Project state management method, system and equipment based on time axis and medium

Abstract

The invention is suitable for the technical field of project management, and provides a project state management method, system, device and medium based on a time axis, and the method comprises the steps: carrying out the unified conversion of the demand state definitions of different teams through employing a state standardization algorithm, and obtaining a demand state data set; based on the demand state data set, constructing a multi-dimensional demand circulation sequence data table, and obtaining demand circulation state parameters; establishing a state transition probability matrix, and identifying an abnormal stagnation demand based on the multi-dimensional demand circulation sequence data table; constructing a demand state time axis with a hierarchical structure according to the demand priority and the stagnation probability; according to the method, the spatial index structure is established in the demand state time axis, the state change event is screened according to the timestamp through the spatial index structure, and the demand query result set is obtained, so that the project management efficiency is improved, the delay risk is reduced, and data support is provided for decision making.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of project management, and particularly relates to a project state management method, system, device and medium based on a time axis. BACKGROUND

[0002] Project requirement management in a software development project is a key link to ensure successful delivery of the project, and real-time monitoring and visual display of the project requirement state directly affect project progress control and resource allocation efficiency. With the continuous expansion of the software project scale and the popularity of the distributed collaboration mode of the development team, the traditional requirement state management method has been difficult to meet the management needs of the complex project environment.

[0003] The existing project requirement state monitoring scheme mainly has problems such as single visual level, static state change display, and non-uniform state standards across teams. The existing system can only provide single-dimensional state display, lacks multi-level perspectives from the global to the details, and is prone to information overload when processing large-scale requirement data, affecting the accuracy and timeliness of management decisions. In a complex multi-team collaboration environment, the time axis visualization of the project state change faces significant technical challenges, such as different teams using their own state definitions and process standards, leading to state mapping and equivalent comparison as technical difficulties. This lack of standardization further exacerbates the complexity of fine processing in the state transition process. In addition, when requirements frequently flow between different states, the system needs to capture changes in real time and dynamically reconstruct, but the traditional scheme often lacks sufficient granularity when processing the intermediate links of state transition, and cannot accurately reflect the real situation of requirement flow. SUMMARY

[0004] The embodiments of the present application provide a project state management method, system, device and medium based on a time axis, which can solve one of the above technical problems.

[0005] In a first aspect, the embodiments of the present application provide a project state management method based on a time axis, comprising: uniformly converting the requirement state definitions of different teams by using a state standardization algorithm to obtain a requirement state data set; based on the requirement state data set, constructing a multi-dimensional requirement flow sequence data table to obtain requirement flow state parameters; establishing a state transition probability matrix and identifying abnormal stagnant requirements based on the multi-dimensional requirement flow sequence data table; constructing a requirement state time axis with a hierarchical structure according to the requirement priority and the stagnation probability; establishing a spatial index structure in the requirement state time axis, and filtering state change events according to the timestamp through the spatial index structure to obtain a requirement query result set.

[0006] Further, the adoption of state standardization algorithm for each team demand state definition uniform conversion, including: Obtain the original demand data of each team, extract the complete state change history of each demand from the original demand data; Based on the complete state change history, all demand state names are summarized, and additional information is associated with each demand state name to generate an original state set; In the original state set, based on the predefined standardized mapping table, through semantic analysis, each demand state name is mapped to obtain a state semantic mapping table.

[0007] Further, the predefined standardized mapping table specifically includes: Define standard state life cycle based on business specification, the standard state life cycle includes standardized states of each demand; Split the sub-state of each standardized state and set the backflow rule to generate a state transition matrix.

[0008] Further, each element of the state transition probability matrix represents the probability of a state transition to another state, and the diagonal element of the state transition matrix represents the probability of the state being in a stagnant state.

[0009] Further, based on the demand state data set, a multi-dimensional demand flow sequence data table is constructed to obtain demand flow state parameters, including: From the demand state data set, obtain the additional information of different demands in the project, classify the additional information by dimension to obtain a dimension matrix; Divide each demand of the project into multiple time buckets according to the same time granularity; Iterate through all demands to obtain the state of each demand in the preset time bucket, and construct a dimension combination state based on the dimension matrix; According to the time sequence, obtain the historical flow state of each demand, extract the state transition event, record the state, timestamp and duration before and after the demand state transition; Aggregate the dimension combination state and the state transition event according to the time bucket to generate a multi-dimensional demand flow sequence data table; Calculate the dimension state distribution characteristics, flow efficiency characteristics and bottleneck detection characteristics in the multi-dimensional demand flow sequence data table.

