Cloud platform cost management service intelligent analysis method and system

Abstract

The invention provides a cloud platform cost management service intelligent analysis method and system, and relates to the technical field of cost service recommendation. The method comprises the following steps: acquiring historical cost service record data of a cloud platform, performing stage association analysis on a plurality of cost management services provided by the cloud platform, and constructing to obtain a stage service association matrix; performing stage contribution analysis on a plurality of cost management services provided by the cloud platform, and constructing to obtain a service local cooperation matrix of each software project; performing service popularity difference analysis on each software project stage and generating a popularity difference distribution matrix of each industry; and after the industry data and the project stage data of the target user are determined, determining a plurality of candidate services about the target user according to the stage service association matrix and the popularity difference distribution matrix, determining a plurality of target cost services from the plurality of candidate services, and generating a cost management service adaptation analysis result. According to the invention, the suitability of the user with different services is accurately analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of cost service recommendation, and in particular to an intelligent analysis method and system for cost management services on a cloud platform. Background Art

[0002] In the modern software development process, project cost management has become an important part of enterprise operations and resource optimization. More and more companies that provide software project cost management services use cloud platforms to carry their own services and provide corresponding cost management services for users' different software project statuses. These cost management services aim to help users reduce development risks, optimize resource allocation, and increase project success rates. However, with the diversification of user needs and the increasing differences in industry characteristics, how to accurately recommend suitable service projects to different users has become a key issue in platform promotion.

[0003] There are certain differences in the demand for cost management services in different industries, and there is a certain combination relationship between multiple services, that is, multiple services can generate higher value when used together. However, some service analysis solutions focus on the recommendation of a single service and pay insufficient attention to the synergy between services. As a result, there are limitations in the process of analyzing the adaptability of multiple services to the user's current project status based on the popularity of different services at different stages of the software project, resulting in a low match between the analysis results and the user's actual needs. Summary of the invention

[0004] The present invention proposes a cloud platform cost management service intelligent analysis method and system, aiming to solve at least one technical problem existing in the above-mentioned background technology.

[0005] A first aspect of the present invention provides a cloud platform cost management service intelligent analysis method, comprising: Obtain historical cost service record data of multiple users on the cloud platform, perform phase correlation analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the selection frequency parameters and global contribution parameters of each cost management service for different software project phases, and construct a phase service correlation matrix between software project phases and cost management services; Extract the industry data of different users from the historical cost service record data, conduct stage contribution analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the local contribution parameters and synergy gain parameters of each cost management service in each software project stage in different industries, and construct the local synergy matrix of services in different industries in each software project stage; According to the historical cost service record data, the service heat difference analysis is carried out for each software project stage, the heat difference parameters of each cost management service in each software project stage in different industries are calculated, and the heat difference distribution matrix of cost management services in each industry is generated; After determining the industry data and project stage data of the target user, multiple candidate services are determined based on the stage service association matrix and the heat difference distribution matrix. Combined with the service local collaboration matrix of the software project stage, multiple target cost services are determined from multiple candidate services to generate the cost management service adaptation analysis results for the target user.

[0006] Preferably, the local contribution parameter and synergy gain parameter of each cost management service in each software project stage in different industries are calculated, and a local synergy matrix of services in different industries in each software project stage is constructed, including: For any software project phase Cost management services , calculated based on historical cost service record data Cost management services The selection frequency parameters and success contribution parameters in different industries are used to modify the success contribution parameters based on the selection frequency parameters to generate the first Cost management services Local contribution parameters within different industries; Calculate the cost based on historical cost service record data Cost management services The global gain parameter, and Cost management services The local gain parameters in different industries are adjusted according to the global gain parameters. Cost management services The multiple local gain parameters are modified to generate the Cost management services The synergy gain parameters in different industries are calculated based on the local contribution parameters and synergy gain parameters. Cost management services The service coordination parameters in different stages of the industry are constructed to obtain the Cost management services About the local synergy matrix of services within different industries.

[0007] Preferably, for the global gain parameter and the local gain parameter, it also includes: Use the following formula to calculate the Cost management services The global gain parameter of : In the formula, For the Cost management services and Cost management services Collaborative Cost management services The global gain parameter, To include Cost management services and Cost management services The success rate of software projects under the conditions of To include Cost management services The success rate of software projects under the conditions of Use the following formula to calculate the Cost management services The local gain parameters in the target industry: In the formula, The first in the target industry Cost management services and Cost management services Collaborative Cost management services The local gain parameter, For the target industry, including Cost management services and Cost management services The success rate of software projects under the conditions of For the target industry, including Cost management services The success rate of software projects under certain conditions.

