A project management method, system, device, and medium

Abstract

This invention provides a project management method, system, device, and medium, belonging to the field of project management. The method includes: collecting project-related data, organizing and cleaning the data to obtain a structured dataset; employing entity recognition and relation extraction algorithms to perform entity annotation and relation extraction processing on the structured dataset to construct a project-related knowledge graph; based on the knowledge graph, using a graph traversal algorithm to decompose tasks, generating a task list and its priority order, and using a dynamic optimization algorithm to monitor task progress and resource utilization in real time to dynamically adjust task priorities and resource allocation strategies. The automatic decomposition of task modules based on the knowledge graph improves the efficiency and accuracy of task decomposition. Combined with dynamic programming and optimization algorithms, it achieves intelligent resource allocation and task priority adjustment, improving team collaboration efficiency and enhancing project flexibility and adaptability.

Description

Technical Field

[0001] This invention belongs to the field of project management technology, and specifically relates to a project management method, system, equipment and medium. Background Technology

[0002] With the rapid development of AI technology, project complexity and uncertainty have increased significantly. Modern project management needs to meet the following requirements: Efficient task decomposition: Quickly identify task modules and their dependencies to reduce manual intervention. Dynamic collaborative optimization: Adjust team resource allocation and task priorities based on real-time data. Intelligent risk management: Proactively predict potential risks and provide response strategies. Cross-domain knowledge integration: Process knowledge and information from different fields to build a complete project knowledge system.

[0003] Traditional project management methods (such as the waterfall model and agile development) are proving inadequate when faced with complex, large-scale AI projects. Specifically:

[0004] Task decomposition lacks intelligence: Traditional methods rely on manual task decomposition, which is time-consuming and prone to missing key nodes. Collaboration efficiency is low: Team collaboration depends on frequent communication and coordination, leading to uneven resource allocation and information silos. Risk management capabilities are limited: Traditional methods lack the ability to predict potential risks, often only responding passively after problems arise. There is a lack of dynamic optimization capabilities: During project execution, if the environment or requirements change, traditional methods struggle to quickly adjust task arrangements and resource allocation; changes in requirements require manual adjustments, increasing time by 50%. Summary of the Invention

[0005] The purpose of this invention is to provide a project management method, system, device, and medium to solve all or at least part of the technical problems existing in the prior art.

[0006] In a first aspect, embodiments of this application provide a project management method, including:

[0007] Collect project-related data, and organize and clean the project-related data to obtain a structured dataset;

[0008] Entity recognition and relation extraction algorithms are used to perform entity annotation and relation extraction on the structured dataset in order to construct a project-related knowledge graph.

[0009] Based on the knowledge graph, a graph traversal algorithm is used to decompose tasks, generate a task list and its priority order, and a dynamic optimization algorithm is used to monitor task progress and resource utilization in real time, so as to dynamically adjust task priority and resource allocation strategies.

[0010] Optionally, collect project-related data, and organize and clean the project-related data to obtain a structured dataset, including:

[0011] Project-related data is collected from multiple sources, including but not limited to project objectives, task descriptions, resource requirements, and team member skills.

[0012] We use NLP (Natural Language Processing) technology to extract key information from project-related data and perform data cleaning to transform unstructured data into structured data, ensuring the consistency and integrity of the data format.

[0013] Optionally, entity recognition and relation extraction algorithms are used to perform entity annotation and relation extraction processing on the structured dataset to construct a project-related knowledge graph, including:

[0014] Based on pre-determined project entities, domain terms are filtered out from the structured dataset and a domain terminology table is generated;

[0015] Define entity label sets, relation label sets, and annotation constraint rules, and annotate the structured dataset to obtain labeled data;

[0016] Entity masking augmentation is performed on the structured dataset, and entity, attribute, and relation triples are extracted from the labeled data to construct a relation pre-training corpus;

[0017] A dual-branch encoder is constructed based on relation pre-training corpus and BERT-base model. The dual-branch encoder includes an entity branch encoder and a relation branch encoder. The entity branch encoder is used to receive token-level input and output entity label distribution. The relation branch encoder is used to receive entity pairs plus context input and output relation classification probability.

[0018] The labeled data is input into the dual-branch encoder, character-level alignment is performed, and a token tag mapping table is generated;

[0019] Based on the Token tag mapping table, string matching and synonym merging are performed, and attribute value validation is used to construct a knowledge graph related to the project.

