Hybrid multi-cloud management platform maturity evaluation method based on CMP-CMM model, storage medium and equipment

Abstract

The invention discloses a CMP-CMM model-based hybrid multi-cloud management platform maturity evaluation method, a storage medium and equipment, and the method comprises the steps: building a CMP-CMM model for a hybrid multi-cloud management platform from a target layer, a key module layer, an element layer and a function point layer, calculating the development workload of all function points in the function point layer corresponding to each capability index in the element layer, and obtaining the score of each capability index in the element layer; determining a weighting coefficient of each capability index in the element layer based on the information uncertainty, and obtaining a score of each key maturity index in the key module layer; determining a weighting coefficient of each key maturity index in the key module layer based on the judgment matrix, and obtaining a score of each maturity index in the target layer; summing the scores of all the maturity indexes in the target layer to obtain an evaluation score of the maturity of the hybrid multi-cloud management platform, and determining the grade of the maturity of the hybrid multi-cloud management platform according to the evaluation score.

Description

Technical Field

[0001] The present invention relates to the field of hybrid multi-cloud management technology, and in particular to a hybrid multi-cloud management platform maturity evaluation method, storage medium and device based on a CMP-CMM model. Background Art

[0002] "New infrastructure" will accelerate the construction of digital infrastructure and digital industry ecology. The industrial architecture and sub-sectors of hybrid multi-cloud will continue to improve, application scenarios will be further expanded, and related technologies will gradually mature. Enterprises will also gradually embrace the hybrid cloud strategy. In recent years, the digital transformation of enterprises has gradually entered the critical stage, and cloud migration and cloud use have entered a new development cycle, showing the development characteristics of cloud migration of resources to cloud architecture, from rough use to fine governance, and from function priority to security and stability, giving birth to a number of cloud network management industry applications.

[0003] With the increase in the number of public cloud, private cloud and virtualization vendors such as Amazon, Alibaba, Tencent, VMware, etc., the normalization of hybrid multi-cloud has led to an upgrade in management requirements and complexity. There is an urgent need for a hybrid multi-cloud management platform to help enterprises integrate multi-cloud management capabilities and avoid being bound by a single cloud service provider. A hybrid multi-cloud management platform refers to a unified management platform that can simultaneously manage multiple public clouds, multiple private clouds, hybrid clouds, and various heterogeneous resources. The platform can provide a unified management interface and portal to help enterprises efficiently operate underlying resources and capabilities. Therefore, the platform should have the design principles of maturity, openness, scalability, ease of use, security, and high performance.

[0004] At present, there are many hybrid multi-cloud management platform products in China. The degree of functional construction of each product is uneven. The product quotations range from free to millions. Such a large span has greatly affected the judgment of enterprises on cloud management platform procurement. However, there is no evaluation method for the maturity of hybrid multi-cloud management platforms. The existing evaluation models are mostly for software capability maturity such as CMM, project management maturity model such as OPM3, enterprise implementation capability maturity model, logistics capability maturity model such as LCMM, data security capability maturity model DSMM, etc. The maturity models of the above business directions are mostly qualitative and quantitative analysis based on the accumulation of industry practice, or the overall evaluation of the enterprise, which cannot be applied to the micro-evaluation of cloud resource management capability maturity. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a hybrid multi-cloud management platform maturity evaluation method, storage medium and device based on the CMP-CMM model. Through the analysis of four-level capabilities, a micro-evaluation of the maturity of the hybrid multi-cloud management platform is performed, the maturity of the hybrid multi-cloud management platform is comprehensively calculated, and the cloud resource management level of the enterprise is evaluated.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution: a hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model, comprising the following steps:

[0007] Step S1, establish a CMP-CMM model of the hybrid multi-cloud management platform from four levels: target layer, key module layer, element layer and function point layer;

[0008] Step S2, calculating the development workload of all function points in the function point layer corresponding to each capability indicator in the element layer of the CMP-CMM model, and combining the actual usage experience score of each capability indicator in the element layer to obtain the score of each capability indicator in the element layer;

