Project resource allocation method, device and equipment

Abstract

The invention provides a project resource allocation method, device and equipment, and the method comprises the steps: obtaining the total resource of a project, the number of characteristics contained in the project, and a project quality influence parameter, and substituting the parameters into a pre-constructed project resource allocation function which is constructed based on the quality function of each characteristic, the quality function of the characteristic is used for representing a non-linear relationship between the resource input to the corresponding characteristic and the project quality; the total resource of the project is taken as a constraint condition, an extreme value of a project resource allocation function is solved, the quantity of resources allocated to each characteristic is determined, an optimal resource allocation scheme can be automatically generated, and an objective, verifiable and optimal resource allocation decision basis is provided for project management, so that the resource allocation efficiency is improved on the premise that the total resource quantity is not changed. And the research and development efficiency and the overall product quality are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of product characteristic analysis, specifically to a method, apparatus, and equipment for project resource allocation. Background Technology

[0002] In the R&D project management of complex products such as communication equipment and software systems, a project typically includes multiple features that need to be developed in parallel. How to rationally allocate limited project resources (such as human and time resources) to different features in order to maximize the overall quality or benefits of the product is the core challenge in project management.

[0003] Currently, project resource allocation within the industry heavily relies on the experience-based estimations of project managers or domain experts. However, this subjective judgment-based model has significant limitations. First, the decision-making process lacks objective and unified quantitative models for support. The rationality of a solution is strongly correlated with the individual experience and capabilities of experts, and solutions from different experts often differ significantly, resulting in poor reproducibility and verifiability. Second, because the relationship between resource input and output benefits cannot be accurately characterized, relying solely on experience makes it difficult to achieve optimal resource allocation, often leading to low allocation efficiency and overall returns falling short of potential optimal levels.

[0004] It is evident that existing resource allocation methods based on experience estimation have inherent shortcomings in terms of objectivity, theoretical rigor, and optimal results, and have become a key bottleneck restricting the continuous improvement of R&D efficiency and product quality. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for project resource allocation, which can solve the technical problem in the prior art that relies on expert experience for project resource allocation, making it difficult to maximize the overall quality or benefits of the project.

[0006] In a first aspect, embodiments of this application provide a project resource allocation method, the project resource allocation method comprising: The total project resources, the number of features included in the project, and the project quality impact parameters are obtained and substituted into a pre-built project resource allocation function. The project resource allocation function is constructed based on the quality function of each feature. The quality function of a feature is used to characterize the non-linear relationship between the resources invested in the corresponding feature and the project quality. Using the total resources of the project as constraints, solve for the extreme value of the project resource allocation function to determine the amount of resources allocated to each characteristic.

[0007] Secondly, embodiments of this application provide a project resource allocation device, the project resource allocation device comprising: The acquisition module is used to acquire the total project resources, the number of features included in the project, and the project quality impact parameters, and then input them into the pre-built project resource allocation function. The project resource allocation function is constructed based on the quality function of each feature, and the quality function of the feature is used to characterize the non-linear relationship between the resources invested in the corresponding feature and the project quality. The allocation module is used to solve for the extreme value of the project resource allocation function with the total resources of the project as a constraint, and to determine the amount of resources allocated to each characteristic.

[0008] Thirdly, embodiments of this application provide a project resource allocation device, which includes a processor, a memory, and a project resource allocation program stored in the memory and executable by the processor, wherein when the project resource allocation program is executed by the processor, it implements the steps of the project resource allocation method as described in any of the above claims.

[0009] The beneficial effects of the technical solutions provided in this application include: By acquiring the total project resources, the number of features included in the project, and project quality impact parameters, and substituting them into a pre-constructed project resource allocation function—which is built based on the quality functions of each feature and characterizes the nonlinear relationship between the resources invested in the corresponding feature and the project quality—and using the total project resources as constraints, the extreme values ​​of the project resource allocation function are solved to determine the amount of resources allocated to each feature. This automatically generates the optimal resource allocation scheme, effectively solving the inherent problems of strong subjectivity, lack of theoretical support, and difficulty in achieving globally optimal configuration in traditional expert estimation methods. It provides objective, verifiable, and optimal resource allocation decision-making basis for project management, thereby significantly improving R&D efficiency and overall product quality while keeping the total amount of resources constant. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating an embodiment of the resource allocation method for this application project; Figure 2 A diagram illustrating the relationship between resource input and product quality; Figure 3 This is a schematic diagram of the functional modules of an embodiment of the resource allocation device of this application. Figure 4 This is a schematic diagram of the hardware structure of the project resource allocation device involved in the embodiment of this application. Detailed Implementation

[0011] 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 are within the scope of protection of the present application.

