Project execution path planning method and device based on multi-path resource detection and multi-strategy decision, and electronic product

Abstract

The embodiment of the invention provides a project execution path planning method and device based on multi-path resource detection and multi-strategy decision, and an electronic product, and the method comprises the steps: analyzing a description file of a target project, so as to recognize function modules defined in the target project from the description file, and matching a target task of each function module; generating a task dependency path tree for the target task, and determining a minimum resource demand when the target project is executed; allocating task initialization configuration for each target task based on the minimum resource demand so as to determine a task execution path; adjusting the task initial configuration and / or the task execution path based on a set plan execution limiting condition until the task initial configuration is matched with a plurality of set planning strategy configurations; and generating a project execution path plan based on the resource configuration and the task execution path during matching.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of artificial intelligence technology, and in particular to a project execution path planning method, device and electronic product based on multi-path resource detection and multi-strategy decision-making. Background Art

[0002] In the field of project management, project planning is a key task, and its quality often plays a decisive role in the success of a project. A well-made plan can provide a clear roadmap for the implementation of the project, effectively coordinate resources, arrange the order of tasks, and clarify the time nodes, thereby ensuring the smooth realization of project goals. Generally, a good project plan can largely predict the success of the project, and in ideal cases, it can even be considered that the project is more than half successful.

[0003] At present, the formulation of project plans within enterprises mainly relies on project managers or project leaders to use simple tools. However, when faced with complex projects or multiple related projects, this approach exposes many problems. It takes a very long time to make plans with manpower, because project managers need to comprehensively consider many factors, such as the detailed content of the task, the reasonable allocation of resources, the logical relationship between tasks, etc. This not only requires project managers to have deep professional knowledge and rich experience, but also requires a lot of time and energy for analysis and calculation. For example, in large-scale engineering projects, there are many construction processes, various types of construction resources (such as manpower, materials, equipment, etc.) and complex dependencies between processes. It is difficult for project managers to formulate a complete plan in a short time with manpower alone.

[0004] Complex project plans involve a lot of complex logic such as calculations, sorting, and dependency sorting. When humans handle these tasks, they are prone to errors due to the limitations of human thinking and fatigue. Moreover, it is difficult to fully consider all possible situations relying solely on humans, and often only local optimal solutions can be obtained, rather than the true optimal solution. For example, when arranging the execution order of multiple tasks, negligence or a lack of in-depth understanding of certain task relationships may lead to an unreasonable task order, which in turn affects the overall progress of the project or resource utilization efficiency.

[0005] In the actual execution of a project, various emergencies are often encountered, such as project delays and personnel changes. When these situations occur, it is necessary to change the project plan in a timely, fast and accurate manner. However, this is an extremely difficult task for manpower. On the one hand, re-evaluating and adjusting the plan requires re-sorting out the logic and resource allocation of the entire project, which is time-consuming and laborious; on the other hand, in an emergency, manpower is prone to more errors due to pressure, and it is difficult to ensure the quality of the changed plan. For example, in a software development project, if a key developer suddenly leaves, tasks need to be reallocated and the schedule needs to be adjusted. It is difficult for manpower to complete these adjustments quickly and accurately, which may cause further delays to the project.

[0006] Most of the current project management tools on the market mainly provide visual interfaces, and their main function is to simplify the daily management work of project managers during project execution, such as task tracking and progress display. However, in the core link of project planning, it still relies heavily on the personal brainpower of project managers. These tools fail to fully utilize the powerful computing power and intelligent algorithms of computers, cannot automatically complete the complex planning process, cannot automatically generate the best project planning plan according to project requirements, and cannot complete planning calculations in a short time (such as seconds), making it difficult to meet the needs of modern project management for efficient and accurate planning. Summary of the invention

[0007] In view of this, an embodiment of the present invention provides a project execution path planning method, device and electronic product based on multi-path resource detection and multi-strategy decision-making to at least partially solve the above problems.

[0008] According to a first aspect of an embodiment of the present invention, a project execution path planning method based on multi-path resource detection and multi-strategy decision-making is provided, which includes:

[0009] Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module;

[0010] Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project;

[0011] Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path;

[0012] Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations;

[0013] Generate project execution path planning based on resource configuration and task execution path during matching.

[0014] A project execution path planning device based on multi-path resource detection and multi-strategy decision-making, comprising:

[0015] A first program unit is used to parse the description file of the target project to identify the function modules defined in the target project and the target tasks matching each function module;

[0016] A second program unit is used to generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project;

[0017] A third program unit is used to allocate a task initialization configuration to each target task based on the minimum resource requirement to determine a task execution path;

[0018] A fourth program unit, configured to adjust the initial configuration of the task and / or the execution path of the task based on the set plan execution constraint conditions until the task matches the set multiple planning strategy configurations;

[0019] The fifth program unit is used to generate a project execution path plan based on the resource configuration and task execution path during matching.

[0020] A computer program product having computer executable instructions stored thereon, wherein when the computer executable instructions are executed, the following steps are performed:

[0021] Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module;

[0022] Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project;

[0023] Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path;

[0024] Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations;

[0025] Generate project execution path planning based on resource configuration and task execution path during matching.

[0026] An electronic device includes a memory, wherein the memory stores computer executable instructions, and when the computer executable instructions are executed, the following steps are performed:

[0027] Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module;

[0028] Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project;

[0029] Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path;

[0030] Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations;

[0031] Generate project execution path planning based on resource configuration and task execution path during matching.

[0032] A project execution path planning method, comprising:

[0033] Generate a task dependency path tree for all target tasks of the target project, and determine the minimum resource requirements when executing the target project;

[0034] Based on the minimum resource requirement, adjusting the task initialization configuration and / or the task execution path allocated to each target task until they match the set planning strategy configuration;

[0035] Generate project execution path planning based on resource configuration and task execution path during matching.

[0036] The solution of the embodiment of the present invention has the following technical advantages:

[0037] 1. Solutions to the Problems of Traditional Human Resource Planning

[0038] 1. Efficient task and resource analysis

[0039] Automatic parsing and identification: This method automatically identifies the functional modules in the target project and the target tasks that match each functional module by parsing the description file of the target project. This avoids the tedious process of human labor to sort out the task content one by one when facing complex projects. For example, in a large software project, the description file may contain many functional modules and task details, and human analysis may take a lot of time. This method can quickly and accurately extract key information, greatly improving the efficiency of task analysis.

[0040] Accurately determine the minimum resource requirements: Generate a task dependency path tree for the target task and determine the minimum resource requirements when executing the project. Compared with human resources estimating resources based on experience, this method is based on system analysis and calculation and can more accurately determine resource requirements. Taking engineering projects as an example, it can accurately calculate the minimum amount of resources such as manpower, materials, and equipment required for each construction process, avoiding over-allocation or under-allocation of resources, improving resource utilization efficiency, and solving the problem of unreasonable resource allocation when human resources make plans.

[0041] 2. Intelligent task execution path determination

[0042] Resource-based initialization configuration: Assign task initialization configuration to each target task based on the minimum resource requirements and determine the task execution path. This process uses system algorithms to comprehensively consider resource constraints and task dependencies and automatically generate a reasonable execution path. When dealing with complex projects, it is difficult for humans to comprehensively consider various resources and task relationships to determine the optimal path. This method overcomes the limitations of human thinking and effectively solves the problem that it is difficult for humans to obtain the best task execution order. For example, in a project with multiple tasks in parallel and limited resources, the system can calculate and find the task execution order that optimizes resource utilization, thereby reducing the overall execution time of the project.

[0043] 2. Advantages of coping with changes during project execution

[0044] 1. Quickly adapt to changes

[0045] When situations such as personnel changes or project delays occur during project execution, this method can quickly adjust the initial configuration of the task and the task execution path based on the set planned execution constraints. Because the system has built a task dependency path tree and clarified information such as resource requirements, it can quickly re-evaluate the impact of the change on the entire project and make corresponding adjustments. Compared with the need for manpower to re-sort out project logic and resource allocation in an emergency, this method is more efficient and accurate. For example, when personnel changes occur, the system can quickly reallocate tasks and adjust execution paths based on the new personnel skills and quantity to ensure that the project can continue smoothly and avoid further project delays caused by changes.

[0046] 2. Ensure the quality of the plan after the change

[0047] This method ensures that the changed plan still meets the overall goals and strategies of the project by continuously adjusting until it matches the multiple planning strategy configurations that are set. When people change plans, they are prone to making mistakes due to pressure and time constraints. However, the system can ensure the rationality of the changed plan in terms of resource utilization, task sequence, etc. through precise calculation and strategy matching, thereby improving the quality of the changed plan. For example, after a project is delayed, the system can adjust the task execution path according to the new time constraints and resource conditions, so that the project can still be advanced in the best way under the new conditions, maintaining high resource utilization efficiency and task execution efficiency.

[0048] 3. Make up for the shortcomings of existing project management tools

[0049] 1. Make full use of computer capabilities to achieve automatic planning

[0050] Existing project management tools rely on human brainpower in the core link of planning, while the method of the present invention fully utilizes the computing power and intelligent algorithms of computers. From parsing files to generating project execution path planning, the entire process does not require a lot of human intervention, realizing the automation of project planning. For example, when faced with complex multi-project management, the system can handle the tasks, resources and dependencies of multiple projects at the same time, and quickly generate an overall planning plan, while traditional tools can only assist manpower in simple task management and cannot achieve such complex automatic planning.

[0051] 2. Get the best planning solution and calculate quickly

[0052] Through multi-channel resource detection and multi-strategy decision-making, this method can obtain the best project planning solution according to project requirements. It traverses multiple resource configurations and execution path possibilities, and selects them in combination with planning strategies, overcoming the defect that existing tools cannot automatically generate the best solution. At the same time, it can complete planning calculations in seconds, meeting the needs of modern project management for efficient planning. For example, in the case of fierce market competition and tight project cycles, enterprises can quickly use this method to develop the best plan, quickly respond to market changes, and improve project competitiveness. However, existing tools cannot provide high-quality planning solutions in a short time due to slow calculation speed and inability to make intelligent decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0054] Figure 1A project execution path planning method, device and electronic product flow chart based on multi-path resource detection and multi-strategy decision-making are provided in an embodiment of the present invention.

[0055] Figure 2 A schematic diagram of a content creation object search device provided in an embodiment of the present invention.

[0056] Figure 3 A structural schematic diagram of an electronic device is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in the field based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0058] It should be understood that the terms "first", "second", and "third" in the claims, specifications, and drawings of the present disclosure are used to distinguish different objects rather than to describe a specific order. The terms "include" and "comprise" used in the specifications and claims of the present disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0059] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0060] Figure 1 A method, device and electronic product flow chart of project execution path planning based on multi-path resource detection and multi-strategy decision-making provided by an embodiment of the present invention. Figure 1 As shown, including:

[0061] According to a first aspect of an embodiment of the present invention, a project execution path planning method based on multi-path resource detection and multi-strategy decision-making is provided, which includes:

[0062] Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module;

[0063] Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project;

[0064] Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path;

[0065] Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations;

[0066] Generate project execution path planning based on resource configuration and task execution path during matching.

[0067] To this end, the project execution path planning method based on multi-way resource detection and multi-strategy decision-making provided by the present invention effectively solves many problems existing in traditional human resource planning and existing project management tools, and brings significant advantages. In solving the problem of traditional human resource planning, by automatically parsing and identifying the functional modules and target tasks of the target project, the tediousness of human resources combing one by one is avoided, and the efficiency of task analysis is greatly improved; the minimum resource requirements are accurately determined, the inaccuracy of human resource estimation is overcome, and the resource utilization efficiency is improved; based on the initial configuration of resources, a reasonable execution path is automatically generated, the limitations of human thinking are overcome, and the order of task execution is optimized. When responding to changes in project execution, the initial configuration and execution path of the task can be quickly adjusted based on the planned execution constraints to avoid aggravated delays; the quality of the plan after the change is guaranteed by matching with the planning strategy configuration. In terms of making up for the shortcomings of existing tools, the computer capabilities are fully utilized to realize project planning automation, and complex multi-project management can be handled; the optimal planning scheme is obtained through multi-way resource detection and multi-strategy decision-making and the calculation is completed in seconds, which meets the efficient needs of modern project management and enhances the competitiveness of enterprise projects.

