An intelligent project resource scheduling method, device, equipment, medium and product

Abstract

Embodiments of the present application provide an intelligent project resource scheduling method, device, equipment, medium and product, which comprises: obtaining project related data of a target project; analyzing and predicting project budget data and historical budget data in the project related data based on a pre-trained time series analysis prediction model, and outputting a cost demand evaluation report corresponding to the target project; determining market risk data based on market analysis data, and determining project risk data based on project progress design data; processing the cost demand evaluation report, the market risk data and the project risk data by using a resource allocation algorithm to obtain target resource allocation information of the target project. The technical scheme provided by the embodiments of the present application solves the technical problem that the allocation scheme is difficult to ensure optimization, and achieves the technical effect of improving the scientificity and efficiency of resource allocation.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of project management technology, and in particular to an intelligent project resource scheduling method, device, equipment, medium and product. Background Technology

[0002] In the operation of modern enterprises and organizations, multiple projects are often carried out simultaneously, and these projects may involve different business areas, technical requirements, and market environments. With the increasing market changes and technological advancements, project management, especially resource management, is becoming increasingly complex.

[0003] Currently, existing methods typically rely on subjective judgment based on human experience to determine resource allocation relationships. However, these methods cannot simultaneously handle the multi-dimensional interactive effects in a multi-project environment, making it difficult to guarantee the optimization of the allocation scheme. Summary of the Invention

[0004] This invention provides an intelligent project resource scheduling method, device, equipment, medium, and product to optimize resource scheduling schemes and improve the scientificity and efficiency of resource allocation.

[0005] According to one aspect of the present invention, an intelligent project resource scheduling method is provided, comprising: Obtain project-related data for the target project, wherein the project-related data includes at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data; Based on the pre-trained time series analysis and prediction model, the project budget data and historical budget data in the project-related data are analyzed and predicted, and a cost requirement assessment report corresponding to the target project is output. Based on the market analysis data, market risk data is determined, and based on the project schedule design data, project risk data is determined. By using a resource allocation algorithm, the cost requirement assessment report, the market risk data, and the project risk data are processed to obtain the target resource allocation information for the target project.

[0006] According to another aspect of the present invention, an intelligent project resource scheduling device is provided, the intelligent project resource scheduling device comprising: The data acquisition module is used to acquire project-related data of the target project, wherein the project-related data includes at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data; The report output module is used to analyze and predict the project budget data and the historical budget data in the project-related data based on the pre-trained time series analysis and prediction model, and output the cost requirement assessment report corresponding to the target project. The data determination module is used to determine market risk data based on the market analysis data and to determine project risk data based on the project schedule design data. The information acquisition module is used to process the cost requirement assessment report, the market risk data, and the project risk data using a resource allocation algorithm to obtain the target resource allocation information for the target project.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform an intelligent project resource scheduling method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement an intelligent project resource scheduling method as described in any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is provided, which, when executed by a processor, implements an intelligent project resource scheduling method as described in any of the embodiments of the present invention.

[0010] The technical solution provided by this invention acquires project-related data for a target project and analyzes and predicts project budget data and historical budget data based on a pre-trained time series analysis and prediction model to output a cost requirement assessment report corresponding to the target project. Furthermore, to ensure the scientific nature of resource allocation, market risk data is determined based on market analysis data, and project risk data is determined based on project schedule design data. Finally, a resource allocation algorithm is used to process the cost requirement assessment report, market risk data, and project risk data to obtain target resource allocation information for the target project. This technical solution solves the technical problem of difficulty in guaranteeing optimal allocation schemes and achieves the technical effect of improving the scientific nature and efficiency of resource allocation.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of an intelligent project resource scheduling method provided in an embodiment of the present invention; Figure 2 A flowchart of an intelligent project resource scheduling method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall scheme framework of an intelligent project resource scheduling method provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an intelligent project resource scheduling device provided in an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0017] Figure 1 This is a flowchart of an intelligent project resource scheduling method provided by an embodiment of the present invention. This embodiment can be applied to the situation of resource scheduling for a target project. The method can be executed by an intelligent project resource scheduling device according to an embodiment of the present invention. The device can be implemented in software and / or hardware. It should be noted that resource scheduling can include personnel scheduling, fund scheduling, and cost scheduling, etc.

[0018] like Figure 1 As shown, the method specifically includes the following steps: S110. Obtain project-related data for the target project, including at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data.

[0019] In this embodiment, the target project can be a project that currently requires resource allocation, cost forecasting, or risk assessment. Project-related data can be all input data associated with the target project. Optionally, project-related data may include at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data.

[0020] The project budget data can be the total planned and approved funding amount for the target project, along with its detailed allocation. This can be understood as the budget amount for each stage of the target project and the budget ceiling for various resources. This ceiling is used to assess cost requirements. Project budget data can be obtained through project management systems and financial systems. Optionally, project budget data may include project budget allocations, actual expenditures, and budget adjustment records.