[0008] Preferably, multiple candidate services are determined according to the stage service association matrix and the heat difference distribution matrix, and multiple target cost services are determined from the multiple candidate services in combination with the service local collaboration matrix of the software project stage, including: According to the target user's industry data and project stage data, determine the target user's heat difference parameters for each cost management service from the heat difference distribution matrix, and determine the target user's stage service association parameters for each cost management service from the stage service association matrix; According to the heat difference parameters and the stage service association parameters, multiple candidate services are screened out from the multiple cost management services provided by the cloud platform, including calculating the global matching degree of each cost management service according to the heat difference parameters and the stage service association parameters, screening out the cost management services with a global matching degree greater than a preset matching threshold and recording them as candidate services, determining the adaptation parameters of each candidate service, and determining multiple target cost services from the multiple candidate services according to the adaptation parameters.

[0009] Preferably, determining the adaptation parameters of each candidate service, and determining multiple target cost services from multiple candidate services according to the adaptation parameters, including: Determine the local service coordination matrix corresponding to the target user based on the target user's industry data and project phase data, record the candidate service corresponding to the maximum global matching degree as the benchmark cost service, remove the benchmark cost service from multiple candidate services, and write it into the adaptation analysis list; Analyze multiple candidate services through the service local coordination matrix and the benchmark cost service, determine the stage service coordination parameter of each candidate service under the benchmark cost service according to the service local coordination matrix, sort the multiple candidate services according to the stage service coordination parameter and the benchmark cost service, including weighting the adaptation parameter by the stage service coordination parameter to generate a sorting value, and sort the multiple candidate services based on the sorting value; The benchmark cost service is updated according to the candidate service ranked first, and the updated benchmark cost service is removed from the multiple candidate services and written into the adaptation analysis list. The above operation is repeated until all candidate services are written into the adaptation analysis list, and the adaptation parameters of the multiple candidate services are determined according to the order in which the candidate services are written into the adaptation analysis list. The candidate service whose adaptation parameter is greater than the preset adaptation threshold is recorded as the target cost service.

[0010] Preferably, the heat difference parameter of each cost management service in each software project stage in different industries is calculated to generate a heat difference distribution matrix of cost management services in each industry, including: For any software project phase Cost management services , according to Cost management services The selection frequency parameters in different industries are calculated using the following formula Cost management services The degree of choice difference between any two industries: In the formula, Indicates Cost management services industry and Industry The difference in choice between , Respectively represent Cost management services In the industry and Industry The selection frequency parameter within; The first Cost management services The average value of multiple selection differences in the software project stage is obtained to obtain the selection difference parameter, and the first Cost management services The heat value in the software project stage is modified by selecting the difference parameter to generate the first heat value in the software project stage. Cost management services According to the heat difference parameters of multiple cost management services in different software project stages, a heat difference distribution matrix of cost management services in each industry is constructed.

[0011] A second aspect of the present invention provides a cloud platform cost management service intelligent analysis system, which is used to implement the above-mentioned cloud platform cost management service intelligent analysis method, including: The historical data acquisition module is used to obtain the historical cost service record data of multiple users on the cloud platform; The phase service correlation analysis module is used to perform phase correlation analysis on multiple cost management services provided by the cloud platform based on historical cost service record data, calculate the selection frequency parameters and global contribution parameters of each cost management service for different software project phases, and construct a phase service correlation matrix between software project phases and cost management services; The service local collaboration analysis module is used to extract the industry data of different users from the historical cost service record data, conduct stage contribution analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the local contribution parameters and collaborative gain parameters of each cost management service in each software project stage in different industries, and construct a service local collaboration matrix for different industries in each software project stage; The heat difference distribution analysis module is used to perform service heat difference analysis on each software project stage based on historical cost service record data, calculate the heat difference parameters of each cost management service in each software project stage in different industries, and generate a heat difference distribution matrix for cost management services in each industry; The service adaptation analysis module is used to determine multiple candidate services based on the stage service association matrix and the heat difference distribution matrix after determining the industry data and project stage data of the target user, and to determine multiple target cost services from multiple candidate services in combination with the service local collaboration matrix of the software project stage to generate the cost management service adaptation analysis results for the target user.