[0010] Further, the demand state time axis with hierarchical structure is constructed according to the demand priority and the probability of stagnation, including: From the state transition probability matrix, the probability value of each demand transition between each state is obtained, and the stagnation probability of each demand is calculated; Based on the stagnation probability, and in combination with the corresponding demand priority, a comprehensive weight value is calculated; Based on the comprehensive weight value, the hierarchical ranking order of each demand on the demand state time axis is determined; Based on the hierarchical ranking order, each demand is allocated to the corresponding position on the demand state time axis according to the timestamp.

[0011] Further, the space index structure is established in the demand state time axis, and through the space index structure, the state change event is filtered according to the timestamp to obtain a demand query result set, including: Based on the state transition event of the demand, the mapping space coordinates of the standardized state in the demand state time axis are generated; Based on the mapping space coordinates, a space index structure is constructed; In the space index structure, the mapping space coordinates of the standardized state on the demand state axis and the corresponding state change event in a preset time range are queried to generate a demand query result set, and the demand query result set is used for visual analysis and processing of each dimension data in the demand.

[0012] In a second aspect, the embodiments of the present application provide a project state management system based on a time axis, including: A first processing module: configured to uniformly convert the demand state definitions of different teams by using a state standardization algorithm to obtain a demand state data set; A second processing module: configured to construct a multi-dimensional demand flow sequence data table based on the demand state data set to obtain demand flow state parameters; A third processing module: configured to establish a state transition probability matrix and identify abnormal stagnation demands based on the multi-dimensional demand flow sequence data table; A fourth processing module: configured to construct a demand state time axis with a hierarchical structure according to the demand priority and the stagnation probability; A fifth processing module: configured to establish a space index structure in the demand state time axis, and through the space index structure, the state change event is filtered according to the timestamp to obtain a demand query result set.

[0013] In a third aspect, the embodiments of the present application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the project state management method based on the time axis when executing the computer program.

[0014] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which comprises a computer program stored in the computer readable storage medium, and the computer program is executed by a processor to implement the project state management method based on a timeline.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects: The application discloses a project state management method based on a timeline. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description.

[0017] Figure 1 is a flow diagram of a project state management method based on a timeline provided by an embodiment of the present application; Figure 2 is a structural diagram of a project state management system based on a timeline provided by an embodiment of the present application; Figure 3 is a structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary details.

[0019] It should be understood that the word “comprise” or variations such as “comprises” or “comprising”, when used in this specification and in the accompanying claims, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0020] It should also be understood that the term “and / or” when used in this specification and in the following claims is intended to mean one or the other or both of the associated listed items and that no combinations of one or more items included in the processes, methods, systems or computer readable media are intended to be excluded.

[0021] As used in this specification and in the claims, the terms “if’ and “when” can be interpreted to mean “upon” or “in response to a determination” or “in response to a detection” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted to mean “upon a determination” or “in response to a determination” or “upon detecting [a described condition or event]” or “in response to detecting [a described condition or event]”, depending on the context.

[0022] In addition, the terms “first”, “second”, “third”, etc. are used in the description and in the following claims merely to distinguish one element from another, and are not necessarily intended to imply relative importance.

[0023] Reference in the specification to “one embodiment” or “some embodiments” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase “in one embodiment” or “in some embodiments” or “in other embodiments” or “in still other embodiments” or other similar phrases in the specification are not necessarily all referring to the same embodiment, but are intended to convey that the particular feature, structure, or characteristic described is included in at least one embodiment of the application. The appearances of the phrases “in one embodiment” or “in some embodiments” or “in other embodiments” or “in still other embodiments” or other similar phrases in the specification are not necessarily all referring to the same embodiment, but are intended to convey that the particular feature, structure, or characteristic described is included in at least one embodiment of the application. The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise.

[0024] Reference is made to Figure 1 As shown in the drawings, the application is a time axis-based project status management method, comprising the following steps: S100, uniformly converting the demand state definitions of different teams by using a state standardization algorithm to obtain a demand state data set; The application uniformly converts the demand state definitions of different teams by using a state standardization algorithm, so that the demand states of different teams for different demands in the same project can be uniformly named, and the harmony degree between different teams can be improved.

[0025] In some embodiments, the adoption of the state standardization algorithm includes: Obtaining original requirement data of each team, extracting the complete state change history of each requirement from the original requirement data; Based on the complete state change history, all requirement state names are summarized and additional information is associated with each requirement state name to generate an original state set; In the original state set, based on the predefined standardized mapping table, the requirement state names are mapped through semantic analysis to obtain a state semantic mapping table.