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0013] Firstly, embodiments of this application provide a method for allocating project resources.

[0014] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the resource allocation method for this application. Figure 1 As shown, the project resource allocation methods include: Step S101: Obtain the total project resources, the number of features included in the project, and the project quality impact parameters, and substitute them into the pre-built project resource allocation function. The project resource allocation function is constructed based on the quality function of each feature. The quality function of a feature is used to characterize the non-linear relationship between the resources invested in the corresponding feature and the project quality.

[0015] Step S102: Using the total resources of the project as a constraint, solve for the extreme value of the project resource allocation function to determine the amount of resources allocated to each characteristic.

[0016] It is worth noting that in this embodiment, "project" refers to a specific product development task, such as the development lifecycle of a software version. A project includes at least one feature, which refers to a functional module or component in the project that can be developed and tested independently, such as routing protocol functions or network management functions in communication equipment.

[0017] Before allocating project resources, a project resource allocation function can be pre-built. In this embodiment, the independent variables of the project resource allocation function mainly include total project resources and project quality impact parameters. Total project resources refer to the total resources invested in the project, which can be expressed in person-days, person-months, or funds, etc. Total project resources also represent the upper limit of resources that can be allocated to all features in the project. Project quality impact parameters include: the number of project issues and project complexity. Specifically, the number of project issues includes the total number of issues in the project in historical versions and the number of issues for each feature in historical versions; project complexity includes the total project complexity and the complexity of each feature, and complexity can be quantified by code nesting levels, number of loops, etc.

[0018] Specifically, in this embodiment, the total number of issues in the project in historical versions can be the total number of issues in the previous version, the number of issues for each feature in historical versions can be the number of issues for each feature in the previous version, the total project complexity can be the total project complexity in the previous version, and the complexity of each feature can be the complexity of each feature in the previous version.

[0019] It is important to understand that for a project product, the resources invested, the number of product issues, and the complexity of the code will all affect the product quality.

[0020] Empirical data and data analysis show that product quality is positively correlated with the resources invested. However, as the resources invested continue to increase, the rate of improvement in product quality decreases. In other words, product quality increases logarithmically with the increase in resources invested.

[0021] The number of issues in historical versions is inversely proportional to product quality; that is, the more issues in historical versions, the worse the product quality, which means that more resources are needed to ensure the quality of the current version.

[0022] The greater the code complexity, the higher the product complexity, and this increases exponentially, leading to poorer product quality. Therefore, code complexity and product quality are inversely proportional.

[0023] In one embodiment, when constructing the project resource allocation function, a quality function corresponding to the characteristics is first established based on the nonlinear relationship between the input resources of the project quality impact parameters and characteristics and the project quality.

[0024] Specifically, the relationship between resource input and product quality is as follows: Figure 2 As shown in the figure, the horizontal axis represents the amount of resources invested, and the vertical axis represents the number of problems found within the period. It can be seen that the number of problems increases logarithmically with the increase of resource investment. Given that the total project resources are fixed, this application establishes a relationship model between invested resources and project quality based on the Shannon formula for optimal allocation. The Shannon formula is:

[0025] In Shannon's formula, the channel bandwidth B can be likened to the total resources of a project, and the channel power s can be likened to the amount of resources allocated to specific features within the project. Since there is no direct equivalent to channel noise in the project... The parameters are defined, and the goal of the model relating resource input to project quality is to maximize quality, rather than calculating a fixed value like channel capacity C. Therefore, the Shannon formula can be rationally simplified, and the final expression for fitting the relationship between resource input and project quality for each characteristic is: .in, Indicates the total resources of the project. This represents the amount of resources allocated to the i-th characteristic in the project, and the value of this expression is between (0, 1), which meets the normalization requirements of quality contribution.