[0021] Historical projects can be understood as projects that have been completed. Historical budget data for historical projects can be seen as the actual budget execution records of completed projects that are similar to the target project in terms of business area, technical approach, or project scale. Historical budget data can be obtained from historical project databases and enterprise data warehouses. Optionally, historical budget data may include budgets, expenditures, completion times, and cost variances of similar past projects.

[0022] Market analysis data provides quantitative information reflecting the state and trends of the external market environment in which a target project operates. This quantitative information is used to determine market risk data. Market analysis data can be obtained from market research reports, third-party market analysis agencies, and online market databases. This data may include material costs, labor costs, market price indices, and inflation rates.

[0023] Project schedule design data is a structured description of the time plan and task dependencies within a target project. This description is used to identify project risk data. Project schedule design data can be obtained using project schedule tracking systems and project management software.

[0024] Specifically, based on the target project, relevant project data is obtained, including project budget data for assessing cost requirements, historical budget data for determining market risk data, market analysis data for determining market risk data, and project schedule design data for determining project risk data. This enables automated collection and standardized integration of target project-related data, providing a high-quality data foundation for subsequent resource allocation.

[0025] It should be noted that, optionally, the target project includes multiple sub-projects, each of which is a sub-project to achieve the target project; or, the target project includes multiple sub-projects, each of which is an independent project.

[0026] In this context, a sub-project can be understood as a manageable unit at the next level of the target project. It's important to note that, based on the target project's internal organizational structure and management model, sub-projects can be categorized into two types: First, a sub-project is a component of the target project. In this case, it's a sub-task unit that must be executed to achieve the final deliverables of the target project. Second, a sub-project is an independent project, meaning a single project with its own independent objective within the framework of the target project. This can be understood as having no task dependencies between sub-projects.

[0027] Furthermore, the process of acquiring project-related data is described in detail. Optionally, project-related data for the target project may be acquired, including: The project budget allocation, actual resource support, and resource adjustment records for the target project are obtained from the project management system and resource management system and used as project budget data. Historical budget data for multiple historical projects associated with the target project are obtained from the historical project database. This historical budget data includes at least historical project expenditures, completion times, and cost deviation information. Project schedule design data for the target project is obtained from the project schedule tracking system. This project schedule design data includes planned project schedule, actual project schedule, schedule deviations, and key milestone schedule information. Market analysis data is obtained from market research reports, market analysis agencies, and online market information databases. This market analysis data includes the target project's material costs, labor costs, and market return on investment.

[0028] In this embodiment, the project management system can be a software system used for information management of project planning and execution. The resource management system can be a software system used for unified configuration and scheduling of various resources within an enterprise. It should be noted that the resource management system can be linked with the project management system to record the allocation, occupancy, and release of resources across different projects.

[0029] Project budget allocation refers to the resource allocation plan devised during the project initiation phase, based on overall resources and the needs and priorities of each component. Actual resource support refers to the quantified costs corresponding to the various resources actually invested and consumed during the execution of the project. This data can be used to reflect the budget execution status. Resource adjustment records are operation logs showing modifications to the original resource allocation plan or budget amount for various reasons throughout the project lifecycle.

[0030] A historical project database is a database system that stores various execution data of completed projects. This database can be used to provide historical experience samples for reference in current target projects, supporting project retrieval and data analysis.

[0031] It should be noted that historical budget data includes at least historical project expenditures, completion times, and cost variance information. Historical project expenditures can be the total cost actually incurred from the start to the end of a historical project. Completion time can be the number of working days from the official start of a historical project to meeting acceptance criteria and passing acceptance. Cost variance information can be the degree of deviation between the actual cost and the planned cost of a historical project.

[0032] A project progress tracking system is a software system used to record and update the execution status of project tasks in real time. Project schedule design data can be structured data obtained from the project progress tracking system. This structured data can describe the target project's planned timeline and actual execution status.

[0033] It should be noted that project schedule design data includes planned project schedule, actual project schedule, schedule deviations, and key milestone schedule information. The planned project schedule refers to the start and end times established during the project initiation phase based on the work breakdown structure and task dependencies. This data indicates the expected completion of the target project. The actual project schedule refers to the actual progress collected through the schedule tracking system during project execution. This schedule reflects the true progress of the target project.

[0034] Schedule variability can be the difference between the actual project progress and the planned project progress. Critical milestone schedule information can be the time execution status of tasks located on the critical path in the project plan.

[0035] Market research reports can be analytical reports prepared by professional departments. These reports analyze specific industries or products. Market analysis agencies are organizations that collect and analyze market data. Online market databases can be online platforms or application programming interfaces (APIs) that provide real-time market access services.

[0036] Market analysis data can be multi-dimensional data obtained from market research reports, market analysis agencies, and online market information databases. This data is used to quantify the external market environment in which the target project operates.

[0037] It should be noted that market analysis data can include the target project's material costs, labor costs, and market rate of return. Material costs can be the market prices of all materials required for the project's execution. Labor costs can be the market salary levels for the various human resources needed for the project. Market rate of return can be the expected rate of return for investors in the industry to which the target project belongs. This data can be used to calculate the project's cost of capital.

