Method for evaluating maturity of whole process of industry private cloud construction

Abstract

The invention relates to the technical field of big data, in particular to an industry private cloud construction whole process maturity evaluation method, which comprises the following steps: analyzing a to-be-evaluated cloud platform to obtain cloud platform structure information; extracting cloud platform function modules from the to-be-evaluated cloud platform, and classifying the cloud platform function modules according to domain categories; for each domain category, respectively constructing a corresponding test case according to the cloud platform description file, then testing the to-be-evaluated cloud platform by adopting the test case, and respectively obtaining test data of the cloud platform function module under the domain category in the test process; and generating an evaluation result under the domain category according to the test data. In order to solve the problems that in the prior art, a cloud platform architecture evaluation method is relatively single in dimension and one-sided in evaluation result, according to the scheme, all functions of a cloud platform are divided into five functional domains, a test process is designed for each functional domain for testing and evaluation, and the maturity level of the cloud platform is comprehensively reflected.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a method for evaluating the maturity of the entire process of building an industry private cloud. Background Art

[0002] An industry private cloud is built for a single customer, thus providing the most effective control over data, security, and service quality. The company owns the infrastructure and can control how applications are deployed on this infrastructure. Private clouds can be deployed within the firewall of an enterprise data center or in a secure hosting location. The core attribute of a private cloud is proprietary resources. Since an industry private cloud is a cloud system built by an enterprise itself, third-party users often cannot judge whether the industry private cloud is reliable when it is provided to a third party.

[0003] To address this problem, the prior art usually requires the introduction of relevant evaluation methods.

[0004] For example, Chinese patent CN202410593909.6 discloses a cloud native technology architecture maturity evaluation method, which relates to the cloud native architecture technology field. The cloud native technology architecture maturity evaluation method includes the following operation steps: S1, cloud native technology architecture; S2, architecture characteristics;

[0005] S3, calculation of the maturity of cloud native technology architecture features; S4, comprehensive maturity assessment of cloud native technology architecture; S5, straightforward explanation. The cloud native technology architecture maturity assessment method calculates the maturity of architecture features such as elasticity, high availability, self-healing, observability, and automation, and obtains the assessment results based on the comprehensive calculation of the maturity of resource management domain, operation and maintenance domain, R&D test domain, and application service domain. The content and results of the assessment process are directly translated through video explanations and examples, so that ordinary users can intuitively and clearly understand the assessment process and results.

[0006] However, the inventor believes that the construction of an industry private cloud platform, as a systematic project, is an iterative process of continuous development and upgrading through stages such as planning and design, construction and development, operation and maintenance management, and operational empowerment. The existing design perspective is relatively single and fails to fully consider the entire life cycle of construction and the requirements and characteristics of the continuous process at different stages, and the evaluation results are relatively one-sided. Summary of the invention

[0007] In view of the above problems existing in the prior art, a method for evaluating the maturity of the entire process of industry private cloud construction is now provided.

[0008] The specific technical solutions are as follows:

[0009] A method for evaluating the maturity of the entire process of building an industry private cloud, including:

[0010] Step S1: obtaining a cloud platform description file for the cloud platform to be evaluated, and obtaining cloud platform structure information according to the analysis of the cloud platform description file;

[0011] Step S2: extracting cloud platform functional modules from the cloud platform to be evaluated according to the cloud platform structure information, and classifying the cloud platform functional modules according to domain categories;

[0012] The domain categories include hardware planning domain, architecture construction domain, operation and maintenance domain, operation domain and application domain;

[0013] Step S3: for each of the domain categories, construct corresponding test cases according to the cloud platform description file, then use the test cases to test the cloud platform to be evaluated, and obtain test data of the cloud platform function modules under the domain category during the test process;

[0014] Step S4: Generate an evaluation result under the domain category according to the test data.

[0015] On the other hand, the step S1 comprises:

[0016] Step S11: obtaining the cloud platform description file, extracting a document style from the cloud platform description file, and determining title information according to the document style;

[0017] Step S12: traversing the cloud platform description file according to the title information, and inserting a segmentation identifier according to the position of the title information in the cloud platform description file;

[0018] Step S13: performing a first semantic recognition process on the title information to construct a title vector, and determining the title information closest to the structural description information as the structural title information according to the title vector;

[0019] Step S14: searching for the structural title associated paragraphs according to the structural title information and the corresponding segment identifiers;

[0020] Step S15: Perform a second semantic recognition process based on the structural title associated paragraphs to obtain semantics and organize the cloud platform structure information.

