A research and development resource visual scheduling method and system
By constructing a matching degree between task entities and resource entities and a multi-objective optimization function, the problem of local optima in existing R&D resource scheduling is solved, and global optimal resource allocation and scheduling are achieved.
Patent Information
- Application Number
- CN202610563448.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-24
AI Technical Summary
Existing R&D resource scheduling methods struggle to quantify and balance multiple optimization objectives, resulting in scheduling schemes that are often locally optimal rather than globally optimal.
By constructing the matching degree between task entities and resource entities, static business value scores, time urgency factors, and task dependency coupling strength, a final multi-objective optimization function is formed, and the scheduling scheme is displayed through a visual dashboard.
It enables automated and intelligent resource scheduling, breaks down information silos, provides a precise data foundation, and ensures the global optimal allocation of resources across multiple objectives.
Smart Images
Figure CN122453017A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of project management technology, and in particular to a method and system for visually scheduling R&D resources. Background Technology
[0002] In a business era where innovation is the core driving force, R&D capabilities directly determine a company's market position and future prospects.
[0003] In the fields of software, internet, and high-tech product development, efficient resource allocation is the core of project success and improved R&D efficiency.
[0004] Current resource scheduling methods mainly rely on the experience of project managers or department heads for manual or semi-manual scheduling. Based on experience and intuition, it is difficult to quantify and weigh multiple optimization objectives at the same time, resulting in scheduling solutions that are often locally optimal rather than globally optimal. Summary of the Invention
[0005] To address the shortcomings of existing technologies, the present invention aims to provide a research and development resource visualization scheduling method and system, which solves the technical problem that existing manual or semi-manual resource scheduling technologies are difficult to quantify and weigh multiple optimization objectives, resulting in scheduling schemes that are often locally optimal rather than globally optimal.
[0006] To achieve the above objectives, firstly, embodiments of this application provide a method for visually scheduling R&D resources, comprising the following steps: Raw data is obtained from the R&D management data source. Based on the raw data, a first entity set including several task entities and a second entity set including several resource entities are constructed, and the matching degree between the task entities and the resource entities is obtained. Obtain the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity, and obtain the priority coefficient based on the static business value score, the time urgency factor, and the task dependency coupling strength; Decision variables are constructed based on the task entity and the resource entity, and a final multi-objective optimization function is constructed. Specific values of the decision variables are obtained based on the final multi-objective optimization function to form a resource scheduling scheme, which is then displayed through a visual dashboard.
[0007] Furthermore, the step of obtaining the matching degree between the task entity and the resource entity includes: Construct a task description vector corresponding to the task entity and a resource description vector corresponding to the resource entity. The task description vector includes a first performance attribute identifier, an attribute requirement value, and an attribute weight. The resource description vector includes a second performance attribute identifier and an attribute quantification value. The matching degree between the task entity and the resource entity is obtained based on the task description vector and the resource description vector.
[0008] Furthermore, the step of obtaining the matching degree between the task entity and the resource entity based on the task description vector and the resource description vector includes: If the first performance attribute identifier is the same as the second performance attribute identifier, then a mapping function is obtained based on the attribute requirement value and the attribute quantification value; The matching degree between the task entity and the resource entity is obtained based on the mapping function and the attribute weight.
[0009] Furthermore, the formula for obtaining the mapping function is: , in, This represents the mapping function between the attribute quantization value and the attribute requirement value corresponding to the k-th attribute. This represents the quantized value of the attribute corresponding to the k-th attribute. This represents the attribute requirement value corresponding to the k-th attribute. This represents the excess reward coefficient, and 0 < <1; The formula for obtaining the matching degree is: , in, Represents a resource entity. Represents the task entity. This indicates the degree of matching between resource entities and task entities. This represents the attribute weight corresponding to the k-th attribute.
[0010] Furthermore, the steps of obtaining the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity include: Obtain the task type from the task entity, and convert the task type into a static business value score based on a preset mapping rule; Obtain the task deadline from the task entity, and obtain the time urgency factor based on the task deadline; Construct dependency relationships between the task entities, obtain the single dependency coupling strength of the task entities based on the dependency relationships, and combine several single dependency coupling strengths into a task dependency coupling strength.