[0026] In this embodiment, the original requirement data of different teams in the project management process is obtained from various tools for managing project cycles such as Jira, Azure DevOps, Trello, etc. Further, the complete state change history of each project requirement is extracted from the original requirement data, including requirement ID, state name, state start time, state end time, and changer information.

[0027] In this embodiment, all state names used by the team are obtained from the complete state change history to form an original state set, such as ["in development", "in progress", "to be tested", "blocked"], and the information carried by each state name is associated and synchronized to the original state set.

[0028] Further, since different teams usually have different naming names for the same state, different state names are aggregated into multiple state groups through semantic analysis, and states with the same semantics are merged into unified standard states. Specifically, based on the predefined standardized mapping table, the number of aggregated state groups is determined, then the BERT model is used for vectorization processing of each state name to obtain a state name vector, and DBSCAN or K-Means aggregation algorithm is used for clustering analysis of each state name vector to obtain multiple state groups, for example, {"Dev", "in development", "Coding"} are all mapped to "in development", and finally a state semantic mapping table is generated.

[0029] In some embodiments, the vectorization processing process and the aggregation processing process are integrated, and a weighted average method is used to calculate the confidence of each state name mapping in the state semantic mapping table to improve the credibility of the state name standardization process. The specific calculation formula is: confidence = α*(1-average vector distance) + β*(cluster size / maximum cluster size), where α and β represent weight coefficients, which are adjusted according to actual conditions. The average vector distance is the average distance between state name vectors in the same cluster, and the cluster size represents the number of state names in the current cluster. The maximum cluster size represents the maximum number of state names in all clusters.

[0030] In some embodiments, the state semantic mapping table includes information such as the original state name, standardized state ID, and confidence. For example, "Dev" corresponds to the standardized state ID "DEV" with a confidence of 0.98; "Encoding" corresponds to the standardized state ID "DEV" with a confidence of 0.95; "Blocked" corresponds to the standardized state ID "BLOCKED" with a confidence of 1.0.

[0031] In this embodiment, during the project status management process, the corresponding project team determines the corresponding standardized status ID for the requirement status name corresponding to each requirement of the project based on the above-mentioned status semantic mapping table, and finally generates a requirement status data set. It can be understood that each requirement in the generated requirement status data set is associated with project-related information to ensure the integrity of the data, which is conducive to subsequent data analysis and management of project requirements. Specifically, the information is analyzed to obtain additional information. The additional information generally includes requirement ID, team ID, requirement type, priority, standardized status ID, status start and end timestamps, duration, and whether it is a blocked state, etc.

[0032] In this way, the project team can determine the corresponding standardized status ID for the requirement status name of each requirement in the project. That is, based on the above status semantic mapping table, different management teams of the same project can manage the project with the same standards and the same naming method for each requirement status, thereby improving the coordination between different teams.

[0033] In some embodiments, the predefined standardized mapping table specifically includes: Defining a standard state lifecycle based on business specifications, wherein the standard state lifecycle includes the standardized state of each requirement; Each of the standardized states is split into sub-states, and reflux rules are set to generate a state transition matrix.

[0034] In the present application, one project is usually implemented by multiple teams in the implementation process, and thus the project may have a composite state in the demand state when the A team is "released" and the B team is "online" in the management process. Therefore, in the process of predefining the standardized mapping table, the entire life cycle of the project is measured to solve the conflict between different teams in the state transition process.

[0035] Specifically, different standard state life cycles are defined for different business specifications. For example, the standard state life cycle of a software development project is specifically demand collection→demand analysis→development→testing→online, and the standardized states in the above standard state life cycle are further split into sub-states, such as "development-encoding" and "development-code review".

[0036] Further specifically, the backflow rules are set for each standardized state, and each standardized state is defined to distinguish from the flow state of the standard state life cycle, such as allowing the "testing" to backflow to the "development", but prohibiting the "online" to backflow to the "development", and setting the allowed parallel states, such as allowing the "testing" and the "development" to be in the parallel stage at the same time. Thus, the state transition matrix is generated, and the legal state transition relationship is usually defined by a directed graph.

[0037] Specifically, the state transition is verified for legality by the above state transition matrix during the implementation of the project. If there is an illegal jump between the current state and the previous state, it is processed, such as inserting an intermediate state or marking an exception. For example, if the current state is not allowed to be transferred from the previous state in the state transition matrix, and the current state belongs to a recoverable state, it is automatically corrected to a blocked state; otherwise, an error log is recorded, and finally a standardized state sequence is generated.

