Project resource configuration method and device, computer equipment and storage medium

Abstract

The invention relates to a project resource configuration method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring project description data of historical projects; determining resource configuration reference data of each project resource in the historical project according to the project description data of the historical project; the project resources comprise physical resources, task resources and member resources; and determining target resource configuration data of the target project according to the resource configuration reference data and the resource configuration basic data of each project resource in the target project. By adopting the method, resource allocation can be accurately performed on each project resource in the target project, so that the resource utilization rate and the project efficiency of the target project are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a project resource configuration method, device, computer equipment and storage medium. Background Art

[0002] With the development of information technology, the field of R&D management has gradually evolved towards intelligence and automation. Modern enterprises usually face the challenge of multi-dimensional resource allocation during the R&D process of complex projects. The effective solution to the above challenges is directly related to the success of the project. Therefore, how to reasonably allocate resources within the project life cycle has become an important issue in R&D management.

[0003] Traditional technologies mostly rely on fixed processes and pre-set plans, lacking the ability to utilize real-time data and respond quickly. When a project encounters changes in the external environment or adjustments in internal needs during execution, traditional technologies often respond slowly, leading to delays in project schedules, waste of resources, or even ultimate project failure.

[0004] Therefore, the existing technology usually has the problem of inaccurate resource allocation, which leads to a lot of resource waste and low project efficiency. Summary of the invention

[0005] Based on this, it is necessary to provide a project resource configuration method, device, computer equipment and storage medium that can accurately configure project resources in response to the above technical problems.

[0006] In a first aspect, the present application provides a project resource configuration method, comprising:

[0007] Get project description data for historical projects;

[0008] Determine the resource allocation reference data of each project resource in the historical project based on the project description data of the historical project; project resources include physical resources, task resources and member resources;

[0009] Determine the target resource configuration data of the target project based on the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0010] In one embodiment, determining resource configuration reference data of each project resource in the historical project according to the project description data of the historical project includes:

[0011] Input the project description data of historical projects into the collaborative filtering prediction model to obtain the allocation of different project resources in historical projects;

[0012] Use the allocation of resources for different projects as reference data for resource configuration.

[0013] In one embodiment, determining resource configuration reference data of each project resource in the historical project according to the project description data of the historical project includes:

[0014] Extract features from the project description data of historical projects to obtain resource features of each project resource in the historical projects; resource features include physical resource features, task resource features, and member resource features;

[0015] Based on the resource characteristics of each project resource in the historical projects, resource configuration reference data of each project resource in the historical projects are predicted.

[0016] In one embodiment, predicting resource configuration reference data of each project resource in the historical project based on the resource characteristics of each project resource in the historical project includes:

[0017] Determine at least one candidate reference data corresponding to the historical project according to resource characteristics of each project resource in the historical project;

[0018] Resource configuration reference data of the historical project is determined according to at least one candidate reference data.

[0019] In one embodiment, determining resource configuration reference data of a historical project according to at least one candidate reference data includes:

[0020] For each candidate reference data, determining a project success rate of the historical project while allocating project resources in the target project according to the candidate reference data;

[0021] According to the project success rates corresponding to different candidate reference data, resource configuration reference data of historical projects are selected from the candidate reference data.

[0022] In one embodiment, determining target resource configuration data of a target project according to resource configuration reference data and resource configuration basic data of each project resource in the target project includes:

[0023] Perform feature fusion on the resource configuration reference data of the historical project and the resource configuration basic data of the target project to obtain the fused resource configuration data of the target project;

[0024] The fusion resource configuration data is adjusted according to the preset adjustment factor to obtain the target resource configuration data of the target project.

