Resource allocation method and device, computer program product and electronic equipment

Abstract

The invention discloses a resource allocation method and device, a computer program product and electronic equipment. The method comprises the following steps: acquiring project information of a target project; multiple evaluation indexes of the target project are determined according to the project information, index weights of the evaluation indexes are determined based on an index judgment matrix, and matrix elements of the index judgment matrix are used for reflecting the importance proportion between every two evaluation indexes; the feasibility level of the target project is determined based on a fuzzy relation matrix and the index weight of each evaluation index, and matrix elements of the fuzzy relation matrix are used for representing the membership degree of each evaluation index to comments of different levels; and determining a cloud resource allocation strategy of the target project according to the project information and the feasibility level, and allocating corresponding cloud resources to the target project according to the cloud resource allocation strategy. The technical problems of unreasonable resource allocation and untimely risk response existing in a cloud resource scheduling strategy for formulating a project based on static information are solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a resource allocation method and apparatus, a computer program product, and an electronic device. Background Technology

[0002] In today's rapidly developing information technology landscape, enterprises are experiencing an explosive growth in demand for high-quality IT system operation and maintenance projects. Companies undertaking these projects face unprecedented opportunities and challenges, with the efficient allocation of the service provider's cloud resources being crucial for successful project implementation.

[0003] However, when existing technologies schedule resources for information system operation and maintenance projects, they are usually based only on static information such as project scale, historical resource consumption data, and current cloud platform resource utilization, without taking into account dynamic factors such as project risks, changes in technical requirements, and personnel skill matching. This can lead to resource allocation results that do not match the actual needs of the project, resulting in the inefficient use of high-performance cloud resources. At the same time, if technical difficulties or market changes are encountered during project implementation, the expansion or reduction of cloud resources will lag behind demand, increasing project costs and implementation difficulty.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a resource allocation method and apparatus, a computer program product, and an electronic device to at least solve the technical problems of unreasonable resource allocation and untimely risk response in cloud resource scheduling strategies based on static information.

[0006] According to one aspect of the embodiments of this application, a resource allocation method is provided, comprising: obtaining project information of a target project, wherein the project information includes at least: project cost, project requirement information, personnel requirement information, and technical requirement information; determining multiple evaluation indicators of the target project based on the project information, and determining the indicator weights of each evaluation indicator based on a preset indicator judgment matrix, wherein the matrix elements of the indicator judgment matrix are used to reflect the importance ratio between each pair of evaluation indicators; determining the feasibility level of the target project based on a preset fuzzy relation matrix and the indicator weights of each evaluation indicator, wherein the matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of evaluation; determining a cloud resource allocation strategy for the target project based on the project information and the feasibility level, and allocating corresponding cloud resources to the target project according to the cloud resource allocation strategy.

[0007] Optionally, the types of evaluation indicators include at least: project profit margin, personnel skill matching rate, technical capability fulfillment rate, and risk occurrence probability. The determination of multiple evaluation indicators for the target project based on project information includes: obtaining project revenues from multiple other projects in a pre-defined project database whose similarity to the target project's project information exceeds a pre-defined threshold, determining the target project's project revenue based on the revenues of these other projects, and combining this with the target project's project costs to determine the target project's project profit margin; determining the target project's personnel skill matching rate based on the target project's personnel requirements information; determining the target project's technical capability fulfillment rate based on the target project's technical requirements information; and analyzing project demand information using a pre-defined risk identification model to obtain the risk occurrence probability of various potential risks in the target project, where the categories of potential risks include at least one of the following: technical risk, personnel risk, and market risk.

[0008] Optionally, before determining the weights of each evaluation indicator based on the preset indicator judgment matrix, the method further includes: obtaining multiple evaluation indicators for each of several other projects whose similarity to the project information of the target project is higher than a preset threshold from a preset project database; for each evaluation indicator, standardizing and normalizing the evaluation indicators of each other project to obtain the temporary weights of the evaluation indicators of each other project, and using the average of the temporary weights of the evaluation indicators of each other project as the importance coefficient of the evaluation indicator; constructing an indicator judgment matrix with each evaluation indicator as rows and columns, and using the ratio of the importance coefficients of every two evaluation indicators as matrix elements.

[0009] Optionally, the weights of each evaluation indicator are determined based on a preset indicator judgment matrix, including: determining the maximum eigenvalue of the indicator judgment matrix and the eigenvector corresponding to the maximum eigenvalue; normalizing the eigenvector corresponding to the maximum eigenvalue, and using each vector element in the normalized eigenvector as the weight of the corresponding evaluation indicator.

[0010] Optionally, before determining the comprehensive evaluation vector of the target project based on the preset fuzzy relation matrix and the weights of each evaluation indicator, the method further includes: obtaining multiple evaluation indicators and comment sets for each of several other projects whose similarity to the project information of the target project is higher than a preset threshold from a preset project database, wherein each comment set includes: at least one comment of different comment levels of each evaluation indicator of the other projects; determining the membership degree of each evaluation indicator to different comment levels in each comment set based on the comment sets of each other project; constructing a fuzzy relation matrix using each evaluation indicator and each comment level as rows and columns, and using the membership degree of each evaluation indicator to different comment levels in each comment set as matrix elements.