[0038] S200, constructing a multi-dimensional demand flow sequence data table based on the demand state data set, and obtaining a demand flow state parameter; The present application divides the state between different demands according to time to construct a multi-dimensional demand flow sequence data table, so as to analyze the flow state of each demand and facilitate the identification of project risks.

[0039] In some embodiments, the above step S200 includes: Obtaining additional information of different demands in the project from the demand state data set, classifying the additional information by dimensions, and obtaining a dimension matrix; Dividing each demand of the project into multiple time buckets according to the same time granularity; Traverse all requirements, get the state of each requirement in the preset time bucket, and construct the dimension combination state based on the dimension matrix; According to time sorting, obtain the historical flow state of each requirement, extract state transition events, record the state before and after the state transition, time stamp and duration; The dimension combination state and the state transition event are aggregated according to the time bucket to generate a multi-dimensional requirement flow sequence data table; Calculate the dimension state distribution characteristics, flow efficiency characteristics and bottleneck detection characteristics in the multi-dimensional requirement flow sequence data table.

[0040] In this embodiment, as shown in step S100, the generated requirement state data set containing different requirements is associated with relevant additional information, such as requirement ID, team ID, requirement type, priority, standardized state ID, state start and end timestamp, duration and whether it is a blocking state.

[0041] Further, the additional information of each requirement is dimensionally classified. In an embodiment, the dimensions can be time dimension, organization dimension, requirement attribute dimension, state dimension and efficiency dimension, etc. Finally, a dimension matrix is generated. From different dimensions, the corresponding project information can be obtained in the dimension matrix, so as to analyze and manage the project requirements subsequently.

[0042] In this embodiment, each requirement of the project is divided according to the same time granularity, such as hour, day, week, month, etc. For example, when day is taken as the time granularity, every other day is taken as a time bucket from the start date to the end date of the requirement, and the requirement time sequence is generated.

[0043] Further, the information of multiple dimensions in each requirement is combined into a dimension combination state according to the time bucket in this embodiment, which is convenient for cross analysis of the state of the requirement in multiple dimensions. For example, the requirement state distribution of a certain team under a certain priority can be analyzed. In addition, by counting the dimension combination state, the number of requirements in the same state can be determined, which is convenient for subsequent data query, analysis and other operations.

[0044] Specifically, the normalized state ID of each requirement in the corresponding time bucket is checked, and a dimension combination state is constructed based on the state, and the dimension combination state includes a dimension combination key and a corresponding dimension combination key and the number of normalized state IDs. It can be understood that the dimension combination key is the key of the dimension combination state, and in one possible embodiment, the format of the dimension combination key is: (team, requirement type, priority, probability of stagnation, normalized state ID), and the number of normalized state IDs is the value corresponding to the dimension combination key in the dimension combination state. Specifically, all requirements are traversed, the state of each requirement in the time bucket 2025-06-09 is checked, and specifically, at least the following requirements exist on 2023-01-01: requirement 1: (Team_A, functional requirement, P0, 2, DEV), requirement 2: (Team_B, defect repair, P2, 1, TEST), requirement 3: (Team_A, functional requirement, P0, 2, DEV), requirement 4: (Team_C, requirement analysis, P5, 1, ANALYSIS), requirement 5: (Team_D, defect repair, P0, 3, BLOCKED)…, thereby determining the number of different dimension combination keys and the number of dimension combination keys with the same normalized state ID in the time bucket 2025-06-09 by traversing the above requirements, thereby generating a dimension combination state.

[0045] In this embodiment, the demands are sorted in chronological order, and the events of each demand transitioning from one state to another are recorded, as well as the states before and after the transition, the timestamp and the duration. Further, the dimension combination state and the above state transition events are aggregated according to the time bucket to generate a multi-dimensional demand flow sequence data table. Through the multi-dimensional demand flow sequence data table, the dimensional state distribution features, the flow efficiency features and the bottleneck detection features are generated. Specifically, according to the arrangement order of the time bucket, the dimensional combination states are aggregated, and the state transition events of each demand are recorded, and the states before and after the event transition, the timestamp and the duration are recorded, thereby generating a multi-dimensional demand flow sequence data table. The multi-dimensional demand flow sequence data table records the timestamp and the duration of the transition between different states of each demand, and further calculates the average duration of each state transition pair and the state residence time of each state in different demands, to obtain the corresponding flow efficiency features, wherein the state transition pair is specifically DEV -> TEST, etc. In addition, the multi-dimensional demand flow sequence data table also records the dimensional combination state in different time buckets. By statistically analyzing the different dimensions in the dimensional combination state, the corresponding dimensional state distribution features are determined, and for the bottleneck detection features, the probability of the standardized state ID of each demand in the stagnation state in different time buckets is measured, such as BLOCKED or PENDING, and the process bottlenecks of each demand are identified to improve the flow efficiency of the process. In some other embodiments, the dimensional combination key of each demand in the stagnation state in each time bucket is calculated separately, such as the standardized state ID "BLOCKED" in the above demand 5, which indicates that the project demand is in the stagnation state at 2023-01-01. Therefore, it is separately counted to quickly identify risks.