[0025] In a second aspect, the present application also provides a project resource configuration device, including:

[0026] A data acquisition module, used to acquire project description data of historical projects;

[0027] A reference determination module is used to determine the resource configuration reference data of each project resource in the historical project according to the project description data of the historical project; the project resources include physical resources, task resources and member resources;

[0028] The target determination module is used to determine the target resource configuration data of the target project based on the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0029] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0030] Get project description data for historical projects;

[0031] Determine the resource allocation reference data of each project resource in the historical project based on the project description data of the historical project; project resources include physical resources, task resources and member resources;

[0032] Determine the target resource configuration data of the target project based on the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0034] Get project description data for historical projects;

[0035] Determine the resource allocation reference data of each project resource in the historical project based on the project description data of the historical project; project resources include physical resources, task resources and member resources;

[0036] Determine the target resource configuration data of the target project based on the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0037] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0038] Get project description data for historical projects;

[0039] Determine the resource allocation reference data of each project resource in the historical project based on the project description data of the historical project; project resources include physical resources, task resources and member resources;

[0040] Determine the target resource configuration data of the target project based on the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0041] The above-mentioned project resource configuration method, device, computer equipment and storage medium obtain the project description data of the historical project; determine the resource configuration reference data of each project resource in the historical project based on the project description data of the historical project; project resources include physical resources, task resources and member resources; determine the target resource configuration data of the target project based on the resource configuration reference data and the resource configuration basic data of each project resource in the target project. This application can accurately configure the resources of each project in the target project, thereby improving the resource utilization rate and project efficiency of the target project. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] FIG1 is an application environment diagram of a project resource configuration method provided by this embodiment;

[0044] FIG2 is a schematic diagram of a flow chart of a first project resource configuration method provided in this embodiment;

[0045] FIG3 is a schematic diagram of a flow chart of a second project resource configuration method provided in this embodiment;

[0046] FIG4 is a structural block diagram of a project resource configuration device provided by this embodiment;

[0047] FIG5 is an internal structure diagram of a first computer device provided in this embodiment;

[0048] FIG6 is a diagram showing the internal structure of a second type of computer device provided in this embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The project resource configuration method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The computer device obtains the project description data of the historical project; according to the project description data of the historical project, the resource configuration reference data of each project resource in the historical project is determined; the project resources include physical resources, task resources and member resources; according to the resource configuration reference data and the resource configuration basic data of each project resource in the target project, the target resource configuration data of the target project is determined. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0051] In an exemplary embodiment, as shown in FIG2 , a project resource configuration method is provided, which is described by taking the method applied to the computer device in FIG1 as an example, and includes the following steps 201 to 203. Among them:

[0052] Step 200, obtaining project description data of historical projects.

[0053] The project description data may be data used to describe the content of the project.

[0054] Specifically, a project progress data set, resource configuration data, task priority data set and project result data set of historical projects are obtained, and the project progress data set, resource configuration data, task priority data set and project result data set are used as project description data.

[0055] For example, extract project progress data from historical projects, project progress data set: D p ={d p1 ,d p2 ,...,d pi ,...,d pn}; where d pi Represents the progress data of the i-th historical project, including the project start time, milestone nodes and actual completion time. Extract resource configuration data from historical projects, resource configuration data set: D r ={d r1 ,d r2 ,...,d ri ,...,d rn}; where dri Represents the allocation of resources for each project in the i-th historical project, including the number of personnel, equipment usage, and budget allocation. Extract the task priority data from the historical project, task priority data set: D t ={d t1 ,d t2 ,...,d ti ,...,d tn}; where d ti Represents the priority setting of each task in the i-th historical project, including the execution order of tasks and critical path tasks. Extract project result data, project result data set: D o ={d o1 ,d o2 ,...,d oi ,...,d on}; where d oi Represents the final result data of the i-th historical project, including the project completion status, success rate, delay days and budget overrun. By integrating the project schedule data set, resource allocation data set, task priority data set and project result data set, the project description data is constructed: D total ={D p ,D r, D t ,D o}.

[0056] Step 210: Determine resource configuration reference data of each project resource in the historical project according to the project description data of the historical project.

[0057] Among them, project resources include physical resources, task resources and member resources.