[0011] Optionally, based on a preset fuzzy relation matrix and the weights of each evaluation indicator, the feasibility level of the target project is determined, including: weighting the weight vector composed of the weights of each evaluation indicator with the fuzzy relation matrix to obtain a comprehensive evaluation vector of the target project, wherein the comprehensive evaluation vector includes the evaluation values ​​corresponding to different comment levels; determining the comment level with the highest evaluation value in the comprehensive evaluation vector, and using the comment level as the feasibility level of the target project.

[0012] Optionally, the cloud resource allocation strategy for the target project is determined based on project information and feasibility level, including: analyzing project information using a pre-trained resource prediction model to obtain the target project's demand prediction results for various types of cloud resources, and determining the corresponding resource adjustment coefficients based on the feasibility level, wherein the higher the feasibility level, the larger the corresponding resource adjustment coefficient, and the lower the feasibility level, the smaller the corresponding resource adjustment coefficient; adjusting the demand prediction results for various types of cloud resources based on the resource adjustment coefficients, and forming the cloud resource allocation strategy for the target project from the adjusted demand prediction results for various types of cloud resources.

[0013] According to another aspect of the embodiments of this application, a resource allocation device is also provided, comprising: an acquisition module, configured to acquire project information of a target project, wherein the project information includes at least: project cost, project requirement information, personnel requirement information, and technical requirement information; a first determination module, configured to determine multiple evaluation indicators of the target project based on the project information, and determine the indicator weights of each evaluation indicator based on a preset indicator judgment matrix, wherein the matrix elements of the indicator judgment matrix are used to reflect the importance ratio between each pair of evaluation indicators; a second determination module, configured to determine the feasibility level of the target project based on a preset fuzzy relation matrix and the indicator weights of each evaluation indicator, wherein the matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of evaluation; and a resource allocation module, configured to determine a cloud resource allocation strategy for the target project based on the project information and the feasibility level, and allocate corresponding cloud resources to the target project according to the cloud resource allocation strategy.

[0014] According to another aspect of the embodiments of this application, a computer program product is also provided, the computer program product comprising: a computer program, wherein the computer program, when executed by a processor, implements the above-described resource allocation method.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, the electronic device including: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described resource allocation method through the computer program.

[0016] In this embodiment, by performing multi-level analysis and fuzzy comprehensive evaluation of the target project's information, the feasibility of the target project is obtained, achieving a multi-dimensional and refined assessment of project undertaking feasibility. This improves the accuracy of project undertaking decisions and reduces project implementation risks. Furthermore, by analyzing the target project's information and feasibility level, a cloud resource allocation strategy is derived, achieving intelligent adjustment of cloud resource allocation. This optimizes resource allocation and improves project operational efficiency and cost-effectiveness. This solves the technical problems of unreasonable resource allocation and untimely risk response in cloud resource scheduling strategies based on static information. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a flowchart illustrating an optional resource allocation method according to an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of an optional resource allocation device according to an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of the hardware structure of an optional computer terminal for implementing a resource allocation method according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of this application, a resource allocation method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart illustrating a resource allocation method according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102: Obtain project information for the target project, wherein the project information includes at least: project cost, project requirements, personnel requirements, and technical requirements.

[0027] Step S104: Determine multiple evaluation indicators for the target project based on the project information, and determine the weight of each evaluation indicator based on the preset indicator judgment matrix. The matrix elements of the indicator judgment matrix are used to reflect the importance ratio between each pair of evaluation indicators.

[0028] Step S106: Based on the preset fuzzy relation matrix and the indicator weights of each evaluation indicator, determine the feasibility level of the target project. The matrix elements of the fuzzy relation matrix are used to characterize the degree of membership of each evaluation indicator to different levels of comments.

[0029] Step S108: Determine the cloud resource allocation strategy for the target project based on the project information and feasibility level, and allocate the corresponding cloud resources to the target project in accordance with the cloud resource allocation strategy.

[0030] Based on the solutions defined in steps S102 to S108 above, it can be understood that in this embodiment, by performing multi-level analysis and fuzzy comprehensive evaluation of the target project's information, the feasibility of the target project is obtained, achieving the technical effect of multi-dimensional and refined evaluation of project undertaking feasibility, thereby improving the accuracy of project undertaking decisions and reducing project implementation risks. Furthermore, by analyzing the target project's information and feasibility level, a cloud resource allocation strategy for the target project is obtained, achieving the technical effect of intelligently adjusting cloud resource allocation, thereby optimizing resource allocation and improving project operational efficiency and cost-effectiveness.

[0031] The following section explains each step of the resource allocation method in conjunction with the specific implementation process.