[0046] S300, establishing a state transition probability matrix, and identifying abnormal stagnation demands based on the multi-dimensional demand flow sequence data table; In this embodiment, the corresponding standardized state ID is obtained from the historical state data set of the project, and an index is assigned to each standardized state ID, which is initialized as a count matrix for recording the number of transitions between different states. The state history records of each demand within a predetermined time are obtained, and the number of each historical state transition event is counted and assigned to the count matrix. Further, the count matrix is converted into a probability matrix. Specifically, wherein, represents the probability of transitioning from state i to state j.

[0047] In some embodiments, each element of the state transition probability matrix represents a probability of a state transition to another state, and a diagonal element of the state transition matrix represents a probability of the state being in a stagnation state.

[0048] It can be understood that in the probability matrix, each element represents a probability of a state transition to another state, and if the value of an element is zero, it means that there is no record of transition from the state in history. In addition, a diagonal element of the state transition matrix represents a probability of the state being in a stagnation state, and if the probability value is greater than a preset threshold, it is considered that the demand will be in a stagnation state in the corresponding state, and state intervention is required.

[0049] Further, in the embodiment, an exponential decay weight is introduced in the process of converting the count matrix into the probability matrix. It can be understood that for each state transition event, the closer the occurrence time is to the current time, the more reference value it has for the state transition of each demand in the current project. Therefore, for each state transition event, the difference between the occurrence time and the current time is calculated, the weight is calculated by an exponential decay function, and then the weighted count value of each element in the count matrix is obtained. Then, the weighted and calculated technology matrix is converted into a probability matrix. Further, the weighted probability values also need to be normalized so that the sum of the elements in each row is 1. At the same time, based on the historical state data set, the historical duration of each state is calculated as additional information of each element in the probability matrix, which is used to measure the flow efficiency and flow smoothness of each demand in the project flow process.

[0050] In addition, in an embodiment of the present application, a corresponding state transition probability matrix can also be constructed based on different dimensions. For example, in the priority dimension, the probability of priority "P1" from "ANALYSIS" to "DEV" is 0.7, and the probability of priority "P2" from "ANALYSIS" to "DEV" is 0.5.

[0051] In this embodiment, the state transition events of each requirement are obtained from the multi-dimensional requirement flow sequence data table, a requirement state sequence is generated, and then all standardized state IDs of the requirement are obtained from the requirement state sequence. At this time, based on the state transition probability matrix constructed above, the next standardized state ID of each requirement in the flow can be determined, and it is judged whether the duration of the current standardized state ID is greater than the duration of the corresponding state standardized ID in the state transition probability matrix to determine whether the current standardized state ID is in a stagnant state. It can be understood that if the duration of the current standardized state ID is greater than the duration of the corresponding state standardized ID in the state transition probability matrix, the corresponding standardized state ID is changed to "BLOCKED", and the project manager is reminded to follow up the requirement. In addition, the duration of the state transition event that has flowed in each requirement is calculated, which is compared with the historical duration of each state transition event in the state transition probability matrix to judge whether the flow efficiency of each state transition event meets the expected state, which is used to ensure the normal progress of the project schedule.

[0052] Therefore, the application constructs a state transition probability matrix to support various analysis and decision-making scenarios such as requirement prediction and process optimization.

[0053] S400, constructing a requirement state time axis with a hierarchical structure according to requirement priorities and stagnation probabilities; The application generates a requirement state time axis with a hierarchical result by using a visualization technology, and realizes dynamic query by combining a spatial index structure, thereby improving project management efficiency, reducing delay risks, and providing data support for decision-making.

[0054] In some embodiments, the above step S400 includes: From the state transition probability matrix, the probability value of the transition of each requirement between each state is obtained, and the stagnation probability of each requirement is calculated; Based on the stagnation probability and in combination with the corresponding requirement priority, a comprehensive weight value is calculated; Based on the comprehensive weight value, the hierarchical ordering sequence of each requirement on the requirement state time axis is determined; Based on the hierarchical ordering sequence, each requirement is distributed to the corresponding position on the requirement state time axis according to the timestamp.