[0012] The present invention has the following beneficial effects: The present invention conducts stage correlation analysis on multiple cost management services provided by the cloud platform from a global perspective, determines the global correlation characteristics between different cost management services and different software project stages, conducts stage contribution analysis on multiple cost management services from an industry perspective, determines the intra-industry local synergy characteristics between multiple cost management services in different software project stages under different industries, conducts service heat difference analysis on multiple cost management services from the perspective of industry heat difference, determines the heat difference hotline of cost management services in different industries, quantifies the adaptability and synergy benefits of the services from a global and local perspective, and finally analyzes the adaptability between users and different cost management services according to the industry fields associated with users and the actual development stages of software projects, thereby providing users with accurate service recommendations, effectively improving the adaptability and reliability of service recommendations, and providing significant advantages for cloud platforms in terms of improving user satisfaction and optimizing market promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a cloud platform cost management service intelligent analysis method provided in one of the embodiments of the present invention.

[0014] Figure 2 A schematic diagram of the structure of a cloud platform cost management service intelligent analysis system provided in one of the embodiments of the present invention. DETAILED DESCRIPTION

[0015] In order to make those skilled in the art better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] See also Figure 1 , which shows a flow chart of a cloud platform cost management service intelligent analysis method provided by one embodiment of the present invention, the method comprising: Step S1, obtaining historical cost service record data of multiple users from the cloud platform, performing phase correlation analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, and constructing a phase service correlation matrix between software project phases and cost management services.

[0017] Specifically, the enterprise provides cost management-related services for various software projects to different users through the cloud platform, including providing users with cost decomposition and estimation throughout the life cycle of the project, helping users optimize resource allocation through data analysis, reducing unnecessary expenses, and providing cost management suggestions in terms of technology selection, risk assessment, performance optimization, etc., to assist users in making decisions. The historical cost service record data at least includes the record data on multiple users’ selection of related cost management services in the enterprise’s cloud platform, including basic information of users such as industry, company size, budget, etc., service data such as service name, user evaluation, etc., and project results such as whether the project was completed on schedule and whether the budget control was up to standard.

[0018] First, the historical cost service record data is analyzed from a global perspective, including stage correlation analysis of multiple cost management services provided by the cloud platform. In this process, the records of cost management services selected by users in different project stages such as demand, development, testing, operation and maintenance are extracted from the historical cost service record data, so as to calculate the selection frequency parameters and success contribution parameters of each cost management service for different software project stages. Among them, the selection frequency parameter can be the ratio of the number of times a certain cost management service is selected by users in a certain software project stage to the total number of times all cost management services are selected in the software project stage. The success contribution parameter can be the number of times a certain cost management service is selected in a certain stage after the project is successful, and the ratio of the total number of project successes. After correcting the selection frequency parameter with the success contribution parameter, the global contribution parameter of the cost management service in the software project stage is obtained, which characterizes the correlation characteristics between the cost management service and the project success status globally, and the global contribution parameter is used as the stage service correlation parameter of the cost management service in the software project stage. After obtaining the phase service association parameters corresponding to each cost management service in different software project phases in this way, finally, based on the multiple phase service association parameters of each cost management service, a phase service association matrix between software project phases and cost management services is constructed to reflect the selection tendency and contribution of different services in each project phase.

[0019] Step S2: extract the industry data of different users from the historical cost service record data, perform stage contribution analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, and construct a local collaboration matrix of services in different industries at each software project stage.

[0020] Specifically, industry data is used to indicate the industry category corresponding to the software project that the user currently needs to perform cost analysis on, such as medical, financial, education, etc. The purpose of stage contribution analysis is to calculate the local contribution parameters and synergy gain parameters of each cost management service in each software project stage in different industries, where the calculation of local contribution parameters is similar to the aforementioned global contribution parameters, but is specifically limited to a certain industry, that is, for a certain industry, the selection frequency parameters and success contribution parameters of the cost management service in a certain software project stage of the industry are used to calculate the local contribution parameters, which characterize the correlation characteristics between the cost management service and the project success status in the industry. Then, a service synergy analysis is performed for each industry, specifically analyzing the impact relationship between the joint selection of two services and the selection of only a single service, so as to calculate the synergy gain parameters of the cost management service in each software project stage in different industries.