[0011] Furthermore, the formula for obtaining the time urgency factor is: , in, This represents the time urgency factor at time t. This represents the deadline for the i-th task entity. This indicates a preset maximum positive value. This indicates a preset minimum positive value; The formula for obtaining the single dependency coupling strength is: , in, This represents the strength of the single dependency coupling between the i-th task entity and the j-th task entity with which it has a dependency connection. This represents the dependency type between the i-th task entity and the j-th task entity that has a dependency relationship with it. This represents the dependency type weight between the i-th task entity and the j-th task entity with which it has a dependency relationship. This represents the attenuation coefficient, and 0 < <1, This represents the minimum number of dependency connections between the i-th task entity and the j-th task entity with which it has a dependency connection.
[0012] Furthermore, the step of constructing the final multi-objective optimization function includes: Based on the decision variables, a minimum project duration objective and a minimum resource load imbalance objective are constructed. Based on the matching degree and the priority coefficient, a maximum matching degree objective is constructed to form an initial multi-objective optimization function. Constraints are set for the initial multi-objective optimization function, including task completion constraints, resource capacity constraints, task dependency constraints, and skill threshold constraints, so as to transform the initial multi-objective optimization function into a final multi-objective optimization function.
[0013] Furthermore, the expression for maximizing the matching degree objective is: , in, This represents the objective of maximizing the matching degree. This represents the priority coefficient of the b-th task entity at time t. This represents the matching degree between the a-th resource entity and the b-th task entity. Represents decision variables, This represents the total workload of the b-th task entity.
[0014] Secondly, embodiments of this application provide a research and development resource visualization scheduling system, applied to the research and development resource visualization scheduling method described in the first aspect above, the system comprising: The processing module is used to obtain raw data from the self-developed management data source, construct a first entity set including several task entities and a second entity set including several resource entities based on the raw data, and obtain the matching degree between the task entities and the resource entities. The evaluation module is used to obtain the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity, and to obtain the priority coefficient based on the static business value score, the time urgency factor, and the task dependency coupling strength. The execution module is used to construct decision variables based on the task entity and the resource entity, construct a final multi-objective optimization function, obtain specific values for the decision variables based on the final multi-objective optimization function to form a resource scheduling scheme, and display the resource scheduling scheme through a visualization dashboard.
[0015] Thirdly, embodiments of this application provide a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the R&D resource visualization scheduling method as described in the first aspect above.
[0016] Fourthly, embodiments of this application provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the R&D resource visualization scheduling method as described in the first aspect above.
[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: by converting the original data into the first entity set and the second entity set, and quantifying the matching degree between the task entity and the resource entity, information silos are broken down, and a precise and strongly correlated data foundation is provided; by obtaining the priority coefficient, quantitative data basis that changes over time is provided for decision-making; by combining the matching degree and the priority coefficient, the final multi-objective optimization function is constructed, transforming the subjective trade-offs that require experience to weigh multiple optimization objectives in traditional methods into a global optimization problem, thereby realizing automated and intelligent resource scheduling strategy trade-offs. Attached Figure Description
[0018] Figure 1 This is a flowchart of the R&D resource visualization scheduling method in the first embodiment of the present invention; Figure 2 This is a structural block diagram of the R&D resource visualization scheduling system in the second embodiment of the present invention; The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation
[0019] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
[0020] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0022] Please see Figure 1 The R&D resource visualization scheduling method provided in the first embodiment of the present invention includes the following steps: S10: Obtain raw data from the R&D management data source, construct a first entity set including several task entities and a second entity set including several resource entities based on the raw data, and obtain the matching degree between the task entities and the resource entities; Understandably, the R&D management data source includes task data source and resource data source, and the raw data includes task data and resource data. After obtaining the raw data, the raw data is cleaned (e.g., deduplication, completion, format standardization, and terminology standardization). Then, basic task attributes (e.g., task name, task type, task deadline, etc.) are extracted from the task data, and basic resource attributes (e.g., resource name, resource type, etc.) are extracted from the resource data, thereby forming the task entity and the resource entity.
[0023] Step S10 includes: S110: Construct a task description vector corresponding to the task entity and a resource description vector corresponding to the resource entity. The task description vector and the resource entity include a first performance attribute identifier, an attribute requirement value, and an attribute weight. The resource description vector includes a second performance attribute identifier and an attribute quantification value. Understandably, the first performance attribute identifier and the second performance attribute identifier are essentially the same, both pointing to task names / resource names such as Java skills or CPU.