[0021] On the other hand, the step S2 comprises:

[0022] Step S21: performing word segmentation using a preset dictionary according to the cloud platform structure information to obtain a preliminary word segmentation result and assemble it into a preliminary word segmentation sequence;

[0023] Step S22: performing part-of-speech tagging on the preliminary word segmentation sequence to determine the sequence part-of-speech of each preliminary word segmentation result;

[0024] Step S23: adjusting the preliminary word segmentation result according to the sequence part of speech to obtain an actual word segmentation result;

[0025] Step S24: performing a third semantic recognition process on the actual word segmentation result to obtain the cloud platform function module;

[0026] Step S25: for each of the cloud platform function modules, searching for a function module related paragraph in the cloud platform description file;

[0027] Step S26: Classify the cloud platform functional modules according to the functional module associated sections.

[0028] On the other hand, for the hardware planning domain, the step S3 includes:

[0029] Step A31: constructing first-class test cases respectively according to the cloud platform description file;

[0030] The first type of test cases are generated for the scale of the computer room, the scale of operation and maintenance personnel, the backward compatibility length of the product, and the capacity scale under different business dimension parameter combinations;

[0031] The business dimension parameters include the number of platform concurrency and platform computing power requirements;

[0032] Step A32: determining upper and lower limits of simulation parameters according to the first type of test cases, and organizing a first type of evaluation scale;

[0033] Step A33: generating a simulation environment according to the upper and lower limits of the simulation parameters, and executing the first type of test cases in the simulation environment;

[0034] Step A34: obtaining first test data during the test process;

[0035] The step S4 comprises:

[0036] Step A4: Generate a first evaluation result of the hardware planning domain according to the first test data and the first type of evaluation scale.

[0037] On the other hand, for the architecture construction domain, step S3 includes:

[0038] Step B31: parsing the cloud platform description file to obtain the functional dimensions of the cloud platform to be evaluated;

[0039] Step B32: Generate sub-function test cases for each of the function dimensions;

[0040] Step B33: Amplify the performance of the sub-function test case to increase the required concurrency or required computing power of the sub-function test case to form a function amplification case;

[0041] Step B34: Generate corresponding stability test cases and security attack cases according to the cloud platform function modules associated with the function expansion case;

[0042] The second category of test cases includes the function expansion test case, the stability test case and the security attack test case;

[0043] Step B35: synchronously testing the cloud platform function modules using a combination of the second type of test cases, and obtaining second test data during the testing process;

[0044] The step S4 comprises:

[0045] Step B4: Generate a second evaluation result of the architecture construction domain according to the second test data.

[0046] On the other hand, for the operation and maintenance domain, step S3 includes:

[0047] Step C31: performing a fourth semantic recognition process according to the cloud platform description file to search for resource information and corresponding permission configuration information of the cloud platform to be evaluated;

[0048] The authority configuration information includes asset management information and behavior management information;

[0049] Step C32: generating a resource access test case associated with resource access according to the resource information and the permission configuration information;

[0050] Step C33: construct corresponding fault handling test cases according to the resource access test cases;

[0051] The third category of test cases includes the resource access test cases and the fault handling test cases;

[0052] Step C34: synchronously testing the cloud platform function modules using a combination of the third type of test cases, and obtaining third test data during the testing process;

[0053] The step S4 comprises:

[0054] Step C4: generating a third evaluation result of the operation and maintenance domain according to the third test data.

[0055] On the other hand, for the operation domain, the step S3 includes:

[0056] Step D31: extracting a service catalog and a service operation behavior corresponding to the service catalog according to the cloud platform description file;

[0057] Step D32: searching and obtaining service evaluation information and service behavior management information according to the service operation behavior;

[0058] Step D33: designing a fourth type of test case for the service operation behavior;

[0059] The fourth type of test case designs a complete service behavior link for the service behavior management information and the service evaluation information associated with the service operation behavior;

[0060] Step D34: testing the cloud platform function module using the fourth type of test case, and obtaining fourth test data during the testing process;

[0061] The step S4 comprises:

[0062] Step D4: Generate a fourth evaluation result of the operation domain according to the fourth test data.

[0063] On the other hand, for the application domain, step S3 includes:

[0064] Step E31: Obtaining related information according to the cloud platform description file;

[0065] The associated information includes associated application cloud information, resource utilization information, computing power support information and hardware source information;

[0066] Step E32: designing a fifth type of test case according to the associated information;

[0067] Step E33: testing the cloud platform function module using the fifth type of test case, and obtaining fifth test data during the testing process;

[0068] The step S4 comprises:

[0069] Step E4: Generate a fifth evaluation result of the application domain according to the fifth test data.

[0070] A memory includes computer instructions. When a computer device executes the computer instructions, the above evaluation method is executed.