[0026] Besides being directly related to resource input, project quality is also affected by project quality impact parameters. In this embodiment, the project quality impact parameters mainly consider two key factors: the number of problems and code complexity.

[0027] The number of problems has an inverse relationship with project quality; therefore, the relationship between the number of problems and project quality for each characteristic can be expressed by an expression. Quantification is performed, where B represents the total number of issues in the project's historical versions. This represents the number of issues with the i-th feature in historical versions. In this embodiment, the denominator is set to... It is possible that a certain feature had no issues in previous versions (i.e.) When the number of problems for a certain characteristic equals 0, ensure that the formula is meaningful and the value is stable. When the denominator is 1 (e.g., B, which is an extreme case), zero or negative values ​​can also be avoided. This item reflects the proportion of the number of issues for a feature in historical versions to the total number of issues for the project in historical versions, reflecting the degree to which the number of issues for that feature affects the project quality.

[0028] The impact of code complexity on project quality exhibits a non-linear, inversely proportional exponential relationship. Therefore, the relationship between the complexity of various features and project quality can be expressed by an expression. Quantification is performed, where L represents the total complexity of the project. Let represent the complexity of the i-th characteristic, and e be the natural constant. Since the increase in complexity exponentially increases the impact on quality, the formula uses... This exponential term is used to simulate this effect.

[0029] It is worth noting that the impact of the number of historical issues and code complexity on project quality may vary across different projects or development stages. This embodiment introduces a weighting coefficient for each influencing factor to enhance the adaptability and flexibility of the function. Weighting coefficients are assigned to the influencing factor of the number of historical issues. Assign weight coefficients to factors affecting code complexity. , and The values ​​can be set according to requirements. Considering that the range of values ​​for each derived basic formula (such as the logarithmic function and the inverse proportional function) is designed to be within the interval (0, 1), in order to achieve parameter normalization and avoid excessive dominance by any one factor, this embodiment sets a normalization condition for the weight coefficients, that is, requiring the sum of all weight coefficients to be equal to 1. This design ensures the interpretability and stability of the mass function calculation results.

[0030] Based on the above analysis, by integrating the relationship between resource investment, number of historical issues, code complexity, and project quality, and their corresponding weighting coefficients, a quality function for a single characteristic can be established: , ( 1) The sum of the quality functions of all characteristics in the project is used as the project resource allocation function: Q= =

[0031] Where Q represents project quality and N represents the number of characteristics. This represents the quality of the i-th characteristic. This indicates the total number of issues in all versions of the project. Let L represent the number of issues with the i-th feature in historical versions, and L represent the total complexity of the project. Let S represent the complexity of the i-th feature, and let S represent the total project resources. This represents the amount of resources allocated to the i-th characteristic. This represents the first weighting coefficient. This represents the second weighting coefficient, and + =1.

[0032] If other factors affect project quality, their impact can be analyzed and then the relationship between historical problem count and code complexity and project quality can be referenced and added to the feature's quality function, thereby updating the project resource allocation function.

[0033] After the project resource allocation function is built, when project resource allocation is required, obtain the total project resources S, the number of features included in the project N, and the total number of issues in the project's historical versions. The number of issues for each feature in previous versions The total project complexity L, and the complexity of each feature. The parameters affecting project quality are then substituted into the project resource allocation function. Next, with the total project resources S as constraints and the goal of maximizing project quality Q, the project resource allocation function is solved. In this way, the problem of resource allocation is transformed into solving the function f( )= The maximization problem involves calculating the amount of resources allocated to each characteristic. .

[0034] Furthermore, after determining the amount of resources allocated to each characteristic, to further improve resource utilization efficiency, fine-grained resource allocation can be performed internally for each individual characteristic. Specific steps include: Step S201: For any feature, obtain the amount of resources invested in the feature, the number of quality dimensions within the feature, and the feature quality influence parameters, and substitute them into the pre-built feature resource allocation function. The feature resource allocation function is constructed based on the quality functions of each quality dimension within the feature. The quality function of the quality dimension is used to characterize the nonlinear relationship between the resources invested in the corresponding quality dimension and the feature quality.