[0024] The attribute requirement values refer to the following: For skill requirements, the task creator or technical lead selects from predefined level standards (such as "beginner: 3", "advanced: 7") based on the task complexity when creating the task, or the system recommends a level by analyzing the task description text and matching it with similar historical tasks; For hardware requirements, the developer explicitly specifies them in the task (such as "requires at least 16GB of video memory GPU"); For known types of computing tasks (such as model training), the system can automatically associate their typical resource requirements based on the experience database.
[0025] The attribute weight refers to the contribution of the attribute to the overall capability of the resource. For personnel, it can be based on their job responsibilities (for backend developers, Java skills have a higher weight); for hardware, it can be based on its intended use (for AI training servers, GPU performance has a higher weight).
[0026] The attribute quantification value refers to: For skills, the frequency of the person's submissions to a specific technology stack (such as Java), the complexity of the code, and the number of core modules involved can be analyzed in the code repository and mapped to a proficiency score. The score is weighted according to the historical quality (such as defect rate, performance indicators) and efficiency (average completion time) of the relevant tasks completed, and then converted into a proficiency level; For hardware, it can be read directly from the device specification, cloud platform instance type definition, or the return results of system detection tools (such as lscpu). For example, val is the number of CPU cores (16) and the size of GPU memory (24GB).
[0027] S120: Obtain the matching degree between the task entity and the resource entity based on the task description vector and the resource description vector; Specifically, if the first performance attribute identifier is the same as the second performance attribute identifier, then a mapping function is obtained based on the attribute requirement value and the attribute quantification value; The formula for obtaining the mapping function is: , in, This represents the mapping function between the attribute quantization value and the attribute requirement value corresponding to the k-th attribute. This represents the quantized value of the attribute corresponding to the k-th attribute. This represents the attribute requirement value corresponding to the k-th attribute. This represents the excess reward coefficient, and 0 < <1.
[0028] The matching degree between the task entity and the resource entity is obtained based on the mapping function and the attribute weight; The formula for obtaining the matching degree is: , in, Represents a resource entity. Represents the task entity. This indicates the degree of matching between resource entities and task entities. This represents the attribute weight corresponding to the k-th attribute.
[0029] S20: Based on the task progress record and the cost record, construct a number of task nodes and a number of cost nodes respectively, and select the final effective cost node corresponding to the task node from the number of cost nodes; Step S20 includes: S210: Obtain the task type from the task entity and convert the task type into a static business value score based on a preset mapping rule; Understandably, when constructing the task entity, basic task attributes, including task type and deadline, are extracted from the task data. The preset mapping rule refers to a mapping table between task type and score. For example, if the task type is experience optimization, the static business value score is 4; if the task type is edge bug fixing, the static business value score is 2.
[0030] S220: Obtain the task deadline from the task entity, and obtain a time urgency factor based on the task deadline; The formula for obtaining the time urgency factor is: , in, This represents the time urgency factor at time t. This represents the deadline for the i-th task entity. This indicates a preset maximum positive value. This indicates a preset minimum positive value. In this embodiment, The value is 1000. The value is 0.1.
[0031] S230: Construct dependency relationship lines between the task entities, obtain the single dependency coupling strength of the task entities based on the dependency relationship lines, and combine several single dependency coupling strengths into a task dependency coupling strength. The formula for obtaining the single dependency coupling strength is: , in, This represents the strength of the single dependency coupling between the i-th task entity and the j-th task entity with which it has a dependency connection. This represents the dependency type between the i-th task entity and the j-th task entity that has a dependency relationship with it. This represents the dependency type weight between the i-th task entity and the j-th task entity with which it has a dependency relationship. This represents the attenuation coefficient, and 0 < <1, In this embodiment, The value is 0.7. This represents the minimum number of dependency connections between the i-th task entity and the j-th task entity with which it has a dependency connection; The dependency types include FS (Precedence Completed - Subsequent Beginning), SS (Precedence Beginning - Subsequent Beginning), FF (Precedence Completed - Subsequent Completed), and SF (Precedence Beginning - Subsequent Completed), with corresponding dependency type weights of 1, 0.6, 0.8, and 0.3, respectively. Specifically, for any task entity, by traversing all task entities, a dependency relationship is constructed between the task entity and its preceding task entity. For example, if there are four sequentially executed task entities: Requirements Review (T1), Backend Development (T2, dependent on T1), Frontend Development (T3, dependent on T1), and Integration Testing (T4, dependent on both T2 and T3), and the Backend Development task entity is selected, a dependency relationship is constructed between Requirements Review and Backend Development. The value of is 1, the dependency type between the two is FS, and the dependency type weight is determined to be 1. After substituting into the formula, the single dependency coupling strength is 1. Since there is only one dependency relationship, the task dependency coupling strength is 1. If the entity of joint debugging test is selected, the dependency relationship between T1 and it is an indirect dependency, and there are two paths: T1→T2→T4 and T1→T3→T4. The minimum number of dependency connection lines is 2. All dependency types are FS. Then the value of Dep(T1, T4) is 0.7, and the values of Dep(T2, T4) and Dep(T3, T4) are both 1. The sum of the three single dependency coupling strengths is 2.7, which is the task dependency coupling strength.