[0021] Finally, the phase service association parameters of the cost management service in a certain software project stage in the industry are determined according to the local contribution parameters and the synergy gain parameters. In this way, the phase service association parameters corresponding to each cost management service in each industry in different software project stages are obtained. Based on the software project stage, the local synergy matrix of services in different industries in each software project stage is constructed, which is used to comprehensively characterize the correlation characteristics between cost management services and industries after considering industry characteristics.

[0022] Step S3: perform service heat difference analysis on each software project stage according to historical cost service record data, calculate the heat difference parameters of each cost management service in each software project stage in different industries, and generate a heat difference distribution matrix for cost management services in each industry.

[0023] Specifically, for each software project stage, for some of the highly popular cost management services, we conduct an in-depth analysis of the popularity difference levels of these cost management services in different industries, and calculate the popularity difference parameters of each cost management service. The popularity difference parameters further consider the differences between cost management services in different industries on the basis of conventional popularity, and characterize the adaptability of cost management services to multiple industries. If the popularity of a cost management service is high and the popularity difference distribution is small in a certain software project stage, it means that the cost management service is widely applicable in the current software project stage, and the larger the corresponding popularity difference parameter, the more popularity difference distribution matrix of cost management services in the industry is constructed through multiple popularity difference parameters of the cost management service. The popularity difference distribution matrix represents the popularity difference distribution performance of multiple cost management services in different software project stages in one industry.

[0024] Step S4: After determining the industry data and project stage data of the target user, multiple candidate services are determined based on the stage service association matrix and the heat difference distribution matrix. Multiple target cost services are determined from the multiple candidate services in combination with the service local collaboration matrix of the software project stage to generate the cost management service adaptation analysis results for the target user.

[0025] Specifically, for target users who need to analyze software projects through cost management services, when the cloud platform determines the target user's industry data and project stage data, that is, the industry field specifically corresponding to the target user's software project and the current project stage, multiple candidate services are comprehensively determined based on the stage service association matrix and the heat difference distribution matrix. Among them, the stage service association matrix represents the popularity of different cost management services in different software project stages from a global perspective, that is, from the overall perspective of multiple industries, and the heat difference distribution matrix specifically considers the heat distribution of different cost management services in different industries, thereby selecting multiple candidate services. cost management services, and then further analyze the collaborative correlation between different cost management services through the service local collaboration matrix. For example, after selecting a certain service, selecting some other services at the same time may achieve a better linkage effect. Finally, some key service items are determined from multiple candidate services and recorded as target cost services. The cost management service adaptation analysis results obtained after the service adaptation analysis between the target user and the multiple cost management services provided by the cloud platform indicate the matching situation between the target user's current software project status and different cost management services, so as to provide the target user with highly adaptable cost service items.

[0026] Through the above steps, the present invention adopts a stage correlation analysis strategy and an industry local contribution analysis strategy, and analyzes the compatibility between multiple cost management services provided by the cloud platform and different software projects from an overall perspective and an industry local perspective, and further conducts a synergistic benefit analysis between services, organically integrating the multi-dimensional analysis results, and realizing accurate service adaptability analysis based on user characteristics and industry needs. This method significantly improves the adaptability and reliability of the recommended solution, and provides strong technical support for the promotion of cloud platform cost management services.

[0027] In the above content, for step S2, in the process of performing stage contribution analysis on multiple cost management services provided by the cloud platform according to historical cost service record data, the local contribution parameters and synergy gain parameters of each cost management service in each software project stage in different industries are calculated, and the local synergy matrix of services in different industries in each software project stage is constructed, which specifically includes: For any software project phase Cost management services , calculated based on historical cost service record data Cost management services The selection frequency parameters and success contribution parameters in different industries are used to modify the success contribution parameters based on the selection frequency parameters to generate the first Cost management services Local contribution parameters within different industries.

[0028] Specifically, take the first phase of any software project as an example. Cost management services For example, referring to the calculation method of the aforementioned global contribution parameters, according to the specific performance of different cost management services in each industry, the unique selection tendency and successful performance of cost management services in different industries are analyzed according to the historical cost service record data. After comprehensively considering the specific successful performance and actual selection tendency, the local contribution parameters corresponding to the cost management services in each industry are calculated.