[0071] The above technical solution has the following advantages or beneficial effects:

[0072] In view of the problem that the cloud platform architecture evaluation methods in the existing technology are relatively single-dimensional and the evaluation results are one-sided, this solution divides the various functions of the cloud platform into five functional domains, and designs a test process for each functional domain to test and evaluate, so as to comprehensively reflect the maturity level of the cloud platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The embodiments of the present invention will be described more fully with reference to the attached drawings, which are provided for illustration and description only and are not intended to limit the scope of the present invention.

[0074] Figure 1 is an overall schematic diagram of an embodiment of the present invention;

[0075] Figure 2 This is a schematic diagram of step S1 of an embodiment of the present invention;

[0076] Figure 3 This is a schematic diagram of step S2 of an embodiment of the present invention;

[0077] Figure 4 This is a schematic diagram of step A3 of an embodiment of the present invention;

[0078] Figure 5 This is a schematic diagram of step B3 of an embodiment of the present invention;

[0079] Figure 6 This is a schematic diagram of step C3 of an embodiment of the present invention;

[0080] Figure 7 This is a schematic diagram of step D3 of an embodiment of the present invention;

[0081] Figure 8 Schematic diagram of step E3 of an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0083] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0084] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0085] The present invention comprises:

[0086] An evaluation method for the maturity of the entire process of private cloud construction in an industry, such as Figure 1 As shown, including:

[0087] Step S1: Obtain a cloud platform description file for the cloud platform to be evaluated, and parse the cloud platform description file to obtain cloud platform structure information;

[0088] Step S2: extracting cloud platform functional modules from the cloud platform to be evaluated according to the cloud platform structure information, and classifying the cloud platform functional modules according to domain categories;

[0089] Domain categories include hardware planning domain, architecture construction domain, operation and maintenance domain, operation domain, and application domain;

[0090] Step S3: for each domain category, construct corresponding test cases according to the cloud platform description file, then use the test cases to test the cloud platform to be evaluated, and obtain test data of the cloud platform function modules under the domain category during the test process;

[0091] Step S4: Generate evaluation results under domain categories based on test data.

[0092] Specifically, in response to the problem that the cloud platform architecture evaluation methods in the existing technology are relatively single-dimensional and the evaluation results are one-sided, the inventor believes that the construction and development of cloud platforms is a spiral process from planning, construction, operation, and application. Its level classification must comprehensively consider planning, technology, management, effectiveness and other aspects, and it is a systematic project.

[0093] Therefore, the industry private cloud maturity assessment model should be designed according to the concept of "three dimensions and five domains", covering the entire life cycle of construction, including planning, construction, operation and maintenance, operation, and effectiveness.

[0094] 1. Five Domains

[0095] Taking into account the life cycle, functional positioning and management needs of the industry's private cloud platform, the evaluation system is constructed as a "three-dimensional five-domain" model. Based on the three dimensions of technology, management and system, the cloud platform functions are subdivided into five key functional domains: cloud platform planning, cloud platform construction, cloud platform operation and maintenance, cloud platform operation and cloud platform empowerment, in order to comprehensively evaluate the comprehensive performance of the industry's private cloud platform.

[0096] (a) Cloud Platform Planning: Assume the “cloud strategy” function and be responsible for improving the management of cloud platform planning.

[0097] (b) Cloud platform construction: transforming cloud strategy into actual technical architecture and service capabilities.

[0098] (c) Cloud platform operation and maintenance: This is the key link to ensure the stable operation of the cloud platform and improve its efficiency through technical management means.

[0099] (d) Cloud platform operation: Maximize the value of cloud services through effective operation strategies and management methods.

[0100] (e) Cloud platform empowerment: Provide powerful impetus for business innovation and development through the technology and resources of the cloud platform.

[0101] 2. Three-dimensional

[0102] The three dimensions refer to the fact that the maturity assessment of cloud platforms needs to comprehensively consider the three aspects of cloud platform technology, management and application effectiveness. It should be pointed out that: (a) each of the five domains of cloud platforms, i.e. each stage of the cloud platform construction cycle, has technology, management and application effectiveness throughout; (b) if only one of the dimensions is considered, it is impossible to fully reflect the maturity level of the cloud platform.

[0103] Therefore, based on the above concepts, the above evaluation method is constructed based on computer technology to facilitate the evaluation of unspecified cloud platforms.

[0104] Specifically, during the construction process of the cloud platform to be evaluated, relevant documents will be generated as cloud platform description files. The cloud platform description files usually contain relevant descriptive information of the cloud platform to be evaluated, including the system architecture, the hardware scale involved, the role of each functional module in the architecture, the working principle and the agreed data interface, etc. The part involving user interaction and external services also includes service evaluation, behavior control and fault operation and maintenance.