[0068] Optionally, generating a task dependency path tree for the target task includes:

[0069] Determine the internal dependencies between target tasks within each functional module and the cross-module dependencies between target tasks in different functional modules;

[0070] According to the internal dependency and the cross-module dependency, determine the task dependency path to generate a task dependency path tree, wherein the internal dependency and the cross-module dependency are at least one of the following: parent-child relationship, predecessor task, successor task, no earlier than, later than task start, and end.

[0071] To this end, the above process of generating a task dependency path tree for the target task has the following technical advantages:

[0072] 1. Comprehensive and accurate task relationship sorting

[0073] By determining the internal dependencies between the target tasks within the functional modules and the cross-module dependencies between the target tasks of different functional modules, the task relationships in the project can be comprehensively and carefully sorted out. This comprehensive sorting helps to accurately grasp the overall logical structure of the project tasks and ensure that no task relationships are omitted or misunderstood. For example, in a large-scale enterprise-level information system development project, multiple functional modules such as user management, data storage, and business logic processing are involved. Clarifying internal dependencies, such as the relationship between the user registration task (pre-task) and the user login task (post-task) in the user management module, and cross-module dependencies, such as the relationship between the user permission setting task in the user management module and the data access permission control task in the business logic processing module, can make the task flow of the entire project clearly presented and provide a solid foundation for subsequent planning and resource allocation.

[0074] 2. Establish a reasonable task execution order

[0075] According to various types of dependency relationships (parent-child relationships, predecessor tasks, successor tasks, no earlier than, later than task start / end, etc.), the task dependency path is determined and a task dependency path tree is generated, which can reasonably plan the execution order of tasks. This makes the task execution process conform to the inherent logic and actual needs of the project, avoiding resource waste, time delay or project failure caused by the confusion of task execution order. Taking the construction project as an example, the foundation construction task (predecessor task) must be completed before the main building construction task (successor task), and some decoration tasks (such as internal wiring) cannot be completed earlier than a certain stage of the main building structure construction (no earlier than relationship), and some key acceptance tasks must be carried out when the entire project is nearing the end (later than the task end relationship). By accurately constructing the task dependency path tree, the optimal task execution order can be determined, such as following the order of foundation construction, main building construction, internal wiring, other decoration tasks, and key acceptance tasks to ensure efficient project advancement.

[0076] 3. Improve the accuracy of resource allocation and plan adjustment

[0077] A detailed and accurate task dependency path tree provides an important basis for resource allocation. Based on the dependency relationship between tasks, human, material, and financial resources can be reasonably allocated according to the order and time requirements of the tasks, so that resources can be invested in the right tasks at the right time. For example, in a software development project, if a task depends on the completion of another task before it can start, then when allocating resources, you can first ensure that the developers, equipment, and other resources required for the predecessor task are sufficient to avoid idle or overly tight resources. At the same time, when changes occur during the execution of the project (such as task extensions or resource adjustments), the task dependency path tree can clearly display the scope and path of the affected tasks, helping project managers to quickly and accurately adjust plans and reduce the impact of changes on the overall progress and quality of the project.

[0078] 4. Enhance the controllability and predictability of the project

[0079] A clear task dependency path tree enables project managers to better control the project progress. Through the visualization of task dependencies (in the form of a path tree), managers can intuitively understand the project's critical path, bottleneck tasks, and the parallel and serial relationships between tasks. This helps to predict possible problems and risks in advance, such as the risk of delays in tasks on the critical path, so that timely measures can be taken to prevent and respond. For example, in a new product development project, if a key technology development task (on the critical path) is found to have a time delay risk through the task dependency path tree, managers can allocate more resources or adjust task plans in advance to ensure that the project is completed on time. At the same time, this controllability and predictability also helps to improve the communication efficiency between project team members, so that each member is clear about the upstream and downstream relationships and importance of their tasks, so as to better collaborate to complete the project.

[0080] In addition, the process of generating a task dependency path tree for the target task is as follows:

[0081] 1. Mathematical modeling and quantitative representation of dependency data

[0082] Matrix representation of tasks and relationships: First, in order to facilitate computer processing, the target tasks in the project and the dependencies between them are mathematically modeled. Suppose there are n target tasks in the project, and define an n×n matrix R, where the element r ij represents the dependency relationship between task i and task j. If task i is the predecessor of task j (including the case where the parent task comes first in the parent-child relationship), then r ij =1; if task j is a subsequent task of task i, then r ji =1; if there is a relationship that task i cannot start earlier than task j, define a time difference variable Δt ij≥0, indicating that task i must start at least Δt after task j starts ij time to start; for the relationship of later than task end, assume that task i must end later than task j, and define the time difference variable Δt ji′ ≥0, indicating that task i must be completed after task j is completed Δt ji′ If there is no direct dependency between task i and task j, then r ij =r ji =0.

[0083] Division and identification of functional modules: The project contains m functional modules, and a vector F = [f1, f2, ..., f n ], where f k represents the functional module to which task k belongs, f k ∈{1,2,…,m}. In this way, through the matrix R and the vector F, the dependencies between the target tasks and the functional modules to which they belong can be mathematically described.

[0084] 2. Algorithm for determining internal dependencies

[0085] Dependency judgment based on matrix operations: For the target tasks within the functional module, the internal dependency relationship is determined by traversing the submatrices in matrix R corresponding to the same functional module (determined by vector F). Assume that task i and task j belong to the same functional module. If r ij =1 and f i =f j , then task i is the internal predecessor task of task j (or the parent task in the parent-child relationship). ji =1 and f i =f j , then task j is the internal post-task of task i. Through this matrix operation method, the dependency relationship between tasks in the module can be determined quickly and accurately, avoiding the inefficiency of task-by-task analysis in the traditional method and improving processing speed and accuracy.

[0086] Dependency calculation under time constraints: For internal dependencies involving not earlier than or later than the start / end of a task, in addition to the above matrix judgment, time constraints also need to be considered. Suppose task i and task j are in the same module and have a not earlier than task start relationship, that is, r ij =1 and f i =f j , and Δt ij , then the actual start time of task i is The inequality must be satisfied: in is the start time of task j. Similarly, for the later than task end relationship, if r ji =1,f i=f j And there is Δt ji′ , then the start time of task i Need to meet in is the end time of task j. By introducing these time constraint inequalities, the time order requirements of task execution can be considered more accurately when determining internal dependencies, which can better adapt to the time constraints of task execution in actual projects.

[0087] 3. Algorithm for determining cross-module dependencies

[0088] Cross-module dependency search and association: Find cross-module dependency relationships by traversing the elements between the sub-matrices corresponding to different functional modules in matrix R. For task i and task j, if r ij =1 and f i ≠f j , then task i is a cross-module predecessor task of task j. ji =1 and f i ≠f j , then task j is the cross-module post-task of task i. At the same time, for the cross-module relationship involving time constraints, according to Δt ij and Δt ji’ The judgment and calculation are performed as shown in the above time constraint formula, but at this time, task i and task j belong to different functional modules. This cross-module dependency search algorithm can fully explore the relationship between tasks in different functional modules, breaking through the limitations of inter-module relationship processing in traditional project management, and helping to build a more complete and accurate task dependency network.

[0089] Integration and optimization of cross-module relationships: After determining the cross-module dependencies, in order to build a reasonable task dependency path tree, these cross-module relationships need to be integrated and optimized. Let the set of tasks with cross-module dependencies be M. For each task i∈M, find the set of all its cross-module predecessor tasks P i ={j|r ji =1,f i ≠f j} and the post-task set S i ={j|r ij =1,f i ≠f j Then, according to the logical relationship and time constraints between tasks, the position of tasks in the overall dependency structure is adjusted to ensure that the dependency paths between cross-module tasks are reasonable and conflict-free. Integrating and optimizing cross-module relationships through these algorithms is an important innovative means for this technology to ensure the effectiveness and feasibility of the task dependency path tree when dealing with multi-module complex projects.

[0090] 4. Task dependency path tree construction algorithm

[0091] Initialization of tree structure and node creation: Create a root node to represent the entire project. Then, for each target task, create a corresponding tree node and organize the nodes according to the functional module to which the task belongs. Let the tree node of task i be N i , its function module is f i , node N i Add to the subtree structure of the corresponding functional module. This step lays the foundation for the subsequent construction of an accurate task dependency path tree through a reasonable node organization method, reflects the rationality of technological innovation in data structure design, and facilitates the computer to efficiently process and manage task nodes.

[0092] Dependency mapping to tree structure: According to the determined internal dependencies and cross-module dependencies, the task nodes are connected to form a task dependency path tree. For each task i, if there is a predecessor task j (either internal or cross-module), that is, r ji =1, then in the tree structure, node N j Connect to node N i The parent node position of i (if j is the only predecessor task of i) or added to node N i in the list of predecessor task nodes of task i (if i has multiple predecessor tasks); similarly, if task i is the predecessor task of task j, that is, r ij =1, then the node N i Connect to node N j At the same time, for dependencies involving time constraints, the corresponding time difference information (such as Δt ij or Δt ji′ ) for reference in the subsequent calculation of task execution time. In this way, the complex task dependency relationship is accurately mapped to the tree structure to build a complete task dependency path tree. This is one of the core innovations of this technology. By building a structured task dependency path tree, it provides an intuitive and efficient basic data structure for project planning and resource allocation, which facilitates rapid analysis and decision-making by computers.

[0093] 5. Path tree verification and optimization algorithm

[0094] Circular dependency detection and elimination: In order to ensure the rationality of the task dependency path tree, it is necessary to detect and eliminate circular dependencies. Use the depth-first search (DFS) or breadth-first search (BFS) algorithm to traverse the task dependency path tree, starting from the root node, and mark each visited node. If a marked node is encountered during the search process, and the node is not the direct ancestor node of the current node, it means that there is a circular dependency. Let the detected circular dependency path be C = {c1, c2, ..., c k}, where task c i Depends on task c i+1 (i=1,2,…,k-1) and task c k To eliminate circular dependencies, the dependencies between tasks in the circular path can be adjusted based on factors such as task priority and resource requirements. For example, if task c1 has a lower priority, it can be removed from task c2. k The task c1 is removed from the list of predecessor tasks, and the execution time and resource allocation of task c1 are adjusted to ensure the correctness of the overall logic of the project. This circular dependency detection and elimination algorithm is an important innovative means for this technology to ensure the feasibility and stability of project plans, avoiding project execution confusion caused by circular dependencies.

[0095] Verification of path integrity and rationality: Verify whether the task dependency path tree covers all target tasks, and whether the dependency of each task complies with the logic and time constraints. Traverse the task list and path tree nodes to check whether there are tasks that are not included in the path tree. At the same time, for each task node, check whether the connection between its predecessor and successor tasks is correct, and whether the time constraints are met. If incomplete or unreasonable situations are found, such as missing tasks or time constraint conflicts, they can be repaired and optimized by backtracking the dependency chain, re-analyzing the task description file, etc. This step ensures the quality of the task dependency path tree through a comprehensive verification and optimization process, realizes the embodiment of attention to detail and accuracy when dealing with project task relationships, and helps to improve the reliability and executability of project plans.

[0096] Optionally, the determining the minimum resource requirement for executing the target project includes:

[0097] Evaluate the multiple resource requirements required to execute each target task under each functional module to filter out the minimum resource requirements when executing the target project, and each resource requirement includes the type and quantity of available resources.

[0098] To this end, the above technical process for determining the minimum resource requirements for executing the target project has the following technical benefits:

[0099] 1. Accurate resource planning and optimal allocation

[0100] By evaluating the multi-channel resource requirements of the target tasks under each functional module and screening out the minimum resource requirements, accurate resource planning can be achieved. Taking software development projects as an example, for tasks under different functional modules (such as user interface design, database management, algorithm development, etc.), the requirements for various types of resources (such as programmer manpower, server resources, software tools, etc.) are analyzed in detail. This can avoid over-allocation of resources and prevent certain tasks from occupying too many unnecessary resources, while ensuring that each task can obtain sufficient resources to support its smooth execution. For example, in the database management functional module, the number and performance configuration of the required database servers are accurately determined according to the complexity of the task and the amount of data, rather than blindly allocating a large number of server resources, thereby improving resource utilization efficiency and reducing project costs.