[0035] Step S202: Using the resource quantity of the characteristic as a constraint, solve for the extreme value of the characteristic resource allocation function to determine the resources to be invested in each quality dimension.

[0036] The quality dimensions within a feature refer to the dimensions or standards used to evaluate and measure the quality of a feature. For example, the quality dimensions of a feature may include performance, security, reliability, etc. A feature must include at least one quality dimension.

[0037] Specifically, before allocating resources to each quality dimension within a characteristic, it is necessary to first analyze the characteristic quality influence parameters that affect its quality. Based on the characteristic quality influence parameters and the nonlinear relationship between the input resources of the quality dimension and the characteristic quality, a quality function for the corresponding quality dimension is established, and then a characteristic resource allocation function is constructed.

[0038] The parameters affecting feature quality include: the number of issues for the feature in historical versions, the number of issues for each quality dimension in historical versions, the number of new requirements for the feature, and the number of new requirements for each quality dimension. The number of new requirements represents the number of new or changed requirements for that feature in the current version for each quality dimension *e*. Analysis shows that, because new requirements typically introduce new complexity and potential risks, the number of new requirements is inversely proportional to feature quality.

[0039] Based on the above analysis, quality functions can be established for each quality dimension within the characteristics. This embodiment draws on the modeling concept of project quality functions, applying the inverse proportional relationship as the basic model.

[0040] Therefore, the quality function of the e-th quality dimension within characteristic i It can be represented as:

[0041] The sum of the quality functions for all quality dimensions is used as the characteristic resource allocation function: =

[0042] in, M represents the quality of characteristic i, and M represents the number of quality dimensions. This represents the quality of the e-th quality dimension. This indicates the number of issues with feature i in historical versions. This represents the number of issues in the e-th quality dimension across historical versions. This represents the number of new requirements for feature i. This represents the number of new requirements in the e-th quality dimension. This represents the amount of resources allocated to characteristic i. This represents the number of resources allocated to the e-th quality dimension. This represents the third weighting coefficient. Indicates the fourth weighting coefficient and + =1.

[0043] After the feature resource allocation function is constructed, when feature resource allocation is required, taking the resource allocation within feature i as an example: obtain the resources allocated to feature i. The number of quality dimensions M contained in feature i, and the number of issues with feature i in previous versions. The number of issues in each quality dimension in historical versions The number of new requirements for feature i And the number of new requirements in each quality dimension. The parameters affecting the quality of characteristics are then substituted into the characteristic resource allocation function, and then the resources allocated to characteristic i are used... As constraints, with characteristic quality With the objective of maximizing resource allocation, we solve for the project resource allocation function. This transforms the resource allocation problem into solving for the function f( )= The maximization problem calculates the amount of resources allocated to each quality dimension of characteristic i. .

[0044] In a specific embodiment, taking the S16800 product development project as an example, the project includes three features: Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), and Address Resolution Protocol (ARP). In the first round of testing during the System Verification Design (SDV) phase, OSPF required 12 person-days of manpower, identifying 7 issues, with 42 issues found in previous versions; BGP required 12 person-days, identifying 4 issues, with 23 issues found in previous versions; and ARP required 10 person-days, identifying 10 issues, with 67 issues found in previous versions. The total manpower for these three features was 34 person-days, representing the total project resources. The total number of issues in previous versions was 42 + 23 + 67 = 132. Substituting this data into the project resource allocation function, resources allocated to OSPF are defined as S1, resources allocated to BGP as S2, and resources allocated to ARP as S3. Three specific weights can also be set; in this embodiment, all three specific weights are set to be equal. ,get:

[0045]

[0046] Based on the original resource allocations for each feature in the project (OSPF: 12 person-days, S1=12; BGP: 12 person-days, S2=12; ARP: 10 person-days, S3=10), the project quality can be calculated. = 0.07442. (This is incomplete and requires further context.) Finding the maximum value, we can find that when the maximum value f(s) = 0.081, S1 = 10, S2 = 4, and S3 = 20. Therefore, we can determine that the resources allocated to each feature in the new round of testing are 10 person-days for OSPF, 4 person-days for BGP, and 20 person-days for ARP.