[0009] Step S3: confirm the weighting coefficient of each capability indicator in the factor layer based on the information uncertainty, and weight the scores of all capability indicators in the factor layer corresponding to each key maturity indicator in the key module layer to obtain the score of each key maturity indicator;

[0010] Step S4: Determine the weight coefficient of each key maturity indicator in the key module layer based on the judgment matrix, and weight each maturity indicator in the target layer corresponding to the score of all key maturity indicators in the key module layer to obtain the score of each maturity indicator;

[0011] Step S5: sum the scores of all maturity indicators in the target layer to obtain an evaluation score of the maturity of the hybrid multi-cloud management platform, and determine the maturity level of the hybrid multi-cloud management platform based on the evaluation score.

[0012] Furthermore, in step S1, the target layer is divided into availability capability maturity L1, stability and ease of use capability maturity L2, and operation optimization capability maturity L3. The key module layer further refines each capability maturity in the target layer through key maturity indicators. The availability capability maturity L1 is divided into cross-cloud capability maturity Q1 and cloud management capability maturity Q2. The stability and ease of use capability maturity L2 is divided into security capability maturity Q3 and architecture capability maturity Q4. The operation optimization capability maturity L3 is divided into operation capability maturity Q5, operation and maintenance capability maturity Q6, and data capability maturity Q7. The element layer further refines each capability maturity in the key module layer through element indicators. The function point layer decomposes each element indicator in the element layer according to the function of the multi-cloud management platform.

[0013] Furthermore, the cross-cloud capability maturity is divided into public cloud management capability Q11, private cloud management capability Q12, virtualization management capability Q13, bare metal management capability Q14, container cloud management capability Q15, other cloud management capabilities Q16 and cloud plug-in easy expansion capability Q17; the cloud management capability maturity is divided into computing resource full life cycle management capability Q21, storage resource full life cycle management capability Q22, network resource full life cycle management capability Q23, database resource full life cycle management capability Q24, container resource full life cycle management capability Q25, PaaS middleware resource management capability Q26, resource orchestration capability Q27, resource scheduling and elastic expansion capability Q28 and other cloud management value-added capabilities Q29; the security capability The maturity is divided into security system design capability Q31, security management capability Q32, security audit capability Q33, authority management capability Q34, disaster recovery capability Q35 and other security capabilities Q36; the architecture capability maturity is divided into distributed capability Q41, easy deployment capability Q42, high availability capability Q43, compatibility capability Q44, scalability capability Q45 and other architecture capabilities Q46; the operation capability maturity is divided into visualization large screen capability Q51, cloud service abstraction capability Q52, self-service capability Q53, multi-tenant capability Q54, business organization management capability Q55, workflow management capability Q56, metering and billing management capability Q57, operation reporting capability Q58, business availability management capability Q59 and other value-added operation capabilities Q510; the operation and maintenance capability maturity is divided into monitoring capability Q61, alarm capability Q62, configuration management capability Q63 and other operation and maintenance automation and report value-added capabilities Q64; the data capability maturity is divided into trend analysis capability Q71, resource optimization suggestion capability Q72 and other data AI scenario capabilities Q73.

[0014] Furthermore, the calculation process of the score of each capability indicator in the factor layer is:

[0015] Q ij =TW j ×E j

[0016] Among them, Q ij It indicates the score of the i-th key maturity indicator in the key module layer corresponding to the j-th capability indicator in the element layer, TW j Indicates Q ij Corresponding to the development workload of all function points in the function point layer, S for Q ij Corresponding to the number of all function points in the function point layer, s represents the index of S, M s represents the development workload of the sth function point, C s represents the development complexity coefficient of the sth function point, E j Indicates Qij The actual usage experience rating includes four levels: 1 point for elementary, 2 points for intermediate, 3 points for advanced, and 4 points for top.