[0101] 2. Improve overall project efficiency

[0102] Accurately determining the minimum resource requirements helps improve the overall efficiency of the project. Since each task is assigned the most suitable resource according to its actual needs, the resource waiting time and task queuing time are reduced. For example, in a multi-task parallel project, if the minimum resources required for each task can be accurately determined, then these tasks can be started and executed as soon as possible under appropriate resource conditions, avoiding task delays caused by insufficient resources or unreasonable allocation. At the same time, reasonable resource allocation can also reduce resource competition conflicts between tasks, making the project execution process smoother, thereby shortening the project cycle and increasing the speed of project delivery.

[0103] 3. Enhance the project's ability to cope with resource changes

[0104] Clarifying the minimum resource requirements provides a strong basis for the project to cope with resource changes. During the execution of the project, the availability of resources may change, such as some equipment failures, temporary leave of absence of personnel, etc. When this happens, since the minimum resource requirements for each task are already known, project managers can quickly determine which tasks will be affected and make flexible adjustments based on actual conditions. For example, if a server resource fails, the project team can evaluate which tasks can continue to run on the remaining server resources and which tasks need to be suspended or reallocated based on the predetermined minimum resource requirements, thereby minimizing the impact of resource changes on the project schedule and enhancing the stability and risk resistance of the project.

[0105] 4. Support modular management and expansion of projects

[0106] This resource demand assessment method is conducive to the modular management and expansion of the project. The resource requirements under each functional module are independently evaluated and determined, making the resource relationship between modules clearer. When the project is expanded or the functional modules are adjusted, the resource allocation can be easily re-evaluated and adjusted according to the new module requirements. For example, when a payment function module is added to an e-commerce project, its resource requirements can be evaluated based on the characteristics of the tasks under the module, and integrated and optimized with the resource requirements of the existing modules to ensure that the new module can be smoothly integrated into the overall project architecture without causing too much impact on the resource allocation and project execution of the original module, which is convenient for the continuous evolution and expansion of the project.

[0107] Optionally, the technical steps for generating a task dependency path tree and determining minimum resource requirements are implemented as follows:

[0108] 1. Generate task dependency path tree

[0109] 1. Dependency data structure definition and initialization

[0110] Define a two-dimensional array D[n][n] to represent the dependency relationship between tasks, where n is the number of target tasks. If task i is the predecessor task of task j (including the case where the parent task comes first in the parent-child relationship), then D[i][j] = 1; if task j is the successor task of task i, then D[j][i] = 1; if there is no direct dependency relationship between task i and task j, then D[i][j] = D[j][i] = 0. At the same time, define a one-dimensional array F[n] to represent the functional module to which each task belongs. The value of F[i] is the number of the functional module to which task i belongs, and the number ranges from 1 to m (m is the number of functional modules).

[0111] Define a structure array T[n] to store detailed information of tasks. Each structure contains the task name, task execution time estimate (set as t i ) and other fields. In addition, a structure is defined to represent the nodes in the task dependency path tree. The structure contains a task pointer (pointing to the corresponding T[i]), a child node pointer array (used to store its child task nodes), a parent node pointer (pointing to its parent task node), a predecessor task pointer array (used to store its predecessor task nodes), a successor task pointer array (used to store its successor task nodes), and variables related to time constraints (such as not earlier than the task start time difference array Δt ij [n] and the time difference array Δt later than the task end time ji′ [n]).

[0112] 2. Internal Dependency Determination Algorithm

[0113] For each functional module k (k = 1 to m), traverse the tasks within the module. For tasks i and j (i, j belong to functional module k), if D[i][j] = 1 and F[i] = F[j] = k, then task i is the internal predecessor task of task j. When constructing the task dependency path tree, add the node of task j to the child node pointer array of task i, and add the node of task i to the predecessor task pointer array of task j.

[0114] For tasks i and j (i, j belong to functional module k) that involve a relationship that starts no earlier than task i, if there is Δt ij >0 (obtained by parsing the project description file or other input data), then record Δt in the node of task j ij , represents the start time of task j Need to satisfy the inequality in is the start time of task i. For a later than task end relationship, if Δt ji′ >0, then record Δt in the node of task i ji′ , represents the start time of task i Need to meet in is the end time of task j. This step accurately describes the time constraint relationship between tasks through mathematical formulas to ensure the rationality of the task execution sequence. It is a manifestation of technological innovation and helps to deal with complex task dependencies and time constraints.

[0115] 3. Cross-module dependency determination algorithm

[0116] Traverse all tasks, for task i and task j (F[i]≠F[j]), if D[i][j]=1, then task i is the cross-module predecessor task of task j. When building the task dependency path tree, add the node of task j to the cross-module successor task pointer array of task i, and add the node of task i to the cross-module predecessor task pointer array of task j. And, just like the time constraint in the internal dependency processing, according to the time difference information in the project description file, record the no earlier than and later than time difference between the cross-module tasks in the corresponding nodes to ensure that the execution order of the cross-module tasks meets the project requirements.

[0117] In order to optimize the dependencies between cross-module tasks, the shortest path algorithm in graph theory (such as a variant of the Dijkstra algorithm or the Floyd-Warshall algorithm) can be used to calculate the optimal dependency path between cross-module tasks. Let the dependency path length between cross-module tasks i and j (which can be defined by weighted consideration of factors such as time and resources) be L ij , through the algorithm calculation so that L ijThe smallest path adjusts the connection relationship between cross-module tasks in the task dependency path tree to ensure the optimization of the dependency relationship between cross-module tasks and improve the overall execution efficiency of the project. This optimization algorithm is an important innovative means of this technology in dealing with complex dependencies in multi-module projects, which helps to improve the quality of project plans.

[0118] 4. Task dependency path tree construction algorithm

[0119] Create a root node root, representing the entire project. Then, for each task i, create a task node N i , and point its task pointer to the corresponding T[i]. Connect the corresponding nodes according to the relationship between the predecessor and successor tasks of task i. If task i has no predecessor task (except the root node), connect its node to the child node pointer array of the root node; if task i has a predecessor task, connect its node to the child node pointer array of the predecessor task node. Similarly, for successor tasks, connect the successor task node to the successor task pointer array of the node of task i.

[0120] In the process of building the tree, for each node, according to the functional module to which it belongs, it is organized into the corresponding functional module subtree structure (if the functional module subtree does not exist, it is created first). This helps to modularize the task dependency path tree, which is convenient for subsequent resource allocation and project execution analysis. At the same time, by continuously updating the pointer relationship between nodes, a complete task dependency path tree is constructed. This tree structure can accurately reflect the dependency relationship, time constraint relationship and functional module relationship between tasks, and provides a powerful basic data structure support for project planning and execution. It is one of the core innovations of this technology. Through this structured approach, it is convenient for computers to perform efficient analysis, decision-making and resource allocation.

[0121] 5. Path tree verification and optimization algorithm

[0122] Circular dependency detection: Use the depth-first search (DFS) or breadth-first search (BFS) algorithm to traverse the task dependency path tree. Starting from the root node, mark each visited node. If a node that has been marked and is not the direct ancestor of the current node is encountered during the search, it means that there is a circular dependency. Let the detected circular dependency path be C = {c1, c2, ..., c k}(where task c i Depends on task c i+1 , i = 1, 2, ..., k-1, and task c k To eliminate circular dependencies, we can use the priority of the task (set as p i , which can be determined by the project description file or other preset rules), resource requirements (set as r i) and other factors to adjust the dependencies between tasks in the loop path. For example, calculate the comprehensive impact factor I of each task in the loop path i =αp i +βr i (where α and β are weight coefficients, which can be adjusted according to project requirements), select the task with the smallest comprehensive impact factor (set as c m ), remove it from the dependency cycle, that is, adjust the pointer relationship of the relevant nodes so that task c m-1 No longer dependent on task c m , and adjust task c m The execution time and resource allocation of the project are optimized to ensure the correctness of the overall logic of the project. This circular dependency detection and elimination algorithm is an important innovative means for this technology to ensure the feasibility and stability of project plans, avoiding project execution chaos caused by circular dependencies.

[0123] Path integrity and rationality verification: Verify whether the task dependency path tree covers all target tasks, and whether the dependency of each task complies with the logic and time constraints. Traverse the task list and path tree nodes to check whether there are tasks that are not included in the path tree. For each task node, check whether the connection between its predecessor and successor tasks is correct, and whether the time constraints are met. Specifically, for each predecessor task j of task i, check whether D[j][i]=1 holds, and verify whether the start time of task i satisfies the corresponding inequality constraints based on the recorded time difference information. If incomplete or unreasonable situations are found, such as missing tasks or time constraint conflicts, they can be repaired and optimized by backtracking the dependency chain, reanalyzing the task description file, etc. This step ensures the quality of the task dependency path tree through a comprehensive verification and optimization process, which is a reflection of the attention to detail and accuracy of this technology when dealing with project task relationships, and helps to improve the reliability and executability of project plans.

[0124] 2. Determine the minimum resource requirements for executing the target project

[0125] 1. Resource requirement matrix definition and initialization

[0126] Define a three-dimensional array R[m][n][l] to represent the demand for different types of resources for each task under each functional module, where m is the number of functional modules, n is the number of target tasks, and l is the number of resource types. The element R[k][i][j] represents the demand for resource type j for task i under functional module k. At the same time, define a one-dimensional array A[l] to represent the total available amount of each resource type.

[0127] 2. Multi-channel resource demand assessment algorithm

[0128] For each functional module k (k = 1 to m), traverse the tasks i (i = 1 to n) in the module. For each task i, according to the nature and complexity of the task (which can be determined by the relevant parameters in the task description file or the preset task evaluation model), evaluate the number r of different resource types j (j = 1 to l) it requires. ij , and store it in R[k][i][j].

[0129] When evaluating resource requirements, consider the impact of dependencies between tasks on resource requirements. Let the set of predecessor tasks of task i be P i , for each predecessor task p∈P i , if there is a resource sharing or collaboration relationship between task p and task i (which can be determined by the task relationship description in the project description file or the preset resource collaboration rules), then when calculating the resource requirements of task i, it is necessary to comprehensively consider the resource usage of task p. For example, if task p uses a certain resource and releases part of it before task i starts, then the quantity required for the resource by task i needs to be adjusted accordingly. This step improves the accuracy of resource demand assessment by considering the dynamic impact of task dependencies on resource demand. It is a manifestation of technological innovation and helps to plan resources more accurately.

[0130] 3. Minimum resource requirement screening algorithm

[0131] For each resource type j (j = 1 to l), calculate the total demand for that resource type during the entire project execution Then, according to the total available amount A[j] of resource type j, it is determined whether the demand is met, that is, whether T j ≤A[j] is true. If this inequality is satisfied for all resource types, then the current resource allocation scheme (i.e., the number of resource requirements for each task) is feasible.

[0132] In order to find the minimum resource requirement, the greedy algorithm or dynamic programming algorithm is used for optimization. Taking the greedy algorithm as an example, starting from the initial resource requirement evaluation result, the number of resources required by each task is gradually reduced (under the premise of meeting the task execution requirements), and the total resource requirement is recalculated and the feasibility is judged. Let the reduced resource requirement array be R'[m][n][l]

[0133] Each time when reducing, select the task with the least impact on the overall project to adjust the resource demand (determined by calculating the importance coefficient of the task or the sensitivity of resource adjustment, for example, the importance coefficient of the task can be determined based on factors such as the position of the task on the critical path and the degree of impact of the task on the project goal). Repeat this process until a minimum resource demand configuration solution that meets the total available amount constraints of all resource types is found. This optimization algorithm is an important innovative means of resource management in this technology, which can minimize resource waste and improve resource utilization efficiency while ensuring the smooth execution of the project.

