A task progress determination method and device, electronic equipment and storage medium
By constructing a subtask dataset and dependency graph, detecting contradictions, and generating professional descriptions of parent task progress, the problems of low efficiency and inaccurate descriptions in existing technologies are solved, achieving efficient and accurate statistics on parent task progress.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA MOBILE (XIONGAN) ICT CO LTD
- Filing Date
- 2026-06-10
- Publication Date
- 2026-07-21
AI Technical Summary
In existing technologies, summarizing the progress of parent tasks relies on manual methods, which is inefficient and easily affected by subjective factors, leading to inaccurate descriptions or omissions of sub-task progress, and failing to fully and objectively reflect the actual situation.
By identifying the subtask datasets for each subtask, constructing a dependency graph, detecting contradiction analysis results, constructing the parent task progress based on weight coefficients, and using a BERT model and a bidirectional long short-term memory network-conditional random field joint model for text analysis, combined with project management knowledge system, a professional description of the parent task progress is generated.
It achieves a comprehensive and accurate description of the progress of the parent task, improves statistical efficiency, reduces manual intervention, and ensures the objectivity and comprehensiveness of the description.
Smart Images
Figure CN122434461A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mission progress statistics technology, and in particular to a method, apparatus, electronic device and storage medium for determining mission progress. Background Technology
[0002] In project management, a parent task is usually divided into multiple sub-tasks, which are handled by different users who also fill in the task progress descriptions.
[0003] Currently, the summarization of parent task progress descriptions mainly relies on manual methods. Managers collect progress descriptions from each subtask and then manually select, organize, and write the parent task progress description. However, this manual summarization method has several drawbacks: firstly, when there are many subtasks, manual summarization requires a significant amount of time and effort, resulting in low efficiency; secondly, manual summarization is susceptible to subjective factors, potentially leading to omissions of subtask progress, inaccurate descriptions, or redundancy, causing the parent task progress description to fail to comprehensively and objectively reflect the actual situation. Summary of the Invention
[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining task progress, in order to solve the problems of low efficiency and potential omission of sub-task progress in existing summarization methods.
[0005] According to one aspect of the present invention, a method for determining task progress is provided, the method comprising: Determine the subtask dataset for each subtask; subtasks are obtained by splitting the parent task; the subtask dataset includes a unique identifier, importance description, task progress, task progress description, and risk points; Based on the subtask datasets of each subtask, determine the dependency graph; Based on the dependency graph, pre-defined contradictions are detected to obtain contradiction analysis results; The weight coefficients of each subtask are determined based on the importance description of each subtask, the data completeness of the subtask dataset, the timeliness of submission, and the historical accuracy. Based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the results of the contradiction analysis, the task progress of the parent task is constructed.
[0006] According to another aspect of the present invention, a task progress determination apparatus is provided, the apparatus comprising: The data determination module is used to determine the subtask datasets for each subtask; subtasks are obtained by splitting the parent task; the subtask datasets include a unique identifier, an importance description, task progress, task progress description, and risk points; The graph construction module is used to determine the dependency graph based on the subtask datasets of each subtask; The contradiction analysis module is used to detect preset contradictions based on the dependency graph and obtain contradiction analysis results; The weight determination module is used to determine the weight coefficient of each subtask based on the importance description of each subtask, the data completeness of the subtask dataset, the timeliness of submission, and the historical accuracy. The overall progress determination module is used to construct the task progress of the parent task based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the conflict analysis results.
[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the task progress determination method according to any embodiment of the present invention.
[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the task progress determination method of any embodiment of the present invention.
[0009] The technical solution of this invention involves determining the subtask datasets for each subtask; subtasks are obtained by splitting the parent task; the subtask datasets include unique identifiers, importance descriptions, task progress, task progress descriptions, and risk points; based on the subtask datasets of each subtask, a dependency graph is determined; based on the dependency graph, preset contradictions are detected to obtain contradiction analysis results; based on the importance descriptions of each subtask, the data completeness of the subtask datasets, the timeliness of submission, and the historical accuracy, the weight coefficients of each subtask are determined; based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the contradiction analysis results, the task progress of the parent task is constructed, which can achieve a comprehensive description of the progress of the parent task while ensuring statistical efficiency.
[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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.