[0032] In the technical solution provided in step S102 above, the system can first obtain the project information of the target project, which includes the project cost, project requirements information, personnel requirements information, and technical requirements information, each of which is defined as follows:

[0033] (1) Project cost refers to the monetary value of all resources expected to be invested in order to achieve the project objectives during the project life cycle, including but not limited to human resource costs, hardware equipment costs, software licensing costs and training costs.

[0034] (2) Project requirements information refers to the standards set for team members’ professional knowledge, skill level, work experience, communication ability and team structure during the project execution process.

[0035] (3) Personnel requirements information refers to the project’s requirements for the skills and number of team members, including but not limited to professional skills requirements and soft skills requirements for team size.

[0036] (4) Technical requirements information refers to the details of technical specifications, standards, tool selection, platform compatibility and technical performance indicators that need to be followed in the implementation of the project.

[0037] Taking an information system operation and maintenance project as an example, the project cost, project requirements, personnel requirements, and technical requirements information in the project information can be understood as follows:

[0038] (1) The above project cost refers to the total cost expected to occur in the information system operation and maintenance project, which includes, but is not limited to, labor costs, hardware equipment costs, software license costs, and training costs. Among them: labor costs involve the total salary of personnel with different skill levels estimated according to the requirements of project maintenance personnel; hardware equipment costs cover the cost of equipment repair, replacement and procurement; software license costs consider the expenditure required for software licensing and upgrades during system maintenance; training costs refer to the course and teacher fees incurred to improve the skills of maintenance personnel.

[0039] (2) The above-mentioned project requirements information refers to the specific technical requirements and service standards proposed by the customer in the information system operation and maintenance project, which include, but are not limited to, the scope of equipment and system maintenance, the operation and maintenance service response time, and the assessment and evaluation standards. Among them: the scope of maintenance clarifies the specific scope of hardware equipment and software systems that need to be maintained; the operation and maintenance service response time sets the response time requirements of the service provider to the customer's request; and the assessment and evaluation standards define the evaluation criteria for service quality during and after the project execution.

[0040] (3) The above personnel requirements refer to the requirements of the information system operation and maintenance project on the skills and number of maintenance team members, including but not limited to professional skills requirements such as network technology and database management technology, as well as expectations for soft skills such as team size, communication ability and problem-solving ability.

[0041] (4) The above technical requirements refer to the specific requirements for technical solutions in the operation and maintenance of information systems, including but not limited to the application requirements of automated operation and maintenance tools, monitoring technology, and cloud computing technology.

[0042] In the technical solution provided in step S104 above, the system can further determine multiple evaluation indicators for the target project based on the project information. Among these, the types mentioned above include at least: project profit margin, personnel skill matching rate, technical capability satisfaction rate, and risk occurrence probability.

[0043] As an optional implementation, in the technical solution provided in step S104 above, multiple evaluation indicators of the target project can be determined according to the following method:

[0044] Step 1: Obtain the project revenue of multiple other projects from the preset project database whose project information is more similar to the target project than a preset threshold, and determine the project revenue of the target project based on the project revenue of the multiple other projects. Combine the project cost of the target project to determine the project profit margin of the target project.

[0045] Specifically, the first step can be understood as follows: Extract historical project data that is highly similar to the target project from a pre-set project database. By comparing key information such as project scale, service nature, and industry characteristics, select multiple other projects with a similarity higher than a pre-set threshold. The pre-set project database contains at least multiple evaluation indicators for each of the processed projects. Then, based on the actual revenue of these similar projects, use statistical analysis methods or prediction models to analyze the expected revenue of the target project. The prediction model can employ techniques such as multiple regression analysis, time series analysis, or neural networks. The model input can be the project revenue, macroeconomic indicators, and project attributes of similar projects, and the model output is the predicted project revenue of the target project. Finally, subtract the project cost of the target project from the predicted project revenue of the target project, and divide the difference by the project cost of the target project to obtain the project profit margin of the target project.

[0046] Step 2: Determine the skill matching rate of personnel for the target project based on the personnel requirements information of the target project.

[0047] Specifically, the second step above can be understood as: analyzing the personnel requirements information of the target project, extracting skill keywords, matching them with employee skill tags in the preset talent pool, calculating the personnel skill matching degree through an algorithm, and using the obtained personnel skill matching degree as the personnel skill matching rate of the target project.

[0048] Step 3: Determine the technical capability satisfaction rate of the target project based on the technical requirements information of the target project.

[0049] Specifically, the third step above can be understood as: analyzing the technical requirements information of the target project, extracting skill keywords, matching them with technical tags in the preset technical library, calculating the technical capability matching degree through an algorithm, and using the obtained technical capability matching degree as the technical capability satisfaction rate of the target project. The preset technical library stores various technical capabilities obtained by reviewing the technical capabilities of the enterprise or part of the target project and testing the equipment and software compatibility.

[0050] Step 4: Analyze the project requirements information using a pre-set risk identification model to obtain the probability of occurrence of various potential risks in the target project. The categories of potential risks include at least one of the following: technical risks, personnel risks, and market risks.