[0029] Calculate the cost based on historical cost service record data Cost management services The global gain parameter, and Cost management services Local gain parameters in different industries. The calculation process of global gain parameters and local gain parameters includes the following: Use the following formula to calculate the Cost management services The global gain parameter of : In the formula, For the Cost management services and Cost management services Collaborative Cost management services The global gain parameter, To include Cost management services and Cost management services The success rate of software projects under the conditions of To include Cost management services The success rate of software projects under the conditions of , the global gain parameter can be used to evaluate the synergy of two services across the entire industry, that is, on the basis of one of the services, if another service is selected, the implicit success rate of the project will be improved.

[0030] Use the following formula to calculate the Cost management services The local gain parameters in the target industry: In the formula, The first in the target industry Cost management services and Cost management services Collaborative Cost management services The local gain parameter, For the target industry, including Cost management services and Cost management services The success rate of software projects under the conditions of For the target industry, including Cost management services The success rate of software projects under the condition of , the local gain parameter can be used to evaluate the synergy of two services in a specific industry, that is, in a specific industry, how the combination of two services affects the success status of software projects.

[0031] It is worth noting that although cost management services alone cannot guarantee the successful implementation of software projects, because the implementation process of software projects is also affected by other factors, the global gain parameters and local gain parameters can still measure the relative benefits of service combinations to a certain extent, reflecting the marginal contribution of a certain service to the project success rate when used in combination, as a global basis for analyzing service adaptability, and representing the differences in service benefits in specific scenarios, providing industry-specific service adaptation analysis.

[0032] Then the global gain parameter and multiple local gain parameters are fused, that is, for the first Cost management services The local gain parameters in different industries take into account the impact of the global state and adjust the first Cost management services The weighted correction of multiple local gain parameters is performed to generate the Cost management services Synergy gain parameters within different industries.

[0033] Finally, the first Cost management services The service coordination parameters in different stages of industries, that is, considering the coordination relationship between multiple services in the industry, are based on the quantitative performance of the comprehensive cost management service itself in the industry, that is, the local contribution parameter. The product of the local contribution parameter and the coordination gain parameter is used as the service coordination parameter of the cost management service in the corresponding industry. According to the multiple service coordination parameters of the cost management service in each industry, the first Cost management services The local synergy matrix of services within different industries represents the overall synergy between each cost management service and other cost management services within the industry based on itself, that is, the marginal benefits that can be brought by combining with other cost management services on the basis of itself.

[0034] In the above content, for step S3, in the process of analyzing the service heat difference of each software project stage according to the historical cost service record data, the heat difference parameters of each cost management service in each software project stage in different industries are calculated to generate a heat difference distribution matrix of the cost management service in each industry, which specifically includes: For any software project phase Cost management services , according to Cost management services The selection frequency parameters in different industries are calculated using the following formula Cost management services The degree of choice difference between any two industries: In the formula, Indicates Cost management services industry and Industry The difference in choice between , Respectively represent Cost management services In the industry and Industry The selection frequency parameter within. The value range of the selection difference is 0 to 1. If the selection difference of a cost management service is close to 0, it means that the two corresponding industries have similar preferences for this cost management service. Otherwise, it means that there is a big difference in the preferences between the two industries for this cost management service.

[0035] The first Cost management services The average of multiple selection differences in the software project phase is used to obtain the selection difference parameter, which comprehensively represents the overall preference difference of the selection frequency of this cost management service in all industries. Then, the first Cost management services The popularity value in the software project stage can be specifically the frequency of the cost management service being selected in the software project stage. Finally, the popularity of the cost management service itself and the performance differences in different industries are comprehensively considered, and the popularity value is corrected by selecting the difference parameters to generate the first Cost management services The heat difference parameter.

[0036] Among them, the heat difference parameter = heat value × (1-selection difference). It means that when the selection difference of cost management service is high, that is, when the preference difference in different industries is significant, its heat value is greatly revised, reflecting that it may not be applicable in some industries. On the contrary, it means that cost management service has a wider applicability. Finally, the preference difference of comprehensive services in various industries is constructed according to the heat difference parameters of multiple cost management services in different software project stages to obtain the heat difference distribution matrix of cost management services in each industry, reflecting the changes in the adaptability of cost management services in different industries.

[0037] In the above content, for step S4, multiple candidate services are determined according to the stage service association matrix and the heat difference distribution matrix, and multiple target cost services are determined from the multiple candidate services in combination with the service local collaboration matrix of the software project stage, specifically including: According to the industry data and project stage data of the target users, the heat difference parameters of the target users for each cost management service are determined from the heat difference distribution matrix, and the stage service association parameters of the target users for each cost management service are determined from the stage service association matrix; according to the heat difference parameters and the stage service association parameters, multiple candidate services are screened out from the multiple cost management services provided by the cloud platform.