[0047] Analysis of historical data and project test results revealed that ARP had the highest number of issues in both the previous and current versions, followed by OSPF, with BGP having the fewest. Therefore, ARP should be allocated the most resources. The resource allocation strategy conclusion derived from the project's resource allocation function is consistent with the empirical analysis. If the resource investment in the ARP feature is further increased, adjusting it to S1=5, S2=5, S3=24, the calculated f(s) = 0.0734, which is lower than the previously obtained 0.081, reflecting the non-linear relationship between resource investment and quality improvement.

[0048] In another specific embodiment, taking the 6000 N V1R2 product development project as an example, the project includes three features: EOS business, OAM, and extended sub-frame.

[0049] The EOS project invested 8 person-days, identifying 1 issue and 1 issue from a previous version; the OAM project invested 6 person-days, identifying 2 issues and 3 issues from a previous version; the extended subframe project invested 5 person-days, identifying 6 issues and 6 issues from a previous version. The total man-days for these three features represent the project's total resources, amounting to 19 person-days. The total number of issues identified in previous versions is 1 + 3 + 6 = 10.

[0050] Applying this to the formula, EOS is S1, OAM is S2, and the extended sub-frame is S3. To simplify the calculation, we assume that the weights of the three functions are the same, all being 1 / 3, resulting in:

[0051]

[0052] Based on the original resource allocation for each feature in the project, the EOS business requires 8 person-days (S1=8), OAM requires 6 person-days (S2=6), and the extended sub-frame requires 5 person-days (S3=5). The project quality can then be calculated. = 0.07442. (This is incomplete and requires further context.) Finding the maximum value, we get f(s) = 0.07938, where S1 = 3, S2 = 5, and S3 = 11. Therefore, in the new round of testing, the resources allocated to each feature are 3 person-days for EOS, 5 person-days for OAM, and 11 person-days for the extended sub-frame. Observing historical data and test results, we find that the extended sub-frame has the most problems, while the EOS function has the fewest. The resource allocation of S1-S3 at the calculated maximum value aligns with the problem-based allocation method and also satisfies the empirical conclusion that the module with the most problems requires the most resources. If we continue to increase the values, for example, S1 = 3, S2 = 3, and S3 = 13, then f(s) = 0.07855 is less than the maximum value, indicating that the resources allocated to the extended sub-frame are excessive, and the resource allocation for the extended sub-frame needs to be reduced.

[0053] The project resource allocation method provided in this application transforms the resource allocation problem into a mathematical extremum problem by constructing a quantitative quality function model. This achieves a shift from subjective, qualitative, experience-based decision-making to model-based decision-making based on objective data. By introducing a logarithmic function, this scheme accurately characterizes nonlinear relationships such as diminishing marginal returns of resource input, ensuring that a near-optimal allocation scheme can be calculated under limited resource constraints. This significantly improves resource utilization efficiency and overall project output. Furthermore, its modeling framework possesses good scalability and reusability, providing a universal theoretical basis and reliable practical tools for the refined resource management of various complex projects.

[0054] Secondly, embodiments of this application also provide a project resource allocation device.

[0055] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the resource allocation device of this application. Figure 3 As shown, the project resource allocation device includes: The acquisition module is used to acquire the total project resources, the number of features included in the project, and the project quality impact parameters, and then input them into the pre-built project resource allocation function. The project resource allocation function is constructed based on the quality function of each feature, and the quality function of the feature is used to characterize the non-linear relationship between the resources invested in the corresponding feature and the project quality. The allocation module is used to solve for the extreme value of the project resource allocation function under the constraint of the total project resources, and to determine the amount of resources allocated to each characteristic. Furthermore, in one embodiment, the project quality impact parameters include: The total number of issues in the project's historical versions, the number of issues for each feature in historical versions, the total complexity of the project, and the complexity of each feature.