[0051] Specifically, the risk model described above can be a rule-based expert system, decision tree, support vector machine, Bayesian classifier, or other algorithms suitable for handling uncertainty and probability problems. Therefore, the model's input is a feature vector of project requirement information, and its output is the probability of occurrence of various risks.

[0052] By taking the above four steps, we can comprehensively assess the profitability, human resource matching, technical feasibility, and potential risks of the target project, providing a scientific basis for the subsequent analysis of the project's feasibility.

[0053] Furthermore, in the technical solution provided in step S104 above, the system can determine the weight of each evaluation indicator based on a preset indicator judgment matrix. The matrix elements of the indicator judgment matrix reflect the importance ratio between any two evaluation indicators.

[0054] Optionally, the judgment matrix for the above indicators can be constructed as follows:

[0055] Step 1: Retrieve multiple evaluation metrics from a pre-defined project database for each of several other projects whose similarity to the target project exceeds a pre-defined threshold. The pre-defined project database must contain multiple evaluation metrics for each of the processed projects.

[0056] Step 2: For each evaluation indicator, standardize and normalize the evaluation indicators of other items to obtain the temporary weights of the evaluation indicators of other items, and use the average of the temporary weights of the evaluation indicators of other items as the importance coefficient of the evaluation indicator.

[0057] Standardization can be achieved by using range standardization, Z-score standardization, or logarithmic transformation to convert all evaluation indicators into the same dimension, such as "0-100 points", to ensure comparability between different evaluation indicators. Normalization, on the other hand, classifies the value of each evaluation indicator into a uniform range to facilitate subsequent weight calculation.

[0058] Step 3: Construct an indicator judgment matrix with each evaluation indicator as the row and column, and the ratio of the importance coefficients of each pair of evaluation indicators as the matrix elements.

[0059] Specifically, the matrix element in the i-th row and j-th column of the indicator judgment matrix. This can be understood as the relative importance of the i-th evaluation indicator to the j-th evaluation indicator. Therefore, the elements on the main diagonal of the indicator judgment matrix... All elements are 1 and are not on the main diagonal. The following relationship exists: .

[0060] For example, if the importance coefficients of project cost, project requirements information, personnel requirements information, and technical requirements information are 28.3, 8.2, 15.9, and 47.6 respectively, then the ratio of the importance coefficient of technical requirements information to that of project requirements information is approximately 1.68 (47.6 / 28.3), which is mapped to a preset scale value of 2. Similarly, the ratio of the importance coefficient of project cost to that of project requirements information is approximately 3.45 (28.3 / 8.2), which is mapped to a preset scale value of 3.

[0061] It should be noted that, to ensure the rationality and consistency of the above indicator judgment matrix, the following verification operations can be performed on the indicator judgment matrix: Determine the largest eigenvalue of the indicator judgment matrix; calculate the first difference between the largest eigenvalue and the number of evaluation indicators, and divide the first difference by the second difference between the number of evaluation indicators and 1, using the resulting quotient as the consistency index of the indicator judgment matrix; query the preset random consistency index table for the random consistency index corresponding to the number of evaluation indicators; finally, use the quotient of the consistency index and the random consistency index as the consistency ratio of the indicator judgment matrix; if the consistency ratio of the indicator judgment matrix is ​​not higher than the preset small threshold (e.g., 0.1), it indicates that the indicator judgment matrix follows the principles of consistency and logic; conversely, if the consistency ratio of the indicator judgment matrix is ​​higher than the preset small threshold (e.g., 0.1), it indicates that there is a logical conflict or inconsistency in the indicator judgment matrix, and the matrix needs to be adjusted until the consistency ratio drops to an acceptable range.

[0062] Therefore, based on the indicator judgment matrix constructed by the above method, the system can determine the indicator weights of each evaluation indicator according to the following steps: determine the maximum eigenvalue of the indicator judgment matrix and the eigenvector corresponding to the maximum eigenvalue; normalize the eigenvector corresponding to the maximum eigenvalue, and use each vector element in the normalized eigenvector as the indicator weight of the corresponding evaluation indicator.

[0063] The system can use either the "sum-product method" or the "square root method" to calculate the index to determine the largest eigenvalue and the corresponding eigenvector in the matrix.

[0064] In the technical solution provided in step S106 above, the system can determine the feasibility level of the target project based on a preset fuzzy relation matrix and the indicator weights of each evaluation indicator. The matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of evaluation.

[0065] Alternatively, the aforementioned fuzzy relation matrix can be constructed as follows:

[0066] Step 1: Obtain from the preset project database multiple evaluation indicators and comment sets of other projects whose similarity to the target project's project information is higher than a preset threshold. The preset project database contains multiple evaluation indicators and comment sets of multiple processed projects, and each comment set includes at least one comment of different comment levels of various evaluation indicators of other projects.

[0067] Specifically, for each processed project, multiple comments from multiple evaluation subjects on various evaluation indicators of the processed project can be obtained first, and each comment can be analyzed using a preset grading model to obtain the corresponding rating level for each comment, such as "high, medium, low" or "excellent, good, qualified, poor", etc. Then, the multiple comments and rating levels of various evaluation indicators of each processed project are stored in a preset project database.