[0038] Specifically, after determining the specific industry of the target user and the current software project stage of the software project that needs cost analysis based on the target user's industry data and project stage data, determine the heat difference parameters corresponding to multiple cost management services in the industry and stage from the heat difference distribution matrix according to the industry and software project stage. Similarly, determine the stage service association parameters corresponding to multiple cost management services in the same stage service association matrix, that is, the aforementioned global contribution parameters. The two respectively represent the preference of cost management services in a specific industry and the performance status within the entire industry. Then combine the two to determine the candidate services.

[0039] In this process, the global matching degree of each cost management service is calculated based on the heat difference parameter and the stage service association parameter, which can be specifically the product of the two. Those skilled in the art can also make weighted calculations on the heat difference parameter and the stage service association parameter based on actual tendencies, such as quantitatively considering the industry adaptability and the stage adaptability within the overall range, and determine the relative importance to generate a global matching degree. The greater the global matching degree, the higher the industry demand and stage adaptability of the cost management service. Then, the cost management services with a global matching degree greater than the preset matching threshold are screened out and recorded as candidate services to determine dual high-efficiency services that are adapted to industry needs and project stages. The adaptation parameters of each candidate service are further calculated, and multiple target cost services are determined from multiple candidate services based on the adaptation parameters.

[0040] In this process, the calculation of the adaptation parameters of the candidate services and the determination of multiple target cost services from multiple candidate services based on the adaptation parameters specifically include: The local service collaboration matrix corresponding to the target user is determined according to the target user's industry data and project stage data. The candidate service corresponding to the maximum global matching degree is recorded as the benchmark cost service. The benchmark cost service is removed from multiple candidate services and written into the adaptation analysis list.

[0041] Specifically, after determining multiple candidate services, further analyze the correlation between each candidate service and other cost management services, first determine the matching service local synergy matrix according to the industry field and project stage corresponding to the target user, which includes candidate services that are suitable for analysis and have high adaptability to the target user in the current scenario, and the synergy correlation between other services. First, record the candidate service corresponding to the maximum global matching value as the benchmark cost service, that is, as the core service for reference, that is, the most representative service among the multiple candidate services, and then remove it from the multiple candidate services and write it into the adaptation analysis list, which is used to sort the adaptability of multiple candidate services.

[0042] Then, multiple candidate services are analyzed through the service local coordination matrix and the benchmark cost service, and the stage service coordination parameters of each candidate service under the benchmark cost service are determined according to the service local coordination matrix. Multiple candidate services are ranked according to the stage service coordination parameters and the benchmark cost service.

[0043] Specifically, for the remaining multiple candidate services, the benchmark cost service is used as a reference, and the collaborative correlation characteristics between the multiple candidate services and the benchmark cost service are determined through the service local collaboration matrix, that is, the stage service collaboration parameters of each candidate service are determined. In this process, the stage service collaboration parameters between the benchmark cost service and each candidate service are first determined, and then the product of the stage service collaboration parameters and the global matching degree of the candidate service is taken as the ranking value of the candidate service. Specifically, the global matching degree comprehensively considers the adaptation between a single service and the target user, and the stage service collaboration parameters reflect the associated collaborative relationship between the two services. Therefore, after determining the representative benchmark cost service, the collaborative characteristics between the candidate service and the benchmark cost service are further combined to calculate the ranking value of each candidate service, thereby realizing the ranking of the remaining multiple candidate services.

[0044] The benchmark cost service is updated according to the candidate service ranked first, that is, the candidate service ranked first is taken as the most representative service among the remaining multiple candidate services, and it is used as the new benchmark cost service, and then the updated benchmark cost service is removed from the multiple candidate services and written into the adaptation analysis list. For the remaining multiple candidate services, the above operation is repeated until all candidate services are written into the adaptation analysis list. The writing process specifically considers the representativeness of multiple candidate services. Therefore, the adaptation parameters of multiple candidate services can be determined according to the order in which the candidate services are written into the adaptation analysis list. For example, they are numbered in order, and the candidate services with adaptation parameters greater than the preset adaptation threshold are recorded as target cost services. The preset adaptation threshold can be reasonably set according to the number of services to be recommended according to specific needs, and is not specifically limited here.