[0012] Figure 1 This is a flowchart of a task progress determination method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of another task progress determination method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a task progress determination device according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the task progress determination method of the present invention. Detailed Implementation
[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0015] Example 1 Figure 1This is a flowchart of a task progress determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the progress of a parent task is determined. This method can be executed by a task progress determination device, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes: S110. Determine the subtask dataset for each subtask; subtasks are obtained by splitting the parent task; the subtask dataset includes a unique identifier, importance description, task progress, task progress description, and risk points.
[0016] In the existing project management process, a complete project may serve as one or more parent tasks, and each parent task may be further divided into multiple sub-tasks for processing. The progress of the parent task is ultimately determined by the progress of each sub-task.
[0017] However, when there are many subtasks, problems such as long processing times and missing data can easily occur. It is also difficult to accurately determine the progress of the parent task.
[0018] To address this, we first obtain the subtask datasets for each subtask.
[0019] For subtask datasets, the unit responsible for the subtask reports independently, including a unique identifier, an importance description, task progress, a description of task progress, and risk points.
[0020] After collecting the subtask datasets of different subtasks, they are automatically stored in the database, resulting in Table 1.
[0021] Table 1 S120. Based on the subtask datasets of each subtask, determine the dependency graph.
[0022] A dependency graph can be a directed network graph that uses tasks / activities as nodes and represents the sequential logic and constraints between tasks with directed lines.
[0023] Based on the BERT pre-trained model, the text describing the progress of subtasks is segmented and labeled with parts of speech to identify entity information in the text, such as task name, achievement name, problem type, etc., and the completion status of each subtask is analyzed, such as "XX achievement completed" or "XX problem exists".
[0024] A BERT-based model is pre-trained on a general project management corpus, focusing on learning progress expression patterns ("XX% complete", "Entered XX stage"); risk expression patterns ("XX risks exist", "May affect XX"); and outcome expression patterns ("Achieved XX function", "Achieved XX indicator"). A progress dictionary is constructed, containing progress expressions and their corresponding percentage ranges.
[0025] See Table 2: Table 2 in Let i be the progress value corresponding to the i-th expression. Its confidence level.
[0026] The final progress value is calculated using a probabilistic fusion algorithm. ): .
[0027] Design a risk classifier to output a 5-level risk score, as shown in Table 3: Table 3 Identify key entities and scope of impact in risk descriptions through attention mechanisms.
[0028] The system checks the degree of match between the final progress value and the task progress; it also examines the logical relationship between the "risk point" description and the actual progress to determine the accuracy of the subtask dataset. If the accuracy is below the threshold, the subtask dataset must be re-uploaded.
[0029] The process involves identifying and defining the subtasks, dependencies, and time constraints; simultaneously, modeling the temporal relationships among the subtasks to capture the temporal relationships of entities within the text, thus constructing a dependency graph.
[0030] Optionally, based on the subtask datasets of each subtask, a dependency graph is determined, including: A bidirectional long short-term memory network-conditional random field joint model is used to determine the subtasks in the subtask dataset and the dependencies between the subtasks; Based on the Heidelberg time extraction algorithm, the time constraints of each subtask are determined. Dependency graphs are determined based on subtasks in the subtask dataset, the dependencies between subtasks, and time constraints.
[0031] A Bidirectional Long Short-Term Memory Network-Conditional Random Field Joint Model (BiLSTM-CRF) is used to determine the subtasks in the subtask dataset and the dependencies between them. A time expression parser (based on the Heidelberg time decimation algorithm) is used to determine the time constraints of each subtask, and a dependency graph is determined based on the subtasks in the subtask dataset, the dependencies between them, and the time constraints.
[0032] S130. Based on the dependency graph, the preset contradictions are detected to obtain the contradiction analysis results.
[0033] After obtaining the dependency graph, we can determine whether a pre-defined conflict exists based on it. For example, if it is clear that task B can proceed after task A is completed, and if task A is not yet completed but task B already has progress, then a conflict exists. Pre-defined conflicts can be pre-set conflicts that need to be checked.
[0034] Optionally, based on the dependency graph, predefined contradictions are detected to obtain contradiction analysis results, including: The planned schedule curve for a subtask is found to have a time discrepancy with the actual schedule curve. Based on the dependency graph, identify the logical conflicts between the various subtasks; Based on the temporal contradictions and logical conflicts, the results of the contradiction analysis were determined.