[0068] Step 2: Determine the degree of membership of each evaluation indicator to different comment levels within each comment set based on the comment sets of other projects.

[0069] The membership degree can be described using membership functions in fuzzy mathematics (such as triangle, trapezoid, Gaussian distribution, etc.) to describe the relationship between evaluation indicators and rating levels. The membership degree usually ranges from [0,1], and the larger the membership degree value, the higher the degree of membership.

[0070] Step 3: Construct a fuzzy relation matrix using each evaluation indicator and each comment level as rows and columns, and using the membership degree of each evaluation indicator to different comment levels within each comment set as matrix elements.

[0071] As an optional implementation, in the technical solution provided in step S106 above, the system can determine the feasibility level of the target project using the following method:

[0072] Step 1: Weight the weight vector composed of the weights of each evaluation indicator and the fuzzy relation matrix to obtain the comprehensive evaluation vector of the target project. The comprehensive evaluation vector includes the evaluation values ​​corresponding to different rating levels.

[0073] Step 2: Determine the highest rating level within the comprehensive evaluation vector and use that rating level as the feasibility level of the target project.

[0074] For example, if the weight vector composed of the weights of the four evaluation indicators of the target project—project profit margin, personnel skill matching rate, technical capability satisfaction rate, and risk occurrence probability—is... Using four evaluation indicators—project profit margin, personnel skill matching rate, technical capability satisfaction rate, and risk occurrence probability—as rows, and three rating levels (high, medium, and low) as columns, the fuzzy relation matrix is ​​constructed as follows: .

[0075] Therefore, combining the weight vector and fuzzy relation matrix mentioned above, the comprehensive evaluation vector of the target project can be written as: ,Right now In the formula, " "" indicates a fuzzy composition operator, among which commonly used fuzzy composition operators include the main factor determination type, the main factor prominence type, and the weighted average type. The embodiments of this application use the weighted average type algorithm for calculation.

[0076] Furthermore, based on the principle of maximum membership, the rating with the highest evaluation value, "Medium", is selected as the feasibility level of the target project, i.e., the feasibility level of the target project is "Medium".

[0077] In the technical solution provided in step S108 above, the system can determine the cloud resource allocation strategy for the target project based on project information and feasibility level.

[0078] As an optional implementation, in the technical solution provided in step S108 above, the method may include:

[0079] Step 1: Analyze the project information using a pre-trained resource prediction model to obtain the target project's demand prediction results for various cloud resources, and determine the corresponding resource adjustment coefficient based on the feasibility level. The higher the feasibility level, the larger the corresponding resource adjustment coefficient, and the lower the feasibility level, the smaller the corresponding resource adjustment coefficient.

[0080] The resource prediction model is a machine learning-based predictive model that learns the relationship between project information and cloud resource requirements, including but not limited to features such as project cost, project requirements, personnel requirements, and technical requirements, as well as the consumption patterns of cloud resources such as CPU, memory, storage, and network bandwidth. Therefore, this embodiment of the application uses the target project's information as input, analyzes it using the resource prediction model, and outputs the target project's demand prediction results for various cloud resources.

[0081] Simultaneously, based on the feasibility level of the target project obtained from the previous fuzzy comprehensive evaluation, a resource adjustment coefficient is determined. This coefficient reflects the impact of project risk level on resource demand. Specifically, a higher feasibility level means lower project risk, allowing for appropriate increases in resource reservation to ensure high-quality service levels; therefore, the corresponding resource adjustment coefficient is larger. Conversely, a lower feasibility level indicates higher project risk, requiring a smaller resource adjustment coefficient to avoid resource waste. This suggests that resource allocation should be more cautious, with a moderate reduction in resource allocation, while also considering risk mitigation strategies.

[0082] Step 2: Adjust the demand forecast results of various cloud resources based on the resource adjustment coefficient, and form the cloud resource allocation strategy for the target project from the adjusted demand forecast results of various cloud resources.

[0083] Specifically, the adjustment process for demand forecasts of various cloud resources is essentially a penalty calculation on the original forecast results, making the final cloud resource allocation strategy closer to the actual needs and risk levels of the project. For example, if the forecasting model predicts that the target project needs 100GB of storage resources, and the resource adjustment coefficient determined according to its feasibility level is 1.2, then the adjusted storage resource requirement is 120GB.

[0084] Finally, the adjusted demand forecasts for various cloud resources are summarized to form a complete cloud resource allocation strategy. This strategy details the resource configuration scheme for the target project in the cloud computing environment, including the number of CPU cores, memory size, storage capacity, network bandwidth, etc., to guide the cloud platform in resource allocation and optimized scheduling.