[0017] Furthermore, step S3 includes the following sub-steps:

[0018] Step S3.1: Perform multiple expert scoring on each capability indicator in the factor layer, and normalize each expert score:

[0019]

[0020] in, represents the score of the mth expert on the i-th key maturity indicator in the key module layer corresponding to the j-th capability indicator in the element layer, j∈{1,...,t}, t represents the number of capability indicators in the element layer corresponding to the i-th key maturity indicator in the key module layer, express Normalized results;

[0021] Step S3.2: Calculate the proportion of the normalized expert score to the overall expert score Determine the information uncertainty of the jth capability indicator in the factor layer Where k represents the information uncertainty coefficient,

[0022] Step S3.3: Calculate the weight of the jth capability indicator according to the information uncertainty of the jth capability indicator in the factor layer, combined with Get the comprehensive score of the ith key maturity indicator corresponding to the ith capability indicator in the factor layer Where M represents the number of experts, represents the weight of the j-th capability indicator corresponding to the m-th expert,

[0023] Step S3.4: According to b ij Determine the weighting coefficient of the i-th key maturity indicator corresponding to the j-th capability indicator in the factor layer Weight the scores of all capability indicators in the element layer corresponding to the i-th key maturity indicator in the key module layer to obtain the score of the i-th key maturity indicator.

[0024] Furthermore, the construction process of the judgment matrix in step S4 is: a number of experts score the key maturity indicators in all key module layers corresponding to each maturity indicator in the target layer, obtain the score mean matrix of each maturity indicator, and construct the judgment matrix.

[0025] Furthermore, the score M of each maturity indicator in step S4 is Lr The calculation process is:

[0026]

[0027] Among them, L r represents the star rating of the r-th maturity indicator in the target layer, I represents the number of all key maturity indicators in the key module layer corresponding to the r-th maturity indicator in the target layer, i represents the index of I, and α i Represents the weighting coefficient of the i-th key maturity indicator.

[0028] Furthermore, in step S5, the maturity of the hybrid multi-cloud management platform includes: <T<0.25T max When 0.25T max ≤T<0.5T max When 0.5T max ≤T<0.6T max When T≥0.6T, the maturity of the hybrid multi-cloud management platform is level 3 optimization; when T≥0.6T max At this time, the maturity of the hybrid multi-cloud management platform is level 4, full capability.

[0029] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model.

[0030] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model is implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects: the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model of the present invention comprehensively covers cloud management capability, cross-cloud capability, security capability, architecture capability, operation capability, operation and maintenance capability, and data capability by establishing a multi-cloud management platform maturity evaluation (Capability Maturity Model of Cloud Management Platform, CMP-CMM) model from four levels: target layer, key module layer, element layer, and function point layer, and classifies and hierarchically maps various indicators, thereby ensuring the integrity and multi-dimensionality of the maturity evaluation of the hybrid multi-cloud management platform, and being able to three-dimensionally reflect the comprehensive capabilities of the multi-cloud management platform throughout its life cycle from bottom-level management to operation and governance; at the same time, the present invention confirms the weight coefficient of each capability indicator in the element layer based on information uncertainty, and determines the weight coefficient of each key maturity indicator in the key module layer based on a judgment matrix, which can effectively integrate the weight relationship of indicators at each level, making the weight distribution of indicators at each level in the CMP-CMM model more scientific and reasonable, and ensuring the fairness and reliability of the maturity evaluation of the hybrid multi-cloud management platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model of the present invention;

[0033] Figure 2 Schematic diagram of the CMP-CMM model in the present invention;

[0034] Figure 3 This is a detailed diagram of the element layer indicators in the CMP-CMM model. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0036] like Figure 1 The flowchart of the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model of the present invention is as follows:

[0037] Step S1: In view of the drawbacks of the current hybrid cloud management platform maturity evaluation process, in which the function points are flatly listed and the scoring method does not consider the platform characteristics, but only considers the weighted R&D complexity coefficient, the present invention establishes a CMP-CMM model for the hybrid multi-cloud management platform from four levels: the target layer, the key module layer, the element layer, and the function point layer, which is used to present the maturity of the hybrid multi-cloud management platform in a three-dimensional way, thereby concretely evaluating the cloud resource management level of the enterprise; Figure 2The CMP-CMM model in the present invention includes: 3-7-45-N level capability indicators, where "3" is the three maturity indicators in the target layer, including: availability capability maturity L1, stability and ease of use capability maturity L2 and operation optimization capability maturity L3; "7" is the key module layer, which further refines each capability maturity in the target layer through key maturity indicators, and there are 7 key maturity indicators in total: availability capability maturity L1 is divided into cross-cloud capability maturity Q1 and cloud management capability maturity Q2, stability and ease of use capability maturity L2 is divided into security capability maturity Q3 and architecture capability maturity Q4, and operation optimization capability maturity L3 is divided into operation capability maturity Q5, operation and maintenance capability maturity Q6 and data capability maturity Q7; "45" is the element layer, which further refines each capability maturity in the key module layer through element indicators, and there are 45 capability indicators in total; "N" is the function point layer, which decomposes each element indicator in the element layer according to the function of the multi-cloud management platform. The CMP-CMM model established in the present invention is divided into four levels of indicators, which are progressive and can conduct a comprehensive evaluation of the maturity of the hybrid multi-cloud management platform from macro to micro perspectives, comprehensively covering cloud management capabilities, cross-cloud capabilities, security capabilities, architecture capabilities, operation capabilities, operation and maintenance capabilities, and data capabilities, ensuring the integrity and multi-dimensionality of the maturity evaluation of the hybrid multi-cloud management platform.

[0038] like Figure 3The cross-cloud capability maturity is divided into public cloud management capability Q11, private cloud management capability Q12, virtualization management capability Q13, bare metal management capability Q14, container cloud management capability Q15, other cloud management capabilities Q16 and cloud plug-in easy expansion capability Q17; the cloud management capability maturity is divided into computing resource full life cycle management capability Q21, storage resource full life cycle management capability Q22, network resource full life cycle management capability Q23, database resource full life cycle management capability Q24, container resource full life cycle management capability Q25, PaaS middleware resource management capability Q26, resource orchestration capability Q27, resource scheduling and elastic expansion capability Q28 and other cloud management value-added capabilities Q29; the security capability maturity is divided into security system design capability Q31, security management capability Q32, security audit capability Q33, permission management capability Q34, disaster recovery capability Q35 and other security capabilities Q36; the architectural capability maturity is divided into distributed capability Q41, easy deployment capability Q42, high availability capability Q43, compatibility capability Q44, scalability capability Q45 and other architectural capabilities Q46; the operational capability maturity is divided into visual large screen capability Q51, cloud service abstraction capability Q52, self-service capability Q53, multi-tenant capability Q54, business organization management capability Q55, workflow management capability Q56, metering and billing management capability Q57, operational reporting capability Q58, business availability management capability Q59 and other value-added operational capabilities Q510; the operational capability maturity is divided into monitoring capability Q61, alarm capability Q62, configuration management capability Q63 and other operational automation and reporting value-added capabilities Q64; the data capability maturity is divided into trend analysis capability Q71, resource optimization suggestion capability Q72 and other data AI scenario capabilities Q73.

[0039] Step S2: Calculate the development workload of all function points in the function point layer corresponding to each capability indicator in the element layer of the CMP-CMM model, and combine the actual usage experience score of each capability indicator in the element layer to obtain the score of each capability indicator in the element layer.