[0035] Pre-set conflicts include time discrepancies between the planned and actual progress curves, as well as logical conflicts between various sub-tasks.
[0036] To address the time discrepancy between the planned and actual progress curves, the degree of deviation between the planned and actual progress curves is determined as the time discrepancy.
[0037] If the degree of deviation is less than the first preset degree, it is considered a slight deviation; if the degree of deviation is greater than the first preset degree but less than the second preset degree, it is considered a significant deviation; if the degree of deviation is greater than the second preset degree, it is considered a serious deviation.
[0038] Different corresponding decisions can be set for different situations.
[0039] In addition, based on the dependency graph, it can be determined whether there is a progress conflict between the subtasks. For example, if it is clear that task B can be carried out after task A is completed, if A is not completed but B already has progress, it can be determined that there is a conflict.
[0040] Optionally, after detecting preset contradictions based on the dependency graph and obtaining the contradiction analysis results, the following steps are also included: If there is a preset contradiction, determine whether to delete the subtask according to the importance description of the subtask with the preset contradiction.
[0041] Optionally, determining whether to delete the subtask includes: If the importance description of the subtask is high importance, keep the subtask; If the importance description of the subtask is low importance, delete the subtask.
[0042] When the importance description of the subtask is high importance, it means that the subtask cannot be deleted, or the subtask may be associated with many other subtasks. In this case, the subtask can be kept.
[0043] Correspondingly, if the importance description of the subtask is low importance, the subtask can be directly deleted.
[0044] In the case of a preset contradiction, mark the subtask with a more prominent color. Also mark the influence path of the subtask.
[0045] S140. Determine the weight coefficients of each subtask based on the importance description of each subtask, the data integrity of the subtask dataset, the submission timeliness, and the historical accuracy.
[0046] According to the importance description of each subtask, the data integrity of the subtask dataset, the submission timeliness, and the historical accuracy in four dimensions, the weight coefficients of different subtasks can be dynamically adjusted.
[0047] Among them, the calculation process of data integrity is: C = 0.6×(field completeness rate) + 0.4×(semantic richness); Field completeness rate = number of actually filled fields / number of required fields; Semantic richness = 1 - (proportion of stop words)×0.5 + (number of entities / total number of words)×0.5.
[0048] Among them, stop words are words that contribute very little to semantics and only play a grammatical auxiliary role, and will be filtered out in the preprocessing stage. For example: de, le, zhe, di, de, guo, ma, ne, ba, a, ya, wa, la, ba, er yi, etc.
[0049] Calculation of historical accuracy (H): H = 1 / (1 + MAPE); Among them, MAPE is the mean absolute percentage of historical progress prediction error.
[0050] Optionally, after constructing the task progress of the parent task, it further includes: Determine the accuracy of the weight coefficients of each subtask; If the accuracy is less than the preset accuracy, the weight coefficients of each subtask will be readjusted.
[0051] During the initial build, default weight coefficients can be assigned. After the parent task is built, the accuracy of the weight coefficients of each subtask is judged. If the accuracy is less than the preset accuracy, the weight coefficients of each subtask are readjusted.
[0052] S150. Based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the results of the contradiction analysis, construct the task progress of the parent task.
[0053] After determining the weight coefficients of each subtask, the subtask datasets of each subtask, and the results of the contradiction analysis, the weight coefficients of each subtask, the subtask datasets of each subtask, and the results of the contradiction analysis are used to construct the task progress of the parent task, thereby determining the task progress of the parent task.
[0054] The technical solution of this application involves determining the subtask datasets for each subtask; subtasks are obtained by splitting the parent task; the subtask datasets include unique identifiers, importance descriptions, task progress, task progress descriptions, and risk points; based on the subtask datasets of each subtask, a dependency graph is determined; based on the dependency graph, preset contradictions are detected to obtain contradiction analysis results; based on the importance descriptions of each subtask, the data completeness of the subtask datasets, the timeliness of submission, and the historical accuracy, the weight coefficients of each subtask are determined; based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the contradiction analysis results, the task progress of the parent task is constructed, which can achieve a comprehensive description of the progress of the parent task while ensuring statistical efficiency.
