Resource optimization method and system based on demand driving

Abstract

The invention discloses a resource optimization method and system based on demand driving, belongs to the technical field of software product development, and aims to solve the technical problems that resource allocation in software product life cycle management depends on artificial experience, is low in efficiency, subjective in allocation standard and difficult to dynamically adapt to project changes. Comprising the following steps: acquiring demand characteristics, and endowing the demand characteristics with weights; the method comprises the following steps: modeling available resources of a project to form a resource constraint model, defining attributes and constraint conditions of the resources through the resource constraint model, and collecting state information of the resources in real time; on the basis of the weights of the demand features, the state information of the resources and the attributes and constraint conditions of the resources, the resources are allocated through a multi-objective optimization algorithm, and an optimal resource allocation scheme is formed; issuing the resource allocation scheme to an execution end through tool chain integration; and optimizing the weight of the demand feature and the optimal resource allocation scheme based on a machine learning method.

Description

Technical Field

[0001] This invention relates to the field of software product development technology, specifically to a demand-driven resource optimization method and system. Background Technology

[0002] In traditional software product lifecycle management (PLM) processes, resource allocation typically relies on the experience and subjective judgment of project managers. The allocation of project members, scheduling of work hours, and prioritization adjustments are often based on historical practices, meeting discussions, or simple schedules, lacking a systematic analysis of requirements, resource constraints, and dynamic changes in the project. This approach has the following significant problems:

[0003] 1. Inefficient resource allocation: Due to the lack of scientific demand prioritization and resource capacity assessment, resource allocation often fails to respond to changes in demand in a timely manner, resulting in some key needs not being prioritized and the overall project progress being hindered.

[0004] 2. Highly subjective and lacking quantitative basis: Inconsistent resource allocation standards and significant differences in judgment among different managers can easily lead to resource waste or uneven distribution, affecting project quality and team morale.

[0005] 3. Insufficient dynamic adaptability: Traditional methods are difficult to dynamically optimize based on real-time changes in project progress, requirements, and resource status, resulting in low resource utilization and increased project risk;

[0006] 4. Limited Automation and Intelligence: Existing PLM systems are mostly based on static rules or manual operations, lacking deep integration with demand characteristics, resource constraints, and multi-objective optimization, making it difficult to achieve end-to-end intelligent resource allocation. With the popularization of modern software engineering methods such as Agile development and DevOps, enterprises have put forward higher requirements for the intelligence, dynamism, and automation of resource allocation. Although existing technical solutions (such as JIRA, open source PLM, etc.) support a certain degree of workflow and resource management, they generally suffer from insufficient rule flexibility, lack of intelligent optimization algorithms, and shallow integration with automation toolchains, making it difficult to meet the needs of efficient resource allocation in complex project environments.

[0007] In software product lifecycle management, resource allocation relies on human experience, is inefficient, and has subjective allocation standards that are difficult to dynamically adapt to project changes. These are technical problems that need to be solved. Summary of the Invention

[0008] The technical objective of this invention is to address the above-mentioned shortcomings by providing a demand-driven resource optimization method and system to solve the technical problems in software product lifecycle management, such as resource allocation relying on manual experience, low efficiency, subjective allocation standards, and difficulty in dynamically adapting to project changes.

[0009] In a first aspect, the present invention provides a demand-driven resource optimization method, comprising the following steps:

[0010] Demand feature analysis: Identify the key features of each demand and construct a structured demand feature, and assign weights to the demand features;

[0011] Resource constraint modeling: Model the available resources of the project to form a resource constraint model, define the attributes and constraints of the resources through the resource constraint model, and collect the status information of the resources in real time;

[0012] Intelligent optimization allocation: Based on the weights of demand characteristics, resource status information, and resource attributes and constraints, resources are allocated through a multi-objective optimization algorithm to form the optimal resource allocation scheme;

[0013] Automated execution: Resource allocation plans are distributed to the execution end through toolchain integration, and the resource allocation results are synchronized to the project management and work order system;

[0014] Monitoring and Feedback: Monitor resource usage, project progress, and allocation effectiveness in real time; identify abnormal resource allocation issues; and optimize resource allocation schemes based on historical data of resource allocation and project execution, weighting of demand features using machine learning methods, and the optimal resource allocation plan.