[0040] The calculation process of the score of each capability indicator in the factor layer is:

[0041] Q ij =TW j ×E j

[0042] Among them, Q ij It indicates the score of the i-th key maturity indicator in the key module layer corresponding to the j-th capability indicator in the element layer, TW j Indicates Q ij Corresponding to the development workload of all function points in the function point layer, S for Q ijCorresponding to the number of all function points in the function point layer, s represents the index of S, Ms represents the development workload of the sth function point, and C s represents the development complexity coefficient of the sth function point, E j Indicates Q ij The actual usage experience score includes four levels: 1 point for elementary, 2 points for intermediate, 3 points for advanced, and 4 points for top level. The specific actual usage experience scores are shown in Table 1.

[0043] Table 1: Actual embodiment scoring rules used by experts

[0044]

[0045]

[0046] Step S3: based on the information uncertainty, determine the weighting coefficient of each capability indicator in the element layer, and weight the scores of all capability indicators in the element layer corresponding to each key maturity indicator in the key module layer to obtain the score of each key maturity indicator. In the present invention, the information uncertainty is calculated based on the scores of the capability indicators by each expert, so as to determine the contribution of all capability indicators corresponding to each key maturity indicator in the key module layer to its influence, thereby improving the fairness and objectivity of the evaluation; including the following sub-steps:

[0047] Step S3.1: Perform multiple expert scoring on each capability indicator in the factor layer, and normalize each expert score:

[0048]

[0049] in, represents the score of the mth expert on the i-th key maturity indicator in the key module layer corresponding to the j-th capability indicator in the element layer, j∈{1,...,i}, t represents the number of capability indicators in the element layer corresponding to the i-th key maturity indicator in the key module layer, express Normalized results;

[0050] Step S3.2: Calculate the proportion of the normalized expert score to the overall expert score Determine the information uncertainty of the jth capability indicator in the factor layer Where k represents the information uncertainty coefficient,

[0051] Step S3.3: Calculate the weight of the jth capability indicator according to the information uncertainty of the jth capability indicator in the factor layer, combined with Obtain the comprehensive score of the jth capability indicator in the factor layer corresponding to the i-th key maturity indicator Where M represents the number of experts, represents the weight of the th capability indicator corresponding to the mth expert,

[0052] Step S3.4: According to b ij Determine the weighting coefficient of the i-th key maturity indicator corresponding to the j-th capability indicator in the factor layer Weight the scores of all capability indicators in the element layer corresponding to the i-th key maturity indicator in the key module layer to obtain the score of the i-th key maturity indicator.

[0053] Step S4: Several experts score the key maturity indicators in all key module layers corresponding to each maturity indicator in the target layer, obtain the score mean matrix of each maturity indicator, and construct a judgment matrix, wherein each element in the judgment matrix represents the importance of a maturity indicator relative to another maturity indicator. The weighting coefficient of each key maturity indicator in the key module layer is determined based on the judgment matrix. Specifically, the relative weight of each element is determined by calculating the geometric mean of each row of elements in the judgment matrix, thereby obtaining the weighting coefficient of each key maturity indicator. This method decomposes, compares and comprehensively judges the key maturity indicators, greatly reducing the influence of subjective factors on the judgment of the importance of key maturity indicators, so that the evaluation of each maturity indicator in the target layer has high reliability. The scores of all key maturity indicators in the key module layer corresponding to each maturity indicator in the target layer are weighted to obtain the score of each maturity indicator.

[0054] The score M for each maturity indicator Lr The calculation process is:

[0055]

[0056] Among them, L r represents the star rating of the r-th maturity indicator in the target layer, I represents the number of all key maturity indicators in the key module layer corresponding to the r-th maturity indicator in the target layer, i represents the index of I, and α i Represents the weighting coefficient of the i-th key maturity indicator.