[0055] Example 2 Figure 2 This invention provides a flowchart of another method for determining task progress. This embodiment further optimizes the process after constructing the task progress of the parent task in the aforementioned embodiments, based on the above embodiments. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the task progress determination method in this embodiment may include the following steps: S210. Determine the subtask dataset for each subtask; subtasks are obtained by splitting the parent task; the subtask dataset includes a unique identifier, importance description, task progress, task progress description, and risk points.
[0056] S220. Based on the subtask datasets of each subtask, determine the dependency graph.
[0057] S230. Based on the dependency graph, the preset contradictions are detected to obtain the contradiction analysis results.
[0058] S240. Based on the importance description of each subtask, the data completeness of the subtask dataset, the timeliness of submission, and the historical accuracy, determine the weight coefficient of each subtask.
[0059] S250. Based on the importance description and task progress of each subtask, determine the display order of each subtask.
[0060] S260. Based on the weight coefficients of each subtask, determine the contribution of each subtask to the progress of the parent task.
[0061] S270. Based on the display order, the contribution of each subtask, the results of the conflict analysis, and the subtask dataset are combined with the terminology library of the Project Management Body of Knowledge (BOKR) to generate the task progress of the parent task.
[0062] The progress of different subtasks is rearranged according to their importance and task priority. Combined with the Project Management Body of Knowledge (PMBOK) terminology database, it automatically generates professional parent task progress descriptions or reports that conform to project management standards.
[0063] By adopting the technical solution of this application, the display order of each subtask is determined based on the importance description and task progress of each subtask; the contribution of each subtask to the progress of the parent task is determined based on the weight coefficient of each subtask; and based on the display order, the contribution of each subtask, the result of the contradiction analysis, and the subtask dataset are combined with the terminology library of the project management knowledge system guide to generate the task progress of the parent task, thereby achieving a more professional and standardized description or report of the progress of the parent task.
[0064] Example 3 Figure 3 This invention provides a structural block diagram of a task progress determination device, applicable to situations where the progress of a parent task is determined. This task progress determination device can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 3 As shown, the task progress determination device of this embodiment may include: a data determination module 310, a map construction module 320, a contradiction analysis module 330, a weight determination module 340, and a total progress determination module 350. Wherein: The data determination module 310 is used to determine the subtask dataset for each subtask; the subtasks are obtained by splitting the parent task; the subtask dataset includes a unique identifier, an importance description, task progress, task progress description, and risk points; The graph construction module 320 is used to determine the dependency graph based on the subtask datasets of each subtask. The contradiction analysis module 330 is used to detect preset contradictions based on the dependency graph and obtain contradiction analysis results; The weight determination module 340 is used to determine the weight coefficient of each subtask based on the importance description of each subtask, the data completeness of the subtask dataset, the timeliness of submission, and the historical accuracy. The overall progress determination module 350 is used to construct the task progress of the parent task based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the conflict analysis results.
[0065] Based on the above embodiments, optionally, a dependency graph is determined based on the subtask datasets of each subtask, including: A bidirectional long short-term memory network-conditional random field joint model is used to determine the subtasks in the subtask dataset and the dependencies between the subtasks; Based on the Heidelberg time extraction algorithm, the time constraints of each subtask are determined. Dependency graphs are determined based on subtasks in the subtask dataset, the dependencies between subtasks, and time constraints.
[0066] Based on the above embodiments, optionally, based on the dependency graph, preset contradictions are detected to obtain contradiction analysis results, including: The planned schedule curve for a subtask is found to have a time discrepancy with the actual schedule curve. Based on the dependency graph, identify the logical conflicts between the various subtasks; Based on the temporal contradictions and logical conflicts, the results of the contradiction analysis were determined.
[0067] Based on the above embodiments, optionally, after detecting preset contradictions based on the dependency graph and obtaining the contradiction analysis results, the method further includes: If a pre-defined conflict exists, determine whether to delete the subtask based on the importance description of the subtask with the pre-defined conflict.
[0068] Based on the above embodiments, optionally, determining whether to delete the subtask includes: If the importance of a subtask is described as high, then the subtask is retained. If the importance of a subtask is described as low, then the subtask is deleted.
[0069] Based on the above embodiments, optionally, after constructing the task progress of the parent task, the following steps are also included: Determine the accuracy of the weight coefficients for each subtask; If the accuracy is less than the preset accuracy, the weight coefficients of each subtask will be readjusted.