[0058] Specifically, feature extraction is performed on the project description data of historical projects to obtain resource features of each project resource in the historical projects; resource features include physical resource features, task resource features, and member resource features; based on the resource features of each project resource in the historical projects, resource configuration reference data of each project resource in the historical projects are predicted.

[0059] In one embodiment, resource configuration reference data of each project resource in the historical project is predicted based on resource characteristics of each project resource in the historical project, including: determining at least one candidate reference data corresponding to the historical project based on the resource characteristics of each project resource in the historical project; and determining the resource configuration reference data of the historical project based on the at least one candidate reference data.

[0060] In one embodiment, resource configuration reference data of a historical project is determined based on at least one candidate reference data, including: for each candidate reference data, while allocating project resources in a target project based on the candidate reference data, determining the project success rate of the historical project; and selecting resource configuration reference data of the historical project from each candidate reference data based on the project success rates corresponding to different candidate reference data.

[0061] Step 220, determining target resource configuration data of the target project according to the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0062] Specifically, feature fusion is performed on the resource configuration reference data of the historical project and the resource configuration basic data of the target project to obtain a fusion result, which is used as the target resource configuration data of the target project.

[0063] The above project resource allocation method obtains the project description data of the historical project; determines the resource allocation reference data of each project resource in the historical project based on the project description data of the historical project; project resources include physical resources, task resources and member resources; determines the target resource configuration data of the target project based on the resource allocation reference data and the resource allocation basic data of each project resource in the target project. This application can accurately allocate resources to each project resource in the target project, thereby improving the resource utilization rate and project efficiency of the target project.

[0064] In one embodiment, resource configuration reference data of each project resource in the historical project is determined based on the project description data of the historical project, including: inputting the project description data of the historical project into a collaborative filtering prediction model to obtain the allocation of different project resources in the historical project; and using the allocation of different project resources as resource configuration reference data.

[0065] Exemplarily, feature extraction is performed on project description data of historical projects to obtain resource features of each project resource in the historical projects; the resource features of each project resource in the historical projects are input into a collaborative filtering prediction model to obtain resource configuration reference data.

[0066] In a specific embodiment, the training process of the collaborative filtering prediction model includes the following steps:

[0067] S211, project description data D total Preprocessing is performed to extract feature vectors related to resource allocation, task priority, staffing, and each project stage, and the feature vector set is defined as X={x 1 ,x 2 ,…,x i ,…,x m}, each item feature vector x iRepresented as a combination of multi-dimensional features: x i =[x i1 ,x i2 ,…,x ij ,…,x ip ]; where x ij Represents the jth feature dimension of the i-th project, including resource allocation R j , Staffing j and task priority T j .

[0068] S212. Define the project success rate as the target variable y, where y i =g(z i ), represents the success rate of the i-th project, including project on-time delivery rate, budget compliance, product quality and customer satisfaction indicators, z i The evaluation function after comprehensively considering the progress, resource allocation and personnel efficiency of each stage of the project: i =αt i +βr i +γp i ; where t i Represents the project time schedule deviation, r i represents resource consumption deviation, p i represents personnel performance, α, β, and γ are weight coefficients used to balance the impact of different factors on project success rate;

[0069] S213. Based on the feature vector set X and the target variable y, a collaborative filtering prediction model for full-stack R&D management is constructed, as shown in the following formula (1-1):

[0070] (1-1)

[0071] in, is the predicted success rate of the i-th project, h(x ij ) is the mapping feature x ij To the function of project success rate, θ j are collaborative filtering prediction model parameters, is the error term, which is used to capture the random factors in the project that are not fully explained by the characteristics of the collaborative filtering prediction model;

[0072] S214, in the collaborative filtering prediction model training process, using the R&D project dataset D total The collaborative filtering prediction model parameters θ are optimized, and the prediction error L(θ) is minimized so that the collaborative filtering prediction model can predict the progress and resource allocation of the project at each development stage. The objective function is shown in the following formula (1-2):