[0057] Step S5: Sum the scores of all maturity indicators in the target layer to obtain the evaluation score of the maturity of the hybrid multi-cloud management platform, and determine the maturity level of the hybrid multi-cloud management platform according to the evaluation score, including: <T<0.25T max When 0.25T max≤T<0.5T max When 0.5T max ≤T<0.6T max When T≥0.6T, the maturity of the hybrid multi-cloud management platform is level 3 optimization; when T≥0.6T max When the hybrid multi-cloud management platform maturity is 4 full-capability level, this rating method can effectively integrate the weight relationship of indicators at each level, making the weight distribution of indicators at each level in the CMP-CMM model more scientific and reasonable, and ensuring the reliability of the hybrid multi-cloud management platform maturity evaluation. In addition, each maturity indicator in the target layer can also be rated, and the results are shown in Table 2:

[0058] Table 2: Maturity level distribution

[0059]

[0060] The hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model of the present invention integrates the objective evaluation based on development workload as the evaluation standard and the user evaluation based on the actual usage experience of experts as the evaluation standard. It not only considers the platform capabilities from the dimension of technical development workload, but also evaluates the platform's user experience and the practical value of various functions from the perspective of actual usage needs, so that the maturity evaluation of the hybrid multi-cloud management platform can be more in line with the evaluation system of real users. It can not only help enterprises identify the technical maturity of various indicators of the multi-cloud management platform, but also screen them from the perspective of usage experience. At the same time, it helps multi-cloud management platforms on the market identify their own capability deficiencies and improve the overall user experience of the platform in a targeted manner.

[0061] In a technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program, wherein the computer program enables a computer to execute the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model.

[0062] In a technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model is implemented.

[0063] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0064] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model, characterized in that: The steps include: Step S1, establish a CMP-CMM model of the hybrid multi-cloud management platform from four levels: target layer, key module layer, element layer and function point layer; Step S2, calculating the development workload of all function points in the function point layer corresponding to each capability indicator in the element layer of the CMP-CMM model, and combining the actual usage experience score of each capability indicator in the element layer to obtain the score of each capability indicator in the element layer; Step S3: confirm the weighting coefficient of each capability indicator in the factor layer based on the information uncertainty, and weight the scores of all capability indicators in the factor layer corresponding to each key maturity indicator in the key module layer to obtain the score of each key maturity indicator; Step S4: Determine the weight coefficient of each key maturity indicator in the key module layer based on the judgment matrix, and weight each maturity indicator in the target layer corresponding to the score of all key maturity indicators in the key module layer to obtain the score of each maturity indicator; Step S5: sum the scores of all maturity indicators in the target layer to obtain an evaluation score of the maturity of the hybrid multi-cloud management platform, and determine the maturity level of the hybrid multi-cloud management platform based on the evaluation score.

2. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 1, characterized in that: In step S1, the target layer is divided into availability capability maturity L1, stability and ease of use capability maturity L2, and operation optimization capability maturity L3. The key module layer further refines each capability maturity in the target layer through key maturity indicators. The availability capability maturity L1 is divided into cross-cloud capability maturity Q1 and cloud management capability maturity Q2. The stability and ease of use capability maturity L2 is divided into security capability maturity Q3 and architecture capability maturity Q4. The operation optimization capability maturity L3 is divided into operation capability maturity Q5, operation and maintenance capability maturity Q6, and data capability maturity Q7. The element layer further refines each capability maturity in the key module layer through element indicators. The function point layer decomposes each element indicator in the element layer according to the function of the multi-cloud management platform.

3. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 2, characterized in that: The cross-cloud capability maturity is divided into public cloud management capability Q11, private cloud management capability Q12, virtualization management capability Q13, bare metal management capability Q14, container cloud management capability Q15, other cloud management capabilities Q16 and cloud plug-in easy expansion capability Q17; the cloud management capability maturity is divided into computing resource full life cycle management capability Q21, storage resource full life cycle management capability Q22, network resource full life cycle management capability Q23, database resource full life cycle management capability Q24, container resource full life cycle management capability Q25, PaaS middleware resource management capability Q26, resource orchestration capability Q27, resource scheduling and elastic expansion capability Q28 and other cloud management value-added capabilities Q29; the security capability maturity is divided into security system design capability Q31, security management capability Q32, security audit capability Q33, authority management capability Q34, disaster recovery capability Q35 and other Other security capabilities Q36; the architectural capability maturity is divided into distributed capability Q41, easy deployment capability Q42, high availability capability Q43, compatibility capability Q44, scalability capability Q45 and other architectural capabilities Q46; the operational capability maturity is divided into visualization large screen capability Q51, cloud service abstraction capability Q52, self-service capability Q53, multi-tenant capability Q54, business organization management capability Q55, workflow management capability Q56, metering and billing management capability Q57, operational reporting capability Q58, business availability management capability Q59 and other value-added operational capabilities Q510; the operational maintenance capability maturity is divided into monitoring capability Q61, alarm capability Q62, configuration management capability Q63 and other operational maintenance automation and reporting value-added capabilities Q64; the data capability maturity is divided into trend analysis capability Q71, resource optimization suggestion capability Q72 and other data AI scenario capabilities Q73.

4. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 3, characterized in that: The calculation process of the score of each capability indicator in the factor layer is: Q ij =TW j ×E j Among them, Q ij It indicates the score of the i-th key maturity indicator in the key module layer corresponding to the j-th capability indicator in the element layer, TW j Indicates Q ij Corresponding to the development workload of all function points in the function point layer, S for Q ij Corresponding to the number of all function points in the function point layer, s represents the index of S, M s represents the development workload of the sth function point, C s represents the development complexity coefficient of the sth function point, E j Indicates Q ij The actual usage experience rating includes four levels: 1 point for elementary, 2 points for intermediate, 3 points for advanced, and 4 points for top.

5. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 4, characterized in that: Step S3 includes the following sub-steps: Step S3.1: Perform multiple expert scoring on each capability indicator in the factor layer, and normalize each expert score: in, represents the score of the mth expert on the i-th key maturity indicator in the key module layer corresponding to the j-th capability indicator in the element layer, j∈{1,...,t}, t represents the number of capability indicators in the element layer corresponding to the i-th key maturity indicator in the key module layer, express Normalized results; Step S3.2: Calculate the proportion of the normalized expert score to the overall expert score Determine the information uncertainty of the jth capability indicator in the factor layer Where k represents the information uncertainty coefficient, Step S3.3: Calculate the weight of the jth capability indicator according to the information uncertainty of the jth capability indicator in the factor layer, combined with Obtain the comprehensive score of the jth capability indicator in the factor layer corresponding to the i-th key maturity indicator Where M represents the number of experts, represents the weight of the j-th capability indicator corresponding to the m-th expert, Step S3.4: According to b ij Determine the weighting coefficient of the i-th key maturity indicator corresponding to the j-th capability indicator in the factor layer Weight the scores of all capability indicators in the element layer corresponding to the i-th key maturity indicator in the key module layer to obtain the score of the i-th key maturity indicator.

6. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 5, characterized in that: The construction process of the judgment matrix in step S4 is as follows: a number of experts score the key maturity indicators in all key module layers corresponding to each maturity indicator in the target layer, obtain the score mean matrix of each maturity indicator, and construct the judgment matrix.

7. A hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 6, characterized in that: The score M of each maturity indicator in step S4 Lr The calculation process is: Among them, L r represents the star rating of the r-th maturity indicator in the target layer, I represents the number of all key maturity indicators in the key module layer corresponding to the r-th maturity indicator in the target layer, i represents the index of I, and α i Represents the weighting coefficient of the i-th key maturity indicator.

8. The hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model according to claim 7 is characterized in that: The maturity of the hybrid multi-cloud management platform in step S5 includes: <T<0.25T max When 0.25T max ≤T<0.5T max When 0.5T max ≤T<0.6T max When T≥0.6T, the maturity of the hybrid multi-cloud management platform is level 3 optimization; when T≥0.6T max At this time, the maturity of the hybrid multi-cloud management platform is level 4, full capability.

9. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model as described in any one of claims 1 to 8.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the hybrid multi-cloud management platform maturity evaluation method based on the CMP-CMM model as described in any one of claims 1 to 8 is implemented.

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