[0085] The above method uses a pre-trained resource prediction model to intelligently analyze project information, predict cloud resource needs, and then adjusts the prediction results based on the project's feasibility level to generate a cloud resource allocation strategy that both meets project needs and reasonably controls risks. Its core is to transform project uncertainty assessment into specific guidance for resource requirements, achieving an effective connection between project management and cloud resource optimization.

[0086] Furthermore, the system can allocate various types of cloud resources to the target project according to the cloud resource allocation strategy determined above, such as computing resources (central processing unit CPU, graphics processing unit GPU), storage resources (disk, solid-state drive SSD), and network resources (bandwidth, traffic).

[0087] In summary, the project resource allocation method provided in this application has the following technical advantages compared to existing methods:

[0088] (1) The embodiments of this application incorporate project cost, project demand information, personnel requirement information and technical requirement information into the evaluation system, realizing a comprehensive consideration of the feasibility of undertaking the project and improving the comprehensiveness and accuracy of the evaluation results.

[0089] (2) By using advanced decision analysis tools such as the analytic hierarchy process and the fuzzy evaluation method, qualitative analysis is transformed into quantitative indicators, and the weight of each evaluation standard and the membership degree of the project performance are scientifically quantified, making the evaluation process more objective and scientific.

[0090] (3) Based on the project information and the feasibility level of the assessment, the cloud resource allocation strategy of the target project is scientifically formulated, so that the contractor can flexibly adjust various cloud resources according to different situations, thereby enhancing the decision-making flexibility and response capability when facing different projects.

[0091] Example 2

[0092] According to an embodiment of this application, a resource allocation apparatus for implementing the resource allocation method in Embodiment 1 is also provided, such as... Figure 2 As shown, the resource allocation device includes at least: an acquisition module 22, a first determination module 24, a second determination module 26, and a resource allocation module 28, wherein:

[0093] The acquisition module 22 is used to acquire project information of the target project, wherein the project information includes at least: project cost, project requirements information, personnel requirements information, and technical requirements information;

[0094] The first determining module 24 is used to determine multiple evaluation indicators of the target project based on project information, and to determine the weight of each evaluation indicator based on a preset indicator judgment matrix. The matrix elements of the indicator judgment matrix are used to reflect the importance ratio between each pair of evaluation indicators.

[0095] The second determining module 26 is used to determine the feasibility level of the target project based on a preset fuzzy relation matrix and the indicator weights of each evaluation indicator. The matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of comments.

[0096] The resource allocation module 28 is used to determine the cloud resource allocation strategy for the target project based on the project information and feasibility level, and to allocate the corresponding cloud resources to the target project in accordance with the cloud resource allocation strategy.

[0097] The functions of each module of the resource allocation device are explained below in conjunction with the specific implementation process.

[0098] Optionally, the first determining module 24 can determine multiple evaluation indicators for the target project in the following manner:

[0099] Step 1: Obtain the project revenue of multiple other projects from the preset project database whose project information is more similar to the target project than a preset threshold, and determine the project revenue of the target project based on the project revenue of the multiple other projects. Combine the project cost of the target project to determine the project profit margin of the target project.

[0100] Step 2: Determine the skill matching rate of personnel for the target project based on the personnel requirements information of the target project.

[0101] Step 3: Determine the technical capability satisfaction rate of the target project based on the technical requirements information of the target project.

[0102] Step 4: Analyze the project requirements information using a pre-set risk identification model to obtain the probability of occurrence of various potential risks in the target project. The categories of potential risks include at least one of the following: technical risks, personnel risks, and market risks.

[0103] Furthermore, the first determining module 24 can determine the weight of each evaluation indicator based on a preset indicator judgment matrix. The matrix elements of the indicator judgment matrix reflect the importance ratio between each pair of evaluation indicators.

[0104] Optionally, the judgment matrix for the above indicators can be constructed as follows:

[0105] Step 1: Retrieve multiple evaluation metrics from a pre-defined project database for each of several other projects whose similarity to the target project exceeds a pre-defined threshold. The pre-defined project database must contain multiple evaluation metrics for each of the processed projects.

[0106] Step 2: For each evaluation indicator, standardize and normalize the evaluation indicators of other items to obtain the temporary weights of the evaluation indicators of other items, and use the average of the temporary weights of the evaluation indicators of other items as the importance coefficient of the evaluation indicator.

[0107] Step 3: Construct an indicator judgment matrix with each evaluation indicator as the row and column, and the ratio of the importance coefficients of each pair of evaluation indicators as the matrix elements.

[0108] Therefore, the first determining module 24 can determine the weights of each evaluation indicator based on the indicator judgment matrix constructed by the above method, according to the following steps: determine the maximum eigenvalue of the indicator judgment matrix and the eigenvector corresponding to the maximum eigenvalue; normalize the eigenvector corresponding to the maximum eigenvalue, and use each vector element in the normalized eigenvector as the weight of the corresponding evaluation indicator.

[0109] In addition, the second determining module 26 can determine the feasibility level of the target project based on a preset fuzzy relation matrix and the indicator weights of each evaluation indicator. The matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of evaluation.