[0056] Furthermore, in one embodiment, the device further includes a construction module for: Based on the nonlinear relationship between the input resources of the project quality impact parameters and characteristics and the project quality, a quality function corresponding to the characteristics is established. The sum of the quality functions of all characteristics is used as the project resource allocation function: Q= =

[0057] Where Q represents project quality and N represents the number of features. This represents the quality of the i-th characteristic. This indicates the total number of issues in all versions of the project. Let L represent the number of issues with the i-th feature in historical versions, and L represent the total complexity of the project. Let S represent the complexity of the i-th feature, and let S represent the total project resources. This represents the amount of resources allocated to the i-th characteristic. This represents the first weighting coefficient. This represents the second weighting coefficient, and + =1.

[0058] Furthermore, in one embodiment, the allocation module is also used for: Using the total resources of the project as a constraint and the maximization of the project quality as the objective, the project resource allocation function is solved to obtain the amount of resources allocated to each characteristic.

[0059] Furthermore, in one embodiment: The acquisition module is further configured to, for any characteristic, acquire the amount of resources allocated to that characteristic, the number of quality dimensions within the characteristic, and the characteristic quality influence parameters, and substitute them into a pre-built characteristic resource allocation function, wherein the characteristic resource allocation function is constructed based on the quality functions of each quality dimension within the characteristic, and the quality function of the quality dimension is used to characterize the nonlinear relationship between the resources invested in the corresponding quality dimension and the characteristic quality. The allocation module is further configured to, using the resource quantity of a characteristic as a constraint, solve for the extreme value of the characteristic resource allocation function, and determine the resource quantity allocated to each quality dimension.

[0060] Furthermore, in one embodiment, the characteristic quality influence parameter includes the following items: The number of issues for a feature in previous versions, the number of issues for each quality dimension in previous versions, the number of new requirements for the feature, and the number of new requirements for each quality dimension.

[0061] Furthermore, in one embodiment, the building module is also used for: Based on the nonlinear relationship between the characteristic quality influence parameters and the input resources of each quality dimension and the characteristic quality, a quality function corresponding to the quality dimension is established. The sum of the quality functions for all quality dimensions is used as the characteristic resource allocation function: =

[0062] in, M represents the quality of characteristic i, and M represents the number of quality dimensions. This represents the quality of the e-th quality dimension. This indicates the number of issues with feature i in historical versions. This represents the number of issues in the e-th quality dimension across historical versions. This represents the number of new requirements for feature i. This represents the number of new requirements in the e-th quality dimension. This represents the amount of resources allocated to characteristic i. This represents the number of resources allocated to the e-th quality dimension. This represents the third weighting coefficient. Indicates the fourth weighting coefficient and + =1.

[0063] Furthermore, in one embodiment, the allocation module is also used for: With the quantity of resources for a given characteristic as a constraint and the maximum quality of that characteristic as the objective, the resource allocation function for that characteristic is solved to obtain the resources allocated to each quality dimension.

[0064] The functions of each module in the above-mentioned project resource allocation device correspond to the steps in the above-mentioned project resource allocation method embodiment, and their functions and implementation processes will not be described in detail here.

[0065] Thirdly, embodiments of this application provide a project resource allocation device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0066] Reference Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of the project resource allocation device involved in the embodiments of this application. In the embodiments of this application, the project resource allocation device may include a processor, a memory, a communication interface, and a communication bus.

[0067] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0068] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the project resource allocation equipment, as well as interfaces used for interconnecting the project resource allocation equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0069] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0070] The processor can be a general-purpose processor, which can call the project resource allocation program stored in memory and execute the project resource allocation method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the project resource allocation program is called can be referred to in the various embodiments of the project resource allocation method of this application, and will not be repeated here.

[0071] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0072] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0073] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0074] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0075] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0076] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0078] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for allocating project resources, characterized in that, The project resource allocation method includes: The total project resources, the number of features included in the project, and the project quality impact parameters are obtained and substituted into a pre-built project resource allocation function. The project resource allocation function is constructed based on the quality function of each feature. The quality function of a feature is used to characterize the non-linear relationship between the resources invested in the corresponding feature and the project quality. Using the total resources of the project as constraints, solve for the extreme value of the project resource allocation function to determine the amount of resources allocated to each characteristic.

2. The project resource allocation method as described in claim 1, characterized in that, The project quality impact parameters include: The total number of issues in the project's historical versions, the number of issues for each feature in historical versions, the total complexity of the project, and the complexity of each feature.