[0134] 4. Verification and adjustment of rationality of resource requirements

[0135] Verify the rationality of the minimum resource requirement solution. Consider factors such as the parallel use efficiency of resources and the resource switching cost. Suppose the parallel use efficiency coefficient of resource type j is η j (represents the ratio of the actual utilization efficiency of the resource in parallel tasks to the theoretical maximum utilization efficiency, which can be determined through historical project data or simulation experiments), and the resource switching cost is C ij (represents the cost of switching task i from one state to another when using resource type j, such as equipment warm-up time, personnel training cost, etc.) Calculate the resource utilization efficiency index of the project under the minimum resource requirement configuration and the total cost of resource switching

[0136] If the resource utilization efficiency index E is lower than the preset minimum efficiency threshold θ E Or the total resource switching cost C is higher than the preset maximum cost threshold θ C , the minimum resource requirement plan needs to be adjusted. Adjustment methods can include reallocating resource types, adjusting the order of task execution to optimize resource parallel use, etc. For example, if it is found that the parallel use efficiency of a certain resource is too low, you can try to adjust some tasks that rely on this resource to other resources, or adjust the order of task execution to make the switching of resources between different tasks more reasonable, thereby improving resource utilization efficiency and reducing costs. This step further optimizes the minimum resource requirement plan by comprehensively considering multiple practical factors.

[0137] Optionally, allocating a task initialization configuration to each target task based on the minimum resource requirement to determine a task execution path includes:

[0138] Based on the minimum resource requirement, assigning task initialization configuration to each target task;

[0139] Based on the initial configuration of the task, a task execution path is determined.

[0140] To this end, the above process of allocating task initialization configuration to each target task based on the minimum resource requirement to determine the task execution path has the following technical benefits:

[0141] 1. Accurate matching of resources and tasks

[0142] By allocating task initialization configuration for each target task based on the minimum resource requirement, accurate matching of resources and tasks can be achieved. This means that each task can obtain the amount of resources that just meet its execution requirements, avoiding the occurrence of over-allocation or under-allocation of resources. For example, in a construction project, if the minimum resource requirement for the pouring concrete task is 3 concrete mixers and 5 operators, initialization configuration according to such requirements can ensure the smooth implementation of the task, without idle waste due to excessive resources, and without delays due to insufficient resources, so that resource utilization can be optimized and the resource utilization efficiency and cost-effectiveness of the entire project can be improved.

[0143] 2. Enhance the planning and predictability of task execution

[0144] Determining the task execution path based on the initial configuration of the task helps to enhance the planning and predictability of task execution. Once the resources allocated to each task are clear, the order and time schedule of task execution can be reasonably planned based on factors such as resource usage characteristics and dependencies between tasks. For example, in a software development project, a certain number of programmers (resource configuration) are assigned to the code writing task of a certain module. Based on the skill level and work efficiency (resource characteristics) of these programmers, combined with the dependency relationship between the task and other module integration, testing and other tasks, the execution path of the code writing task in the entire project can be accurately determined, such as which predecessor tasks it starts after completion, how long it will take, and which tasks will be connected later, etc., so that the project team can prepare in advance, control the progress, and effectively deal with various situations that may arise during the project execution.

[0145] 3. Improve the flexibility of the project to cope with changes

[0146] When some variables appear during the project execution, such as temporary shortage of some resources or early / delayed progress of tasks, the project team can respond and adjust more flexibly based on the initial configuration of the minimum resource requirement allocation and the determined task execution path. Since the initial configuration of each task is based on the minimum resource requirement, it is possible to clearly determine which resources are indispensable and which resources can be flexibly deployed within a certain range. For example, if a device resource fails (resource change), by checking the initial configuration and execution path of each task, the scope of the affected tasks can be quickly known, and then the execution order of related tasks can be reasonably adjusted or other alternative resources can be deployed to ensure that the overall progress of the project is affected as little as possible, and to improve the project's adaptability and risk resistance in a complex and changing environment.

[0147] 4. Facilitate overall project coordination and monitoring

[0148] This method of allocating and configuring based on the minimum resource requirements and then determining the execution path is conducive to the overall coordination and monitoring of the project. For project managers, each task has a clear resource allocation and a clear execution path, which makes it convenient for them to control the allocation and flow of resources in the project, as well as the coordination and cooperation between tasks from a macro level. Managers can compare the actual execution situation with the established initial configuration and execution path, promptly discover deviations and take corrective measures to ensure that the project proceeds in an orderly manner as planned. For example, in a large manufacturing project, tasks in different production links have their own corresponding resource initial configuration and execution path. Managers can effectively coordinate the relationship between each link by monitoring resource usage and task progress in real time, avoid problems such as resource competition or poor task connection, and ensure the smooth operation of the entire production process.

[0149] Optionally, in a specific scenario, the process of allocating task initialization configuration to each target task based on the minimum resource requirement to determine the task execution path is as follows:

[0150] 1. Assign task initialization configuration to each target task based on minimum resource requirements

[0151] 1. Definition and initialization of relevant data structures

[0152] First, define the following data structures to assist in computing and storing information:

[0153] Assume there are n target tasks and define a two-dimensional array R min [n][m] represents the minimum resource requirement for each target task, where the element R min [i][j] represents the minimum requirement of the i-th target task for the j-th resource, i∈{1,2,…,n}, j∈{1,2,…,m} (m is the number of resource types).

[0154] Define a two-dimensional array A[m] to represent the actual available amount of each resource, that is, A[j] is the total amount of the j-th resource currently available for allocation.

[0155] Define a structure array T[n] to store detailed information of each target task. The structure contains fields such as task number (corresponding to i), task name, and allocated resource amount (initialized to 0). Here, the allocated resource amount is represented by a two-dimensional array R alloc [n][m] means, R alloc [i][j] represents the number of j-th resources currently allocated to the i-th task.

[0156] Define another two-dimensional array C[n][n] to represent the resource coordination coefficient between tasks. If there is a synergistic relationship between task i and task j in resource usage (for example, simultaneous execution can improve resource utilization), then C[i][j]>0; if there is a resource competition relationship (for example, sharing a scarce resource), then C[i][j]<0; if there is no obvious resource association, then C[i][j]=0.

[0157] 2. Resource Allocation Algorithm

[0158] Consider the initial allocation of resource coordination:

[0159] Traverse each target task i (i = 1 to n), for each task i, according to its minimum resource requirement R min [i][j], try to allocate resources for the first time. When allocating, the coordination of resources needs to be considered. Let the set of predecessor tasks of task i be P i , for each resource j, calculate the resource allocation R after the synergistic impact temp [i][j], the calculation formula is as follows:

[0160]

[0161] The idea here is that in addition to the minimum demand of task i, the resource allocation amount will be appropriately adjusted according to the synergy or competition relationship between it and the predecessor task in resource use. For example, if the predecessor task and the current task have a synergistic relationship in the use of a certain equipment resource (C[i][p]>0), and the predecessor task has allocated a certain amount of the resource, then the allocation amount of the resource can be appropriately increased when the current task is first allocated; on the contrary, if there is resource competition (C[i][p]<0), the initial allocation amount will be reduced accordingly to comprehensively consider the overall utilization efficiency of resources in the task chain, which reflects the fine handling of resource synergy in technological innovation.

[0162] Resource availability verification and adjustment:

[0163] For each resource j, after completing the initial allocation calculation of all tasks (get R temp [i][j] array), we need to check resource availability. Calculate the total temporary allocation of all tasks to resource j Then determine whether T j ≤A[j], that is, the total allocation cannot exceed the actual available amount of the resource.

[0164] If for a resource j, T j > A[j], then adjustment is required. The adjustment strategy adopts the method of allocating resources according to task priority. Let the priority of task i be PR i (It can be pre-set according to factors such as the criticality of the task in the project and the time urgency, and the value range is between 0 and 1. The larger the value, the higher the priority). Sort the tasks from high to low priority. Then start with the task with the lowest priority and gradually reduce its temporary allocation of resource j. temp [i][j], each time reducing one unit (can be determined according to the minimum allocation granularity of resources), and recalculating T j , until T is satisfied j ≤A[j]. This adjustment process ensures that critical tasks are given priority and sufficient resources through priority sorting, while also rationally allocating limited resources. It is a reflection of technological innovation in ensuring the rationality of resource allocation.

[0165] Final resource allocation confirmation:

[0166] After the above adjustments, the final resource allocation R temp [i][j] is the amount of allocated resources R assigned to task i alloc [i][j], complete the task initialization configuration of each target task, that is, determine the specific quantity of various resources that can be obtained by each task initially, laying the foundation for the subsequent determination of the task execution path.

[0167] 2. Determine the task execution path based on the initial task configuration

[0168] 1. Execution path related data structure definition

[0169] Define a directed graph data structure G(V,E) to represent the dependencies between tasks and the impact of resource allocation on the execution path, where V is a set of vertices representing all target tasks (corresponding to task numbers 1 to n), and E is a set of directed edges. Each directed edge (i,j) indicates that task j can only start after task i is completed. The weight of the edge can be used to represent some limiting factors between tasks (such as time intervals, resource switching costs, etc.).

[0170] Define a weight array W[i][j][k] for each directed edge (i, j), where k represents different weight factors, for example, k = 1 represents the time interval weight, k = 2 represents the resource switching cost weight, etc. Let the time interval from task i to task j be t ij , the resource switching cost is c ij , then W[i][j][1]=t ij , W[i][j][2]=c ij In addition, according to the initial resource configuration of the task, define a function f res (i, j) to determine the feasibility of resource switching from task i to task j. If the resource configuration meets the switching conditions (for example, the required resources can be deployed in place in time, etc.), then f res (i,j)=1, otherwise f res (i,j)=0.

[0171] 2. Execution path determination based on graph theory algorithm

[0172] Construct the initial directed graph:

[0173] According to the dependencies between tasks (which can be determined based on the previously generated task dependency path tree or pre-set dependency rules), add directed edges between the dependent tasks to the graph G. For example, if the predecessor task of task 3 is task 2, add a directed edge (2,3) to the graph. At the same time, according to the initial configuration of the tasks, calculate the value of the weight array W[i][j][k] corresponding to each edge and the resource switching feasibility function f res The value of (i,j).

[0174] Execute the path search algorithm:

[0175] An improved shortest path algorithm (such as a variant of the Dijkstra algorithm or the A* algorithm) is used to determine the task execution path. The "shortest path" here is the concept of the optimal path that takes into account multiple weight factors. Taking the A* algorithm as an example, a heuristic function h(i) is defined to estimate the comprehensive cost (taking into account factors such as time and resource costs) from task i to the final target task of the project (which can be set to task n). The design of the heuristic function is a key part of this technical innovation, which combines resource allocation and the overall project goals.

[0176] Let the current search node be s, the target node be t (in the entire project, t is ultimately the task node representing the completion of the project), start the search from the starting task node (set to task 1), and calculate the evaluation function value F(s) from each node s to the target node t. The calculation formula is as follows:

[0177] F(s)=g(s)+h(s)

[0178] Among them, g(s) is the actual cost from the starting node to the current node s (obtained by accumulating the weights of the traversed edges, such as the sum of time intervals, the sum of resource switching costs, etc.), and h(s) is the cost from the current node s to the target node t estimated by the heuristic function. By continuously selecting the node with the smallest F(s) value to perform an extended search until the target node t is found, the node sequence passed through is the determined task execution path. During the search process, according to the resource switching feasibility function f res (i, j) is used to screen feasible edges to avoid infeasible paths caused by the inability to allocate resources in a timely manner, and to ensure that the found execution path is feasible in terms of resource allocation and task dependency. This reflects the advantage of technological innovation in comprehensively considering multiple factors to determine the execution path.

[0179] 3. Execution path optimization and verification

[0180] Path Optimization:

[0181] After finding the preliminary task execution path, you can also optimize it. Considering factors such as the idle time of resources, suppose that after task i is executed, the resource j it occupies will have an idle time of idle ij , define a resource idle utilization function U idle (i, j) to measure the utilization value of resource idle time (for example, idle time can be used for other auxiliary tasks, bringing certain benefits, etc.). By adjusting the task execution order (under the premise of satisfying task dependencies and resource availability), try to maximize the total resource idle utilization Intelligent optimization algorithms such as simulated annealing algorithm and genetic algorithm are used to search for better task execution paths, further explore resource utilization potential and optimize project execution.