[0020] Optionally, based on the knowledge graph, a graph traversal algorithm is used to decompose the tasks, generate a task list and its priority order, and a dynamic optimization algorithm is used to monitor task progress and resource utilization in real time to dynamically adjust task priorities and resource allocation strategies, including:

[0021] A directed acyclic graph is generated based on the resource dependency attributes and execution time weights of task nodes. A task dependency matrix is ​​established during the task list generation process. Conflict detection is performed on task nodes with resource contention. Mutual exclusive access to shared resources is ensured by inserting virtual synchronization nodes. The task priority is sorted based on the product of the node's out-degree value and the path weighted duration.

[0022] Construct a multi-dimensional monitoring vector of task progress deviation, resource utilization entropy value, and node load balancing factor, and trigger priority reassessment when the task progress deviation, resource utilization entropy value, and node load balancing factor all exceed the preset threshold.

[0023] The dynamic adjustment strategy includes resource preemptive reallocation, which preempts reserved resources for low-priority tasks based on an urgency index for delayed tasks, while recording resource lending relationships for subsequent compensation scheduling. The urgency index is determined by the ratio of the square of the remaining workload to the remaining resource elasticity.

[0024] Optionally, the project management method further includes:

[0025] Assign the optimized tasks to the appropriate team members and use collaboration tools to track task progress.

[0026] Employ real-time communication technology to ensure instant information exchange among team members;

[0027] Design a collaboration interface to display task assignments, progress updates, and communication records.

[0028] Optionally, the project management method further includes:

[0029] Use a front-end framework to build the user interface and set different access permissions based on user roles;

[0030] Users can adjust task priorities or reallocate resources through release operations.

[0031] Secondly, embodiments of this application also provide a project management system, including:

[0032] The data acquisition and preprocessing module is used to collect project-related data and perform preliminary sorting and cleaning of the project-related data to obtain a structured dataset.

[0033] The knowledge graph construction module is used to perform entity annotation and relation extraction processing on the structured dataset using entity recognition and relation extraction algorithms to construct a project-related knowledge graph.

[0034] The task decomposition and optimization module is used to decompose tasks based on the knowledge graph using a graph traversal algorithm, generate a task list and its priority order, and monitor task progress and resource utilization in real time based on a dynamic optimization algorithm to dynamically adjust task priorities and resource allocation strategies.

[0035] Optionally, the project management system further includes:

[0036] The system integration and extension module is used to divide the system into multiple independent services, ensuring modular design, and using an API gateway to manage communication and service discovery between modules.

[0037] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the project management method described above.

[0038] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the project management method described above.

[0039] As can be seen from the above technical solutions, the present invention has the following advantages:

[0040] The project management methods, systems, equipment, and media provided in this application automatically decompose task modules based on knowledge graphs, improving the efficiency and accuracy of task decomposition. Combined with dynamic programming and optimization algorithms, they achieve intelligent resource allocation and task priority adjustment, thereby improving team collaboration efficiency and enhancing the flexibility and adaptability of projects. Attached Figure Description

[0041] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating the implementation of a project management method provided in this embodiment of the invention;

[0043] Figure 2 A schematic diagram illustrating the knowledge graph construction process provided in an embodiment of the present invention;

[0044] Figure 3 A flowchart for project management based on knowledge graphs is provided as an embodiment of the present invention;

[0045] Figure 4A schematic diagram of the structure of a project management system provided in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0047] Various embodiments of this disclosure will be described more fully in the following detailed description. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0048] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions or operations and do not limit the addition of one or more functions or operations. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing, or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing.

[0049] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] See Figure 1 The diagram shows a flowchart of a project management method in a specific embodiment, including the following execution steps:

[0052] Step 100: Collect project-related data, and organize and clean the project-related data to obtain a structured dataset.

[0053] Specifically, when executing step 100, the following steps can be performed:

[0054] S1000: Collect project-related data from multiple sources, including but not limited to project objectives, task descriptions, resource requirements, and team member skills.

[0055] For example, project-related data can be collected from requirements documents, technical specifications, and team member information.