[0045] Finally, the cost management service adaptation analysis results for the target users can be generated based on the determined multiple target cost services. The cloud platform can generate accurate cost management service recommendation solutions for the target users based on the cost management service adaptation analysis results.

[0046] See also Figure 2 , which shows a schematic diagram of the structure of a cloud platform cost management service intelligent analysis system provided by one embodiment of the present invention, the system is specifically used to implement the above-mentioned cloud platform cost management service intelligent analysis method, including: The historical data acquisition module is used to obtain the historical cost service record data of multiple users on the cloud platform; The phase service correlation analysis module is used to perform phase correlation analysis on multiple cost management services provided by the cloud platform based on historical cost service record data, calculate the selection frequency parameters and global contribution parameters of each cost management service for different software project phases, and construct a phase service correlation matrix between software project phases and cost management services; The service local collaboration analysis module is used to extract the industry data of different users from the historical cost service record data, conduct stage contribution analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the local contribution parameters and collaborative gain parameters of each cost management service in each software project stage in different industries, and construct a service local collaboration matrix for different industries in each software project stage; The heat difference distribution analysis module is used to perform service heat difference analysis on each software project stage based on historical cost service record data, calculate the heat difference parameters of each cost management service in each software project stage in different industries, and generate a heat difference distribution matrix for cost management services in each industry; The service adaptation analysis module is used to determine multiple candidate services based on the stage service association matrix and the heat difference distribution matrix after determining the industry data and project stage data of the target user, and to determine multiple target cost services from multiple candidate services in combination with the service local collaboration matrix of the software project stage to generate the cost management service adaptation analysis results for the target user.

[0047] It can be understood that the functions or modules included in the system provided by the embodiment of the present invention can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment, and will not be repeated here for the sake of brevity.

[0048] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A cloud platform cost management service intelligent analysis method, characterized in that: include: Obtain historical cost service record data of multiple users on the cloud platform, perform phase correlation analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the selection frequency parameters and global contribution parameters of each cost management service for different software project phases, and construct a phase service correlation matrix between software project phases and cost management services; Extract the industry data of different users from the historical cost service record data, conduct stage contribution analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the local contribution parameters and synergy gain parameters of each cost management service in each software project stage in different industries, and construct the local synergy matrix of services in different industries in each software project stage; According to the historical cost service record data, the service heat difference analysis is carried out for each software project stage, and the heat difference parameters of each cost management service in each software project stage in different industries are calculated to generate the heat difference distribution matrix of cost management services in each industry; After determining the industry data and project stage data of the target user, multiple candidate services are determined based on the stage service association matrix and the heat difference distribution matrix. Combined with the service local collaboration matrix of the software project stage, multiple target cost services are determined from multiple candidate services to generate the cost management service adaptation analysis results for the target user.

2. According to claim 1, a cloud platform cost management service intelligent analysis method is characterized in that: Calculate the local contribution parameters and synergy gain parameters of each cost management service in each software project stage in different industries, and construct the local synergy matrix of services in different industries in each software project stage, including: For any software project phase Cost management services , calculated based on historical cost service record data Cost management services The selection frequency parameters and success contribution parameters in different industries are used to modify the success contribution parameters based on the selection frequency parameters to generate the first Cost management services Local contribution parameters within different industries; Calculate the cost based on historical cost service record data Cost management services The global gain parameter, and Cost management services The local gain parameters in different industries are adjusted according to the global gain parameters. Cost management services The multiple local gain parameters are modified to generate the Cost management services The synergy gain parameters in different industries are calculated based on the local contribution parameters and synergy gain parameters. Cost management services The service coordination parameters in different stages of the industry are constructed to obtain the Cost management services About the local synergy matrix of services within different industries.

3. According to claim 2, a cloud platform cost management service intelligent analysis method is characterized in that: For global gain parameters and local gain parameters, it also includes: Use the following formula to calculate the Cost management services The global gain parameter of : In the formula, For the Cost management services and Cost management services Collaborative Cost management services The global gain parameter, To include Cost management services and Cost management services The success rate of software projects under the conditions of To include Cost management services The success rate of software projects under the conditions of Use the following formula to calculate the Cost management services The local gain parameters in the target industry: In the formula, The first in the target industry Cost management services and Cost management services Collaborative Cost management services The local gain parameter, For the target industry, including Cost management services and Cost management services The success rate of software projects under the conditions of For the target industry, including Cost management services The success rate of software projects under certain conditions.