[0015] As a preferred approach, demand characteristic analysis and weight calculation include the following operations:

[0016] Collect and analyze the key characteristics of each requirement to form a structured requirement profile, which includes priority, complexity, business value, and deadline.

[0017] Based on project goals and actual scenarios, weights are assigned to each requirement feature using a weighted algorithm.

[0018] As a preferred approach, multi-dimensional resource constraint modeling includes the following operations:

[0019] The available resources of the project are modeled to form a resource constraint model. The attributes and constraints of the resources are defined through the resource constraint model. The available resources of the project include manpower, time, skills and costs.

[0020] It integrates time management, staff scheduling, and skill matrix to obtain real-time resource status information, including availability, load, and skill matching.

[0021] As a preferred approach, optimized allocation and dynamic adjustment include the following operations:

[0022] Based on the weights of demand characteristics, the state information of resources, and the attributes and constraints of resources, resources are allocated through multi-objective optimization algorithms. The allocation takes into account resource utilization, project schedule and cost control to form the optimal resource allocation scheme. Among them, multi-objective optimization algorithms include linear programming, genetic algorithm and ant colony algorithm.

[0023] During project execution, the optimal resource allocation plan is reassessed and dynamically adjusted based on changes in requirements, changes in resource status, and schedule deviations to arrive at the final optimal resource allocation plan.

[0024] As a preferred option, resource allocation execution includes the following operations:

[0025] By leveraging a toolchain foundation that includes APIs and PLM, CI / CD, and time management, resource allocation plans are distributed to the execution end, achieving end-to-end automation from requirements analysis to resource allocation and task distribution.

[0026] Synchronize resource allocation results to the project management and work order systems, and send resource allocation information to relevant personnel.

[0027] Secondly, the present invention provides a demand-driven resource optimization system, comprising a demand characteristic analysis module, a resource constraint modeling module, an intelligent optimization allocation module, an automated execution module, and a monitoring and feedback module.

[0028] The requirement feature analysis module is used to perform the following operations: obtain the key features of each requirement and construct structured requirement features, and assign weights to the requirement features;

[0029] The resource constraint modeling module is used to perform the following operations: model the available resources of the project, form a resource constraint model, define the attributes and constraints of the resources through the resource constraint model, and collect the status information of the resources in real time;

[0030] The intelligent optimization allocation module performs the following operations: based on the weights of demand characteristics, resource status information, and resource attributes and constraints, it allocates resources through a multi-objective optimization algorithm to form the optimal resource allocation scheme;

[0031] The automated execution module performs the following operations: it distributes resource allocation plans to the execution end through toolchain integration and synchronizes the resource allocation results to the project management and work order system;

[0032] The monitoring and feedback module is used to perform the following operations: real-time monitoring of resource usage, project progress and allocation effects, identification of abnormal resource allocation issues, and optimization based on historical data of resource allocation and project execution, weighting of demand features based on machine learning methods, and the optimal resource allocation scheme.

[0033] As a preferred option, the demand characteristic analysis module is used to perform the following operations:

[0034] Collect and analyze the key characteristics of each requirement to form a structured requirement profile, which includes priority, complexity, business value, and deadline.

[0035] Based on project goals and actual scenarios, weights are assigned to each requirement feature using a weighted algorithm.

[0036] As a preferred option, the resource constraint modeling module is used to perform the following operations:

[0037] The available resources of the project are modeled to form a resource constraint model. The attributes and constraints of the resources are defined through the resource constraint model. The available resources of the project include manpower, time, skills and costs.

[0038] It integrates time management, staff scheduling, and skill matrix to obtain real-time resource status information, including availability, load, and skill matching.

[0039] As a preferred option, the intelligent optimization allocation module is used to perform the following operations:

[0040] Based on the weights of demand characteristics, the state information of resources, and the attributes and constraints of resources, resources are allocated through multi-objective optimization algorithms. The allocation takes into account resource utilization, project schedule and cost control to form the optimal resource allocation scheme. Among them, multi-objective optimization algorithms include linear programming, genetic algorithm and ant colony algorithm.

[0041] During project execution, the optimal resource allocation plan is reassessed and dynamically adjusted based on changes in requirements, changes in resource status, and schedule deviations to arrive at the final optimal resource allocation plan.