[0073] (1-2)

[0074] Among them, n is the number of projects, yi is the actual success rate of the project, and λ is the regularization parameter used to prevent the model from overfitting;

[0075] S215. Based on the optimized collaborative filtering prediction model, generate resource allocation, task priority setting and staffing solutions for each stage of full-stack R&D management, including: in the demand analysis stage, the collaborative filtering prediction model recommends suitable team member configuration and time allocation; in the design and development stage, the collaborative filtering prediction model recommends the best technology stack selection and resource allocation; in the test verification stage, the collaborative filtering prediction model recommends appropriate test tools and time arrangements; in the release and delivery stage, the collaborative filtering prediction model optimizes resource allocation to ensure timely delivery of products and quality compliance with standards;

[0076] S216, collaborative filtering prediction model based on item feature vector x i Output the recommended configuration parameter set for full-stack R&D management: P i ={p i1 ,p i2 ,…,p ij ,…,p ik}; where p ij Represents the recommended configuration of the i-th project at the j-th development stage.

[0077] This embodiment inputs the project description data of historical projects into the collaborative filtering prediction model to obtain the allocation of different project resources in the historical projects; the allocation of different project resources is used as resource configuration reference data, so that the resource configuration reference data can be obtained more accurately.

[0078] In one embodiment, target resource configuration data of a target project is determined based on resource configuration reference data and basic resource configuration data of each project resource in the target project, including: performing feature fusion on resource configuration reference data of historical projects and basic resource configuration data of the target project to obtain fused resource configuration data of the target project; and adjusting the fused resource configuration data according to a preset adjustment factor to obtain the target resource configuration data of the target project.

[0079] In a specific embodiment, the training method of the adaptive optimization model may include the following steps:

[0080] S2200, recommended configuration parameter set P generated based on collaborative filtering prediction model i , define the initial input variable set of the adaptive optimization model: V={v 1 ,v 2 ,v 3 ,v 4}; where v iRepresents key indicators related to project launch, including project schedule deviation v 1 , resource consumption rate v 2 , Team Performance 3 and the external environmental change factor v 4 .

[0081] S2201. Construct the objective function F(Θ) of the adaptive optimization model. The objective function consists of multiple optimization objectives: F(Θ)=α 1 O 1 +β 1 O 2 +γ 1 O 3 +δ 1 O 4 ; Among them, O 1 represents the optimization goal of the project progress, O 2 represents the optimization goal of resource consumption, O 3 represents the optimization goal of team performance, O 4 represents the external environment adaptability optimization target; α 1 , β 1 , γ 1 , δ 1 is the weight coefficient of each optimization objective, Θ represents the parameter set of the adaptive optimization model;

[0082] S2202. Set the initial parameter set of the adaptive optimization model: Θ 0 ={θ 1 ,θ 2 ,θ 3}; where θ j Represents the parameters related to the initial optimization goal, including the project schedule adjustment parameter θ 1 , resource allocation adjustment parameter θ 2 and team performance improvement parameter θ 3 .

[0083] S2203, using real-time project progress, resource consumption, team performance and external environment change data, dynamically adjust the adaptive optimization model and update the optimization parameter set to: Θ(t)={θ 1 (t),θ 2 (t),θ 3 (t)}; where t represents the time variable, reflecting the change of the parameters of the adaptive optimization model over time;

[0084] S2204. By iteratively optimizing the adaptive optimization model, the objective function F(Θ(t)) is optimized during the project execution process, and finally the optimal project management strategy is generated.