[0038] Specifically, project budget data is obtained from the project management system and resource management system, including project forecast allocation, actual resource support, and resource adjustment records. Secondly, historical budget data from multiple related historical projects is retrieved from the historical project database. Thirdly, project schedule design data, including planned schedule, actual schedule, schedule deviations, and key milestone information, is obtained from the project progress tracking system to assess internal execution risks. Finally, market analysis data, including at least the material costs, labor costs, and market return rates required for the target project, is obtained from market research reports, market analysis agencies, and online market data databases to quantify external market risks. This multi-source data collection provides comprehensive and accurate input for subsequent cost requirement assessment, risk quantification, and resource allocation.

[0039] S120. Based on the pre-trained time series analysis and prediction model, analyze and predict the project budget data and historical budget data in the project-related data, and output the cost requirement assessment report corresponding to the target project.

[0040] In this embodiment, the pre-trained time series analysis prediction model can be a time series analysis prediction model obtained after training based on training samples. It should be noted that historical project data can be used as training samples. The time series analysis prediction model can be obtained through supervised learning using time series analysis algorithms.

[0041] In this embodiment, the input data for the time series analysis forecasting model can be time-series budget data related to the target project, such as project budget data and historical budget data. Project budget data can be budget data obtained from project management systems and resource management systems. This budget data is associated with the current plan and execution of the target project. Historical budget data can be budget execution records obtained from a historical project database. This data can be budget data corresponding to multiple completed projects associated with the target project.

[0042] The model's output can be a cost requirement forecast for a preset time period, such as a cost requirement assessment report corresponding to the target project. The cost requirement assessment report can be the data output after processing by the time series analysis forecasting model. This data is used to quantitatively describe the scale and distribution of resources required by the target project in its future execution cycle.

[0043] It should be noted that before inputting project-related data into the time series analysis and forecasting model, the data can be preprocessed to convert it into a unified data format. Optionally, data preprocessing can include, but is not limited to, data cleaning, handling missing values, handling outliers, data standardization, data consistency checks, and data integrity checks. Data cleaning involves checking and correcting errors, incompleteness, irrelevantness, or inaccuracy in the dataset. Handling missing values ​​involves processing samples where certain fields in the dataset are empty. For example, for data with few missing values, interpolation or mean imputation methods can be used; for data with many missing values, their importance needs to be assessed, and if necessary, they should be removed or supplemented by requesting data from the data source.

[0044] Outlier handling involves identifying and processing values ​​in a dataset that significantly deviate from the normal distribution range. This includes using statistical methods to detect outliers and deciding whether to remove or correct them based on business rules.

[0045] Data standardization is the process of converting data with different units and value ranges into a single scale. For example, unifying time formats to a standard time format. Data consistency checks verify whether there are logical contradictions or conflicts between data from different data sources, different fields, or different points in time; for example, checking whether the total amount of budget data and expenditure data for the same project is consistent. Data integrity checks verify whether the dataset contains all necessary fields, whether key fields contain null values, and whether records are complete. For example, checking whether the correspondence between project progress and budget is reasonable.

[0046] Furthermore, project budget data, historical budget data, market analysis data, and project schedule design data are integrated into a single comprehensive dataset based on the project ID. For example, a Structured Query Language (SQL) database is used to perform multi-table joins to integrate project budget data, historical budget data, market analysis data, and project schedule design data into a single comprehensive dataset.

[0047] Specifically, by using budget execution sequences from multiple historical projects as training samples, time series algorithms are used to estimate the parameters of the time series analysis and prediction model, resulting in a final trained model to capture cost trends over time. Furthermore, the target project's budget data is input into the time series analysis and prediction model for processing, enabling forward forecasting of the funding required for each sub-project in the future. The model then outputs a cost requirement assessment report corresponding to the target project, providing a cost requirement dimension for subsequent resource allocation algorithms.

[0048] S130. Based on market analysis data, determine market risk data, and based on project schedule design data, determine project risk data.

[0049] In this embodiment, market risk data can be data obtained through quantitative calculations based on market analysis data. This data is used to measure the probability that a target project may suffer losses due to adverse changes in the external market environment.

[0050] Project risk data can be derived from the analysis and calculation of project schedule design data. This data can be used to measure quantitative indicators of potential delays, cost overruns, or payment failures due to internal schedule deviations in the target project.

[0051] Specifically, it analyzes market analysis data and outputs market risk data; and it outputs project risk data based on project schedule design data, thus achieving objectivity and standardization of risk assessment.

[0052] Furthermore, the process of determining market risk data is described in detail. Optionally, market risk data is determined based on market analysis data, including: Market volatility is determined based on the average returns of multiple markets within the first preset time period in the market analysis data; the average market scheduling cost is determined based on the labor and material costs within the second preset time period in the market analysis data; and market risk data is determined based on market volatility and the average market scheduling cost. The first and second preset time periods include multiple processing cycles, with each processing cycle corresponding to one day.