[0110] Alternatively, the aforementioned fuzzy relation matrix can be constructed as follows:

[0111] Step 1: Obtain from the preset project database multiple evaluation indicators and comment sets of other projects whose similarity to the target project's project information is higher than a preset threshold. The preset project database contains multiple evaluation indicators and comment sets of multiple processed projects, and each comment set includes at least one comment of different comment levels of various evaluation indicators of other projects.

[0112] Step 2: Determine the degree of membership of each evaluation indicator to different comment levels within each comment set based on the comment sets of other projects.

[0113] Step 3: Construct a fuzzy relation matrix using each evaluation indicator and each comment level as rows and columns, and using the membership degree of each evaluation indicator to different comment levels within each comment set as matrix elements.

[0114] Optionally, the second determining module 26 can determine the feasibility level of the target project by the following method:

[0115] Step 1: Weight the weight vector composed of the weights of each evaluation indicator and the fuzzy relation matrix to obtain the comprehensive evaluation vector of the target project. The comprehensive evaluation vector includes the evaluation values ​​corresponding to different rating levels.

[0116] Step 2: Determine the highest rating level within the comprehensive evaluation vector and use that rating level as the feasibility level of the target project.

[0117] Finally, the resource allocation module 28 can determine the cloud resource allocation strategy for the target project as follows: First, analyze the project information using a pre-trained resource prediction model to obtain the target project's demand prediction results for various types of cloud resources, and determine the corresponding resource adjustment coefficients based on the feasibility level. The higher the feasibility level, the larger the corresponding resource adjustment coefficient, and the lower the feasibility level, the smaller the corresponding resource adjustment coefficient. Second, adjust the demand prediction results for various types of cloud resources based on the resource adjustment coefficients, and form the target project's cloud resource allocation strategy from the adjusted demand prediction results for various types of cloud resources.

[0118] It should be noted that each module in the resource allocation device in this application embodiment corresponds one-to-one with each implementation step of the resource allocation method in embodiment 1. Since embodiment 1 has been described in detail, some details not shown in this embodiment can be referred to embodiment 1, and will not be elaborated further here.

[0119] Example 3

[0120] According to an embodiment of this application, a computer program product is also provided, which includes a computer program, wherein the computer program implements the resource allocation method in embodiment 1 when executed by a processor.

[0121] According to an embodiment of this application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the resource allocation method in Embodiment 1 by running the computer program.

[0122] According to an embodiment of this application, a processor is also provided for running a computer program, wherein the computer program executes the resource allocation method in embodiment 1 during runtime.

[0123] According to an embodiment of this application, an electronic device is also provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the resource allocation method of embodiment 1 through the computer program.

[0124] Specifically, the computer program executes the following steps during runtime: acquiring project information for the target project, including at least: project cost, project requirements, personnel requirements, and technical requirements; determining multiple evaluation indicators for the target project based on the project information, and determining the weights of each evaluation indicator based on a pre-defined indicator judgment matrix, wherein the matrix elements of the indicator judgment matrix reflect the importance ratio between each pair of evaluation indicators; determining the feasibility level of the target project based on a pre-defined fuzzy relation matrix and the weights of each evaluation indicator, wherein the matrix elements of the fuzzy relation matrix characterize the membership degree of each evaluation indicator to different levels of evaluation; determining the cloud resource allocation strategy for the target project based on the project information and feasibility level, and allocating corresponding cloud resources to the target project according to the cloud resource allocation strategy.

[0125] As an alternative implementation, the above-mentioned electronic device may exist in the form of a mobile terminal, a computer terminal, or a similar computing device. Figure 3 A hardware block diagram of a computer terminal for implementing a resource allocation method is shown. Figure 3As shown, the computer terminal 30 may include one or more processors 302 (shown as 302a, 302b, ..., 302n in the figure) 302 (processor 302 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 304 for storing data, and a transmission device 306 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the computer terminal described above. For example, computer terminal 30 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0126] It should be noted that the aforementioned one or more processors 302 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 30. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0127] The memory 304 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the resource allocation method in this embodiment. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, thereby implementing the above-mentioned application vulnerability detection method. The memory 304 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include memory remotely located relative to the processor 302, and these remote memories can be connected to the computer terminal 30 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0128] The transmission device 306 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 30. In one example, the transmission device 306 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 306 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0129] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 30.

[0130] The sequence numbers of the above embodiments are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0134] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0136] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A resource allocation method, characterized in that, include: Obtain project information for the target project, wherein the project information includes at least: project cost, project requirements, personnel requirements, and technical requirements. Based on the project information, multiple evaluation indicators for the target project are determined, and the weight of each evaluation indicator is determined based on a preset indicator judgment matrix, wherein the matrix elements of the indicator judgment matrix are used to reflect the importance ratio between each pair of evaluation indicators. Based on a preset fuzzy relation matrix and the weights of each evaluation indicator, the feasibility level of the target project is determined, wherein the matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of comments. Based on the project information and the feasibility level, a cloud resource allocation strategy for the target project is determined, and corresponding cloud resources are allocated to the target project in accordance with the cloud resource allocation strategy.