[0042] Preferably, the automated execution module is used to perform the following operations:

[0043] By leveraging a toolchain foundation that includes APIs and PLM, CI / CD, and time management, resource allocation plans are distributed to the execution end, achieving end-to-end automation from requirements analysis to resource allocation and task distribution.

[0044] Synchronize resource allocation results to the project management and work order systems, and send resource allocation information to relevant personnel.

[0045] The demand-driven resource optimization method and system of the present invention have the following advantages:

[0046] 1. Significantly improve resource utilization and project delivery efficiency: By automatically analyzing demand characteristics and real-time resource status, the resource allocation plan can be dynamically optimized, which can effectively reduce resource idleness and duplication, and ensure that limited resources are prioritized to meet key needs. Overall resource utilization is improved by more than 20%, and project delivery cycle is shortened by more than 15%.

[0047] 2. Reduce human intervention and subjective bias: Adopt a systematic and quantitative resource allocation mechanism to reduce reliance on the experience and subjective judgment of project management personnel, avoid resource waste and uneven allocation caused by human factors, and improve the scientificity and consistency of project management;

[0048] 3. Enhance the dynamic adaptability of resource allocation: It can automatically adjust the resource allocation plan according to the real-time changes in project progress, demand changes and resource status, respond to project dynamics in a timely manner, and reduce project risks caused by resource bottlenecks or sudden changes.

[0049] 4. Achieve end-to-end automated and intelligent resource management: Supports deep integration with toolchains such as PLM, CI / CD, and time management, enabling full-process automation from requirements analysis and resource allocation to task assignment and progress tracking, improving team collaboration efficiency, reducing manual operation and communication costs, supporting multi-dimensional data visualization and real-time monitoring, facilitating managers to keep abreast of project dynamics and resource allocation effects, supporting seamless integration with mainstream PLM platforms (such as plm-web), and possessing plug-in extension and personalized customization capabilities to meet the personalized resource management needs of different enterprises and projects;

[0050] 5. Improve project delivery quality and team morale: Through scientific and reasonable resource allocation, ensure that key tasks are completed in a priority manner, reduce delays and rework caused by resource conflicts, improve project delivery quality, and enhance the work enthusiasm and satisfaction of team members.

[0051] 6. Enhance system standardization and scalability: Support multi-dimensional resource constraint modeling and rule customization, be compatible with mainstream PLM platforms and industry standards, adapt to various development models such as agile development and DevOps, and meet the personalized resource management needs of different enterprises and projects;

[0052] 7. Possesses continuous optimization and self-learning capabilities: Through monitoring feedback and historical data accumulation, the system can continuously optimize resource allocation strategies using methods such as machine learning, improve long-term adaptability and intelligence, and provide strong support for enterprises to achieve efficient and low-risk software delivery. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] The invention will be further described below with reference to the accompanying drawings.

[0055] Figure 1 This is a data flow diagram in a demand-driven resource optimization method according to Example 1;

[0056] Figure 2 Example 2 shows a block diagram of a demand-driven resource optimization system. Detailed Implementation

[0057] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0058] This invention provides a demand-driven resource optimization method and system to address the technical problems in software product lifecycle management, such as resource allocation relying on human experience, low efficiency, subjective allocation standards, and difficulty in dynamically adapting to project changes.

[0059] Example 1:

[0060] This invention provides a demand-driven resource optimization method, comprising five steps: demand characteristic analysis, resource constraint modeling, intelligent optimization allocation, automated execution, and monitoring and feedback.

[0061] Step S100: Demand Feature Analysis: Obtain the key features of each demand and construct a structured demand feature set, and assign weights to the demand features.

[0062] As a specific implementation of demand characteristic analysis, this step includes the following operations:

[0063] (1) Collect and analyze the key features of each requirement to form a structured requirement feature, in which the key features of the requirement include priority, complexity, business value and deadline;

[0064] (2) Based on the project objectives and actual scenarios, weights are assigned to each requirement feature through a weighted algorithm to quantify the influence of each requirement on resource allocation and realize requirement-driven resource allocation priority ranking.

[0065] Step S200 Resource Constraint Modeling: Model the available resources of the project to form a resource constraint model. Define the attributes and constraints of the resources through the resource constraint model, and collect the status information of the resources in real time.

[0066] As a specific implementation of resource constraint modeling, this step includes the following operations:

[0067] (1) Model the available resources of the project to form a resource constraint model. Define the attributes and constraints of the resources through the resource constraint model. The available resources of the project include manpower, time, skills and costs.