[0055] In this embodiment, the probability that each state of each requirement is in a stagnant state, i.e., the probability value of the diagonal element in the state transition probability matrix, is obtained from the state transition probability matrix constructed in step S300. Then, the average number algorithm is used to comprehensively calculate the stagnation probability of the corresponding requirement, and then the stagnation probability of each requirement is normalized so that the sum of the stagnation probabilities of all requirements is 1.

[0056] In this embodiment, the demand priority is specifically the demand priority extracted from the original demand data in step S100. It can be understood that during the project process, the project manager usually assigns corresponding priorities to each demand in the project according to the importance of the business and the dependency relationship between different demands, and finally promotes the smooth delivery of the project.

[0057] Further, in this embodiment, the weighted average method is used to calculate the comprehensive weight value of each demand in the priority and stagnation risk, which is used for visualizing the hierarchical distribution of each demand on the demand state time axis.

[0058] Specifically, the demands are sorted according to the comprehensive weight value to determine the corresponding hierarchical order, and each demand is assigned a corresponding display parameter, which includes display color, display size, and position on the time axis. Specifically, for the display color, the corresponding color is assigned according to the priority of each demand. Further, for the display size, it is determined according to the stagnation probability of each demand. Specifically, the display size is proportional to the stagnation probability. The larger the stagnation probability, the larger the display size, which can provide a detailed explanation of the demand. Then, the demands are sorted by timestamp and assigned to the corresponding position on the demand state time axis. Specifically, according to the residence time of the demand in each state, the position is proportionally distributed on the time axis. For the case where multiple demands have overlapping timestamps on the demand state time axis, the layout is adjusted according to the comprehensive weight value of multiple demands. For example, high-weight-value demands are displayed on the upper layer, and low-weight-value demands are offset to avoid overlapping. Specifically, a hierarchical spacing is preset, and then the hierarchical height is adjusted according to the weight difference between two demands. For example, in one embodiment, the comprehensive weight value of demand A is 15, and the comprehensive weight value of demand B is 10. The hierarchical height = hierarchical spacing x 5.

[0059] Further specifically, different shapes are used to mark different standardized state IDs, and the color of the standardized state ID of different demands corresponds to the display color of the corresponding demand to distinguish the standardized state ID of different demands.

[0060] S500, a spatial index structure is established in the demand state time axis, and through the spatial index structure, a state change event is filtered according to the timestamp to obtain a demand query result set; In some embodiments, the above step S500 includes: Based on the state transition event of the demand, a mapping space coordinate of the standardized state in the demand state time axis is generated; Based on the mapping space coordinate, a spatial index structure is constructed; In the space index structure, the mapping space coordinates of the standardized states of the requirements in a preset time range on the demand state axis and the corresponding state change events are queried to generate a demand query result set, which is used for visual analysis and processing of the dimensional data in the demand.

[0061] In this embodiment, a space coordinate is set for the position of each standardized state of the requirement on the demand state time axis in a two-dimensional plane. It can be understood that the vertical coordinates of the standardized states of the same requirement on the demand state time axis are the same, and the horizontal coordinates are sorted in time sequence based on the residence time of different standardized states. Further, the vertical coordinates between different requirements are determined based on the hierarchical height obtained in step S400, thereby mapping the standardized states of each requirement in the demand state time axis, and each standardized state of each requirement is provided with a mapping space coordinate. The corresponding requirement can be obtained through the mapping space coordinate, and the flow time between two adjacent standardized states can be obtained based on two adjacent mapping space coordinates, which facilitates subsequent analysis and processing of the requirement data.

[0062] Further, based on the mapping space coordinates of each standardized state, a space index coordinate is constructed. It can be understood that each standardized state is grouped according to the requirement to form an initial MBR. Specifically, the MBR refers to the smallest rectangle that can completely enclose a group of space coordinates. Specifically, in one possible embodiment, there are the following requirements and standardized states: Requirement ID Standardization status ID Timestamp Mapping space coordinates (x, y) R001 DEV 2023-01-05 (10, 20) R001 TEST 2023-01-10 (15, 20) R001 DONE 2023-01-15 (20, 20) R002 DEV 2023-01-08 (13, 30) R002 TEST 2023-01-15 (20, 30) R003 DEV 2023-01-20 (25, 60) Based on the above information, it can be known that (10, 20), (15, 20) and (20, 20) correspond to the R001_DEV node, the R001_TEST node and the R001_DONE node in the demand state time axis respectively. Similarly, (13, 30) and (20, 30) correspond to the R002_DEV node and the R002_TEST node in the demand state time axis respectively, and (25, 60) corresponds to the R003_DEV node. Therefore, the above standardized states are divided into three groups according to the requirements, and the initial MBR of the requirement R001 is [(10, 20), (20, 20)], the initial MBR of the requirement R002 is [(13, 30), (20, 30)], and the initial MBR of the requirement R003 is (25, 60), (25, 60)]. Thus, the space index structure of each requirement is generated.