[0182] Path Validation:

[0183] Verify whether the determined task execution path meets all constraints, including task dependencies, rationality of resource allocation, feasibility of resource switching, and overall time and cost requirements of the project. Traverse each task node and the corresponding edge on the execution path to check whether there are any violations of the above conditions. For example, check whether the resource allocation of each task is consistent with the initial configuration of the task, whether the dependencies between tasks are correct, and whether the resource switching cost is within an acceptable range. If it is found that the constraints are not met, return to the execution path determination step and readjust the path until the verification is passed, ensuring that the final determined task execution path is a high-quality solution that meets the actual needs of the project, reflecting the rigor of the technology in ensuring the accuracy and feasibility of the execution path.

[0184] Optionally, adjusting the initial configuration of the task and / or adjusting the task execution path based on the set plan execution constraint until matching the set multiple planning strategy configurations includes:

[0185] Calculate the estimated time required for the target task to determine the overall time required to complete the target project;

[0186] Based on the task execution path and the overall time requirement indicator for completing the project, assign a task start time and a task end time to each target task;

[0187] Based on the set plan execution constraint conditions, according to the task start time and task end time assigned to each target task, each task dependency path on the task dependency path tree is traversed to determine whether there is a resource conflict between the target tasks. If there is a conflict, the initial resource configuration of the task is adjusted and / or the task execution path is adjusted until there is no resource conflict;

[0188] Generate project planning result-related data structures based on resource configuration and task execution paths corresponding to resource conflict-free situations;

[0189] Calculating the actual resource consumption index when executing the target project based on the planning result task tree, and matching it with the set multiple planning strategy configurations to evaluate the matching degree;

[0190] If the matching degree does not reach the set matching degree threshold, adjusting the initial resource configuration of the task and / or adjusting the task execution path until the matching degree reaches the set matching degree threshold;

[0191] When the matching degree reaches a set matching degree threshold, it is determined that the adjusted resource configuration and / or task execution path matches the set multiple planning strategy configurations.

[0192] To this end, the above-mentioned technical processing process of adjusting the initial configuration of the task and / or adjusting the task execution path based on the set plan execution constraint conditions until matching the set multiple planning strategy configurations has the following technical benefits:

[0193] 1. Accurate time control and efficient resource utilization

[0194] By calculating the estimated time requirement of the target task to determine the overall time requirement for completing the target project, project managers can clearly grasp the time span of the project from a macro level and make planning work such as resource allocation and schedule arrangement in advance. Based on this, a specific start time and end time are assigned to each target task, so that each task can be carried out in an orderly manner in the time dimension, avoiding confusion in task execution, improving the time utilization efficiency of the entire project execution process, ensuring that resources can be put into use in the appropriate time period, reducing idle or overused resources, and maximizing the efficient use of resources. For example, in a large-scale event planning project, accurately estimating the time requirement of each sub-activity (target task) and reasonably arranging its sequence and start and end time can enable resources such as venues, props, and personnel to coordinate with the needs of each stage of the activity in an orderly manner, avoiding the problem of idle waste of resources in certain periods and insufficient supply in key links.

[0195] 2. Prevent and resolve resource conflicts in advance

[0196] Based on the set plan execution constraints, the task dependency path tree is traversed to determine whether there is a resource conflict between the target tasks. This mechanism can detect potential unreasonable resource allocation problems in advance. Before or during project execution, resource conflict is one of the key factors affecting the smooth progress of the project, such as when multiple tasks in the same period require specific key equipment or professionals. Through this active detection method, resource conflicts can be detected in a timely manner, and the initial resource configuration of the task and the task execution path can be adjusted in a targeted manner until the conflict is eliminated, ensuring the rationality and feasibility of resource allocation during project execution, avoiding problems such as task delays and project stagnation caused by resource conflicts, and ensuring that the project can proceed in an orderly manner as planned. For example, in a software development project, if the testing phase and the new function development phase both require experienced senior programmers (resource conflict), this conflict can be resolved by reasonably adjusting the task execution order or allocating additional human resources, so that both tasks can be carried out smoothly.

[0197] 3. Generate scientific project planning results

[0198] The data structure related to the project planning results is generated based on the resource allocation and task execution path without resource conflicts, providing a clear, accurate and complete execution blueprint for the project. This data structure not only includes the execution order and time schedule of the tasks, but also covers the specific allocation of resources, which is convenient for project team members to fully understand the project planning, clarify their respective responsibilities and key nodes of task execution, and also facilitate managers to monitor and dynamically adjust the project in real time. For example, in a construction project, based on this data structure, the construction team can clearly know when each construction process (task) starts and ends, what construction materials and construction equipment (resources) are needed, and the project manager can also accurately control the project progress and resource usage based on it.

[0199] 4. Match planning strategies to achieve project optimization

[0200] Calculate the actual resource consumption indicators when executing the target project based on the planning result task tree, and match and evaluate the matching degree with the set multiple planning strategy configurations. This process can measure the degree of fit between the current planning scheme and the preset goals. Different projects have different focuses, such as pursuing planning strategies such as the lowest cost, the most balanced resource consumption, or the shortest project completion time. Through this matching evaluation mechanism, it can be found whether the current planning scheme meets the expected development direction of the project. If the matching degree does not reach the threshold, the initial resource configuration of the task and the task execution path are continuously adjusted and optimized until the set threshold is reached to ensure that the final resource configuration and execution path of the project can closely fit the preset planning strategy, realize the optimization of the project in many aspects, and improve the overall benefits of the project. For example, for a research and development project with a tight budget, by constantly adjusting the resource configuration and task sequence to make it more in line with the lowest cost planning strategy, the project can be completed with high quality within limited funds.

[0201] 5. Ensure that the planning scheme meets the overall requirements of the project

[0202] When the matching degree reaches the set threshold, it is determined that the adjusted resource configuration and task execution path match the multiple planning strategy configurations set. This mechanism ensures that the final project planning scheme is rigorously verified and meets the overall requirements of the project. It avoids the situation where unreasonable planning schemes are adopted due to human misjudgment or lack of consideration, improves the scientificity, accuracy and reliability of project planning, and lays a solid foundation for the successful implementation of the project. Whether it is a simple project or a complex large-scale project, this planning scheme determination method based on strict matching verification will help enhance the project's ability to cope with various internal and external changes, ensure the smooth delivery of the project and achieve the expected goals.

[0203] Optionally, the technical process of adjusting the initial configuration of the task and / or adjusting the task execution path based on the set plan execution constraint conditions until the task matches the set multiple planning strategy configurations is as follows:

[0204] 1. Calculate the estimated time required for the target task to determine the overall time required to complete the target project

[0205] 1. Related data structure and parameter definition

[0206] Assume that the project contains n target tasks and define a one-dimensional array T est [n] to store the estimated required time indicator of each target task, where T est [i] represents the estimated execution time of the i-th target task (the unit can be set according to the actual situation of the project, such as hours, days, etc.), i∈{1,2,…,n}.

[0207] Define a directed graph structure G(V,E) to represent the dependency relationship between tasks, where V is a vertex set representing all target tasks (corresponding to the task number), E is a directed edge set, and edge (i,j) means that task j can only start after task i is completed.

[0208] Suppose there is a two-dimensional array D[n][n]. If task i is the direct predecessor of task j (that is, there is a directed edge (i, j)∈E), then D[i][j]=1, otherwise D[i][j]=0.

[0209] 2. Calculation algorithm for estimated demand time index

[0210] Forward recursive calculation based on task dependencies:

[0211] First, for a task without a predecessor task (i.e., a vertex task with an in-degree of 0), its estimated required time indicator is its original estimated execution time. That is, if for task k, but ( is the initial estimated basic execution time of task k).

[0212] Then, for other tasks j with predecessor tasks, the estimated required time index is calculated by the following formula:

[0213]

[0214] This formula means that the estimated time required for task j is the time required for the task with the longest estimated time required among all its predecessor tasks, plus its own basic execution time. By traversing all tasks and calculating them in sequence according to the task dependencies, we can finally get the estimated time required for each task. This recursive calculation method based on dependencies fully considers the impact of task sequence on time and is the basis for accurate time estimation in technological innovation. It is more in line with actual project conditions than simple average estimation and other methods.

[0215] Determine the overall time requirement indicator:

[0216] After calculating the estimated time requirements for all tasks, determine the overall time requirement for completing the target project. total , that is, take the maximum value of the estimated time required for all tasks:

[0217] T total =max i∈{1,2,…,n} (T est [i])

[0218] Because the project completion time depends on the last completed task, using the longest task chain time as the time requirement indicator for the overall project can accurately reflect the actual time span required for the project and provide an important basis for the time allocation of subsequent tasks.

[0219] 2. Assign a start time and end time to each target task based on the task execution path and the overall time required to complete the project

[0220] 1. Data structure expansion and initialization

[0221] Based on the previously defined data structure, a new one-dimensional array S is added tart [n] to store the start time of each target task, and a one-dimensional array E nd [n] is used to store the end time of each target task, and the initial value is set to 0.

[0222] Assume that the task execution path is represented as a task sequence P = {p1, p2, ..., p m}(m≤n,p i The execution path is determined by combining the task dependency path tree generated earlier with factors such as resource configuration.

[0223] 2. Task time allocation algorithm

[0224] Allocate time in order of execution paths:

[0225] First determine the start time S of the first task (i.e. task p1) tart[p1] = 0 (the project starts at time 0), and its end time is:

[0226] E nd [p1]=S tart [p1]+T est [p1]

[0227] Then, for the subsequent task p in the execution path k (k>1), its start time is the end time of the predecessor task, that is:

[0228] S tart [p k ]=E nd [p k-1 ]

[0229] The end time is:

[0230] E nd [p k ]vS tart [p k ]+T est [p k ]

[0231] By calculating the start and end time of each task in sequence according to the task execution path, we ensure that the tasks are connected in an orderly manner in time, and fully consider the estimated required time of each task itself, so that the time allocation conforms to the task execution logic. This reflects the characteristics of technological innovation that combines execution paths and time estimates for detailed time arrangements, which helps to improve the planning and efficiency of project execution.

[0232] 3. Based on the set plan execution constraints, according to the task start time and task end time assigned to each target task, traverse each task dependency path on the task dependency path tree to determine whether there is a resource conflict between the target tasks. If there is a conflict, adjust the initial resource configuration of the task and / or adjust the task execution path until there is no resource conflict

[0233] 1. Resource-related data structure definition and plan execution constraint expression

[0234] Assume there are r resource types and define a three-dimensional array R alloc [n][r][2] represents the resource allocation of each target task to different resource types, where R alloc [i][j][0] indicates the number of resource type j assigned to task i, R alloc [i][j][1] indicates the time interval during which task i uses resource type j (which can be represented by the start time and the end time, for example [S tart [i],E nd[i]]).

[0235] Plan execution constraints are expressed as a set of inequalities or rules, such as:

[0236] Total resource limit: For each resource type j, there is Among them A vail [j] represents the total available amount of resource type j.

[0237] Resource concurrent usage restrictions: If a resource type j is specified to be used by at most C max [j] tasks are used, then for any time point t, That is, the number of tasks using the resource at the same time cannot exceed the limit.

[0238] Resource switching cost constraint: Let the cost of switching resource type j between task i and task k be C switch [i][k][j], stipulates that the total cost of each resource switching cannot exceed a certain threshold but (The actual calculation needs to consider the task execution order and the actual resource switching situation).

[0239] 2. Resource conflict detection algorithm

[0240] Traverse the task dependency path tree to detect conflicts:

[0241] The task dependency path tree (i.e., graph G) is traversed by using the depth-first search (DFS) or breadth-first search (BFS) algorithm. For each task pair (i, j) on the task dependency path (satisfying the existence of a directed edge (i, j) ∈ E), check whether the task pair (i, j) is in their respective task time intervals ([S tart [i],E nd [i]] and [S tart [j],E nd [j]]) to see if there is a resource conflict. Specifically check the following situations:

[0242] Total resource conflict detection: For each resource type s, check whether That is, check whether the total amount of resources allocated to the task using the resource exceeds the available amount of resources within the time overlap interval of the two tasks. If so, there is a total resource conflict.