[0105] In view of the above content, the semantic recognition method can be used to identify this part of information, so as to extract the cloud platform structure information that can describe the overall structure of the cloud platform.

[0106] Based on the cloud platform structure information, multiple cloud platform functional modules can be extracted from the cloud platform to be evaluated, and the functional modules can be classified according to the role of each cloud platform functional module.

[0107] The classification is based on the above-mentioned five-domain three-dimensional model, dividing the cloud platform functional modules into hardware planning domain, architecture construction domain, operation and maintenance domain, operation domain and application domain.

[0108] For the cloud platform functional modules involved in each domain category, corresponding test processes and test cases can be designed according to the description information in the cloud platform description file, and the test cases can be used to test the cloud platform to be evaluated.

[0109] Taking into account the dependencies between the functional modules of each cloud platform, during the testing process, the test cases will conduct an overall test on the cloud platform to be evaluated based on the preset interface, and obtain the test data of specific cloud platform functional modules under the domain category respectively during the testing process. Finally, the evaluation results are obtained by combining the previously compiled evaluation methods, such as scales.

[0110] Generally speaking, considering factors such as cloud computing service planning level, service process, resource allocation, etc., the maturity of industry private cloud platforms can be assessed into the following levels:

[0111] L1 (initial level): In the traditional chimney-style business construction and management mode, local dispersion, non-reusable software and infrastructure; resources are scattered, and a stable and reliable cloud infrastructure platform has not been formed.

[0112] L2 (basic level): In the initial construction stage of cloud implementation, resource pool resources begin to be standardized, and have collection monitoring and process engines; resources are gradually shared at different levels, underlying IT resources and business are built in layers, and the online cycle begins to shorten to the monthly level.

[0113] L3 (Enhanced): The service catalog is gradually clarified, and IT personnel are oriented to business services. Technically, a shared architecture is adopted, with rich cloud management functions, business-level monitoring, and the platform is gradually automated. The degree of resource sharing is high, and the service launch cycle reaches the weekly level.

[0114] L4 (Excellent): Build an operational process system oriented towards service capabilities; service-driven technology, decoupled architecture, stable development, and strong automation capabilities; rich types of resource sharing, and the business launch cycle reaches the daily level.

[0115]

[0116]

[0117] In one embodiment, Figure 2 As shown, step S1 includes:

[0118] Step S11: obtaining a cloud platform description file, extracting a document style from the cloud platform description file, and determining title information according to the document style;

[0119] Step S12: traverse the cloud platform description file according to the title information, and insert segment identifiers according to the position of the title information in the cloud platform description file;

[0120] Step S13: performing a first semantic recognition process on the title information to construct a title vector, and determining the title information closest to the structural description information as the structural title information according to the title vector;

[0121] Step S14: searching for the structural title associated paragraphs according to the structural title information and the corresponding segmentation identifiers;

[0122] Step S15: Perform a second semantic recognition process based on the structural title associated paragraphs to obtain semantics and organize the cloud platform structure information.

[0123] Specifically, in order to extract the structural information of the cloud platform to be evaluated, in this embodiment, first, the cloud platform description file is obtained, the document style is extracted for the cloud platform description file, and the title information is determined according to the document style. Since the cloud platform description file is a document type file, it usually uses different styles to mark different parts of the article. By extracting title styles at all levels and filtering the file content according to the title style, different title information can be obtained.

[0124] Then, the cloud platform description file is traversed according to the title information, and segment identifiers are inserted according to the position of the title information in the cloud platform description file. There are two segment identifiers corresponding to each title information, the starting position is after the current title information, and the ending position is before the next title information.

[0125] At the same time, the title information is subjected to a first semantic recognition process, including inputting it into a natural language model for processing, extracting semantic features, and then constructing a title vector based on the semantic features. Classification can be performed based on the title vector to determine the roughly corresponding content in each title information, and then the title information closest to the structural description information is determined as the structural title information, that is, the title of the paragraph that describes the functional modules of the cloud platform.

[0126] Then, according to the structure title information and the corresponding segmentation identifier, the structure title related paragraphs are found and the content is parsed, including extracting nouns and contextual relationships between nouns, etc., so as to obtain semantics and organize the cloud platform structure information.

[0127] In one embodiment, Figure 3 As shown, step S2 includes:

[0128] Step S21: using a preset dictionary to perform word segmentation according to the cloud platform structure information to obtain preliminary word segmentation results and assemble them into a preliminary word segmentation sequence;

[0129] Step S22: performing part-of-speech tagging on the preliminary word segmentation sequence to determine the sequence part-of-speech of each preliminary word segmentation result;

[0130] Step S23: adjusting the preliminary word segmentation result according to the sequence part of speech to obtain the actual word segmentation result;

[0131] Step S24: performing a third semantic recognition process on the actual word segmentation result to obtain a cloud platform function module;

[0132] Step S25: for each cloud platform functional module, searching for a functional module related paragraph in the cloud platform description file;

[0133] Step S26: Classify the cloud platform functional modules according to the functional module associated sections.