[0063] Further specifically, when the user performs a timestamp-based state change event query, based on a preset time range, whether there is a corresponding time intersection in each demand spatial index structure is queried, if there is a time intersection, all demand state nodes in the control index structure are obtained, a candidate node set is generated, and demand state nodes overlapping with the preset time range are selected from the candidate node set to generate a time matching node set, each demand state node in the time matching node set is sorted according to the timestamp, and the corresponding state change event is obtained, and finally a demand query result is generated. Through the demand query result, the user can query all state change events in the preset time range in the demand and the corresponding mapping space coordinates, thereby facilitating analysis and processing of the corresponding data. Specifically, from the demand query result of each demand, the spatio-temporal distribution of each demand is obtained, such as R001 moving from (10, 20) to (15, 20), and then displaying the state change of R001 on 2023-01-05 and 2023-01-10 in the demand state time axis. Further, the demand distribution scatter plot of R001 at (10, 20) and (15, 20) is displayed on the spatial plane. After obtaining the demand distribution scatter plot of different demands according to the above manner, the demand distribution scatter plots of multiple demands are combined to form a demand distribution scatter plot, a visual query result is generated, and the user can intuitively understand the change and distribution of the demand state.

[0064] Referring to Figure 2 The application further provides a project state management system based on a time axis, which comprises: A first processing module 201 is configured to uniformly convert demand state definitions of different teams by using a state standardization algorithm, and obtain a demand state data set; A second processing module 202 is configured to construct a multi-dimensional demand flow sequence data table based on the demand state data set, and obtain demand flow state parameters; A third processing module 203 is configured to establish a state transition probability matrix, and identify abnormal stagnant demands based on the multi-dimensional demand flow sequence data table; A fourth processing module 204 is configured to construct a demand state time axis with a hierarchical structure according to demand priorities and stagnation probabilities; A fifth processing module 205 is configured to establish a spatial index structure in the demand state time axis, and filter state change events according to timestamps through the spatial index structure to obtain a demand query result set.

[0065] It can be understood that, as Figure 1The content of the illustrated time axis-based project status management method embodiment is applicable to the present time axis-based project status management system embodiment, the function implemented by the present time axis-based project status management system embodiment is the same as that of the illustrated time axis-based project status management method embodiment, and the beneficial effects achieved are the same as those of the illustrated time axis-based project status management method embodiment. Figure 1 The content of the illustrated time axis-based project status management method embodiment is applicable to the present time axis-based project status management system embodiment, the function implemented by the present time axis-based project status management system embodiment is the same as that of the illustrated time axis-based project status management method embodiment, and the beneficial effects achieved are the same as those of the illustrated time axis-based project status management method embodiment. Figure 1 The content of the illustrated time axis-based project status management method embodiment is applicable to the present time axis-based project status management system embodiment, the function implemented by the present time axis-based project status management system embodiment is the same as that of the illustrated time axis-based project status management method embodiment, and the beneficial effects achieved are the same as those of the illustrated time axis-based project status management method embodiment.

[0066] It should be noted that the information interaction and execution process between the above systems are based on the same concept as the method embodiments, and the specific functions and technical effects can be referred to the method embodiments, which will not be described here.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the above described functions. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of the functional units and modules are only for mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can be referred to the corresponding process in the foregoing method embodiments, which will not be described here.

[0068] Please refer to Figure 3 The present embodiment further provides a computer device 3, which comprises a memory 302, a processor 301 and a computer program 303 stored in the memory 302, and when the computer program 303 is executed on the processor 301, the time axis-based project status management method according to any one of the above methods is implemented.

[0069] The computer device 3 can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device 3 can include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that Figure 3 The computer device 3 is only an example and does not limit the computer device 3, which can include more or fewer components than shown, or combine certain components, or different components, for example, can also include input / output devices, network access devices and the like.