[0053] In this embodiment, the first preset duration can be the length of a time window used to collect market return rate data, which can be preset. The market return rate can be the market rate of return of the industry in which the target project operates within a single processing cycle. It should be noted that the market return rate can reflect the change in the market's profitability level within the cycle. The average return rate can be the arithmetic mean of the market returns corresponding to all processing cycles within the first preset duration. It should be noted that this average can be used as a benchmark for measuring the average market return level.

[0054] Market volatility can be a statistic calculated based on a first preset time period and the mean return. This statistic can be used to measure the dispersion of market returns.

[0055] The second preset duration can be the length of the time window used to collect labor cost and material cost data. It should be noted that both the first and second preset durations include multiple processing cycles. A processing cycle can be understood as the smallest unit of time for data collection and calculation. For example, if each processing cycle corresponds to one day, then both the first and second preset durations contain multiple processing cycles.

[0056] The market scheduling average can be a comprehensive cost benchmark calculated based on the labor and material costs collected over multiple processing cycles within a second preset time period. This benchmark reflects the average cost level required for the target project to acquire resources in a market environment.

[0057] Specifically, the market returns for multiple processing periods within a first preset time period are obtained, and the arithmetic mean of all market returns within that time period, i.e., the mean return, is calculated. Then, market volatility is calculated based on the degree of deviation between the market returns of each period and the mean return. The calculation process for market volatility is as follows: ; in, For market volatility, Let be the market return rate on day i. Let N be the average rate of return, and N be the number of days in the time period.

[0058] Simultaneously, the labor and material costs for multiple processing cycles within the second preset time period are obtained, and the average market scheduling cost is calculated using the averaging method, i.e.: ; in, Let be the average market scheduling value on day t. Let be the market price on day ti, and n be the second preset duration.

[0059] Finally, the market volatility and the market scheduling mean are input into a preset risk quantification function to output market risk data, namely: ; in, For market risk data, and For preset weight parameters, For market volatility, Let be the average market scheduling value on day t.

[0060] By using the combined calculation of market volatility and market scheduling mean, the one-sidedness of a single indicator assessment is avoided, and short-term fluctuations in market returns and costs are captured in a timely manner, providing quantitative external risk dimension input data for subsequent resource allocation algorithms.

[0061] Furthermore, to quantify the uncertainties in project execution, project risk data is output. The process for determining this project risk data is further elaborated. Optionally, project risk data can be determined based on project schedule design data, including: Obtain the planned progress from multiple actual progress data and project progress design data within the third preset time period, and determine the progress deviation for each processing cycle within the third preset time period; based on the progress deviation for each processing cycle within the third preset time period and the first weighting coefficient for each processing cycle, determine the project risk data.

[0062] In this embodiment, the third preset duration can be the length of a time window used to collect actual and planned progress data. This duration can consist of several processing cycles. By setting the third preset duration, the recent progress of the target project can be captured.

[0063] Actual progress refers to the actual completion status at the end of a specific processing cycle, collected by a project progress tracking system during the execution of the target project. This status reflects the true speed of progress of the target project.

[0064] The planned schedule can be the completion status that should be achieved in a specific processing cycle, which is pre-set during the project planning phase based on the work breakdown structure and task dependencies.

[0065] Schedule variability can be defined as the difference between actual progress and planned progress within the same processing cycle. This difference represents the degree to which the schedule deviates from the plan. The first weighting parameter can be the multiplier factor assigned to the schedule variability for each processing cycle within a third preset duration. It should be noted that this parameter can adjust the contribution of different cycle variability to the final project risk data.

[0066] Specifically, first, the actual progress of multiple processing cycles within the third preset time period is obtained, along with the planned progress of the corresponding processing cycles in the project schedule design data. For each processing cycle within the third preset time period, the difference between the actual progress and the planned progress is calculated to obtain the progress deviation for that processing cycle, i.e.: ; in, Let be the schedule deviation for stage i. This represents the actual progress of stage i. This represents the planned schedule for stage i.

[0067] Furthermore, a first weighting coefficient is assigned to each processing cycle within the third preset time period. This weighting coefficient can be configured according to the time decay principle. The progress deviation of each processing cycle is weighted and calculated with its corresponding first weighting coefficient to finally obtain the project risk data, namely: ; in, For project risk data, Let be the weight coefficient of the i-th node. Let M be the schedule deviation at node i, and M be the total number of project phases.

[0068] The project's risk data quantifies the degree of delay risk that may result from the accumulation of schedule deviations during the recent execution of the target project, thereby reducing the execution risk of the target project portfolio.

[0069] It should be noted that after determining market risk data and project risk data, comprehensive risk data can be further determined based on these data. The process for determining comprehensive risk data will be described below. Optional methods also include: Comprehensive risk data is determined based on market risk data, project risk data, and the corresponding second weighting coefficient.

[0070] The second weighting coefficient can be a weighting parameter assigned to market risk data and project risk data. This parameter can be used to adjust the relative importance of market risk data and project risk data in the overall risk data. It should be noted that the second weighting parameter can be preset.

