the present disclosure.

[0047] Figure 2 A flowchart illustrating a cloud platform interface generation method according to an embodiment of the present disclosure is shown schematically.

[0048] Figure 3 One of the flowcharts for a cloud platform data classification method according to an embodiment of the present disclosure is illustrated schematically;

[0049] Figure 4 A second flowchart illustrating a method for classifying cloud platform data according to an embodiment of this disclosure is shown schematically.

[0050] Figure 5 This schematically illustrates a flowchart of establishing a target model from first target data and second target data according to an embodiment of the present disclosure;

[0051] Figure 6 A flowchart illustrating a method for generating a customized interface according to an embodiment of this disclosure is shown schematically.

[0052] Figure 7 This schematically illustrates a structural block diagram of a cloud platform interface generation apparatus according to embodiments of the present disclosure; and

[0053] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a cloud platform interface generation method according to an embodiment of the present disclosure. Detailed Implementation

[0054] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0055] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0056] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0057] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0058] As Docker technology matures, it has spurred the development of various leading distributed architecture solutions based on container technology, including Kubernetes. With the accelerated development of on-premises platforms and the increasing scale of applications migrating to the cloud, various customized, visual cloud platform management systems based on open-source distributed architecture frameworks have emerged. These systems provide application operations personnel with convenient visual interfaces by encapsulating the interfaces of open-source distributed architecture frameworks.

[0059] Meanwhile, due to limitations in the cloud platform management interface, it's impossible to directly provide convenient, fast, and effective API call interfaces. However, bypassing the cloud platform management interface and directly using the underlying framework logic for calls presents two problems: firstly, the inconsistency between the underlying and encapsulated logic prevents a convenient solution; secondly, bypassing the cloud platform management system to directly call the underlying layer increases usage risks. Therefore, a cloud platform-based interface development method is needed. This would facilitate automated operation and maintenance development for developers and operations personnel, while avoiding the security risks associated with bypassing the management platform.

[0060] Based on the aforementioned technical problems, embodiments of this disclosure provide a cloud platform interface generation method, comprising: acquiring cloud management platform data through web crawler technology; classifying the cloud platform data to determine first target data and second target data, wherein the first target data includes environmental information data and application information data, and the second target data includes application node information and container information; establishing a target model for the first target data and the second target data, wherein the target model includes an application layer, a template layer, and a container layer; and encapsulating the target model to generate interface information, wherein the interface information includes interface calling methods and interface documentation.

[0061] Figure 1 The illustration schematically depicts application scenarios of cloud platform interface generation methods, apparatus, devices, media, and program products according to embodiments of the present disclosure.

[0062] like Figure 1As shown, application scenario 100 according to this embodiment may include a cloud platform interface usage scenario. Network 104 is used as a medium to provide a communication link between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links or fiber optic cables, etc.

[0063] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0064] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0065] Server 105 can be a server deployed on a cloud platform, providing cloud application management support services to users. For example, it can be a backend server that responds to interface call commands issued by users using terminal devices 101, 102, and 103, providing cloud management service support (this is just an example). The backend server can analyze and process the received user call requests or interface generation requests, and feed back the processing results (such as data obtained or generated according to user requests) to the terminal devices.

[0066] It should be noted that the cloud platform interface generation method provided in this disclosure embodiment can generally be executed by server 105. Correspondingly, the cloud platform interface generation device provided in this disclosure embodiment can generally be located in server 105. The cloud platform interface generation method provided in this disclosure embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the cloud platform interface generation device provided in this disclosure embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0067] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0068] It should be noted that the cloud platform interface generation method and apparatus determined in the embodiments of this disclosure can be used in the application of cloud computing in the financial field, or in any field other than the financial field. The application field of the cloud platform interface generation method and apparatus determined in the embodiments of this disclosure is not limited.

[0069] The following will be based on Figure 1 The described scene, through Figures 2-6 The cloud platform interface generation method of the disclosed embodiments is described in detail.

[0070] Figure 2 A flowchart illustrating a cloud platform interface generation method according to an embodiment of this disclosure is shown schematically. Figure 2 As shown, the cloud platform interface generation method of this embodiment includes operations S210 to S240, which can be executed by a server or other computing device.