[0085] In another embodiment, based on an adaptive optimization algorithm, the resource configuration basic data of the target project is iteratively updated according to the resource configuration reference data of the historical project to obtain the target resource configuration data of the target project, including:

[0086] S2210, based on the recommended configuration parameter set P of the collaborative filtering prediction model i , generate initial resource allocation plans, task priority settings, and team member configuration plans for new projects;

[0087] S2211. Optimize the initial configuration using the adaptive optimization model, and set the initial optimization objective function as shown in the following formula (1-3):

[0088] (1-3)

[0089] Among them, ω j and η l The weight coefficients for optimizing objectives in different dimensions, f(p ij ) and g(q il ) are respectively for the configuration parameters p ij and q il The optimization function, h(p ij ) and s(q il ) is the feasibility constraint function of the parameter, and It is a control term used to prevent over-optimization under extreme conditions.

[0090] S2212. When the project is started, the initial configuration is iteratively optimized according to the project requirements and resource constraints. The optimized configuration is determined by the following formula (1-4):

[0091] (1-4)

[0092] Among them, P i '(t) is the optimized configuration parameter set at time t, α 2 , β 2 , γ 2 is the learning rate parameter, which is used to control the adjustment amplitude of each iteration. To optimize the gradient of the objective function, δ ij and σ il are respectively related to the configuration parameters p ij and q il Related constraint factors;

[0093] S2213. During the optimization process, the actual needs of the project and resource constraints are considered, and each configuration parameter is dynamically adjusted. The optimal startup plan S is determined by the following formula (1-5): opt:

[0094] (1-5)

[0095] Among them, R i '(t), T i '(t),M i '(t) are the optimized resource allocation, task priority setting and team member configuration scheme, κ i (t) is the adjustment factor, which reflects the dynamic changes of various configurations over time.

[0096] In another embodiment, resource configuration reference data of historical projects and basic resource configuration data of target projects are input into the adaptive optimization model to obtain target resource configuration data of the target project, including:

[0097] S2220. During the project execution, monitor the project progress P(t), resource usage R(t) and environmental changes E(t) in real time, input the monitoring data into the adaptive optimization model, and construct a dynamic input variable set D(t);

[0098] S2221. Using the adaptive optimization model, dynamically adjust the resource allocation, task priority setting and team member configuration plan of the project according to the dynamic input variable set D(t), and set the optimization objective function as shown in the following formula (1-6):

[0099] (1-6)

[0100] Among them, J(Θ(t)) is the optimization objective function, λ 1, λ 2, λ 3 ,λ 4 is the weight coefficient, g 1 ,g 2 ,g 3 ,g 4 are the optimization functions of progress status, resource allocation, task priority and personnel arrangement, respectively, and Θ(t) is the model parameter at time t;

[0101] S2222. Deriving the optimization objective function J(Θ(t)), obtaining the adjustment direction of each configuration parameter, and updating the configuration parameter set as follows: ; where η 2 is the learning rate, which controls the magnitude of each adjustment during the optimization process. It is the gradient of the objective function, which is used to determine the direction and magnitude of the next adjustment;

[0102] S2223. Dynamically adjust resource configuration R according to the updated configuration parameter set Θ(t+1) i'(t+1), task priority T i '(t+1) and personnel arrangement M i '(t+1), so that the project remains in the optimal state in the next time period;

[0103] S2224. Loop through steps S2220 to S2223 until the project is completed. During the entire project execution process, the project's resource allocation, task priorities, and personnel arrangements are dynamically adjusted based on real-time data and environmental changes, and are always in the optimal state.

[0104] It should be noted that in this embodiment, during the project's demand analysis phase, design and development phase, test and verification phase, release and delivery phase, and subsequent operation and maintenance phase, the recommended configuration parameter set generated by the collaborative filtering prediction model is applied to provide optimization suggestions for each phase; at the start of each project phase, based on the recommendation results of the collaborative filtering prediction model and the actual situation of the project, an initial optimization plan is generated using the adaptive optimization model, and customized optimization settings are made for the specific needs of each phase; in each stage of the project, the initial optimization plan is dynamically adjusted based on real-time feedback data, so that the resource allocation, task priority, and personnel configuration of each stage respond to environmental changes and project requirements; during the execution of each stage of the project, the project management strategy is continuously monitored and optimized, so that each stage remains in the optimal state throughout the entire process from project launch to final delivery; after the project is completed, the data and optimization results of each stage are reviewed and analyzed, and the collaborative filtering prediction model and the adaptive optimization model are updated.