[0071] Comprehensive risk data can be obtained by combining market risk data and project risk data through a preset fusion function, and adjusting for the difference by introducing a corresponding second weighting coefficient. This data represents the overall risk level of the target project.

[0072] Specifically, after determining the market risk data and project risk data, the comprehensive risk data is further determined based on the two types of risk data and their corresponding second weighting coefficients, namely: ; in, To integrate risk data, For market risk data, For project risk data, and This is the second weighting coefficient.

[0073] Based on the above formula, comprehensive risk data is calculated to achieve the integration and computability of multi-dimensional risks, thereby improving the scientific nature and flexibility of risk management in a multi-project environment.

[0074] S140. Using resource allocation algorithms, process the cost demand assessment report, market risk data, and project risk data to obtain target resource allocation information for the target project.

[0075] In this embodiment, the resource allocation algorithm can be a computational method for solving optimal resource allocation schemes. This method can be used to solve resource allocation schemes under project resource constraints. The input data for the resource allocation algorithm includes at least a cost requirement assessment report, market risk data, and project risk data.

[0076] The target resource allocation information can be a specific plan used to guide the actual resource scheduling of the target project. This plan is output by the resource allocation algorithm.

[0077] Specifically, market risk data and project risk data are integrated into a comprehensive risk index, which serves as a risk penalty term or constraint in the objective function. By solving this multi-objective optimization problem, the algorithm automatically calculates the optimal or near-optimal target resource allocation information under the condition of limited total resources, thereby ensuring the maximization of resource utilization efficiency and the minimization of comprehensive risk.

[0078] The technical solution provided by this invention acquires project-related data for a target project and analyzes and predicts project budget data and historical budget data based on a pre-trained time series analysis and prediction model to output a cost requirement assessment report corresponding to the target project. Furthermore, to ensure the scientific nature of resource allocation, market risk data is determined based on market analysis data, and project risk data is determined based on project schedule design data. Finally, a resource allocation algorithm is used to process the cost requirement assessment report, market risk data, and project risk data to obtain target resource allocation information for the target project. This technical solution solves the technical problem of difficulty in guaranteeing optimal allocation schemes and achieves the technical effect of improving the scientific nature and efficiency of resource allocation.

[0079] Figure 2 This is a flowchart of an intelligent project resource scheduling method provided by an embodiment of the present invention. Based on the above embodiment, this embodiment further refines the process of obtaining target resource allocation information.

[0080] like Figure 2 As shown, the method includes: S210. Obtain project-related data for the target project, including at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data. S220. Based on the pre-trained time series analysis and prediction model, analyze and predict the project budget data and historical budget data in the project-related data, and output the cost requirement assessment report corresponding to the target project. S230. Based on market analysis data, determine market risk data, and based on project schedule design data, determine project risk data; S240. With the optimization objectives of maximizing resource utilization and minimizing resource value, determine the objective function, which includes the resource utilization benefits of each sub-project in the target project, the comprehensive risk data of the sub-project, and the number of sub-projects.

[0081] In this embodiment, resource utilization rate can be defined as the degree to which various resources are actually and effectively utilized in a resource allocation scheme. It can be understood as the ratio of actual usage to total available resources. For example, a higher resource utilization rate indicates less idle resources and a higher efficiency in the allocation scheme.

[0082] The resource value can be the comprehensive quantified cost corresponding to the resources allocated to the target project in a resource allocation scheme. The objective function can be an expression used in the resource allocation algorithm to quantitatively evaluate the merits of the resource allocation scheme.

[0083] It should be noted that the objective function includes the resource utilization efficiency of each sub-project within the target project, the comprehensive risk data of the sub-project, and the number of sub-projects. Resource utilization efficiency refers to the expected return that each sub-project can generate after allocating specific resources. Comprehensive risk data is a quantitative indicator obtained by integrating market risk data and project risk data. The number of projects refers to the number of sub-projects included in the target project.

[0084] The optimization objective can be the direction pursued by the resource allocation algorithm. For example, the optimization objective can be to maximize resource utilization and minimize resource value.

[0085] Specifically, the resource allocation algorithm aims to maximize resource utilization and minimize resource usage, and constructs a corresponding objective function, namely: ; Where U represents resource utilization efficiency. For the resource utilization efficiency of the j-th sub-project, For the comprehensive risk data of the j-th sub-project, This is the risk aversion coefficient, which is used to balance benefits and risks. This represents the number of sub-projects.

[0086] The objective function is constructed using the above formula, achieving a dynamic balance between full resource utilization and reducing total resource consumption. S250. Transform the objective function and constraints into a linear programming model, and determine the target resource allocation information for the target project based on the introduced obstacle function.

[0087] In this embodiment, the constraints can be restrictive conditions that must be satisfied in the resource allocation problem. These conditions define the range of feasible solutions. The linear programming model can be an optimization mathematical model where both the objective function and the constraints are linear functions. The barrier function can be a penalty function used to handle inequality constraints during the linear programming solution process. This function is defined within the feasible region; when the solution vector approaches the boundary of the feasible region, the function value increases or decreases sharply, thereby preventing the iteration point from going beyond the boundary. The target resource allocation information can be the optimal resource allocation scheme output after solving the linear programming model with the barrier function introduced above.