[0134] Specifically, in order to achieve effective extraction of functional modules, in this embodiment, firstly, a preset dictionary is used for word segmentation according to the cloud platform structure information to obtain preliminary word segmentation results, and the front and back position relationship of each preliminary word segmentation result after word segmentation is retained to form a preliminary word segmentation sequence.

[0135] Since some compound words, modifiers and other contents may appear in the description process of the functional module, which may affect the accuracy of word segmentation, it is necessary to perform part-of-speech tagging on the preliminary word segmentation sequence to determine the sequence part-of-speech of each preliminary word segmentation result.

[0136] Then, the preliminary segmentation results are adjusted according to the sequence part of speech to obtain the actual segmentation results. For example, the combination of modifier + noun is judged to determine whether it meets the characteristics of compound words and then synthesized.

[0137] Finally, the third semantic recognition process is performed on the actual word segmentation results to extract the module names to obtain the cloud platform function modules.

[0138] After obtaining the cloud platform function module, the name of the cloud platform function module is used as a keyword to search for the title information, thereby finding the associated title information and paragraph content, and determining the function module associated paragraph according to the identifier.

[0139] Finally, semantic recognition is performed based on the associated paragraphs of the functional modules to determine their functions and classify them into corresponding functional domains.

[0140] In one embodiment, for a hardware planning domain, such as Figure 4 As shown, step S3 includes:

[0141] Step A31: construct the first type of test cases respectively according to the cloud platform description file;

[0142] The first type of test cases are generated for the scale of the data center, the scale of operation and maintenance personnel, the backward compatibility length of the product, and the capacity scale under different business dimension parameter combinations;

[0143] Business dimension parameters include platform concurrency and platform computing power requirements;

[0144] Step A32: Determine the upper and lower limits of simulation parameters according to the first type of test cases, and organize the first type of evaluation scale;

[0145] Step A33: generating a simulation environment according to the upper and lower limits of the simulation parameters, and executing the first type of test cases in the simulation environment;

[0146] Step A34: obtaining first test data during the test process;

[0147] Step S4 includes:

[0148] Step A4: Generate a first evaluation result of the hardware planning domain according to the first test data and the first type of evaluation scale.

[0149] Specifically, in order to achieve a better testing process for the hardware planning domain, in this embodiment, the parameters of the dimensions of computer room scale, operation and maintenance personnel scale, product backward compatibility length, and capacity scale required by the hardware planning domain are first searched from the cloud platform description file through semantic vectors, thereby determining the approximate quantity range corresponding to the above parameters.

[0150] On this basis, the first type of test cases are constructed, which are mainly used to simulate the operation of cloud platforms at different scales, including capacity expansion and reduction of computer rooms under different business needs, and operation and maintenance under emergencies.

[0151] Combined with the aforementioned extracted quantity range, the upper and lower limits of simulation parameters can be determined for the first type of test cases, and the first type of evaluation scale can be organized, that is, for the achievement rates of different upper and lower limits, the corresponding evaluation results can be converted.

[0152] Then, a simulation environment is generated for the upper and lower limits of the simulation parameters, and the first type of test cases are executed in the simulation environment. The simulation environment mainly simulates the computer room environment. For example, for operation and maintenance test cases, corresponding test computer room simulation scenarios and simulated operation and maintenance personnel are constructed. The occupancy of the workstations during the operation and maintenance process is simulated to determine whether the current hardware planning can meet the needs.

[0153] Finally, first test data is obtained during the test process, and a first evaluation result of the hardware planning domain is generated according to the first test data and the first type of evaluation scale.

[0154] In one embodiment, for the architecture construction domain, such as Figure 5 As shown, step S3 includes:

[0155] Step B31: parse the cloud platform description file to obtain the functional dimensions of the cloud platform to be evaluated;

[0156] Step B32: Generate sub-function test cases for each functional dimension;

[0157] Step B33: Perform performance amplification on the sub-function test case to increase the required concurrency or required computing power of the sub-function test case to form a function amplification case;

[0158] Step B34: Generate corresponding stability test cases and security attack cases according to the cloud platform function modules associated with the function expansion case;

[0159] The second category of test cases includes function expansion test cases, stability test cases and security attack test cases;

[0160] Step B35: Performing synchronous testing on the cloud platform functional modules using a combination of the second type of test cases, and obtaining second test data during the testing process;

[0161] Step S4 includes:

[0162] Step B4: Generate a second evaluation result of the architecture construction domain according to the second test data.