[0071] When operating the S210, data from the cloud platform is obtained through web crawling technology.

[0072] In one example, a simulated login to a cloud platform management (system) is implemented using web crawling technology, with signature verification possible according to security requirements. After the simulated login, relevant environmental data information related to cloud-native applications is extracted from the cloud management platform, i.e., cloud platform data, such as environment information, application information, application node (POD) information, UID, storage configuration, and directories. The methods for obtaining data using web crawling technology can be found in any existing technology, and will not be elaborated upon here.

[0073] In operation S220, the cloud platform data is classified to determine the first target data and the second target data.

[0074] According to embodiments of this disclosure, the first target data includes environmental information data and application information data, and the second target data includes application node information and container information.

[0075] In one example, due to significant differences in the speed and timeliness of data acquisition across different dimensions—such as application-layer information like environment, version, and application information—real-time access is possible. However, when accessing specific application or server (container) information for a particular application type, the access speed decreases dramatically. In other words, the acquisition speed gradually decreases as information density increases. For a specific application or application type template, the acquired information remains essentially unchanged unless there are significant fluctuations in the operational environment.

[0076] Therefore, to reduce the load pressure on interface access, the cloud platform data obtained from operation S210 is classified according to different dimensions into first target data and second target data. The first target data includes environmental information data and application information data, while the second target data includes application node information and container information. For details on the data classification process, please refer to [link to relevant documentation]. Figure 3 Operations S221 to S222 shown will not be described again here.

[0077] In operation S230, a target model is established for the first target data and the second target data.

[0078] According to embodiments of this disclosure, the target model includes an application layer, a template layer, and a container layer.

[0079] In one example, after filtering and classifying application-related information obtained through web crawling, a target model is generated based on three dimensions: the application layer, the template layer, and the container layer. This target model is then used for subsequent interface encapsulation and invocation. The application layer primarily handles information matching for interface access and the acquisition of basic application information, while the template and container layers are mainly used for obtaining detailed application information and handling application container operations. There are certain coupling relationships between the different model layers. Before modeling, data extraction from the cloud platform is necessary to identify the essential data relationships. The process of building the target model can be found in [reference needed]. Figure 4 Operations S231 to S232 are described above.

[0080] In operation S240, the target model is encapsulated to generate interface information.

[0081] According to embodiments of this disclosure, the interface information includes interface calling methods and interface documentation.

[0082] In one example, after modeling the extracted data, the target model is encapsulated into interfaces based on three dimensions: application, template, and container. This encapsulation results in usable interface information, including API call methods and API documentation. The encapsulated API interfaces can not only be used to directly obtain information but also to assist developers in secondary development. For instance, in existing technologies, users perform operations such as obtaining and managing cloud application information by logging into a cloud platform management system, which is cumbersome. Using the method provided in this embodiment, users can directly call the generated cloud platform interface according to the corresponding interface documentation, inputting data that meets the format requirements to obtain the corresponding results. Furthermore, the interfaces generated through operations S210 to S240 meet most usage needs. When developers or operations personnel have other special usage requirements, they can also perform secondary development based on these interfaces, reducing the workload of developers and improving their development efficiency.

[0083] The cloud platform interface generation method provided by this disclosure simulates logging into the cloud platform using web crawler technology to obtain cloud platform data. The cloud platform data is then categorized according to different dimensions to generate first target data and second target data, thereby improving data access efficiency. Target models are established for the first and second target data across three dimensions: application layer, template layer, and container layer. These target models are then encapsulated to generate interface information. Data extraction and integration are performed using web crawler data extraction methods, and the data information is abstracted, modeled, and encapsulated into interfaces. This achieves seamless and secure access to the cloud platform while ensuring the usability of interface encapsulations across different dimensions. It provides developers with an intuitive, convenient, secure, and effective method for calling interfaces, facilitating secondary development and improving development efficiency.

[0084] Figure 3 One of the flowcharts illustrating a method for classifying cloud platform data according to an embodiment of the present disclosure is shown schematically. Figure 4 A second flowchart illustrating a method for classifying cloud platform data according to an embodiment of this disclosure is shown schematically. Figure 3 As shown, operation S220 includes operations S221 to S222.