[0056] S1001: Use NLP (Natural Language Processing) technology to extract key information from project-related data, and perform data cleaning to transform unstructured data into structured data, ensuring the consistency and integrity of data format.

[0057] For example, cleaning and standardizing data includes removing noise; for text data, this includes removing punctuation, converting to lowercase, and removing stop words. Natural Language Processing (NLP) techniques are used to extract task modules and dependencies from unstructured text data, and machine learning is used to predict task completion time, resource requirements, and potential risks.

[0058] Step 101: Using entity recognition and relation extraction algorithms, entity annotation and relation extraction are performed on the structured dataset to construct a project-related knowledge graph.

[0059] For details, please refer to Figure 2 As shown, when executing step 101, the following steps can be specifically performed:

[0060] S1010: Based on the pre-determined project entities, filter out the domain terms in the structured dataset and generate a domain terminology table.

[0061] For example, predefined project entities include task modules, resources, and team members.

[0062] S1011: Define the entity label set, relation label set, and annotation constraint rules, and annotate the structured dataset to obtain labeled data.

[0063] For example, a task entity must contain an action word (such as "analyze"), a resource entity must carry a quantitative attribute (quantity / type), and a skill entity must be associated with a member entity. Specifically, this can be represented as a task must be associated with ≥1 resource, a member skill must be associated with a specific member, and the number of resources must be ≥1.

[0064] S1012: Perform entity masking augmentation on the structured dataset and extract entity, attribute, and relation triples from the labeled data to construct a relation pre-training corpus.

[0065] S1013: Construct a dual-branch encoder based on relation pre-training corpus and BERT-base model. The dual-branch encoder includes an entity branch encoder and a relation branch encoder. The entity branch encoder is used to receive token-level input and output entity label distribution. The relation branch encoder is used to receive entity pairs plus context input and output relation classification probability.

[0066] S1014: Input the labeled data into the dual-branch encoder, perform character-level alignment, and generate a token tag mapping table.

[0067] S1015: Based on the Token tag mapping table, string matching and synonym merging are performed, and attribute value verification is used to construct a knowledge graph related to the project.

[0068] For example, for resource entities, cross-validation is performed using quantity units (units / person) and type (tools / human resources) to eliminate ambiguity (such as "3 days" being a non-resource).

[0069] For example, the following graph conflict detection rules are constructed: task duration ≥ sum of subtask durations (±10% fluctuation); total resources ≤ available resources (e.g., servers × 2, inventory must ≥ 2); member skill proficiency ≥ task requirements (mapped through skill ontology library); detected conflicts (confidence ≥ 0.8) are automatically marked as nodes awaiting manual review.

[0070] For example, a visual knowledge graph can be used to display the relationships between task modules, resources, and team member machines, and the knowledge graph can be stored and managed using a graph database such as Neo4j.

[0071] In one specific embodiment, a domain-adaptive BERT model is employed, and knowledge graph construction is optimized through pre-training tasks (such as entity annotation and relation extraction). Specifically, fine-tuning is performed using an annotated dataset containing 1000 project management entities (task modules, resource requirements, and team member skills), achieving a model accuracy of 92%. A graph database (such as Neo4j) is used to store the entities and their relationships. A semantic network is constructed between task modules, resources, and team members. The knowledge graph is then visualized using a visualization tool (such as Gephi) to facilitate user understanding of the relationships between task modules.

[0072] Step 102: Based on the knowledge graph, a graph traversal algorithm is used to decompose the task, generate a task list and its priority order, and based on a dynamic optimization algorithm, the task progress and resource utilization are monitored in real time to dynamically adjust the task priority and resource allocation strategy.

[0073] For details, please refer to Figure 3 As shown, when executing step 102, the following steps can be specifically performed:

[0074] S1020: Generate a directed acyclic graph based on the resource dependency attributes and execution time weights of task nodes, establish a task dependency matrix during the task list generation process, perform conflict detection on task nodes with resource contention, and ensure mutual exclusion access to shared resources by inserting virtual synchronization nodes.

[0075] The task priority is sorted based on the product of the node's out-degree value and the path weighted duration.

[0076] It should be understood that the priority of virtual nodes is dynamically adjusted based on the scarcity coefficient of competing resources.