[0088] Specifically, the constraints are determined as follows: ; ; in, B represents the total budget, which is the resource allocated to the j-th sub-project. The number of sub-projects is determined; after determining the objective function and constraints, a linear programming model is constructed based on the objective function and constraints, and the linear programming model is solved using the interior point method based on the introduced obstacle function, thereby obtaining the target resource allocation information for the target project.

[0089] It should be noted that the process of solving the linear programming model based on the interior point method is as follows: Transform the objective function and constraints into a standard linear programming model: ; ; ; ; in, To maximize the objective function, the value of the objective function should be as large as possible; c is the coefficient vector of the objective function. For the decision variable vector, The objective function value, Here is the constraint coefficient matrix, and E represents resource utilization efficiency. To comprehensively assess risk data, Resources for the j-th project.

[0090] Choose an initial point initial point Satisfying all constraints does not occur on the boundary; the obstacle function method ensures the stability and optimality of the solution process, achieving a precise conversion from a multidimensional optimization objective to an executable allocation scheme.

[0091] Furthermore, after transforming the resource allocation problem into a linear programming model and introducing a barrier function to handle inequality constraints, an iterative solution is performed. Next, the process for determining the target resource allocation information is refined. Optionally, based on the introduced barrier function, the target resource allocation information for the target project is determined, including: The obstacle function is superimposed on the objective function to obtain the parameter optimization function; based on the parameter optimization function, the first gradient and the objective matrix are determined; based on the first gradient and the objective matrix, the objective data is determined; based on the resource information corresponding to each sub-item in the objective data, the objective resource allocation information of the objective project is determined.

[0092] In this embodiment, the parameter optimization function can be an optimizable function obtained by superimposing the objective function and the obstacle function. This function is defined within the feasible region, and by gradually adjusting the parameters, the optimal solution sequence of the parameter optimization function approximates the optimal solution of the original constraint problem.

[0093] The first gradient can be the vector of first-order partial derivatives of the parameter optimization function with respect to the decision variables. This parameter indicates the steepest ascent direction of the function value in the variable space. The objective matrix can be the matrix of second-order partial derivatives of the parameter optimization function with respect to the decision variables. The objective data can be the vector of the optimal solution obtained by iteratively solving the parameter optimization function.

[0094] Specifically, a barrier function is introduced and added to the objective function to obtain a parameter optimization function, forming a parameterized optimization problem: ; ; in, The obstacle coefficient, Where P is the obstacle function value, and P is the number of sub-items. Allocate resources to the i-th sub-project, where i is the sub-project index. Assign vectors to resources.

[0095] The above optimization problem is solved using Newton's method, and the updated variables are obtained through calculation. First, calculate the first gradient and the target matrix. The first gradient is... ;in, Let be the first gradient, and c be the coefficient vector of the objective function. These are obstacle parameters. The target matrix is... ,in, Given a diagonal matrix, the Newton direction can be determined by... Where H is the Hessian matrix, i.e., the target matrix. Let this be the search direction vector. Furthermore, a preset step size is chosen so that the updated points remain within the feasible region and the objective function value decreases. Variable updates are performed based on the chosen preset step size, i.e.: ; in, For the decision variable vector, To preset the step size, Let the search direction vector be denoted by . If the range of the gradient satisfies . , If the threshold for convergence is reached, the iteration stops; otherwise, it continues.

[0096] The system acquires resource information corresponding to each sub-project in the target data and outputs the target resource allocation information for the target project. This achieves a complete transformation from the mathematically optimal solution to an engineering-executable allocation scheme, ensuring that the resource allocation results not only meet the global optimization objective but also have practical operability.

[0097] Figure 3This is a schematic diagram of the overall scheme framework of an intelligent project resource scheduling method provided by an embodiment of the present invention. Based on the above embodiment, an optional example is provided, which can be used for project resource scheduling scenarios.

[0098] like Figure 3 As shown, firstly, in order to allocate resources for the target project, relevant project data is collected. This data may include project budget data, historical data (historical budget data of historical projects related to the target project), market data (market analysis data), and project schedule data (project schedule design data).

[0099] Furthermore, the project-related data undergoes preprocessing, namely, converting it into a unified data format. Then, project budget data, historical data, market data, and project progress data are linked and integrated based on the project ID to generate a comprehensive dataset encompassing budget, historical, market, and progress dimensions.

[0100] The pre-trained time series analysis prediction model is obtained, and the project budget and historical data in the comprehensive dataset are used as inputs. The time series analysis prediction model compares the recent budget consumption of the target project with the cost evolution of similar historical projects at various time stages to make forward predictions on the resource requirements of each sub-project in the future, and outputs a structured funding requirement assessment report (cost requirement assessment report).