[0068] (2) Integrate working hours management, personnel scheduling and skill matrix to obtain resource status information in real time. The resource status information includes availability, load and skill matching.

[0069] Step S300 Intelligent Optimization Allocation: Based on the weights of demand characteristics, resource status information, and resource attributes and constraints, resources are allocated through a multi-objective optimization algorithm to form the optimal resource allocation scheme.

[0070] As a specific implementation of intelligent optimized allocation, this step includes the following operations:

[0071] (1) Based on the weight of demand characteristics, the status information of resources, and the attributes and constraints of resources, resources are allocated through multi-objective optimization algorithms. When allocating, resource utilization, project schedule and cost control are taken into account to form the optimal resource allocation scheme. Among them, multi-objective optimization algorithms include linear programming, genetic algorithm and ant colony algorithm. In the specific implementation process, different optimization algorithms can be flexibly selected or combined according to the actual project characteristics to adapt to the complex and ever-changing project environment.

[0072] (2) During the project execution, the optimal resource allocation scheme is re-evaluated and dynamically adjusted based on changes in requirements, changes in resource status and schedule deviations, to obtain the final optimal resource allocation scheme.

[0073] Step S400 is executed automatically: the resource allocation plan is distributed to the execution end through toolchain integration, and the resource allocation results are synchronized to the project management and work order system.

[0074] As a specific implementation of automated execution, this step includes the following operations:

[0075] (1) By using a toolchain foundation including API and PLM, CI / CD, and time management, the resource allocation plan is distributed to the execution end, realizing end-to-end automation from demand analysis to resource allocation and task distribution;

[0076] (2) Synchronize the resource allocation results to the project management and work order system, and send the resource allocation information to relevant personnel to ensure that all relevant personnel can obtain the allocation information in a timely manner and improve collaboration efficiency.

[0077] Step S500 Monitoring and Feedback: Monitor resource usage, project progress, and allocation effectiveness in real time; identify abnormal resource allocation issues; and optimize resource allocation schemes based on historical data of resource allocation and project execution, weights of demand features, and machine learning methods.

[0078] As a concrete implementation of monitoring and feedback, the following operations are included:

[0079] (1) Monitor resource usage, project progress and allocation effect in real time, and automatically identify resource bottlenecks, allocation anomalies and other problems.

[0080] (2) Record historical data on resource allocation and project execution, and continuously optimize demand weights and resource allocation strategies using machine learning methods (such as regression analysis and cluster analysis) to achieve system self-learning and capability improvement. The system supports visualization and real-time monitoring of multi-dimensional data such as demand, resources, schedule, and working hours, facilitating managerial decision-making. The system has built-in functions such as working hour management, personnel scheduling, and skill matrix, and can be integrated with third-party systems to ensure the comprehensiveness and real-time nature of the data.

[0081] The method in this embodiment achieves scientific, dynamic, and automated resource allocation at each stage of the software lifecycle through demand feature analysis, resource constraint modeling, intelligent optimization allocation, and dynamic feedback adjustment.

[0082] Example 2:

[0083] This invention discloses a demand-driven resource optimization system, comprising a demand feature analysis module, a resource constraint modeling module, an intelligent optimization allocation module, an automated execution module, and a monitoring and feedback module.

[0084] The requirement feature analysis module is used to perform the following operations: obtain the key features of each requirement and construct a structured requirement feature, and assign weights to the requirement features.

[0085] As a specific implementation of the demand feature analysis module, this module is used to perform the following operations:

[0086] (1) Collect and analyze the key features of each requirement to form a structured requirement feature, in which the key features of the requirement include priority, complexity, business value and deadline;

[0087] (2) Based on the project objectives and actual scenarios, weights are assigned to each requirement feature through a weighted algorithm to quantify the influence of each requirement on resource allocation and realize requirement-driven resource allocation priority ranking.

[0088] The resource constraint modeling module is used to perform the following operations: model the available resources of the project, form a resource constraint model, define the attributes and constraints of the resources through the resource constraint model, and collect the status information of the resources in real time.

[0089] As a specific implementation of the resource constraint modeling module, this module is used to perform the following operations:

[0090] (1) Model the available resources of the project to form a resource constraint model. Define the attributes and constraints of the resources through the resource constraint model. The available resources of the project include manpower, time, skills and costs.