[0243] Resource concurrency conflict detection: For a resource type s, calculate the number of tasks that use the resource in overlapping time intervals. If N s >C max [s], there is a resource concurrency conflict.

[0244] Conflict adjustment algorithm (taking resource configuration adjustment as an example):

[0245] Resource allocation strategy: If a resource conflict is detected, first try to adjust the initial resource configuration of the task. In the case of a total resource conflict, the resource allocation priority strategy is adopted, and the resource allocation priority of task i is set to PR res [i] (It can be determined based on factors such as task importance and resource dependency, with a value range of 0 to 1, and the higher the value, the higher the priority). Sort the tasks involved in the conflict from low to high priority. Starting with the task with the lowest priority, gradually reduce its allocation of conflicting resources (one unit at a time, and the unit is determined based on the minimum resource allocation granularity), and re-check the resource conflict until the conflict is eliminated.

[0246] Execution path adjustment (optional): If adjusting resource configuration alone cannot resolve the conflict, or the adjustment cost is too high, consider adjusting the task execution path. This can be done by replanning task dependencies, such as adjusting conflicting tasks to other times, or inserting some buffer tasks to change the order of task execution so that resources can be reasonably allocated in time. The adjusted task execution path needs to be re-verified to see if it meets task dependencies and other planned execution constraints. By continuously iteratively adjusting resource configuration and task execution paths until there are no resource conflicts, this flexible conflict resolution mechanism, which comprehensively considers resource and execution path adjustments, is a reflection of technological innovation in dealing with complex project resource management issues, and can effectively ensure the smooth progress of the project.

[0247] 4. Generate data structures related to project planning results based on resource allocation and task execution paths corresponding to the situation without resource conflicts

[0248] 1. Define the project planning results data structure

[0249] Define a structure array PLAN[n] to store project planning result related information. Each structure corresponds to a target task, including task number, task name, task start time (S tart [i]), task end time (E nd [i]), the type and amount of resources allocated (through R alloc [i][j][0] indicates) and other fields. This data structure will fully record the detailed execution plan after project planning, which is convenient for subsequent query, display and further analysis.

[0250] 2. Data filling and construction

[0251] Traverse all target tasks, and for each task i, fill the corresponding relevant information into the structure PLAN[i], and assign values ​​according to the task start time, end time and resource configuration determined in the above case without resource conflicts, so as to construct a complete project planning result data structure, clearly present the project execution blueprint, and provide accurate guidance for project implementation.

[0252] 5. Calculate the actual resource consumption indicators when executing the target project based on the planning result task tree, and match them with the set multiple planning strategy configurations to evaluate the matching degree

[0253] 1. Calculation of actual resource consumption indicators

[0254] Define several variables to calculate the actual resource consumption index. Suppose the actual total resource consumption vector R actual [r] represents the actual total consumption of each resource type during the project execution, and the initial values ​​are all 0.

[0255] For each resource type j, traverse all tasks i in the project planning result data structure and calculate the actual resource consumption:

[0256]

[0257] Here, the number of resources used by each task is multiplied by its usage time, and the actual total consumption of each resource is accumulated to accurately reflect the resource consumption during the actual execution of the project, providing a quantitative basis for matching with the planning strategy.

[0258] Other relevant indicators can also be defined, such as resource utilization balance index (used to measure whether resources are used evenly at different stages), resource idle rate index (reflecting resource idleness), etc., and calculated through corresponding calculation formulas (the specific formula can be determined according to actual project needs and definitions). Resource consumption is comprehensively evaluated from multiple dimensions, reflecting the characteristics of technological innovation in the refined evaluation of resource management.

[0259] 2. Matching with planning strategy configuration and matching degree calculation

[0260] Assume that the planning strategy configuration is represented by a set of target vectors and thresholds. For example, for the resource consumption minimization strategy, there is a target resource consumption vector R target [r] and the allowed deviation threshold vector ΔR[r], which represent the target amount of each resource expected to be consumed and the acceptable deviation range.

[0261] There are many ways to calculate the matching degree. The following is an example:

[0262] First, calculate the resource consumption deviation vector ΔR diff [r]=|Ractual [r]-R target [r]| represents the absolute value of the difference between the actual consumption and the target consumption of each resource.

[0263] Then, the matching degree M is calculated through a comprehensive evaluation function, for example:

[0264]

[0265] Among them, ò is a very small positive number, which is used to avoid the situation where the denominator is 0. The value range of the matching degree M is between 0 and 1. The closer the value is to 1, the more the actual resource consumption is matched with the planning strategy configuration. This matching degree calculation method that comprehensively considers the deviation of multiple resource types can comprehensively and accurately measure the degree of fit between the planning scheme and the preset strategy. It is a key manifestation of technological innovation in planning effect evaluation and helps to scientifically judge the rationality of project planning.

[0266] 6. If the matching degree does not reach the set matching degree threshold, adjust the initial resource configuration of the task and / or adjust the task execution path until the matching degree reaches the set matching degree threshold

[0267] 1. Adjust strategy and iterative optimization

[0268] When M<M threshold (M threshold When the matching degree reaches the set matching degree threshold, the adjustment mechanism is started. The adjustment process is similar to the adjustment method for solving resource conflicts mentioned above, but the adjustment goal is to improve the matching degree.

[0269] Resource configuration adjustment strategy:

[0270] Analyze the consumption deviation of each resource. For resource types with large deviations, determine the adjustment direction based on the task's dependence on the resource and the adjustment cost. For example, if the actual consumption of a resource far exceeds the target consumption, consider reducing the resource allocation for tasks that are highly dependent on the resource and relatively less important. This can be done by re-evaluating the task's resource demand function (dynamically adjusting the resource demand estimate based on task characteristics, complexity, and other factors), and reducing the allocation by a certain proportion (such as determining the adjustment proportion based on the size of the deviation), then recalculating the actual resource consumption index and matching degree, and observing the change in matching degree. If the matching degree improves, continue to optimize. If it does not improve or causes a larger deviation in other resources, roll back the adjustment and try other adjustment strategies.

[0271] Task execution path adjustment strategy:

[0272] From the perspective of the entire project, consider the impact of adjusting the task execution path on resource consumption. For example, by appropriately postponing or advancing the execution of some tasks during peak resource consumption periods (based on the feasibility of the task's time elasticity, dependencies, and other factors), observe the distribution changes in resource use over time, and then affect the actual resource consumption indicators and matching degree. Intelligent optimization algorithms such as simulated annealing algorithms and genetic algorithms can be used to maximize the matching degree, perform global search and optimization on the task execution path, and continuously iterate and adjust resource allocation and task execution paths until the matching degree reaches the set threshold. This iterative adjustment mechanism based on intelligent optimization algorithms is a prominent embodiment of technological innovation in the pursuit of optimal planning solutions. It can continuously approach the optimal planning results that meet the project's preset strategies and improve the overall benefits of the project.

[0273] 7. When the matching degree reaches the set matching degree threshold, it is determined that the adjusted resource configuration and task execution path match the set multiple planning strategy configurations.

[0274] Once M ≥ M threshold , determining that the currently adjusted resource configuration and task execution path meet the matching requirements of the set multiple planning strategy configurations, means that the current project planning scheme has achieved the preset project goals in terms of resource consumption, task execution sequence, etc. This scheme can be used as the final project execution plan to ensure that the project can be carried out smoothly in a scientific, reasonable and expected manner, reflecting the rigor and reliability of technological innovation in ensuring the quality and effectiveness of project planning.

[0275] Optionally, adjusting the initial configuration of the task and / or adjusting the task execution path based on the set plan execution constraint until matching the set multiple planning strategy configurations includes:

[0276] Calculate the estimated resource requirements for the target task to determine the overall resource requirements for completing the target project;

[0277] Based on the task execution path and the overall resource requirement indicators for completing the project, assign a task start time and a task end time to each target task;

[0278] Based on the set plan execution constraint conditions, according to the task start time and task end time assigned to each target task, each task dependency path on the task dependency path tree is traversed to determine whether there is a time conflict between the target tasks. If there is a conflict, the initial time configuration of the task is adjusted and / or the task execution path is adjusted until there is no time conflict;

[0279] Generate project planning result-related data structures based on resource allocation and task execution paths corresponding to the situation without time conflicts;

[0280] Calculate the actual time consumption index when executing the target project based on the planning result task tree, and match it with the set multiple planning strategy configurations to evaluate the matching degree;

[0281] If the matching degree does not reach the set matching degree threshold, adjusting the task initial time configuration and / or adjusting the task execution path until the matching degree reaches the set matching degree threshold;

[0282] When the matching degree reaches a set matching degree threshold, it is determined that the adjusted time configuration and / or task execution path matches the set multiple planning strategy configurations.

[0283] To this end, the technical benefits brought by the technical steps of adjusting the initial configuration of the task and / or adjusting the task execution path based on the set plan execution constraints until the task matches the set multiple planning strategy configurations are as follows:

[0284] 1. Accurate resource control and overall coordination

[0285] By calculating the estimated resource demand indicators of the target task to determine the overall resource demand indicators for completing the target project, project managers can clarify the overall requirements of the entire project at the resource level in advance and achieve macro-control of resources. This helps to coordinate resource planning from a global perspective and avoid project execution problems caused by insufficient or excessive resource estimates. For example, in a manufacturing project, accurately calculating the resource indicators such as raw materials and equipment usage time required for each production link (target task) and then determining the overall resource demand of the project can make procurement, deployment and other work more targeted, ensure the coordination of resource supply in each link of the project, and improve the overall efficiency of resource utilization.

[0286] 2. Reasonable time arrangement and orderly execution

[0287] Based on the task execution path and the overall resource demand indicators, each target task is assigned a task start time and task end time, so that the tasks can be planned in an orderly manner in the time dimension. Each task has a clear time range, which can effectively avoid the disorder and chaos of task execution and ensure that the project is carried out at a reasonable pace. For example, in a software development project, according to the resource requirements and interdependencies of different functional module development tasks (target tasks), the respective start and end times are arranged, so that developers can clearly know the sequence and time nodes of the work, reduce the time waste in the task connection process, and improve the overall execution efficiency of the project.

[0288] 3. Check and resolve time conflicts in advance

[0289] Based on the planned execution constraints, by traversing the task dependency path tree to determine whether there is a time conflict between the target tasks, this mechanism can detect potential unreasonable time scheduling problems in advance. In actual projects, time conflicts may lead to adverse consequences such as task delays and idle resources, affecting the project progress. For example, if two interdependent tasks are scheduled to be carried out in the same period, or the time scheduling of tasks on the critical path is unreasonable, by actively detecting and timely adjusting the initial time configuration of the task or the task execution path until the conflict is eliminated, the temporal consistency and rationality of each task during the project execution process can be guaranteed, ensuring that the project can be delivered on time.

[0290] 4. Generate a clear and effective project planning blueprint

[0291] The data structure related to the project planning results is generated based on the corresponding resource allocation and task execution path without time conflicts, creating a clear, accurate and complete execution blueprint for the project. This data structure covers key information such as task scheduling and resource allocation, which makes it convenient for project team members to fully understand the project planning, clarify their respective responsibilities and the key time nodes and resource requirements of each task, and also facilitate managers to monitor and dynamically adjust the project in real time. Taking a construction project as an example, the construction team can clearly know the specific construction time, required materials and equipment and other resources of each construction process (task) based on this data structure, and the project manager can accurately control the project progress and resource usage status based on this.

[0292] 5. Match planning strategies and optimize project execution

[0293] Calculate the actual time consumption index when executing the target project based on the planning result task tree, and match it with the set multiple planning strategy configurations to evaluate the matching degree. This process can measure the degree of fit between the current planning scheme and the preset target in the time dimension. Different projects may have different time management focuses, such as pursuing the shortest construction period, balanced time allocation, or meeting specific phased time requirements. Through the matching evaluation mechanism, it can be found whether the current planning scheme meets the expected time management direction of the project. If the matching degree does not reach the threshold, the initial time configuration of the task and the task execution path are continuously adjusted and optimized until the set threshold is reached, ensuring that the final time arrangement and execution path of the project can closely fit the preset planning strategy, realize the optimization of the project in time management, and improve the overall benefits of the project.