[0070] The processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0071] The memory 302 can be an internal storage unit of the computer device 3 in some embodiments, for example, a hard disk or a memory of the computer device 3. The memory 302 can also be an external storage device of the computer device 3 in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 302 can include both an internal storage unit and an external storage device of the computer device 3. The memory 302 is used to store an operating system, an application program, a boot loader, data and other programs, for example, program codes of the computer program, etc. The memory 302 can also be used to temporarily store data that has been output or is to be output.

[0072] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is run by a processor to implement the time axis-based project state management method according to any one of the above methods.

[0073] In this embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / computer equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunication signal.

[0074] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A project status management method based on a timeline, characterized in that: include: Use the state standardization algorithm to uniformly convert the demand state definitions of different teams to obtain the demand state data set; Based on the demand status data set, a multi-dimensional demand flow sequence data table is constructed to obtain demand flow state parameters; Establishing a state transition probability matrix and identifying abnormal stagnant demand based on the multi-dimensional demand flow sequence data table; Construct a hierarchical demand status timeline based on demand priority and stagnation probability; A spatial index structure is established in the demand state time axis, and state change events are filtered according to timestamps through the spatial index structure to obtain a demand query result set.

2. The method according to claim 1, wherein The state standardization algorithm is used to uniformly convert the requirement state definitions of each team, including: Obtain the original requirement data of each team, and extract the complete status change history of each requirement from the original requirement data; Based on the complete state change history, all required state names are aggregated, and additional information is associated with each required state name to generate an original state set; In the original state set, based on a predefined standardized mapping table, each required state name is mapped through semantic analysis to obtain a state semantic mapping table.

3. The method according to claim 2, wherein The predefined standardized mapping table specifically includes: Defining a standard state lifecycle based on business specifications, wherein the standard state lifecycle includes the standardized state of each requirement; Each of the standardized states is split into sub-states, and reflux rules are set to generate a state transition matrix.

4. The method according to claim 1, wherein Each element of the state transition probability matrix represents the probability of one state transferring to another state, and the diagonal elements of the state transition matrix represent the probability of the state being in a stagnant state.

5. The method according to claim 1, wherein The step of constructing a multi-dimensional demand flow sequence data table based on the demand state data set and obtaining demand flow state parameters includes: Obtaining additional information of different requirements in the project from the requirement status dataset, and classifying the additional information into dimensions to obtain a dimension matrix; Divide each requirement of the project into multiple time buckets according to the same time granularity; Traverse all requirements, obtain the status of each requirement in a preset time bucket, and construct a dimension combination state based on the dimension matrix; Sort by time, obtain the historical flow status of each requirement, extract the state transition events, and record the state, timestamp, and duration before and after the requirement state transition; Aggregate the dimension combination state and the state transition event according to time buckets to generate a multi-dimensional demand flow sequence data table; Calculate the state distribution characteristics, flow efficiency characteristics and bottleneck detection characteristics of each dimension in the multi-dimensional demand flow sequence data table.

6. The method according to claim 1, wherein According to the demand priority and stagnation probability, a hierarchical demand status timeline is constructed, including: Obtaining the probability value of each demand transitioning between each state from the state transition probability matrix, and calculating the stagnation probability of each demand; Calculate a comprehensive weight value based on the stagnation probability and in combination with the corresponding demand priority; Determining the hierarchical order of each demand on the demand status timeline based on the comprehensive weight value; Based on the hierarchical sorting order, each requirement is assigned to a corresponding position on the requirement status timeline according to a timestamp.

7. The method according to claim 1, wherein The step of establishing a spatial index structure in the demand state time axis and filtering state change events according to timestamps through the spatial index structure to obtain a demand query result set includes: Generate the mapping space coordinates of the standardized state in the demand state timeline based on the demand state transition event; Constructing a spatial index structure based on the mapping space coordinates; In the spatial index structure, the mapping spatial coordinates of the standardized state within a preset time range on the demand state axis and the corresponding state change events are queried to generate a demand query result set, which is used for visual analysis and processing of data of various dimensions in the demand.

8. A timeline-based project status management system, characterized in that: include: The first processing module is used to uniformly convert the requirement status definitions of different teams using a status standardization algorithm to obtain a requirement status data set; The second processing module is used to construct a multi-dimensional demand flow sequence data table based on the demand status data set and obtain demand flow status parameters; The third processing module is used to establish a state transition probability matrix and identify abnormal stagnant demand based on the multi-dimensional demand flow sequence data table; The fourth processing module is used to construct a hierarchical demand status timeline based on demand priority and stagnation probability; The fifth processing module is used to establish a spatial index structure in the demand state time axis, and filter state change events according to timestamps through the spatial index structure to obtain a demand query result set.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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