[0085] In operation S221, the acquisition speed information and data timeliness information of the cloud platform data are determined.

[0086] In operation S222, the cloud platform data classification first target data and second target data are determined based on the acquisition speed information and the data timeliness information.

[0087] In one example, during simulated access using web crawler technology, significant differences exist in the speed and timeliness of data acquisition across different dimensions. For instance, application-layer information such as environment, version, and application information updates frequently, thus requiring high timeliness and fast acquisition speed. Conversely, when accessing server (container) information for a specific application or application type, the update frequency is lower, the timeliness requirement is less stringent, and the access speed is slower. For a particular application or application type template, the acquired information remains largely unchanged unless there are significant fluctuations in the operational environment. Therefore, to reduce the access load on the cloud platform and improve the overall acquisition speed of interface data, cloud platform data is categorized based on its acquisition speed and timeliness for subsequent data modeling and interface encapsulation.

[0088] like Figure 4 As shown, operation S222 includes operation S2221 and operation S2222.

[0089] In operation S2221, the method of acquiring cloud platform data is determined based on the acquisition speed information and the data timeliness information.

[0090] In operation S2222, the cloud platform data is classified into first target data and second target data according to the method of acquiring the cloud platform data.

[0091] According to embodiments of this disclosure, the first target data is obtained through real-time access and retrieval, and the second target data is obtained through data persistence.

[0092] In one example, for encapsulated interfaces such as environment and application that require direct calls and can be retrieved quickly (in real-time), a real-time access and retrieval method is used to extract relevant information; that is, the first data is obtained through real-time access and retrieval. However, for application nodes (PODs), information such as UID, storage configuration, and container environment configuration, which cannot be quickly obtained from the cloud platform and whose information changes without requiring interface calls, is persisted in the database; that is, the second target data is obtained through data persistence. Updates to the second target data are performed in batches on a scheduled basis, thereby reducing the load on the cloud management platform and improving interface access speed.

[0093] Figure 5 A flowchart illustrating the process of establishing a target model from first target data and second target data according to an embodiment of this disclosure is shown. Figure 5 As shown, operation S230 includes operations S231 to S232.

[0094] In operation S231, an application layer is established based on the first target data.

[0095] According to embodiments of this disclosure, the application layer includes application information, version information, environment information, basic partition information, and basic parameter information.

[0096] In operation S232, a template layer and a container layer are established based on the second target data.

[0097] According to an embodiment of this disclosure, the template layer includes the application node information, and the container layer includes the container information.

[0098] According to embodiments of this disclosure, the application node information includes application node data information and application node template information. The application node data information includes the application node name, application node ID, host IP, and container information contained in the node. The application node template information includes the application node template tag, application node template version information, and application node template hardware and software limitation information.

[0099] According to embodiments of this disclosure, the container information includes container ID, container name, image information corresponding to the container, mapping port information, and storage directory configuration.

[0100] In one example, after filtering and classifying the extracted information, a model is built based on the following three dimensions: the application layer, the (application) template layer, and the container layer. The application layer is primarily used for matching information from API access points and obtaining basic application information; this information is retrieved in real-time. The template and container layers are mainly used for obtaining detailed application information and operating the application container; this information is processed through data persistence and periodic updates.

[0101] The application layer primarily contains the required application information, version information, environment information, basic partition information, and basic parameter information. The template layer primarily contains application node data information and application node template information. Application node information mainly includes the application node name, application node ID, host IP address, and contained container information; application node template information mainly includes application node template tags, application node template version information, and application node template hardware and software limitations. The container layer primarily contains the corresponding container's container ID, container name, corresponding image information, mapped port information, and storage directory configuration.

[0102] Figure 6 A flowchart illustrating a method for generating a customized interface according to an embodiment of this disclosure is shown schematically. Figure 6 As shown, this includes operations S310 to S320.

[0103] When operating the S310, receive user request information.

[0104] In operation S320, the target model is encapsulated and a customized interface is generated based on the required information.

[0105] In one example, unlike the generic interface generated by operations S210 to S240, after establishing the three-dimensional data model, a customized interface can be generated by encapsulating the target model based on user requirements. Specifically, it receives user-uploaded requirements, such as querying the working status of an application, retrieving logs from a container, or configuring a container. Customized interfaces are generated by encapsulating the target model across the application layer, template layer, and container layer based on different requirements.