[0101] After obtaining the cost requirement assessment report, the following two risk assessment processes are executed in parallel. First, market fluctuations and trends are analyzed based on market data (market analysis data), and market risk indicators (market risk data) are calculated. Second, based on project schedule data (project schedule design data), the schedule deviation for each processing cycle within the pre-set market is calculated and weighted to assess project schedule deviations and potential risks, resulting in project risk data (project risk indicators). Finally, the market risk indicators and project risk indicators are weighted and merged using a second weighting coefficient to generate a comprehensive risk indicator (comprehensive risk data).

[0102] Finally, using the predicted cost value in the funding needs assessment report as the resource demand constraint and the comprehensive risk index as the risk penalty term, an objective function is constructed with the optimization goals of maximizing resource utilization and minimizing resource value. This objective function and constraints are then transformed into a linear programming model, and a barrier function is introduced for solving. The solution yields a funding allocation scheme (target resource allocation information) to ensure maximum efficiency and minimum risk in resource utilization.

[0103] Figure 4This is a schematic diagram of an intelligent project resource scheduling device provided in an embodiment of the present invention. This embodiment is applicable to project resource scheduling scenarios. The device can be implemented using software and / or hardware, and can be integrated into any device that provides intelligent project resource scheduling functionality, such as… Figure 4 As shown, the intelligent project resource scheduling device specifically includes: a data acquisition module 310, a report output module 320, a data determination module 330, and an information acquisition module 340.

[0104] The system includes a data acquisition module 310 for acquiring project-related data for the target project, including at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data. A report output module 320 analyzes and predicts the project budget data and historical budget data in the project-related data based on a pre-trained time series analysis and prediction model, and outputs a cost requirement assessment report corresponding to the target project. A data determination module 330 determines market risk data based on the market analysis data and project risk data based on the project schedule design data. An information acquisition module 340 processes the cost requirement assessment report, the market risk data, and the project risk data using a resource allocation algorithm to obtain target resource allocation information for the target project.

[0105] The technical solution provided by this invention acquires project-related data for a target project and analyzes and predicts project budget data and historical budget data based on a pre-trained time series analysis and prediction model to output a cost requirement assessment report corresponding to the target project. Furthermore, to ensure the scientific nature of resource allocation, market risk data is determined based on market analysis data, and project risk data is determined based on project schedule design data. Finally, a resource allocation algorithm is used to process the cost requirement assessment report, market risk data, and project risk data to obtain target resource allocation information for the target project. This technical solution solves the technical problem of difficulty in guaranteeing optimal allocation schemes and achieves the technical effect of improving the scientific nature and efficiency of resource allocation.

[0106] Based on the above embodiments, the data acquisition module includes: The budget data determination unit is used to obtain the project budget allocation, actual resource support, and resource adjustment records of the target project from the project management system and the resource management system, and use them as the project budget data. The historical budget data acquisition unit is used to acquire historical budget data of multiple historical projects associated with the target project from the historical project database, wherein the historical budget data includes at least historical project expenditures, completion time, and cost deviation information; The design data acquisition unit is used to acquire the project progress design data of the target project from the project progress tracking system. The project progress design data includes the planned project progress, the actual project progress, the progress deviation, and the progress information of key nodes. The data acquisition unit is used to acquire market analysis data from market research reports, market analysis agencies, and online market information databases. The market analysis data includes the material costs, labor costs, and market return rates of the target project.

[0107] Based on the above embodiments, the data determination module includes: The volatility determination unit is used to determine market volatility based on the returns and average returns of multiple markets within a first preset time period in the market analysis data. The scheduling average determination unit is used to determine the market scheduling average based on the labor cost and material cost within a second preset time period in the market analysis data; A risk data determination unit is used to determine the market risk data based on the market volatility and the market scheduling mean. The first preset duration and the second preset duration include multiple processing cycles, with each processing cycle corresponding to one day.

[0108] Based on the above embodiments, the data determination module includes: The deviation determination unit is used to obtain multiple actual progresses within a third preset time period and the planned progress in the project progress design data, and to determine the progress deviation of each processing cycle within the third preset time period. The risk data determination unit is used to determine project risk data based on the progress deviation of each processing cycle within the third preset time period and the first weighting coefficient of each processing cycle.

[0109] Based on the above embodiments, the device further includes: The comprehensive risk data determination module is used to determine comprehensive risk data based on the market risk data, the project risk data, and the corresponding second weighting coefficient.

[0110] Based on the above embodiments, the information acquisition module includes: The objective function determination unit is used to determine the objective function with the optimization objectives of maximizing resource utilization and minimizing resource value. The objective function includes the resource utilization benefits of each sub-project in the objective project, the comprehensive risk data of the sub-project, and the number of sub-projects. The allocation information module is used to convert the objective function and constraints into a linear programming model, and determine the target resource allocation information of the target project based on the introduced obstacle function.

[0111] Based on the above embodiments, the allocation information module includes: The parameter optimization function acquisition unit is used to superimpose the obstacle function onto the target function to obtain the parameter optimization function; The target data determination unit is used to determine the first gradient and the target matrix based on the parameter optimization function, and to determine the target data based on the first gradient and the target matrix. The information determination unit is used to determine the target resource allocation information of the target project based on the resource information corresponding to each sub-project in the target data.