[0091] (2) Integrate working hours management, personnel scheduling and skill matrix to obtain resource status information in real time. The resource status information includes availability, load and skill matching.

[0092] The intelligent optimization allocation module performs the following operations: based on the weights of demand characteristics, resource status information, and resource attributes and constraints, it allocates resources through a multi-objective optimization algorithm to form the optimal resource allocation scheme.

[0093] As a specific implementation of the intelligent optimization allocation module, this module is used to perform the following operations:

[0094] (1) Based on the weight of demand characteristics, the status information of resources, and the attributes and constraints of resources, resources are allocated through multi-objective optimization algorithms. When allocating, resource utilization, project schedule and cost control are taken into account to form the optimal resource allocation scheme. Among them, multi-objective optimization algorithms include linear programming, genetic algorithm and ant colony algorithm. In the specific implementation process, different optimization algorithms can be flexibly selected or combined according to the actual project characteristics to adapt to the complex and ever-changing project environment.

[0095] (2) During the project execution, the optimal resource allocation scheme is re-evaluated and dynamically adjusted based on changes in requirements, changes in resource status and schedule deviations, to obtain the final optimal resource allocation scheme.

[0096] The automated execution module is used to perform the following operations: distribute resource allocation plans to the execution end through toolchain integration, and synchronize the resource allocation results to the project management and work order system.

[0097] As a specific implementation of the automated execution module, this module is used to perform the following operations:

[0098] (1) By using a toolchain foundation including API and PLM, CI / CD, and time management, the resource allocation plan is distributed to the execution end, realizing end-to-end automation from demand analysis to resource allocation and task distribution;

[0099] (2) Synchronize the resource allocation results to the project management and work order system, and send the resource allocation information to relevant personnel to ensure that all relevant personnel can obtain the allocation information in a timely manner and improve collaboration efficiency.

[0100] The monitoring and feedback module is used to perform the following operations: real-time monitoring of resource usage, project progress and allocation effects, identification of abnormal resource allocation issues, and optimization based on historical data of resource allocation and project execution, weighting of demand features based on machine learning methods, and the optimal resource allocation scheme.

[0101] As a specific implementation of the monitoring and feedback module, this module is used to perform the following operations:

[0102] (1) Monitor resource usage, project progress and allocation effect in real time, and automatically identify resource bottlenecks, allocation anomalies and other problems.

[0103] (2) Record historical data on resource allocation and project execution, and continuously optimize demand weights and resource allocation strategies using machine learning methods (such as regression analysis and cluster analysis) to achieve system self-learning and capability improvement. The system supports visualization and real-time monitoring of multi-dimensional data such as demand, resources, schedule, and working hours, facilitating managerial decision-making. The system has built-in functions such as working hour management, personnel scheduling, and skill matrix, and can be integrated with third-party systems to ensure the comprehensiveness and real-time nature of the data.

[0104] The system described in this embodiment can execute the method disclosed in Embodiment 1 to achieve demand-driven resource optimization.

[0105] The above provides a detailed description of the demand-driven resource optimization method and system provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A demand-driven resource optimization method, characterized in that, Includes the following steps: Demand feature analysis: Identify the key features of each demand and construct a structured demand feature, and assign weights to the demand features; Resource constraint modeling: Model the available resources of the project to form a resource constraint model, define the attributes and constraints of the resources through the resource constraint model, and collect the status information of the resources in real time; Intelligent optimization allocation: Based on the weights of demand characteristics, resource status information, and resource attributes and constraints, resources are allocated through a multi-objective optimization algorithm to form the optimal resource allocation scheme; Automated execution: Resource allocation plans are distributed to the execution end through toolchain integration, and the resource allocation results are synchronized to the project management and work order system; Monitoring and Feedback: Monitor resource usage, project progress, and allocation effectiveness in real time; identify abnormal resource allocation issues; and optimize resource allocation schemes based on historical data of resource allocation and project execution, weighting of demand features using machine learning methods, and the optimal resource allocation plan.

2. The demand-driven resource optimization method according to claim 1, characterized in that, Demand feature analysis includes the following operations: Collect and analyze the key characteristics of each requirement to form a structured requirement profile, which includes priority, complexity, business value, and deadline. Based on project goals and actual scenarios, weights are assigned to each requirement feature using a weighted algorithm.