[0106] It is scalable. When an external interface is called, the caller is verified and the interface call data is recorded. The request initiated by the caller is matched with data. At the same time, multi-dimensional auxiliary monitoring can be implemented according to different needs, such as recording the frequency of interface calls and storing relevant call information in the database. Data analysis and data mining can be performed based on the actual access situation.

[0107] Based on the above-described cloud platform interface generation method, this disclosure also provides a cloud platform interface generation apparatus. The following will be combined with... Figure 7 The device is described in detail.

[0108] Figure 7 A schematic block diagram of a cloud platform interface generation apparatus according to an embodiment of the present disclosure is shown.

[0109] like Figure 7 As shown, the cloud platform interface generation device 800 of this embodiment includes an acquisition module 810, a data classification module 820, a data modeling module 830, and an interface generation module 840.

[0110] The acquisition module 810 is used to acquire data from the cloud platform using web crawler technology. In one embodiment, the acquisition module 810 can be used to perform the operation S210 described above, which will not be repeated here.

[0111] The data classification module 820 is used to classify the cloud platform data to determine first target data and second target data. The first target data includes environmental information data and application information data, and the second target data includes application node information and container information. In one embodiment, the data classification module 820 can be used to perform the operation S220 described above, which will not be repeated here.

[0112] The data modeling module 830 is used to establish a target model for the first target data and the second target data. The target model includes an application layer, a template layer, and a container layer. In one embodiment, the data modeling module 830 can be used to perform the operation S230 described above, which will not be repeated here.

[0113] The interface generation module 840 is used to encapsulate the target model to generate interface information, wherein the interface information includes interface calling methods and interface documentation. In one embodiment, the interface generation module 840 can be used to perform the operation S240 described above, which will not be repeated here.

[0114] According to an embodiment of this disclosure, the data classification module 820 includes: a first determination submodule 821 and a second determination submodule 822.

[0115] The first determining submodule 821 is used to determine the acquisition speed information and data timeliness information of the cloud platform data. In one embodiment, the first acquisition submodule 821 can be used to perform the operation S221 described above, which will not be repeated here.

[0116] The second determining submodule 822 is used to determine the first target data and the second target data of the cloud platform data classification based on the acquisition speed information and the data timeliness information. In one embodiment, the second determining submodule 822 can be used to perform the operation S222 described above, which will not be repeated here.

[0117] According to embodiments of this disclosure, the second determining submodule 822 includes a determining unit 8221 and a classifying unit 8222.

[0118] The determining unit 8221 is used to determine the acquisition method of the cloud platform data based on the acquisition speed information and the data timeliness information; in one embodiment, the determining unit 8221 can be used to perform the operation S2221 described above, which will not be repeated here.

[0119] The classification unit 8222 is used to classify the cloud platform data into first target data and second target data according to the acquisition method of the cloud platform data. The first target data is acquired through real-time access and retrieval, while the second target data is acquired through data persistence. In one embodiment, the classification unit 8222 can be used to perform the operation S2222 described above, which will not be repeated here.

[0120] According to an embodiment of this disclosure, the data modeling module 830 includes: a first data modeling submodule 831 and a second data modeling submodule 832.

[0121] The first data modeling submodule 831 is used to establish an application layer based on the first target data, wherein the application layer includes application information, version information, environment information, basic partition information, and basic parameter information. In one embodiment, the first data modeling submodule 831 can be used to perform the operation S231 described above, which will not be repeated here.

[0122] The second data modeling submodule 832 is used to establish a template layer and a container layer based on the second target data, wherein the template layer includes the application node information and the container layer includes the container information. In one embodiment, the second data modeling submodule 832 can be used to perform the operation S232 described above, which will not be repeated here.

[0123] According to embodiments of this disclosure, the cloud platform interface generation apparatus includes a receiving module 850 and an interface customization module 860.

[0124] The receiving module 850 is used to receive user request information. In one embodiment, the receiving module 850 can be used to perform the operation S310 described above, which will not be repeated here.