[0163] Specifically, in order to achieve a better evaluation effect on the architecture construction domain, in this embodiment, the functional dimension of the cloud platform to be evaluated is firstly obtained by parsing the cloud platform description file, and the functional dimension is used to characterize the business functions that the cloud platform to be evaluated needs to support.

[0164] Then, for each different functional dimension, sub-functional test cases are generated respectively, and each type of sub-functional test case is used to simulate the test user's operation behavior on the corresponding business segment.

[0165] Accordingly, in order to simulate the performance of the cloud platform, after obtaining the sub-function test cases, the sub-function test cases are also amplified, mainly by modifying the request parameters and other contents to form different test cases, and amplifying them respectively for the required concurrency and required computing power. For example, multiple test cases with the same function and different request objects are combined to form test cases with different concurrency, and the required computing power of the test cases is adjusted by increasing the requested data range and algorithm type. This amplification method realizes the simultaneous verification of the computing power and concurrency performance of the platform during the functional testing process.

[0166] Accordingly, in order to verify the security and stability of the platform, while generating function expansion use cases, corresponding stability test cases and security attack use cases are also generated for the cloud platform function modules that will be called by the function expansion use cases, which together serve as the second type of test cases.

[0167] During the actual testing process, the above test cases are combined and tested to obtain second test data, and a second evaluation result of the architecture construction domain is generated according to the second test data.

[0168] In one embodiment, for the operation and maintenance domain, such as Figure 6 As shown, step S3 includes:

[0169] Step C31: performing a fourth semantic recognition process according to the cloud platform description file to find out the resource information and corresponding permission configuration information of the cloud platform to be evaluated;

[0170] Permission configuration information includes asset management information and behavior management information;

[0171] Step C32: Generate a resource access test case associated with resource access according to the resource information and the permission configuration information;

[0172] Step C33: Construct corresponding fault handling test cases according to resource access test cases;

[0173] The third category of test cases includes resource access test cases and fault handling test cases;

[0174] Step C34: synchronously testing the cloud platform functional modules using a combination of the third type of test cases, and obtaining third test data during the testing process;

[0175] Step S4 includes:

[0176] Step C4: Generate a third evaluation result of the operation and maintenance domain according to the third test data.

[0177] Specifically, in order to achieve a better evaluation effect on the operation and maintenance domain, in this embodiment, the fourth semantic recognition process is first performed on the cloud platform description file to find the resource information of the relevant resources stored in the storage system in the cloud platform to be evaluated; accordingly, in order to achieve the management of resources, asset management information and behavior management information should usually also be configured to constrain the relevant calling and reading behaviors of resources.

[0178] A resource access test case associated with resource access is generated for the collected resource information and permission configuration information to simulate a case in which a user accesses a resource.

[0179] Accordingly, the user's abnormal access behavior should be simulated to build fault handling test cases.

[0180] Finally, the cloud platform functional modules are tested synchronously using a combination of the third type of test cases, and the third test data is obtained during the test process to generate the third evaluation results of the operation and maintenance domain.

[0181] In one embodiment, for the operation domain, such as Figure 7 As shown, step S3 includes:

[0182] Step D31: extracting a service catalog and a service operation behavior corresponding to the service catalog according to the cloud platform description file;

[0183] Step D32: Search and obtain service evaluation information and service behavior management information according to the service operation behavior;

[0184] Step D33: Design the fourth type of test cases for service operation behaviors;

[0185] The fourth type of test case designs a complete service behavior chain for the service behavior management information and service evaluation information associated with the service operation behavior;

[0186] Step D34: testing the cloud platform function module using the fourth type of test case, and obtaining fourth test data during the testing process;

[0187] Step S4 includes:

[0188] Step D4: Generate a fourth evaluation result of the operation domain according to the fourth test data.

[0189] Specifically, in order to realize the evaluation of the operation domain of the cloud platform, in this embodiment, the service catalog and the service operation behaviors corresponding to the service catalog are first extracted according to the cloud platform description file, which mainly include the services provided by the cloud platform to external users, and the service operation behaviors provided by the cloud platform to support related services.

[0190] For each service operation behavior, we further search for service evaluation information and service behavior management information, and then design a fourth type of test case for the service operation behavior to simulate the complete service behavior chain associated with each service operation behavior, including the service behavior management process that needs to be responded to in the background, and the evaluation process after the service behavior is completed.

[0191] Finally, the cloud platform functional modules are tested using the fourth type of test cases, fourth test data are obtained during the testing process, and a fourth evaluation result of the operation domain is generated.