[0032] By setting value weights, time weights, and intensity weights corresponding to the static business value score, the time urgency factor, and the task dependency coupling strength, the static business value score, the time urgency factor, and the task dependency coupling strength are weighted into the priority coefficient. In this embodiment, the value weight is 0.5, the time weight is 0.2, and the intensity weight is 0.3.
[0033] S30: Construct decision variables based on the task entity and the resource entity, and construct a final multi-objective optimization function. Obtain specific values for the decision variables based on the final multi-objective optimization function to form a resource scheduling scheme. Display the resource scheduling scheme through a visual dashboard. The expression for the decision variable is: Specifically, this means that at time t, the a-th resource entity is assigned to the b-th task entity.
[0034] Step S30 includes: S310: Based on the decision variables, construct the objectives of minimizing the project duration and minimizing the resource load imbalance; based on the matching degree and the priority coefficient, construct the objective of maximizing the matching degree to form an initial multi-objective optimization function. The expression for the minimum project duration objective is: , in, This indicates the goal of minimizing the project duration. This represents the completion time of the b-th task. It is understandable that once the allocation between resource entities and task entities is completed based on the decision variables, the completion time of that task entity can be predicted. For a research and development project, there are multiple task entities that proceed sequentially. The completion of the task entity at the last step is equivalent to the completion of the entire project's research and development. The task entity at the last step will inevitably have the longest completion time; therefore, it can be selected as the target for minimizing the project duration.
[0035] The expression for the objective of minimizing resource load imbalance is: , in, This represents the objective of minimizing resource load imbalance. Indicates the first The workload allocated to each resource entity ∈ , Indicates the total number of resource entities. This represents the standard deviation function. Understandably, once the allocation between resource entities and task entities is completed based on the decision variables, the workload assigned to each resource entity can be predicted.
[0036] The expression for maximizing the matching degree objective is: , in, This represents the objective of maximizing the matching degree. This represents the priority coefficient of the b-th task entity at time t. This represents the matching degree between the a-th resource entity and the b-th task entity. Represents decision variables, This represents the total workload of the b-th task entity. By using the priority coefficient and the matching degree simultaneously as evaluation factors, the optimization process no longer simply pursues static task matching, ensuring that high-quality resources are prioritized for tasks crucial to project success.
[0037] S320: Set constraints for the initial multi-objective optimization function, including task completion constraints, resource capacity constraints, task dependency constraints, and skill threshold constraints, so as to transform the initial multi-objective optimization function into a final multi-objective optimization function; Task completion constraints ensure that for each task entity, the total workload of the allocated resource entities is exactly equal to the total workload of that task entity; resource capacity constraints ensure that the workload allocated to each resource entity is less than its maximum available workload; task dependency constraints ensure the sequential relationship between different task entities, i.e., ensure the logical order of tasks; skill threshold constraints ensure that when a resource entity is assigned to a task entity, the matching degree between the two reaches the minimum requirement. After the final multi-objective optimization function is constructed, the known data (task entities, resource entities, matching degree, priority coefficients, etc.) are substituted into the final multi-objective optimization function, and the solution is obtained through a multi-objective optimization algorithm (such as multi-objective evolutionary algorithm or particle swarm optimization algorithm, etc.). The values of the minimum project duration objective, the minimum resource load imbalance objective, and the maximum matching objective are then evaluated and constraints are checked. Finally, a set of Pareto optimal solutions is approximated, which represents resource scheduling schemes that achieve different trade-offs among the three optimization objectives.