[0125] The interface customization module 860 is used to encapsulate the target model and generate a customized interface based on the requirement information. In one embodiment, the interface customization module 860 can be used to perform the operation S320 described above, which will not be repeated here.

[0126] According to embodiments of this disclosure, any multiple modules among the acquisition module 810, data classification module 820, data modeling module 830, and interface generation module 840 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the acquisition module 810, data classification module 820, data modeling module 830, and interface generation module 840 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 810, data classification module 820, data modeling module 830, and interface generation module 840 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0127] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a cloud platform interface generation method according to an embodiment of the present disclosure.

[0128] like Figure 8 As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0129] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0130] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0131] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the cloud platform interface generation method according to the embodiments of this disclosure.

[0132] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0133] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the cloud platform interface generation method provided in the embodiments of this disclosure.

[0134] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0135] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0136] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0137] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0139] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0140] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for generating cloud platform interfaces, characterized in that, include: Obtain data from cloud platforms using web crawling technology; The cloud platform data is classified according to the acquisition speed and timeliness of the data to determine the first target data and the second target data. The first target data includes environmental information data and application information data, and the second target data includes application node information and container information. The first target data is obtained through real-time access and retrieval, and the second target data is obtained through data persistence. The classification is used to reduce the access load of the cloud platform interface. A target model is established for the first target data and the second target data. The target model includes an application layer, a template layer, and a container layer. The step of establishing a target model for the first target data and the second target data includes: An application layer is established based on the first target data, wherein the application layer includes application information, version information, environment information, basic partition information, and basic parameter information; Based on the second target data, a template layer and a container layer are established. The template layer includes the application node information, the container layer includes the container information, the application layer is used for interface access information matching and real-time retrieval of basic application information, the template layer is used for persistent storage and periodic updating of application node data information and application node template information, and the container layer is used for persistent storage and periodic updating of container information; and The target model is encapsulated to generate interface information, wherein the interface information includes interface calling methods and interface documentation.

2. The method according to claim 1, characterized in that, The process of classifying the cloud platform data to determine the first target data and the second target data includes: Determine the data acquisition speed information and data timeliness information of the cloud platform; and Based on the acquisition speed information and the data timeliness information, the cloud platform data classification first target data and second target data are determined.

3. The method according to claim 2, characterized in that, The step of determining the first target data and the second target data for cloud platform data classification based on the acquisition speed information and the data timeliness information includes: The method for acquiring cloud platform data is determined based on the acquisition speed information and the data timeliness information; and The cloud platform data is classified into first target data and second target data according to the method of acquiring the cloud platform data.

4. The method according to claim 1, characterized in that, The application node information includes application node data information and application node template information. The application node data information includes the application node name, application node ID, host IP, and container information contained in the node. The application node template information includes the application node template tag, application node template version information, and application node template hardware and software limitation information.

5. The method according to claim 4, characterized in that, The container information includes container ID, container name, corresponding image information, mapped port information, and storage directory configuration.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Receive user request information; and Based on the required information, the target model is encapsulated to generate a customized interface.

7. A cloud platform interface generation device, comprising: The acquisition module is used to acquire data from the cloud platform using web crawler technology. The data classification module is used to classify the cloud platform data according to the acquisition speed and timeliness of the cloud platform data to determine the first target data and the second target data. The first target data includes environmental information data and application information data, and the second target data includes application node information and container information. The first target data is obtained through real-time access and retrieval, and the second target data is obtained through data persistence. The classification is used to reduce the access load of the cloud platform interface. A data modeling module is used to establish a target model for the first target data and the second target data, wherein the target model includes an application layer, a template layer, and a container layer; and An interface generation module is used to encapsulate the target model to generate interface information, wherein the interface information includes interface calling methods and interface documentation; The data modeling module is also used to establish an application layer based on the first target data, wherein the application layer includes application information, version information, environment information, basic partition information, and basic parameter information; and to establish a template layer and a container layer based on the second target data, wherein the template layer includes the application node information, and the container layer includes the container information. The application layer is used for information matching of interface access and real-time retrieval of basic application information, the template layer is used for persistent storage and periodic updating of application node data information and application node template information, and the container layer is used for persistent storage and periodic updating of container information.

8. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 6.

Images