[0070] Based on the above embodiments, optionally, the task progress of the parent task can be constructed based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the conflict analysis results, including: The display order of each subtask is determined based on its importance description and progress. Based on the weight coefficients of each subtask, determine the contribution of each subtask to the progress of the parent task. Based on the display order, the contribution of each subtask, the results of the conflict analysis, and the subtask datasets are combined with the terminology library of the Project Management Body of Knowledge (PMBOK) guide to generate the task progress of the parent task.
[0071] The task progress determination device provided in the embodiments of the present invention can execute the task progress determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0072] Example 4 Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0073] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0074] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0075] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the task progress determination method.
[0076] In some embodiments, the task progress determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the task progress determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the task progress determination method by any other suitable means (e.g., by means of firmware).
[0077] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0078] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0079] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0081] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0082] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0083] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0084] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for determining task progress, characterized in that, include: Determine the subtask dataset for each subtask; The subtasks are obtained by splitting the parent task; The subtask dataset includes a unique identifier, an importance description, a task progress, a task progress description, and risk points; Based on the subtask datasets of each subtask, determine the dependency graph; Based on the dependency graph, preset contradictions are detected to obtain contradiction analysis results; The weight coefficients of each subtask are determined based on the importance description of each subtask, the data completeness of the subtask dataset, the timeliness of submission, and the historical accuracy. Based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the results of the contradiction analysis, the task progress of the parent task is constructed.
2. The method according to claim 1, characterized in that, Based on the subtask datasets of each subtask, a dependency graph is determined, including: A bidirectional long short-term memory network-conditional random field joint model is used to determine the subtasks in the subtask dataset and the dependencies between the subtasks; Based on the Heidelberg time extraction algorithm, the time constraints of each subtask are determined. Dependency graphs are determined based on subtasks in the subtask dataset, the dependencies between subtasks, and time constraints.
3. The method according to claim 2, characterized in that, Based on the dependency graph, preset contradictions are detected to obtain contradiction analysis results, including: The planned schedule curve for a subtask is found to have a time discrepancy with the actual schedule curve. Based on the dependency graph, the logical conflicts between the subtasks are determined; Based on the aforementioned temporal contradiction and logical conflict, the result of the contradiction analysis is determined.
4. The method according to claim 3, characterized in that, After detecting preset contradictions based on the dependency graph and obtaining the contradiction analysis results, the process further includes: If a pre-defined conflict exists, determine whether to delete the subtask based on the importance description of the subtask with the pre-defined conflict.
5. The method according to claim 4, characterized in that, Determine whether to delete the subtask, including: If the importance of a subtask is described as high, then the subtask is retained. If the importance of a subtask is described as low, then the subtask is deleted.
6. The method according to claim 1, characterized in that, After constructing the task progress of the parent task, it also includes: Determine the accuracy of the weight coefficients for each subtask; If the accuracy is less than the preset accuracy, the weight coefficients of each subtask will be readjusted.
7. The method according to claim 1, characterized in that, Based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the results of the contradiction analysis, the task progress of the parent task is constructed, including: The display order of each subtask is determined based on its importance description and progress. Based on the weight coefficients of each subtask, determine the contribution of each subtask to the progress of the parent task. Based on the display order, the contribution of each subtask, the results of the conflict analysis, and the subtask datasets are combined with the terminology library of the Project Management Body of Knowledge (PMBOK) guide to generate the task progress of the parent task.
8. A task progress determination device, characterized in that, include: The data determination module is used to determine the subtask datasets for each subtask. The subtasks are obtained by splitting the parent task; The subtask dataset includes a unique identifier, an importance description, a task progress, a task progress description, and risk points; The graph construction module is used to determine the dependency graph based on the subtask datasets of each subtask; The contradiction analysis module is used to detect preset contradictions based on the dependency graph and obtain contradiction analysis results; The weight determination module is used to determine the weight coefficient of each subtask based on the importance description of each subtask, the data completeness of the subtask dataset, the timeliness of submission, and the historical accuracy. The overall progress determination module is used to construct the task progress of the parent task based on the weight coefficients of each subtask, the subtask datasets of each subtask, and the conflict analysis results.
9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the task progress determination method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the task progress determination method according to any one of claims 1-7.