[0077] In some implementations, critical path analysis uses an improved PERT algorithm, which sets optimistic time to, pessimistic time tp, and probability weight w for task nodes, and calculates the weighted expected time te=(w×to + tp) / (w+1), where the value of w is dynamically adjusted according to the coefficient of variation of historical execution data.

[0078] S1021: Construct a multi-dimensional monitoring vector of task progress deviation, resource utilization entropy value, and node load balancing factor, and trigger priority reassessment when the task progress deviation, resource utilization entropy value, and node load balancing factor all exceed the preset threshold.

[0079] Specifically, the task progress deviation is calculated using the following formula: ΔT = Actual time spent / Estimated time spent. The resource utilization entropy value is calculated using the following formula:

[0080]

[0081] The node load balancing factor is calculated using the following formula: β = 1 - σ(CPU utilization) / μ(CPU utilization).

[0082] It should be understood that the preset thresholds can be set according to specific application scenarios. For example, the task progress deviation threshold can be set to 1.2, the resource utilization entropy threshold can be set to 0.6, and the node load balancing factor can be set to 0.3. When ΔT>1.2, H<0.6 and β>0.3, priority reassessment is triggered.

[0083] S1022: The dynamic resource allocation adjustment strategy includes resource preemption-based reallocation, which preempts the reserved resources of low-priority tasks according to the urgency index for lagging tasks, and records the resource lending relationship for subsequent compensation scheduling.

[0084] The urgency index is determined by the ratio of the square of the remaining workload to the remaining resource elasticity.

[0085] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0086] In one specific implementation, the project management method execution process further includes assigning optimized tasks to corresponding team members and using collaboration tools to track task progress; employing real-time communication technology to ensure instant information transmission among team members; and designing a collaboration interface to display task assignments, progress updates, and communication records.

[0087] For example, collaboration tools such as Jira and real-time communication technologies such as WebSocket ensure instant information delivery between team members, provide discussion or chat functions to facilitate team members to communicate problems and solutions, design intuitive collaboration interfaces to display task assignments, progress updates and communication records, and support team members to directly adjust tasks and submit feedback within the interface.

[0088] In one specific implementation, the project management method execution process further includes using a front-end framework to build a user interface and setting different access permissions according to user roles; supporting users to adjust task priorities or reallocate resources through release operations.

[0089] For example, use a front-end framework (such as React) to build an intuitive user interface. Provide personalization settings, allowing users to customize views according to their needs and set different access permissions based on user roles (project manager, team member, client). Ensure the security and privacy of sensitive information. Support users in adjusting task priorities or reallocating resources via drag-and-drop operations. Provide a one-click report generation function for users to quickly obtain a project status summary.

[0090] In one specific embodiment, system integration and expansion are also included: Microservice architecture design: The system is divided into multiple independent services (such as data acquisition services, knowledge graph construction services, etc.) to ensure modular design. Each service runs independently, facilitating expansion and maintenance. API gateway management: An API gateway is used to manage communication and service discovery between modules. A unified interface standard is provided to simplify the integration process of third-party tools. Third-party tool integration: Commonly used project management tools (such as Jira, Trello) and data analysis tools (such as Tableau) are integrated. This ensures seamless integration between the system and other tools, improving overall work efficiency.

[0091] The technical effects achieved by this application are as follows:

[0092] 1. Intelligent and Efficient Task Decomposition: Knowledge graph technology automatically identifies task modules and their dependencies, significantly reducing manual intervention and improving the efficiency and accuracy of task decomposition. It can quickly extract key task modules from complex requirements documents and technical specifications. It automatically identifies the logical relationships between tasks, avoiding the omission of key nodes. It improves the traceability and explainability of task decomposition.

[0093] 2. Flexibility and Real-Time Optimization of Dynamic Collaboration: Based on real-time data, task priorities and resource allocation are dynamically adjusted to improve team collaboration efficiency. Tasks are intelligently allocated according to team members' workloads and skill sets. Task plans are quickly adjusted when project requirements or resource conditions change. This reduces resource waste and task delays, improving project execution efficiency.

[0094] 3. Cross-domain knowledge integration and utilization: Construct a complete project knowledge system to promote knowledge sharing and collaboration across departments and domains. Integrate multi-source knowledge, including requirements documents, technical specifications, and team member experience. Utilize knowledge graphs to achieve semantic understanding and correlation analysis of knowledge, thereby improving the team's overall cognitive ability regarding complex projects.