[0105] This embodiment performs feature fusion on the resource configuration reference data of the historical project and the resource configuration basic data of the target project to obtain the fused resource configuration data of the target project; adjusts the fused resource configuration data according to a preset adjustment factor to obtain the target resource configuration data of the target project, so that the target resource configuration data can be obtained more accurately, and the resource configuration reference data can be obtained more accurately.

[0106] In one embodiment, this embodiment provides an optional method for project resource configuration, and takes the method applied to a server as an example for explanation. As shown in FIG3 , the method includes the following steps:

[0107] Step 301, obtaining project description data of historical projects;

[0108] Step 302, inputting the project description data of the historical projects into the collaborative filtering prediction model to obtain the allocation of different project resources in the historical projects;

[0109] Step 303, using the allocation of resources of different projects as resource configuration reference data;

[0110] Step 304, feature fusion is performed on the resource configuration reference data of the historical project and the resource configuration basic data of the target project to obtain fused resource configuration data of the target project;

[0111] Step 305: Adjust the fusion resource configuration data according to the preset adjustment factor to obtain the target resource configuration data of the target project.

[0112] In this embodiment, through the collaborative filtering prediction model, based on historical project data and real-time feedback, the key requirements of each stage of the project can be accurately identified, and resource allocation, task priority setting and staffing plans can be intelligently recommended. Different from the traditional static resource allocation method, it realizes the full-process intelligent management of the initial configuration at the start of the project and the dynamic adjustment during the execution process, ensuring the optimal use of resources at different stages of the project. Through the adaptive optimization model, it can respond to changes in the project in real time, automatically adjust resource allocation and task priority, thereby improving the overall efficiency of the project and reducing resource waste.

[0113] Based on the collaborative filtering prediction model, this embodiment further combines the adaptive optimization model to achieve full life cycle management from project demand analysis, design and development, test verification, release and delivery to subsequent operation and maintenance. Compared with the traditional management method that can only optimize at a specific stage of the project, the present invention enables the project to be adaptively adjusted at each stage through real-time monitoring and feedback adjustment, always maintaining the optimal state, greatly improving the project success rate, and avoiding management failures and progress delays caused by environmental changes or demand adjustments.

[0114] The adaptive optimization model in this embodiment is highly adaptable and scalable. Through dynamic learning and adjustment of real-time data, the adaptive optimization model can automatically adjust the optimization parameters and objective functions according to the specific needs of different projects, and adapt to the complexity and variability of different projects. At the same time, the structural design of the adaptive optimization model is also highly scalable, and can flexibly adjust the optimization strategy and computing load of the model according to changes in project scale and complexity, thereby ensuring that the model can run efficiently in projects of different scales and complexities.

[0115] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0116] Based on the same inventive concept, the embodiment of the present application also provides a project resource configuration device for implementing the project resource configuration method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more project resource configuration device embodiments provided below can refer to the limitations on the project resource configuration method above, and will not be repeated here.

[0117] In an exemplary embodiment, as shown in FIG4 , a project resource configuration device is provided, comprising: a data acquisition module 10, a reference determination module 11 and a target determination module 12, wherein:

[0118] A data acquisition module 10 is used to acquire project description data of historical projects;

[0119] The reference determination module 11 is used to determine the resource configuration reference data of each project resource in the historical project according to the project description data of the historical project; the project resources include physical resources, task resources and member resources;

[0120] The target determination module 12 is used to determine the target resource configuration data of the target project according to the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

[0121] In one embodiment, the reference determination module 11 is further used to input the project description data of the historical project into the collaborative filtering prediction model to obtain the allocation of different project resources in the historical project; and use the allocation of different project resources as resource configuration reference data.