4. According to claim 3, a cloud platform cost management service intelligent analysis method is characterized in that: According to the phase service association matrix and the heat difference distribution matrix, multiple candidate services are determined. Combined with the service local coordination matrix of the software project phase, multiple target cost services are determined from multiple candidate services, including: According to the target user's industry data and project stage data, determine the target user's heat difference parameters for each cost management service from the heat difference distribution matrix, and determine the target user's stage service association parameters for each cost management service from the stage service association matrix; According to the heat difference parameters and the stage service association parameters, multiple candidate services are screened out from the multiple cost management services provided by the cloud platform, including calculating the global matching degree of each cost management service according to the heat difference parameters and the stage service association parameters, screening out the cost management services with a global matching degree greater than a preset matching threshold and recording them as candidate services, determining the adaptation parameters of each candidate service, and determining multiple target cost services from the multiple candidate services according to the adaptation parameters.

5. According to claim 4, a cloud platform cost management service intelligent analysis method is characterized in that: Determine the adaptation parameters of each candidate service, and determine multiple target cost services from multiple candidate services according to the adaptation parameters, including: Determine the local service coordination matrix corresponding to the target user based on the target user's industry data and project stage data, record the candidate service corresponding to the maximum global matching degree as the benchmark cost service, remove the benchmark cost service from multiple candidate services and write it into the adaptation analysis list; Analyze multiple candidate services through the service local coordination matrix and the benchmark cost service, determine the stage service coordination parameter of each candidate service under the benchmark cost service according to the service local coordination matrix, sort the multiple candidate services according to the stage service coordination parameter and the benchmark cost service, including weighting the adaptation parameter by the stage service coordination parameter to generate a sorting value, and sort the multiple candidate services based on the sorting value; The benchmark cost service is updated according to the candidate service ranked first, and the updated benchmark cost service is removed from the multiple candidate services and written into the adaptation analysis list. The above operation is repeated until all candidate services are written into the adaptation analysis list, and the adaptation parameters of the multiple candidate services are determined according to the order in which the candidate services are written into the adaptation analysis list. The candidate service whose adaptation parameter is greater than the preset adaptation threshold is recorded as the target cost service.

6. According to claim 2, a cloud platform cost management service intelligent analysis method is characterized in that: Calculate the heat difference parameters of each cost management service in each software project stage in different industries, and generate the heat difference distribution matrix of cost management services in each industry, including: For any software project phase Cost management services , according to Cost management services The selection frequency parameters in different industries are calculated using the following formula Cost management services The degree of choice difference between any two industries: In the formula, Indicates Cost management services industry and Industry The difference in choice between , Respectively represent Cost management services In the industry and Industry The selection frequency parameter within; The first Cost management services The average value of multiple selection differences in the software project stage is obtained to obtain the selection difference parameter, and the first Cost management services The heat value in the software project stage is modified by selecting the difference parameter to generate the first heat value in the software project stage. Cost management services According to the heat difference parameters of multiple cost management services in different software project stages, a heat difference distribution matrix of cost management services in each industry is constructed.

7. A cloud platform cost management service intelligent analysis system, characterized in that: The system is used to implement a cloud platform cost management service intelligent analysis method as described in any one of claims 1 to 6, including: The historical data acquisition module is used to obtain the historical cost service record data of multiple users on the cloud platform; The phase service correlation analysis module is used to perform phase correlation analysis on multiple cost management services provided by the cloud platform based on historical cost service record data, calculate the selection frequency parameters and global contribution parameters of each cost management service for different software project phases, and construct a phase service correlation matrix between software project phases and cost management services; The service local collaboration analysis module is used to extract the industry data of different users from the historical cost service record data, conduct stage contribution analysis on multiple cost management services provided by the cloud platform based on the historical cost service record data, calculate the local contribution parameters and collaborative gain parameters of each cost management service in each software project stage in different industries, and construct a service local collaboration matrix for different industries in each software project stage; The heat difference distribution analysis module is used to perform service heat difference analysis on each software project stage based on historical cost service record data, calculate the heat difference parameters of each cost management service in each software project stage in different industries, and generate a heat difference distribution matrix for cost management services in each industry; The service adaptation analysis module is used to determine multiple candidate services based on the stage service association matrix and the heat difference distribution matrix after determining the industry data and project stage data of the target user, and to determine multiple target cost services from multiple candidate services in combination with the service local collaboration matrix of the software project stage to generate the cost management service adaptation analysis results for the target user.

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