[0192] In one embodiment, for an application domain, such as Figure 8 As shown, step S3 includes:

[0193] Step E31: Obtaining related information according to the cloud platform description file;

[0194] Related information includes related application cloud information, resource utilization information, computing power support information, and hardware source information;

[0195] Step E32: designing a fifth type of test case according to the associated information;

[0196] Step E33: Testing the cloud platform function module using the fifth type of test case, and obtaining fifth test data during the testing process;

[0197] Step S4 includes:

[0198] Step E4: Generate a fifth evaluation result of the application domain according to the fifth test data.

[0199] Specifically, in order to achieve effective testing of the application domain, in this embodiment, firstly, the associated information is obtained according to the cloud platform description file, including the associated application cloud information, resource utilization information, computing power support information and hardware source information. Among them, the associated application cloud information refers to the coverage of the business link corresponding to the cloud platform in the cloud platform, the resource utilization information refers to the application degree of the existing business data in the functional application, and the computing power support information includes the support of the enterprise's hardware for the algorithm module, as well as the hardware source information, such as the localization rate.

[0200] The fifth type of test cases are designed based on the above content, which mainly includes designing evaluation indicators, conducting evaluation according to the evaluation indicators, and finally obtaining the fifth evaluation results.

[0201] A memory includes computer instructions. When a computer device executes the computer instructions, the above evaluation method is executed.

[0202] Those skilled in the art will appreciate that various aspects of the present invention, or possible implementations of various aspects, may be specifically implemented as systems, methods, or computer program products. Therefore, various aspects of the present invention, or possible implementations of various aspects, may take the form of complete hardware embodiments, complete software embodiments (including firmware, resident software, etc.), or embodiments of combined software and hardware aspects, all collectively referred to herein as "circuits," "modules," or "systems." In addition, various aspects of the present invention, or possible implementations of various aspects, may take the form of computer program products, which refer to computer instructions stored in a memory.

[0203] The memory may be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or apparatuses, or any suitable combination of the foregoing, such as random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable read-only memory (CD-ROM).

[0204] The processor in the computer reads the computer instructions stored in the memory, so that the processor can execute the functional actions specified in each step or the combination of steps in the flowchart; and generate a device for implementing the functional actions specified in each block or the combination of blocks in the block diagram.

[0205] It should be understood that the processor in the computer can be understood as one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components implemented to execute the aforementioned computer instructions.

[0206] Computer instructions can be executed completely on the user's local computer, partially on the user's local computer, as a separate software package, partially on the user's local computer and partially on a remote computer, or completely on a remote computer or server. It should also be noted that in some alternative embodiments, the functions noted in each step in the flow chart or each block in the block diagram may not occur in the order noted in the figure. For example, depending on the functions involved, two steps or two blocks shown in succession may actually be executed roughly simultaneously, or these blocks may sometimes be executed in reverse order.

[0207] Of course, in actual application, the various components in the computer system are coupled together through the bus system. It can be understood that the bus system is used to realize the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0208] Those skilled in the art will appreciate that one or more embodiments of the present application may be provided as a method, system or computer program product. Therefore, one or more embodiments of the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, one or more embodiments of the present application may take the form of a computer program product implemented on one or more computer-usable storage media (which may include but are not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0209] The term "and / or" in the present application means at least one of the two. For example, "A and / or B" may include three options: A, B, and "A and B".

[0210] Each embodiment in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the data processing device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0211] The above describes a specific embodiment of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0212] The above are only preferred embodiments of the present invention, and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the maturity of the entire process of private cloud construction in an industry, characterized in that: include: Step S1: obtaining a cloud platform description file for the cloud platform to be evaluated, and parsing the cloud platform description file to obtain cloud platform structure information; Step S2: extracting cloud platform functional modules from the cloud platform to be evaluated according to the cloud platform structure information, and classifying the cloud platform functional modules according to domain categories; The domain categories include hardware planning domain, architecture construction domain, operation and maintenance domain, operation domain and application domain; Step S3: for each of the domain categories, construct corresponding test cases according to the cloud platform description file, then use the test cases to test the cloud platform to be evaluated, and obtain test data of the cloud platform function modules under the domain category during the test process; Step S4: Generate an evaluation result under the domain category according to the test data.

2. The evaluation method according to claim 1, characterized in that: The step S1 comprises: Step S11: obtaining the cloud platform description file, extracting a document style from the cloud platform description file, and determining title information according to the document style; Step S12: traversing the cloud platform description file according to the title information, and inserting a segmentation identifier according to the position of the title information in the cloud platform description file; Step S13: performing a first semantic recognition process on the title information to construct a title vector, and determining the title information closest to the structural description information as the structural title information according to the title vector; Step S14: searching for the structural title associated paragraphs according to the structural title information and the corresponding segment identifiers; Step S15: Perform a second semantic recognition process based on the structural title associated paragraphs to obtain semantics and organize the cloud platform structure information.