[0294] 6. Ensure that the plan meets the overall project requirements

[0295] When the matching degree reaches the set matching degree threshold, it is determined that the adjusted time configuration and task execution path match the set multiple planning strategy configurations, which ensures that the final project planning scheme is rigorously verified and meets the overall time management requirements of the project. This planning scheme determination method based on strict matching verification avoids the situation where unreasonable time planning schemes are adopted due to human misjudgment or inconsideration, improves the scientificity, accuracy and reliability of project planning, lays a solid foundation for the smooth completion of the project on schedule, and enhances the project's ability to cope with various internal and external changes that affect the time schedule.

[0296] Optionally, the steps of adjusting the initial configuration of the task and / or adjusting the task execution path based on the set plan execution restriction conditions until the task matches the set multiple planning strategy configurations may specifically include:

[0297] 1. Calculate the estimated resource requirements for the target task to determine the overall resource requirements for completing the target project

[0298] 1. Data structure definition and initialization

[0299] Assume that the project contains n target tasks and m resource types. Define a two-dimensional array R est [n][m] is used to store the estimated resource requirements of each target task for different resource types, where R est [i][j] represents the estimated quantity of the j-th resource required for the ith target task (the unit depends on the specific resource, such as the number of manpower, the number of equipment, etc.), i∈{1,2,…,n}, j∈{1,2,…,m}.

[0300] At the same time, define a one-dimensional array R total [m] is used to store the overall resource requirement indicator for each resource to complete the entire target project. The initial values ​​are all set to 0.

[0301] 2. Calculation algorithm for estimated demand resource indicators

[0302] Resource estimation based on task characteristics: For each target task i, estimate the demand for each resource j based on the nature, scale, complexity and other factors of the task. For example, if task i is a functional module development task in software development, its demand for programmer manpower (resource type j) can be determined by comprehensively considering factors such as the code volume estimation formula and the functional complexity coefficient. Suppose that through a specific estimation function F res (i,j) to calculate, that is:

[0303] R est [i][j]=F res (i,j)

[0304] Here the estimated function F res (i,j) can be a mathematical model constructed based on historical project data, industry experience and specific project requirements. It reflects the characteristics of combining multiple factors to accurately estimate resources in technological innovation, and is more scientific and accurate than simple empirical estimates.

[0305] Determine the overall resource demand index: After calculating the estimated resource demand index for each task for each resource, determine the overall resource demand index for each resource to complete the entire project by adding them up. For each resource type j, the calculation formula is as follows:

[0306]

[0307] That is, the estimated demand for the resource for all tasks is summed up to obtain the overall demand for each resource for the project, providing a basic resource-level basis for subsequent task time allocation and overall planning.

[0308] 2. Assign a start time and end time to each target task based on the task execution path and the overall resource requirement indicators for completing the project.

[0309] 1. Related data structures and execution path representation

[0310] The task execution path is represented by a directed graph G(V,E), where V is a vertex set representing all target tasks (corresponding to the task number), E is a directed edge set, and edge (i,j) means that task j can only start after task i is completed, reflecting the dependency relationship between tasks.

[0311] Define a one-dimensional array S tart [n] to store the start time of each target task, and a one-dimensional array E nd [n] is used to store the end time of each target task, and the initial value is set to 0.

[0312] Suppose there exists a sequence P = {p1, p2, ..., p k}(k≤n, and the tasks that satisfy the dependency relationship are arranged in sequence). This sequence can be obtained by performing topological sorting on the task dependency path tree and other algorithms (reflecting the characteristics of technological innovation in using appropriate algorithms to determine the execution order), providing a sequential basis for subsequent time allocation.

[0313] 2. Task time allocation algorithm

[0314] Allocate time in order of execution paths:

[0315] First, determine the start time S of the first task (i.e., task p1) tart[p1] = 0 (the project starts at time 0), and its end time is determined by the estimated execution time of the task and the resource allocation. Assume that the resource demand of task p1 is calculated by R est [p1][j] represents (calculated in the previous step). The resource allocation satisfaction affects the task execution efficiency, and then affects the execution time. Suppose there is a time adjustment function T based on resource allocation adj (p1,R est [p1]) to calculate the actual execution time of task p1 (taking into account the impact of factors such as resource sufficiency and resource allocation efficiency on time, which is a reflection of the careful consideration of the relationship between resources and time in technological innovation), then the end time of task p1 is:

[0316] E nd [p1]=S tart [p1]+T adj (p1,R est [p1])

[0317] For the subsequent task p in the execution path sequence s (s>1), its start time is the end time of the predecessor task, that is:

[0318] S tart [p s ]=E nd [p s-1 ]

[0319] The end time also takes into account the impact of resource allocation on execution time and is calculated through the time adjustment function:

[0320] E nd [p s ]=S tart [p s ]+T adj (p s ,R est [p s ])

[0321] By calculating the start and end time of each task in sequence according to the task execution path, the task dependencies and the impact of resources on task execution time are fully considered, making time allocation more in line with the actual project execution situation, which helps to improve the planning and efficiency of project execution.

[0322] 3. Based on the set plan execution constraints, according to the task start time and task end time assigned to each target task, traverse each task dependency path on the task dependency path tree to determine whether there is a time conflict between the target tasks. If there is a conflict, adjust the task initial time configuration and / or adjust the task execution path until there is no time conflict

[0323] 1. Data structures related to plan execution constraints and time conflict judgment

[0324] Plan execution constraints can be expressed as a set of inequalities or rules. For example:

[0325] Task order constraints: Based on the task dependency, for a directed edge (i, j) ∈ E (task i is the predecessor task of task j), E must be satisfied nd [i]≤S tart [j], that is, the end time of the predecessor task must be earlier than or equal to the start time of the successor task. This is the most basic manifestation of the task sequence constraint.

[0326] Time interval constraint: It stipulates that there must be a minimum time interval between certain task pairs. Let the minimum time interval required between task i and task k be ΔT min [i][k], then S tart [k]-E nd [i]≥ΔT min [i][k] To ensure that there is enough buffer time during the task execution process to meet some special requirements of the actual project, such as equipment cooling and personnel handover, which reflects the characteristics of technological innovation in considering the time requirements of actual operation details.

[0327] Define a two-dimensional array C time [n][n] is used to help determine time conflicts. If the time intervals of task i and task j overlap (i.e. ), then C time [i][j]=1, indicating the possibility of time conflict, otherwise C time [i][j]=0.

[0328] 2. Time conflict detection algorithm

[0329] Traverse the path tree to detect conflicts:

[0330] The task dependency path tree (i.e., graph G) is traversed through the depth-first search (DFS) or breadth-first search (BFS) algorithm. For each task pair (i, j) on the task dependency path (satisfying the existence of a directed edge (i, j) ∈ E), its time interval is checked to see whether it meets the planned execution constraints and whether there is time overlap (through C time [i][j] judgment). The specific inspection is as follows:

[0331] For the task sequence constraints, verify whether E is satisfied. nd [i]≤S tart [j], if not satisfied, it is determined that there is a time conflict.

[0332] For time interval constraints, check whether S is satisfied for the task pairs with corresponding requirements. tart [j]-E nd [i]≥ΔT min [i][j], if not satisfied, it is also determined that there is a time conflict.

[0333] Conflict adjustment algorithm (taking time configuration adjustment as an example): Priority-based time adjustment strategy: If a time conflict is detected, first try to adjust the initial time configuration of the task. Let the time adjustment priority of task i be PR time [i] (It can be determined based on multiple factors such as task importance, resource dependency, and impact on project progress. The value range is between 0 and 1. The higher the value, the higher the priority). Sort the conflicting tasks from low to high priority.

[0334] For a task pair (i, j) with a conflicting order, if E nd [i]>S tart [j], starting from the task with the lower priority (task i), gradually postpone its start time (one time unit each time, the unit can be set according to the minimum time granularity of the project, such as minutes, hours, etc.), that is, update S tart [i] and E nd [i], recalculate the time interval and check if there is still a conflict until the order constraint is met.

[0335] For task pairs with insufficient time interval conflicts, the start or end time of the related tasks can be appropriately adjusted to meet the minimum time interval requirement based on the flexibility and adjustable space of the tasks (for example, some task times are relatively fixed, while others are relatively flexible). For example, if the time interval between task k and task l is insufficient, and the end time of task k is relatively more flexible, E can be appropriately extended. nd The value of [k] is adjusted, and the time configuration of subsequent dependent tasks is adjusted accordingly, and conflicts are rechecked until all conflicts are eliminated.

[0336] Execution path adjustment: If adjusting the time configuration alone is difficult to resolve the conflict or the adjustment cost is too high (such as causing new conflicts in other tasks), consider adjusting the task execution path. This can be done by reanalyzing the task dependencies and adjusting the conflicting tasks to other appropriate locations for execution. For example, by swapping the execution order of two parallel tasks (provided that their respective predecessor and successor task dependencies are met), or inserting some buffer tasks to change the execution order and schedule of the tasks, so that the overall time configuration is more reasonable. The adjusted task execution path needs to be re-verified to see whether it meets all planned execution constraints and whether new time conflicts are introduced. By continuously iteratively adjusting the time configuration and task execution path until there is no time conflict, this flexible conflict resolution mechanism, which comprehensively considers the adjustment of time and execution path, is a reflection of technological innovation in dealing with complex project time management issues, and can effectively ensure the smooth progress of the project in the time dimension.

[0337] 4. Generate the data structure related to the project planning results based on the resource allocation and task execution path corresponding to the situation without time conflict

[0338] 1. Define the project planning results data structure

[0339] Define a structure array PLAN[n] to store project planning result related information. Each structure corresponds to a target task, including task number, task name, task start time (S tart [i]), task end time (E nd [i]), the type and quantity of resources allocated (the R est This data structure will fully record the detailed execution plan after project planning, which is convenient for subsequent query, display and further analysis. It reflects the advantages of technological innovation in integrating planning information and structuring its representation, making it easier for project team members to intuitively understand and execute project plans.

[0340] 2. Data filling and construction

[0341] Traverse all target tasks, and for each task i, fill the corresponding relevant information into the structure PLAN[i], and assign values ​​according to the task start time, end time and existing resource configuration determined in the above case without time conflict, so as to construct a complete project planning result data structure, clearly present the project execution blueprint, and provide accurate guidance for project implementation.

[0342] 5. Calculate the actual time consumption index when executing the target project based on the planning result task tree, and match it with the set multiple planning strategy configurations to evaluate the matching degree

[0343] 1. Calculation of actual time consumption indicators

[0344] Define a variable T_{actual} to represent the actual time consumption indicator when executing the target project based on the planning result task tree, that is, the total time actually spent on the entire project from start to finish. During the project execution process, the actual execution time of each task is recorded in turn according to the task execution path. Let the actual execution time of task i be t actual [i] (This can be obtained by subtracting the actual start time from the actual end time of the task. The actual start time and end time are recorded during project execution). Then:

[0345]

[0346] By accumulating the actual execution time of all tasks, the total time actually spent on the project can be accurately obtained, providing an objective and quantitative time indicator basis for matching with the planning strategy configuration, reflecting the characteristics of technological innovation in accurately measuring the actual execution situation.

[0347] 2. Matching with planning strategy configuration and matching degree calculation

[0348] The planning strategy configuration is represented by a set of target time indicators and thresholds. For example, for the strategy of pursuing the shortest duration, there is a target total time T target and the allowed time deviation threshold ΔT, which represents the expected project completion time and the acceptable deviation range from the target time.

[0349] There are many ways to calculate the matching degree. The following is an example:

[0350] First calculate the time deviation value ΔT diff =|T actual -T target |, represents the absolute value of the difference between the actual total time and the target total time.