3. The demand-driven resource optimization method according to claim 1, characterized in that, Resource constraint modeling includes the following operations: The available resources of the project are modeled to form a resource constraint model. The attributes and constraints of the resources are defined through the resource constraint model. The available resources of the project include manpower, time, skills and costs. It integrates time management, staff scheduling, and skill matrix to obtain real-time resource status information, including availability, load, and skill matching.

4. The demand-driven resource optimization method according to claim 1, characterized in that, Intelligent optimization allocation includes the following operations: Based on the weights of demand characteristics, the state information of resources, and the attributes and constraints of resources, resources are allocated through multi-objective optimization algorithms. The allocation takes into account resource utilization, project schedule and cost control to form the optimal resource allocation scheme. Among them, multi-objective optimization algorithms include linear programming, genetic algorithm and ant colony algorithm. During project execution, the optimal resource allocation plan is reassessed and dynamically adjusted based on changes in requirements, changes in resource status, and schedule deviations to arrive at the final optimal resource allocation plan.

5. The demand-driven resource optimization method according to claim 1, characterized in that, Automated execution includes the following operations: By leveraging a toolchain foundation that includes APIs and PLM, CI / CD, and time management, resource allocation plans are distributed to the execution end, achieving end-to-end automation from requirements analysis to resource allocation and task distribution. Synchronize resource allocation results to the project management and work order systems, and send resource allocation information to relevant personnel.

6. A demand-driven resource optimization system, characterized in that, It includes a demand characteristic analysis module, a resource constraint modeling module, an intelligent optimization allocation module, an automated execution module, and a monitoring and feedback module; The requirement feature analysis module is used to perform the following operations: obtain the key features of each requirement and construct structured requirement features, and assign weights to the requirement features; The resource constraint modeling module is used to perform the following operations: model the available resources of the project, form a resource constraint model, define the attributes and constraints of the resources through the resource constraint model, and collect the status information of the resources in real time; The intelligent optimization allocation module performs the following operations: based on the weights of demand characteristics, resource status information, and resource attributes and constraints, it allocates resources through a multi-objective optimization algorithm to form the optimal resource allocation scheme; The automated execution module performs the following operations: it distributes resource allocation plans to the execution end through toolchain integration and synchronizes the resource allocation results to the project management and work order system; The monitoring and feedback module is used to perform the following operations: real-time monitoring of resource usage, project progress and allocation effects, identification of abnormal resource allocation issues, and optimization based on historical data of resource allocation and project execution, weighting of demand features based on machine learning methods, and the optimal resource allocation scheme.

7. The demand-driven resource optimization system according to claim 6, characterized in that, The requirement feature analysis module is used to perform the following operations: Collect and analyze the key characteristics of each requirement to form a structured requirement profile, which includes priority, complexity, business value, and deadline. Based on project goals and actual scenarios, weights are assigned to each requirement feature using a weighted algorithm.

8. The demand-driven resource optimization system according to claim 6, characterized in that, The resource constraint modeling module is used to perform the following operations: The available resources of the project are modeled to form a resource constraint model. The attributes and constraints of the resources are defined through the resource constraint model. The available resources of the project include manpower, time, skills and costs. It integrates time management, staff scheduling, and skill matrix to obtain real-time resource status information, including availability, load, and skill matching.

9. The demand-driven resource optimization system according to claim 6, characterized in that, The intelligent optimization allocation module is used to perform the following operations: Based on the weights of demand characteristics, the state information of resources, and the attributes and constraints of resources, resources are allocated through multi-objective optimization algorithms. The allocation takes into account resource utilization, project schedule and cost control to form the optimal resource allocation scheme. Among them, multi-objective optimization algorithms include linear programming, genetic algorithm and ant colony algorithm. During project execution, the optimal resource allocation plan is reassessed and dynamically adjusted based on changes in requirements, changes in resource status, and schedule deviations to arrive at the final optimal resource allocation plan.

10. The demand-driven resource optimization system according to claim 6, characterized in that, The automated execution module is used to perform the following operations: By leveraging a toolchain foundation that includes APIs and PLM, CI / CD, and time management, resource allocation plans are distributed to the execution end, achieving end-to-end automation from requirements analysis to resource allocation and task distribution. Synchronize resource allocation results to the project management and work order systems, and send resource allocation information to relevant personnel.

Images