2. The method according to claim 1, characterized in that, The types of evaluation indicators include at least: project profit margin, personnel skill matching rate, technical capability fulfillment rate, and risk occurrence probability. Among these, multiple evaluation indicators for the target project, determined based on the project information, include: The project revenue of multiple other projects with a similarity to the project information of the target project higher than a preset threshold is obtained from a preset project database. The project revenue of the target project is determined based on the project revenue of the multiple other projects. The project profit margin of the target project is determined by combining the project cost of the target project. Determine the personnel skill matching rate for the target project based on the personnel requirement information of the target project; Determine the technical capability fulfillment rate of the target project based on the technical requirements information of the target project; The project requirement information is analyzed using a preset risk identification model to obtain the probability of occurrence of various potential risks in the target project, and the categories of potential risks include at least one of the following: technical risk, personnel risk, and market risk.

3. The method according to claim 1, characterized in that, Before determining the weights of each evaluation indicator based on a preset indicator judgment matrix, the method further includes: Multiple evaluation indicators are obtained from a preset project database for each of several other projects whose similarity to the target project's project information is higher than a preset threshold. For each of the aforementioned evaluation indicators, the evaluation indicators of each of the other items are standardized and normalized to obtain the temporary weights of the evaluation indicators of each of the other items, and the average value of the temporary weights of the evaluation indicators of each of the other items is used as the importance coefficient of the evaluation indicator. The indicator judgment matrix is ​​constructed by using each of the aforementioned evaluation indicators as rows and columns, and by using the ratio of the importance coefficients of every two of the aforementioned evaluation indicators as matrix elements.

4. The method according to claim 3, characterized in that, The weights of each evaluation indicator are determined based on a preset indicator judgment matrix, including: Determine the largest eigenvalue of the indicator judgment matrix and the eigenvector corresponding to the largest eigenvalue; The eigenvector corresponding to the largest eigenvalue is normalized, and each vector element in the normalized eigenvector is used as the index weight of the corresponding evaluation index.

5. The method according to claim 1, characterized in that, Before determining the comprehensive evaluation vector of the target project based on a preset fuzzy relation matrix and the weights of each evaluation indicator, the method further includes: The project database retrieves multiple evaluation indicators and comment sets for each of several other projects whose project information is more similar to the target project than a preset threshold. Each comment set includes at least one comment of different comment levels for each evaluation indicator of the other projects. Determine the membership degree of each evaluation indicator to different comment levels within each of the other item comment sets based on the comment sets of each item; The fuzzy relation matrix is ​​constructed using the evaluation indicators and the comment levels as rows and columns, and the membership degree of each evaluation indicator to different comment levels within each comment set as matrix elements.

6. The method according to claim 5, characterized in that, Based on a preset fuzzy relation matrix and the weights of each evaluation indicator, the feasibility level of the target project is determined, including: The weight vector composed of the weights of each of the evaluation indicators is weighted and then weighted with the fuzzy relation matrix to obtain the comprehensive evaluation vector of the target project. The comprehensive evaluation vector includes the evaluation values ​​corresponding to different rating levels. The highest rating level within the comprehensive evaluation vector is determined, and this rating level is used as the feasibility level of the target project.

7. The method according to claim 1, characterized in that, Determining the cloud resource allocation strategy for the target project based on the project information and the feasibility level includes: The project information is analyzed using a pre-trained resource prediction model to obtain the target project's demand prediction results for various cloud resources, and the corresponding resource adjustment coefficient is determined based on the feasibility level. The higher the feasibility level, the larger the corresponding resource adjustment coefficient, and the lower the feasibility level, the smaller the corresponding resource adjustment coefficient. The demand forecast results for various cloud resources are adjusted based on the resource adjustment coefficient, and the adjusted demand forecast results for various cloud resources constitute the cloud resource allocation strategy for the target project.

8. A resource allocation device, characterized in that, include: The acquisition module is used to acquire project information of the target project, wherein the project information includes at least: project cost, project requirements information, personnel requirements information, and technical requirements information; The first determining module is used to determine multiple evaluation indicators of the target project based on the project information, and to determine the indicator weight of each evaluation indicator based on a preset indicator judgment matrix, wherein the matrix elements of the indicator judgment matrix are used to reflect the importance ratio between each pair of evaluation indicators. The second determining module is used to determine the feasibility level of the target project based on a preset fuzzy relation matrix and the indicator weights of each evaluation indicator, wherein the matrix elements of the fuzzy relation matrix are used to characterize the membership degree of each evaluation indicator to different levels of comments. The resource allocation module is used to determine the cloud resource allocation strategy for the target project based on the project information and the feasibility level, and to allocate corresponding cloud resources to the target project according to the cloud resource allocation strategy.

9. A computer program product, characterized in that, include: A computer program, wherein when executed by a processor, the computer program implements the resource allocation method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the resource allocation method of any one of claims 1 to 7 through the computer program.

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