[0351] Then, the matching degree M is calculated through a matching degree function, for example:

[0352]

[0353] Among them, ò is a very small positive number, which is used to avoid the situation where the denominator is 0. The value range of the matching degree M is between 0 and 1. The closer the value is to 1, the more the actual time consumption situation matches the planning strategy configuration. This matching degree calculation method based on the relationship between deviation and threshold can intuitively and effectively measure the degree of fit between the planning scheme and the preset strategy in the time dimension. It is a key manifestation of technological innovation in the evaluation of planning effects and helps to scientifically judge the rationality of project planning.

[0354] 6. If the matching degree does not reach the set matching degree threshold, adjust the task initial time configuration and / or adjust the task execution path until the matching degree reaches the set matching degree threshold

[0355] 1. Adjust strategy and iterative optimization

[0356] When M<M threshold (M threshold When the matching degree threshold is reached, the adjustment mechanism is started. The adjustment process is similar to the adjustment method for solving time conflicts, but the adjustment goal is to improve the matching degree.

[0357] Time configuration adjustment strategy: Analyze the reasons for time deviation. If the actual total time exceeds the target total time, focus on those tasks that take a long time to execute and have a greater impact on the overall time (this can be determined by analyzing the critical path of the task, time elasticity and other factors). For these tasks, try to adjust their time configuration, for example, by optimizing resource allocation (because resource configuration affects task execution time) to shorten the task execution time. You can use the correlation function between resources and time (such as the T mentioned above) to calculate the time configuration of the task. adj (i,R est [i]) function) re-evaluates and adjusts resource allocation, thereby changing the actual execution time of the task, recalculates the actual time consumption index and matching degree, and observes the change in matching degree. If the matching degree improves, continue to optimize; if it does not improve or causes other problems (such as new time conflicts), roll back the adjustment and try other adjustment strategies.

[0358] Task execution path adjustment strategy: From the perspective of the entire project, consider the impact of adjusting the task execution path on the total time. For example, by advancing or postponing the execution of some tasks on non-critical paths (based on the feasibility of the task's time elasticity, dependencies, and other factors), observe the changes in the actual total time of the project, thereby affecting the matching degree. Intelligent optimization algorithms such as simulated annealing algorithms and genetic algorithms can be used to maximize the matching degree, conduct global search and optimization of the task execution path, and continuously iterate and adjust the time configuration and task execution path until the matching degree reaches the set threshold. This iterative adjustment mechanism based on intelligent optimization algorithms is a prominent manifestation of technological innovation in the pursuit of optimal planning solutions. It can continuously approach the optimal planning results that meet the preset project strategies and improve the overall benefits of the project.

[0359] 7. When the matching degree reaches the set matching degree threshold, it is determined that the adjusted time configuration and task execution path match the set multiple planning strategy configurations.

[0360] Once M ≥ M threshold, determining that the currently adjusted time configuration and task execution path meet the matching requirements of the set multiple planning strategy configurations, means that the current project planning scheme has achieved the preset project goals in terms of time arrangement, task execution sequence, etc. This scheme can be used as the final project execution plan to ensure that the project can be carried out smoothly in a scientific, reasonable and expected manner, reflecting the rigor and reliability of technological innovation in ensuring the quality and effectiveness of project planning.

[0361] Figure 2 A schematic diagram of a project execution path planning device based on multi-path resource detection and multi-strategy decision-making provided in an embodiment of the present application. Figure 2 As shown, it includes: a first program unit, which is used to parse the description file of the target project to identify the functional modules defined in the target project and the target tasks that match each functional module; a second program unit, which is used to generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project; a third program unit, which is used to assign a task initialization configuration to each target task based on the minimum resource requirement to determine the task execution path; a fourth program unit, which is used to adjust the task initial configuration and / or the task execution path based on the set planned execution constraints until it matches the set multiple planning strategy configurations; a fifth program unit, which is used to generate a project execution path plan based on the resource configuration and task execution path during matching.

[0362] The embodiment of the present application also provides a computer program product having computer executable instructions stored thereon, and when the computer executable instructions are executed, the following steps are performed: parsing a description file of a target project to identify therefrom functional modules defined in the target project and target tasks matching each functional module; generating a task dependency path tree for the target task, and determining the minimum resource requirement when executing the target project; based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path; based on set planned execution constraints, adjusting the task initial configuration and / or adjusting the task execution path until it matches the set multiple planning strategy configurations; generating a project execution path plan based on the resource configuration and task execution path during matching.

[0363] In the above computer program product embodiment, the exemplary explanation of the technical processing process of each step can be found in the above Figure 1 Records of.

[0364] An embodiment of the present application also provides an electronic device, which includes a memory and, the memory stores computer executable instructions, and when the computer executable instructions are run, the following steps are performed: parsing a description file of a target project to identify therefrom the functional modules defined in the target project and the target tasks matching each functional module; generating a task dependency path tree for the target task, and determining the minimum resource requirement when executing the target project; based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine the task execution path; based on the set planned execution constraints, adjusting the task initial configuration and / or adjusting the task execution path until it matches the set multiple planning strategy configurations; generating a project execution path plan based on the resource configuration and task execution path during matching.

[0365] The embodiment of the present application also provides a project execution path planning method, which includes: generating a task dependency path tree for all target tasks of the target project, and determining the minimum resource requirement when executing the target project; based on the minimum resource requirement, adjusting the task initialization configuration and / or the task execution path assigned to each target task until it matches the set planning strategy configuration; based on the resource configuration and task execution path at the time of matching, generating a project execution path plan. In the above-mentioned project execution path planning method embodiment, the exemplary explanation of the technical processing process of each step execution can be found in the above-mentioned Figure 1 Records of.

[0366] Figure 3 A schematic diagram of the structure of an electronic device is provided in accordance with an embodiment of the present invention. Figure 3 As shown, the electronic device includes a memory, and a computer executable program is stored on the memory, and the computer executable program is run to implement the following steps: parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module; generating a task dependency path tree for the target task, and determining the minimum resource requirement when executing the target project; based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine the task execution path; based on the set plan execution constraints, adjusting the task initial configuration and / or adjusting the task execution path until it matches the set multiple planning strategy configurations; generating a project execution path plan based on the resource configuration and task execution path during matching.

[0367] Above Figure 3 In the embodiment, the exemplary explanation of the technical processing process of each step can be found in the above Figure 1 Records of.

[0368] The above implementation methods are only used to illustrate the embodiments of the present invention, and are not intended to limit the embodiments of the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims. The systems, devices, modules or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

Claims

1. A project execution path planning method based on multi-path resource detection and multi-strategy decision-making, characterized in that: include: Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module; Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project; Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path; Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations; Generate project execution path planning based on resource configuration and task execution path during matching.

2. According to claim 1, a project execution path planning method based on multi-path resource detection and multi-strategy decision-making is characterized in that: The step of generating a task dependency path tree for the target task includes: Determine the internal dependencies between target tasks within each functional module and the cross-module dependencies between target tasks in different functional modules; According to the internal dependency and the cross-module dependency, determine the task dependency path to generate a task dependency path tree, wherein the internal dependency and the cross-module dependency are at least one of the following: parent-child relationship, predecessor task, successor task, no earlier than, later than task start, and end.

3. The project execution path planning method based on multi-path resource detection and multi-strategy decision-making according to claim 1 is characterized in that: The determining of the minimum resource requirement for executing the target project includes: Evaluate the multiple resource requirements required to execute each target task under each functional module to filter out the minimum resource requirements when executing the target project, and each resource requirement includes the type and quantity of available resources.

4. The project execution path planning method based on multi-path resource detection and multi-strategy decision-making according to claim 1 is characterized in that: The allocating task initialization configuration to each target task based on the minimum resource requirement to determine the task execution path includes: Based on the minimum resource requirement, assigning task initialization configuration to each target task; Based on the initial configuration of the task, a task execution path is determined.

5. The project execution path planning method based on multi-path resource detection and multi-strategy decision-making according to claim 1 is characterized in that: The adjusting the initial configuration of the task and / or the execution path of the task based on the set plan execution constraint condition until the task matches the set multiple planning strategy configurations includes: Calculate the estimated time required for the target task to determine the overall time required to complete the target project; Based on the task execution path and the overall time requirement indicator for completing the project, assign a task start time and a task end time to each target task; Based on the set plan execution constraint conditions, according to the task start time and task end time assigned to each target task, each task dependency path on the task dependency path tree is traversed to determine whether there is a resource conflict between the target tasks. If there is a conflict, the initial resource configuration of the task is adjusted and / or the task execution path is adjusted until there is no resource conflict; Generate project planning result-related data structures based on resource configuration and task execution paths corresponding to resource conflict-free situations; Calculating the actual resource consumption index when executing the target project based on the planning result task tree, and matching it with the set multiple planning strategy configurations to evaluate the matching degree; If the matching degree does not reach the set matching degree threshold, adjusting the initial resource configuration of the task and / or adjusting the task execution path until the matching degree reaches the set matching degree threshold; When the matching degree reaches a set matching degree threshold, it is determined that the adjusted resource configuration and / or task execution path matches the set multiple planning strategy configurations.

6. The project execution path planning method based on multi-path resource detection and multi-strategy decision-making according to claim 1 is characterized in that: The adjusting the initial configuration of the task and / or the execution path of the task based on the set plan execution constraint condition until the task matches the set multiple planning strategy configurations includes: Calculate the estimated resource requirements for the target task to determine the overall resource requirements for completing the target project; Based on the task execution path and the overall resource requirement indicators for completing the project, assign a task start time and a task end time to each target task; Based on the set plan execution constraint conditions, according to the task start time and task end time assigned to each target task, each task dependency path on the task dependency path tree is traversed to determine whether there is a time conflict between the target tasks. If there is a conflict, the initial time configuration of the task is adjusted and / or the task execution path is adjusted until there is no time conflict; Generate project planning result-related data structures based on resource allocation and task execution paths corresponding to the situation without time conflicts; Calculate the actual time consumption index when executing the target project based on the planning result task tree, and match it with the set multiple planning strategy configurations to evaluate the matching degree; If the matching degree does not reach the set matching degree threshold, adjusting the task initial time configuration and / or adjusting the task execution path until the matching degree reaches the set matching degree threshold; When the matching degree reaches a set matching degree threshold, it is determined that the adjusted time configuration and / or task execution path matches the set multiple planning strategy configurations.

7. A project execution path planning device based on multi-path resource detection and multi-strategy decision-making, characterized in that: include: A first program unit is used to parse the description file of the target project to identify the function modules defined in the target project and the target tasks matching each function module; A second program unit is used to generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project; A third program unit is used to allocate a task initialization configuration to each target task based on the minimum resource requirement to determine a task execution path; A fourth program unit, configured to adjust the initial configuration of the task and / or the execution path of the task based on the set plan execution constraint conditions until the task matches the set multiple planning strategy configurations; The fifth program unit is used to generate a project execution path plan based on the resource configuration and task execution path during matching.

8. A computer program product, characterized in that Computer executable instructions are stored thereon, and when the computer executable instructions are executed, the following steps are performed: Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module; Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project; Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path; Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations; Generate project execution path planning based on resource configuration and task execution path during matching.

9. An electronic device, characterized in that: The method comprises a memory, wherein the memory stores computer executable instructions, and when the computer executable instructions are executed, the following steps are performed: Parsing the description file of the target project to identify the functional modules defined in the target project and the target tasks matching each functional module; Generate a task dependency path tree for the target task and determine the minimum resource requirement when executing the target project; Based on the minimum resource requirement, assigning a task initialization configuration to each target task to determine a task execution path; Based on the set plan execution constraint conditions, the initial configuration of the task and / or the task execution path are adjusted until they match the set multiple planning strategy configurations; Generate project execution path planning based on resource configuration and task execution path during matching.

10. A project execution path planning method, characterized in that: include: Generate a task dependency path tree for all target tasks of the target project, and determine the minimum resource requirements when executing the target project; Based on the minimum resource requirement, adjusting the task initialization configuration and / or the task execution path allocated to each target task until they match the set planning strategy configuration; Generate project execution path planning based on resource configuration and task execution path during matching.

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