3. The project resource allocation method as described in claim 2, characterized in that, The steps for constructing the project resource allocation function include: Based on the nonlinear relationship between the input resources of the project quality impact parameters and characteristics and the project quality, a quality function corresponding to the characteristics is established. The sum of the quality functions of all characteristics is used as the project resource allocation function: Q= = Where Q represents project quality and N represents the number of features. This represents the quality of the i-th characteristic. This indicates the total number of issues in all versions of the project. Let L represent the number of issues with the i-th feature in historical versions, and L represent the total complexity of the project. Let S represent the complexity of the i-th feature, and let S represent the total project resources. This represents the amount of resources allocated to the i-th characteristic. This represents the first weighting coefficient. This represents the second weighting coefficient, and + =1.

4. The project resource allocation method as described in claim 3, characterized in that, The step of solving for the extreme value of the project resource allocation function, using the total project resources as constraints, and determining the amount of resources allocated to each characteristic, includes: Using the total resources of the project as a constraint and the maximization of the project quality as the objective, the project resource allocation function is solved to obtain the amount of resources allocated to each characteristic.

5. The project resource allocation method as described in claim 1, characterized in that, After determining the amount of resources allocated to each characteristic, the following is also included: For any given characteristic, obtain the amount of resources allocated to that characteristic, the number of quality dimensions within the characteristic, and the characteristic quality impact parameters, and substitute them into a pre-built characteristic resource allocation function. The characteristic resource allocation function is constructed based on the quality functions of each quality dimension within the characteristic. The quality function of the quality dimension is used to characterize the nonlinear relationship between the resources invested in the corresponding quality dimension and the characteristic quality. Using the resource quantity of a characteristic as a constraint, the extreme value of the characteristic resource allocation function is solved to determine the amount of resources allocated to each quality dimension.

6. The project resource allocation method as described in claim 5, characterized in that, The attribute quality influence parameter package includes: The number of issues for a feature in previous versions, the number of issues for each quality dimension in previous versions, the number of new requirements for the feature, and the number of new requirements for each quality dimension.

7. The project resource allocation method as described in claim 6, characterized in that, The pre-construction of the feature resource allocation function includes: Based on the nonlinear relationship between the characteristic quality influence parameters and the input resources of the quality dimension and the characteristic quality, a quality function corresponding to the quality dimension is established. The sum of the quality functions for all quality dimensions is used as the characteristic resource allocation function: = in, M represents the quality of characteristic i, and M represents the number of quality dimensions. This represents the quality of the e-th quality dimension. This indicates the number of issues with feature i in historical versions. This represents the number of issues in the e-th quality dimension across historical versions. This represents the number of new requirements for feature i. This represents the number of new requirements in the e-th quality dimension. This indicates the amount of resources allocated to characteristic i. This represents the number of resources allocated to the e-th quality dimension. This represents the third weighting coefficient. Indicates the fourth weighting coefficient and + =1.

8. The project resource allocation method as described in claim 5, characterized in that, The process of finding the extreme value of the characteristic resource allocation function, using the quantity of resources of a characteristic as a constraint, to determine the resources allocated to each quality dimension, includes: With the quantity of resources for a given characteristic as a constraint and the maximum quality of that characteristic as the objective, the resource allocation function for that characteristic is solved to obtain the resources allocated to each quality dimension.

9. A project resource allocation device, characterized in that, The project resource allocation device includes: The acquisition module is used to acquire the total project resources, the number of features included in the project, and the project quality impact parameters, and then input them into the pre-built project resource allocation function. The project resource allocation function is constructed based on the quality function of each feature, and the quality function of the feature is used to characterize the non-linear relationship between the resources invested in the corresponding feature and the project quality. The allocation module is used to solve for the extreme value of the project resource allocation function with the total resources of the project as a constraint, and to determine the amount of resources allocated to each characteristic.

10. A project resource allocation device, characterized in that, The project resource allocation device includes a processor, a memory, and a project resource allocation program stored in the memory and executable by the processor, wherein when the project resource allocation program is executed by the processor, it implements the steps of the project resource allocation method as described in any one of claims 1 to 8.

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