[0122] In one embodiment, the reference determination module 11 is also used to extract features from the project description data of the historical project to obtain resource features of each project resource in the historical project; the resource features include physical resource features, task resource features and member resource features; based on the resource features of each project resource in the historical project, the resource configuration reference data of each project resource in the historical project is predicted.

[0123] In one embodiment, the reference determination module 11 is further used to determine at least one candidate reference data corresponding to the historical project according to resource characteristics of each project resource in the historical project; and determine resource configuration reference data of the historical project according to the at least one candidate reference data.

[0124] In one embodiment, the reference determination module 11 is also used to determine the project success rate of the historical project for each candidate reference data, while allocating project resources in the target project according to the candidate reference data; and select the resource configuration reference data of the historical project from each candidate reference data according to the project success rates corresponding to different candidate reference data.

[0125] In one embodiment, the target determination module 12 is also used to perform feature fusion on the resource configuration reference data of the historical project and the resource configuration basic data of the target project to obtain fused resource configuration data of the target project; and adjust the fused resource configuration data according to a preset adjustment factor to obtain target resource configuration data of the target project.

[0126] Each module in the above-mentioned project resource configuration device can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0127] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a project resource configuration method is implemented.

[0128] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a project resource configuration method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0129] Those skilled in the art will understand that the structures shown in FIGS. 5 and 6 are merely block diagrams of partial structures related to the scheme of the present application, and do not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than those shown in the figures, or combine certain components, or have a different arrangement of components.

[0130] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0132] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0133] It should be noted that the data involved in this application (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0135] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A project resource configuration method, characterized in that: The method comprises: Get project description data for historical projects; Determine resource configuration reference data of each project resource in the historical project according to the project description data of the historical project; the project resources include physical resources, task resources and member resources; The target resource configuration data of the target project is determined according to the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

2. The method according to claim 1, characterized in that Determining resource configuration reference data of each project resource in the historical project according to the project description data of the historical project includes: Inputting the project description data of the historical project into the collaborative filtering prediction model to obtain the allocation of different project resources in the historical project; The allocation conditions of the resources of the different projects are used as the resource configuration reference data.

3. The method according to claim 1, characterized in that Determining resource configuration reference data of each project resource in the historical project according to the project description data of the historical project includes: Extracting features from the project description data of the target project to obtain resource features of each project resource in the historical project; the resource features include physical resource features, task resource features, and member resource features; Resource configuration reference data of each project resource in the historical project is predicted according to the resource characteristics of each project resource in the historical project.

4. The method according to claim 3, characterized in that The predicting resource configuration reference data of each project resource in the historical project according to the resource characteristics of each project resource in the historical project includes: Determining at least one candidate reference data corresponding to the historical project according to resource characteristics of each project resource in the historical project; Resource configuration reference data of the historical project is determined according to at least one candidate reference data.

5. The method according to claim 4, characterized in that The step of determining resource configuration reference data of the historical project according to at least one candidate reference data includes: For each candidate reference data, determining the project success rate of the historical project while allocating project resources in the target project according to the candidate reference data; According to the project success rates corresponding to different candidate reference data, resource configuration reference data of the historical project is selected from the candidate reference data.

6. The method according to claim 1, characterized in that Determining target resource configuration data of the target project according to the resource configuration reference data and resource configuration basic data of each project resource in the target project includes: Perform feature fusion on the resource configuration reference data of the historical project and the resource configuration basic data of the target project to obtain the fused resource configuration data of the target project; The fusion resource configuration data is adjusted according to a preset adjustment factor to obtain the target resource configuration data of the target project.

7. A project resource configuration device, characterized in that: The device comprises: A data acquisition module, used to acquire project description data of historical projects; A reference determination module, used to determine resource configuration reference data of each project resource in the historical project according to the project description data of the historical project; the project resources include physical resources, task resources and member resources; The target determination module is used to determine the target resource configuration data of the target project according to the resource configuration reference data and the resource configuration basic data of each project resource in the target project.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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