[0038] By converting the raw data into the first entity set and the second entity set, and quantifying the matching degree between the task entity and the resource entity, information silos are broken down, providing a precise and strongly correlated data foundation. By obtaining the priority coefficient, quantitative data basis that changes over time is provided for decision-making. By combining the matching degree and the priority coefficient, the final multi-objective optimization function is constructed, transforming the subjective trade-offs that traditional methods require relying on experience to weigh multiple optimization objectives into a global optimization problem, realizing automated and intelligent resource scheduling strategy trade-offs.
[0039] Please see Figure 2The second embodiment of the present invention provides a research and development resource visualization scheduling system, which is applied to the research and development resource visualization scheduling method described in the above embodiments, and will not be repeated hereafter. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0040] The system includes: Processing module 10 is used to obtain raw data from the self-developed management data source, construct a first entity set including several task entities and a second entity set including several resource entities based on the raw data, and obtain the matching degree between the task entities and the resource entities. The processing module 10 includes: The first unit is used to construct a task description vector corresponding to the task entity and a resource description vector corresponding to the resource entity. The task description vector includes a first performance attribute identifier, an attribute requirement value, and an attribute weight. The resource description vector includes a second performance attribute identifier and an attribute quantification value. The second unit is used to obtain the matching degree between the task entity and the resource entity based on the task description vector and the resource description vector; The second unit is specifically used to obtain a mapping function based on the attribute requirement value and the attribute quantification value if the first performance attribute identifier is the same as the second performance attribute identifier; and to obtain the matching degree between the task entity and the resource entity based on the mapping function and the attribute weight. Evaluation module 20 is used to obtain the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity, and to obtain the priority coefficient based on the static business value score, the time urgency factor, and the task dependency coupling strength. The evaluation module 20 includes: The third unit is used to obtain the task type from the task entity and convert the task type into a static business value score based on a preset mapping rule. The fourth unit is used to obtain the task deadline from the task entity and obtain the time urgency factor based on the task deadline. The fifth unit is used to construct the dependency relationship connection between the task entities, obtain the single dependency coupling strength of the task entities based on the dependency relationship connection, and combine several single dependency coupling strengths into the task dependency coupling strength. The execution module 30 is used to construct decision variables based on the task entity and the resource entity, construct a final multi-objective optimization function, obtain specific values of the decision variables based on the final multi-objective optimization function to form a resource scheduling scheme, and display the resource scheduling scheme through a visual dashboard. The execution module 30 includes: The sixth unit is used to construct a minimum project duration objective and a minimum resource load imbalance objective based on the decision variables, and to construct a maximum matching degree objective based on the matching degree and the priority coefficient, so as to form an initial multi-objective optimization function; The seventh unit is used to set constraints for the initial multi-objective optimization function, including task completion constraints, resource capacity constraints, task dependency constraints, and skill threshold constraints, so as to transform the initial multi-objective optimization function into the final multi-objective optimization function.
[0041] The present invention also provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the R&D resource visualization scheduling method as described in the above technical solutions.
[0042] The present invention also provides a storage medium on which a computer program is stored, which, when executed by a processor, implements the R&D resource visualization scheduling method as described in the above technical solution.
[0043] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0044] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.
Claims
1. A method for visually scheduling research and development resources, characterized in that, Includes the following steps: Raw data is obtained from the R&D management data source. Based on the raw data, a first entity set including several task entities and a second entity set including several resource entities are constructed, and the matching degree between the task entities and the resource entities is obtained. Obtain the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity, and obtain the priority coefficient based on the static business value score, the time urgency factor, and the task dependency coupling strength; Decision variables are constructed based on the task entity and the resource entity, and a final multi-objective optimization function is constructed. Specific values of the decision variables are obtained based on the final multi-objective optimization function to form a resource scheduling scheme, which is then displayed through a visual dashboard.