3. The evaluation method according to claim 1, characterized in that: The step S2 comprises: Step S21: performing word segmentation using a preset dictionary according to the cloud platform structure information to obtain a preliminary word segmentation result and assemble it into a preliminary word segmentation sequence; Step S22: performing part-of-speech tagging on the preliminary word segmentation sequence to determine the sequence part-of-speech of each preliminary word segmentation result; Step S23: adjusting the preliminary word segmentation result according to the sequence part of speech to obtain an actual word segmentation result; Step S24: performing a third semantic recognition process on the actual word segmentation result to obtain the cloud platform function module; Step S25: for each of the cloud platform function modules, searching for a function module related paragraph in the cloud platform description file; Step S26: Classify the cloud platform functional modules according to the functional module associated sections.

4. The evaluation method according to claim 1, characterized in that: For the hardware planning domain, step S3 includes: Step A31: constructing first-class test cases respectively according to the cloud platform description file; The first type of test cases are generated for the scale of the computer room, the scale of operation and maintenance personnel, the backward compatibility length of the product, and the capacity scale under different business dimension parameter combinations; The business dimension parameters include the number of platform concurrency and platform computing power requirements; Step A32: determining upper and lower limits of simulation parameters according to the first type of test cases, and organizing a first type of evaluation scale; Step A33: generating a simulation environment according to the upper and lower limits of the simulation parameters, and executing the first type of test cases in the simulation environment; Step A34: obtaining first test data during the test process; The step S4 comprises: Step A4: Generate a first evaluation result of the hardware planning domain according to the first test data and the first type of evaluation scale.

5. The evaluation method according to claim 1, characterized in that: For the architecture construction domain, step S3 includes: Step B31: parsing the cloud platform description file to obtain the functional dimensions of the cloud platform to be evaluated; Step B32: Generate sub-function test cases for each of the function dimensions; Step B33: Amplify the performance of the sub-function test case to increase the required concurrency or required computing power of the sub-function test case to form a function amplification case; Step B34: Generate corresponding stability test cases and security attack cases according to the cloud platform function modules associated with the function expansion case; The second category of test cases includes the function expansion test case, the stability test case and the security attack test case; Step B35: synchronously testing the cloud platform function modules using a combination of the second type of test cases, and obtaining second test data during the testing process; The step S4 comprises: Step B4: Generate a second evaluation result of the architecture construction domain according to the second test data.

6. The evaluation method according to claim 1, characterized in that: For the operation and maintenance domain, step S3 includes: Step C31: performing a fourth semantic recognition process according to the cloud platform description file to search for resource information and corresponding permission configuration information of the cloud platform to be evaluated; The authority configuration information includes asset management information and behavior management information; Step C32: generating a resource access test case associated with resource access according to the resource information and the permission configuration information; Step C33: construct corresponding fault handling test cases according to the resource access test cases; The third category of test cases includes the resource access test cases and the fault handling test cases; Step C34: synchronously testing the cloud platform function modules using a combination of the third type of test cases, and obtaining third test data during the testing process; The step S4 comprises: Step C4: generating a third evaluation result of the operation and maintenance domain according to the third test data.

7. The evaluation method according to claim 1, characterized in that: For the operation domain, step S3 includes: Step D31: extracting a service catalog and a service operation behavior corresponding to the service catalog according to the cloud platform description file; Step D32: searching and obtaining service evaluation information and service behavior management information according to the service operation behavior; Step D33: designing a fourth type of test case for the service operation behavior; The fourth type of test case designs a complete service behavior link for the service behavior management information and the service evaluation information associated with the service operation behavior; Step D34: testing the cloud platform function module using the fourth type of test case, and obtaining fourth test data during the testing process; The step S4 comprises: Step D4: Generate a fourth evaluation result of the operation domain according to the fourth test data.

8. The evaluation method according to claim 1, characterized in that: For the application domain, step S3 includes: Step E31: Obtaining related information according to the cloud platform description file; The associated information includes associated application cloud information, resource utilization information, computing power support information and hardware source information; Step E32: designing a fifth type of test case according to the associated information; Step E33: testing the cloud platform function module using the fifth type of test case, and obtaining fifth test data during the testing process; The step S4 comprises: Step E4: Generate a fifth evaluation result of the application domain according to the fifth test data.

9. A memory comprising computer instructions, characterized in that: When the computer device executes the computer instructions, the evaluation method according to any one of claims 1 to 8 is performed.

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