[0112] Based on the above embodiments, the target project includes multiple sub-projects, each of which is a sub-project for implementing the target project; or, the target project includes multiple sub-projects, each of which is an independent project.

[0113] The above-described products can perform the methods provided in any embodiment of the present invention, and have the corresponding functional modules and beneficial effects for performing the methods.

[0114] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0115] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0116] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as an intelligent project resource scheduling method.

[0118] In some embodiments, an intelligent project resource scheduling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the intelligent project resource scheduling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform an intelligent project resource scheduling method by any other suitable means (e.g., by means of firmware).

[0119] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0123] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0124] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0125] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0126] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements an intelligent project resource scheduling method according to any embodiment of the invention.

[0127] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent project resource scheduling method, characterized in that, include: Obtain project-related data for the target project, wherein the project-related data includes at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data; Based on the pre-trained time series analysis and prediction model, the project budget data and historical budget data in the project-related data are analyzed and predicted, and a cost requirement assessment report corresponding to the target project is output. Based on the market analysis data, market risk data is determined, and based on the project schedule design data, project risk data is determined. By using a resource allocation algorithm, the cost requirement assessment report, the market risk data, and the project risk data are processed to obtain the target resource allocation information for the target project.

2. The method according to claim 1, characterized in that, The acquisition of project-related data for the target project includes: The project budget allocation, actual resource support, and resource adjustment records of the target project are obtained from the project management system and resource management system and used as the project budget data. Obtain historical budget data from a historical project database for multiple historical projects associated with the target project, wherein the historical budget data includes at least historical project expenditures, completion times, and cost deviation information; Obtain the project schedule design data of the target project from the project schedule tracking system, wherein the project schedule design data includes the planned project schedule, the actual project schedule, the schedule deviation, and the schedule information of key nodes. Market analysis data is obtained from market research reports, market analysis agencies, and online market information databases. The market analysis data includes the material costs, labor costs, and market profitability of the target project.

3. The method according to claim 1, characterized in that, The determination of market risk data based on the market analysis data includes: Market volatility is determined based on the market analysis data, which includes the returns and average returns of multiple markets over a first preset time period. Based on the labor and material costs within the second preset time period in the market analysis data, determine the average market scheduling value; The market risk data is determined based on the market volatility and the market scheduling mean. The first preset duration and the second preset duration include multiple processing cycles, with each processing cycle corresponding to one day.

4. The method according to claim 1, characterized in that, The determination of project risk data based on the project schedule design data includes: Obtain multiple actual progresses within a third preset time period and the planned progress in the project progress design data, and determine the progress deviation for each processing cycle within the third preset time period; Based on the progress deviation of each processing cycle within the third preset time period and the first weighting coefficient of each processing cycle, project risk data is determined.

5. The method according to claim 1, characterized in that, The method further includes: Based on the market risk data, the project risk data, and the corresponding second weighting coefficient, the comprehensive risk data is determined.

6. The method according to claim 1, characterized in that, The process of using a resource allocation algorithm to process the cost requirement assessment report, the market risk data, and the project risk data to obtain the target resource allocation information for the target project includes: With the optimization objectives of maximizing resource utilization and minimizing resource value, an objective function is determined. The objective function includes the resource utilization efficiency of each sub-project in the objective project, the comprehensive risk data of the sub-project, and the number of sub-projects. The objective function and constraints are converted into a linear programming model, and the target resource allocation information for the target project is determined based on the introduced obstacle function.

7. The method according to claim 6, characterized in that, The determination of the target resource allocation information for the target project based on the introduced barrier function includes: The obstacle function is superimposed on the objective function to obtain the parameter optimization function; Based on the parameter optimization function, the first gradient and the target matrix are determined, and the target data is determined based on the first gradient and the target matrix. Based on the resource information corresponding to each sub-project in the target data, the target resource allocation information of the target project is determined.

8. The method according to claim 1, characterized in that, The target project includes multiple sub-projects, each of which is a sub-project for implementing the target project; or, The target project includes multiple sub-projects, each of which is an independent project.

9. An intelligent project resource scheduling device, characterized in that, include: The data acquisition module is used to acquire project-related data of the target project, wherein the project-related data includes at least project budget data, historical budget data of historical projects related to the target project, market analysis data, and project schedule design data; The report output module is used to analyze and predict the project budget data and the historical budget data in the project-related data based on the pre-trained time series analysis and prediction model, and output the cost requirement assessment report corresponding to the target project. The data determination module is used to determine market risk data based on the market analysis data and to determine project risk data based on the project schedule design data. The information acquisition module is used to process the cost requirement assessment report, the market risk data, and the project risk data using a resource allocation algorithm to obtain the target resource allocation information for the target project.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform an intelligent project resource scheduling method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute an intelligent project resource scheduling method according to any one of claims 1-8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements an intelligent project resource scheduling method according to any one of claims 1-8.

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