2. The R&D resource visualization scheduling method according to claim 1, characterized in that, The step of obtaining the matching degree between the task entity and the resource entity includes: Construct a task description vector corresponding to the task entity and a resource description vector corresponding to the resource entity. The task description vector includes a first performance attribute identifier, an attribute requirement value, and an attribute weight. The resource description vector includes a second performance attribute identifier and an attribute quantification value. The matching degree between the task entity and the resource entity is obtained based on the task description vector and the resource description vector.
3. The R&D resource visualization scheduling method according to claim 2, characterized in that, The step of obtaining the matching degree between the task entity and the resource entity based on the task description vector and the resource description vector includes: If the first performance attribute identifier is the same as the second performance attribute identifier, then a mapping function is obtained based on the attribute requirement value and the attribute quantification value; The matching degree between the task entity and the resource entity is obtained based on the mapping function and the attribute weight.
4. The R&D resource visualization scheduling method according to claim 3, characterized in that, The formula for obtaining the mapping function is: ， in, This represents the mapping function between the attribute quantization value and the attribute requirement value corresponding to the k-th attribute. This represents the quantized value of the attribute corresponding to the k-th attribute. This represents the attribute requirement value corresponding to the k-th attribute. This represents the excess reward coefficient, and 0 < <1; The formula for obtaining the matching degree is: ， in, Represents a resource entity. Represents the task entity. This indicates the degree of matching between resource entities and task entities. This represents the attribute weight corresponding to the k-th attribute.
5. The R&D resource visualization scheduling method according to claim 1, characterized in that, The steps of obtaining the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity include: Obtain the task type from the task entity, and convert the task type into a static business value score based on a preset mapping rule; Obtain the task deadline from the task entity, and obtain the time urgency factor based on the task deadline; Construct dependency relationships between the task entities, obtain the single dependency coupling strength of the task entities based on the dependency relationships, and combine several single dependency coupling strengths into a task dependency coupling strength.
6. The R&D resource visualization scheduling method according to claim 5, characterized in that, The formula for obtaining the time urgency factor is: ， in, This represents the time urgency factor at time t. This represents the deadline for the i-th task entity. This indicates a preset maximum positive value. This indicates a preset minimum positive value; The formula for obtaining the single dependency coupling strength is: ， in, This represents the strength of the single dependency coupling between the i-th task entity and the j-th task entity with which it has a dependency connection. This represents the dependency type between the i-th task entity and the j-th task entity that has a dependency relationship with it. This represents the dependency type weight between the i-th task entity and the j-th task entity with which it has a dependency relationship. This represents the attenuation coefficient, and 0 < <1, This represents the minimum number of dependency connections between the i-th task entity and the j-th task entity with which it has a dependency connection.
7. The R&D resource visualization scheduling method according to claim 1, characterized in that, The steps for constructing the final multi-objective optimization function include: Based on the decision variables, a minimum project duration objective and a minimum resource load imbalance objective are constructed. Based on the matching degree and the priority coefficient, a maximum matching degree objective is constructed to form an initial multi-objective optimization function. Constraints are set for the initial multi-objective optimization function, including task completion constraints, resource capacity constraints, task dependency constraints, and skill threshold constraints, so as to transform the initial multi-objective optimization function into a final multi-objective optimization function.
8. The R&D resource visualization scheduling method according to claim 7, characterized in that, The expression for maximizing the matching degree objective is: ， in, This represents the objective of maximizing the matching degree. This represents the priority coefficient of the b-th task entity at time t. This represents the matching degree between the a-th resource entity and the b-th task entity. Represents decision variables, This represents the total workload of the b-th task entity.
9. A research and development resource visualization scheduling system, applied to the research and development resource visualization scheduling method as described in any one of claims 1 to 8, characterized in that, The system includes: The processing module is used to obtain raw data from the self-developed management data source, construct a first entity set including several task entities and a second entity set including several resource entities based on the raw data, and obtain the matching degree between the task entities and the resource entities. The evaluation module is used to obtain the static business value score, time urgency factor, and task dependency coupling strength corresponding to the task entity, and to obtain the priority coefficient based on the static business value score, the time urgency factor, and the task dependency coupling strength. The execution module is used to construct decision variables based on the task entity and the resource entity, construct a final multi-objective optimization function, obtain specific values for the decision variables based on the final multi-objective optimization function to form a resource scheduling scheme, and display the resource scheduling scheme through a visualization dashboard.
10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the R&D resource visualization scheduling method as described in any one of claims 1 to 8.