[0095] 4. Maximizing Resource Utilization: Resource utilization is improved through dynamic optimization algorithms. Resource allocation is dynamically adjusted based on task priority and resource requirements, avoiding resource idleness or overuse, and ultimately enhancing the overall cost-effectiveness of the project.

[0096] 5. Enhance User Experience and Usability: Improve user experience through a user-friendly interface and interaction design. Provide an intuitive task view and real-time data visualization. Support personalized settings and multi-role permission management. Offer convenient task adjustment and feedback functions.

[0097] like Figure 4 As shown, the following are embodiments of the project management system provided in this disclosure. These embodiments belong to the same inventive concept as the project management methods described above. For details not described in detail in the embodiments of the project management system, please refer to the embodiments of the project management methods described above.

[0098] Project management system, including:

[0099] The data acquisition and preprocessing module is used to collect project-related data and perform preliminary sorting and cleaning of the project-related data to obtain a structured dataset.

[0100] The knowledge graph construction module is used to perform entity annotation and relation extraction processing on the structured dataset using entity recognition and relation extraction algorithms to construct a project-related knowledge graph.

[0101] The task decomposition and optimization module is used to decompose tasks based on the knowledge graph using a graph traversal algorithm, generate a task list and its priority order, and monitor task progress and resource utilization in real time based on a dynamic optimization algorithm to dynamically adjust task priorities and resource allocation strategies.

[0102] Preferably, the project management system further includes: a system integration and extension module, used to divide the system into multiple independent services, ensure modular design, and use an API gateway to manage communication and service discovery between modules.

[0103] In some implementations, the project management system further includes a collaboration and communication module, providing a collaboration platform for team members and supporting task assignment, progress tracking, and communication. Key technologies include: real-time communication technologies (such as WebSocket) to ensure instant information delivery; and integration with collaboration tools (such as Jira and Trello) to improve work efficiency. Inputs include: task assignment information and team member feedback. Outputs include: a collaboration interface and communication records.

[0104] In some implementations, the project management system further includes a real-time monitoring and feedback module to track project progress in real time and provide data visualization and risk warning functions. Key technologies: Data visualization tools (such as Tableau and Power BI) to display project status. Predictive analytics algorithms (such as machine learning models) for risk identification. Input: Real-time project data. Output: Project progress reports and risk warning information.

[0105] In some implementations, the project management system further includes a user interface and interaction module, providing a user-friendly interface for users to view project status, adjust task assignments, and obtain feedback. Key technologies: Front-end frameworks (such as React and Vue.js) build an intuitive user interface. Back-end services (such as RESTful APIs) support data interaction. Input: User operation commands. Output: Dynamically updated project views and interactive feedback.

[0106] This system automatically identifies task modules and their dependencies using knowledge graph technology, significantly reducing manual intervention and improving the efficiency and accuracy of task decomposition. It dynamically adjusts task priorities and resource allocation based on real-time data, enhancing team collaboration efficiency. Through an intelligent collaboration platform and real-time communication tools, it significantly improves team collaboration efficiency.

[0107] Figure 5 This is a schematic diagram of the hardware structure of an electronic device that implements various embodiments of the present invention.

[0108] The project management method provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0109] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0110] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0111] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0112] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0113] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0114] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0115] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0116] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0117] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0118] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0119] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0120] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0121] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0122] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0123] The storage medium provided in this application stores a program product capable of implementing project management methods.

[0124] The project management method includes: collecting project-related data, organizing and cleaning the data to obtain a structured dataset; using entity recognition and relation extraction algorithms to perform entity annotation and relation extraction processing on the structured dataset to construct a project-related knowledge graph; based on the knowledge graph, using a graph traversal algorithm to decompose tasks, generating a task list and its priority order, and using a dynamic optimization algorithm to monitor task progress and resource utilization in real time to dynamically adjust task priorities and resource allocation strategies.

[0125] In some possible implementations, the subject matter of this disclosure, project management method and system, can be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0126] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0127] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A project management method, characterized in that, include: Step 100: Collect project-related data, and organize and clean the project-related data to obtain a structured dataset; wherein, the project-related data includes, but is not limited to, project objectives, task descriptions, resource requirements, and team member skills; Step 101: Using entity recognition and relation extraction algorithms, perform entity annotation and relation extraction processing on the structured dataset to construct a project-related knowledge graph; wherein, project entities include task modules, resources, and team members; Step 101 includes: Based on pre-determined project entities, domain terms are filtered out from the structured dataset and a domain terminology table is generated; Define entity label sets, relation label sets, and annotation constraint rules, and annotate the structured dataset to obtain labeled data; Entity masking augmentation is performed on the structured dataset, and entity, attribute, and relation triples are extracted from the labeled data to construct a relation pre-training corpus; A dual-branch encoder is constructed based on relation pre-training corpus and BERT-base model. The dual-branch encoder includes an entity branch encoder and a relation branch encoder. The entity branch encoder is used to receive token-level input and output entity label distribution. The relation branch encoder is used to receive entity pair plus context input and output relation classification probability. The labeled data is input into the dual-branch encoder, character-level alignment is performed, and a token tag mapping table is generated; Based on the Token tag mapping table, string matching and synonym merging are performed, and attribute value verification is used to construct a knowledge graph related to the project; Step 102: Based on the knowledge graph, a graph traversal algorithm is used to decompose the task, generate a task list and its priority order, and based on a dynamic optimization algorithm, the task progress and resource utilization are monitored in real time to dynamically adjust the task priority and resource allocation strategy. Step 102 includes: generating a directed acyclic graph based on the resource dependency attributes and execution time weights of task nodes, establishing a task dependency matrix during the task list generation process, performing conflict detection on task nodes with resource contention, and ensuring mutual exclusion access to shared resources by inserting virtual synchronization nodes; wherein, task priority is sorted based on the product of the out-degree value of the task node and the path weighted duration, and the priority of the virtual synchronization node is dynamically adjusted according to the scarcity coefficient of the contention resource; Construct a multi-dimensional monitoring vector of task progress deviation, resource utilization entropy value, and node load balancing factor, and trigger task priority reassessment when task progress deviation, resource utilization entropy value, and node load balancing factor all exceed preset thresholds. The dynamic resource allocation adjustment strategy includes resource preemption-based reallocation, which preempts reserved resources for low-priority tasks based on an urgency index for delayed tasks, and records resource lending relationships for subsequent compensation scheduling. The urgency index is determined by the ratio of the square of the remaining workload to the remaining resource elasticity. Calculate the task progress deviation using the following formula: ΔT = Actual time spent / Estimated time spent; Calculate the resource utilization entropy value using the following formula: ； The node load balancing factor is calculated using the following formula: β = 1 - σ(CPU utilization) / μ(CPU utilization).

2. The project management method according to claim 1, characterized in that, Step 100 includes: Collect project-related data from multiple sources; Natural Language Processing (NLP) technology is used to extract key information from project-related data and perform data cleaning to transform unstructured data into structured data, ensuring the consistency and integrity of the data format.

3. The project management method according to claim 1, characterized in that, The project management methods also include: Assign the optimized tasks to the appropriate team members and use collaboration tools to track task progress. Employ real-time communication technology to ensure instant information exchange among team members; Design a collaboration interface to display task assignments, progress updates, and communication records.

4. The project management method according to claim 1, characterized in that, The project management methods also include: Use a front-end framework to build the user interface and set different access permissions based on user roles; Users can adjust task priorities or reallocate resources through release operations.

5. A project management system, characterized in that, The system is used to implement the project management method as described in any one of claims 1 to 4; The system includes: The data acquisition and preprocessing module is used to collect project-related data and perform preliminary sorting and cleaning of the project-related data to obtain a structured dataset. The knowledge graph construction module is used to perform entity annotation and relation extraction processing on the structured dataset using entity recognition and relation extraction algorithms to construct a project-related knowledge graph. The task decomposition and optimization module is used to decompose tasks based on the knowledge graph using a graph traversal algorithm, generate a task list and its priority order, and monitor task progress and resource utilization in real time based on a dynamic optimization algorithm to dynamically adjust task priorities and resource allocation strategies.

6. The project management system according to claim 5, characterized in that, The project management system also includes: The system integration and extension module is used to divide the system into multiple independent services, ensuring modular design, and using an API gateway to manage communication and service discovery between modules.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the project management method as described in any one of claims 1 to 4.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the project management method as described in any one of claims 1 to 4.

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