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CN-121979949-A - Project full life cycle progress visualization method and system

CN121979949ACN 121979949 ACN121979949 ACN 121979949ACN-121979949-A

Abstract

The invention relates to the technical field of project management and discloses a project full life cycle progress visualization method and a system, wherein the method comprises the steps of collecting task dependent data of a plurality of projects and real-time execution data to construct a weighted key partial sequence, extracting a conduction path between the tasks based on the weighted key partial sequence and calculating a conduction coefficient to generate a deviation conduction matrix, receiving the deviation conduction matrix and collecting the real-time deviation data to construct a deviation vector, calculating a cumulative influence value through matrix operation and combining a key measurement value to generate a risk layering quantization pseudo-array, configuring a differentiation early warning strategy based on the risk layering quantization pseudo-array and rendering a visualization early warning mark in a global progress view.

Inventors

  • LIANG YUGANG

Assignees

  • 上海速擎软件有限公司

Dates

Publication Date
20260505
Application Date
20260403

Claims (10)

  1. 1. A method for visualizing full lifecycle progress of an item, the method comprising: Collecting multi-project task dependent data and real-time execution data, constructing a partial sequence lattice structure of task dependent relation, calculating key metric values of each task based on the partial sequence lattice structure, and generating a weighted key partial sequence lattice; Based on the weighted key partial sequence, extracting conduction paths among tasks along the partial sequence relation, and calculating a conduction coefficient by combining the resource redundancy and the buffer time to generate a deviation conduction matrix; Receiving a deviation conduction matrix, collecting real-time deviation data of each task to construct a deviation vector, calculating an accumulated influence value of each task through matrix operation, and generating a risk layering quantization pseudo-array by combining key metric values in a weighted key partial sequence; Based on the risk layering quantitative simulation array, a differential early warning strategy is configured according to the priority of each risk level, instant early warning is generated for high-risk level tasks, a conduction influence range is marked, only observation logs are recorded for low-risk level tasks, and visual early warning identification is rendered according to the levels in a global progress view.
  2. 2. The method for visualizing a full lifecycle schedule of an item as recited in claim 1, wherein the process of constructing a weighted key partial sequence comprises: collecting multi-project task dependent data and real-time execution data, and constructing a multi-project original data set; Analyzing the dependency relationship among tasks based on the multi-project original data set, and constructing a partial sequence lattice structure; calculating time margin parameters of each task node based on the partial sequence structure; and calculating the key metric value of each task based on the time margin parameter and the partial sequence structure, and generating the weighted key partial sequence.
  3. 3. The project full life cycle progress visualization method of claim 2, wherein the construction flow of the partial sequence structure is as follows: Extracting all task nodes and the dependency relationships thereof from the multi-project original data set, taking each task node as an element of a partial sequence, and converting the predecessor and successor dependency relationships among tasks into a partial sequence relationship; Performing hierarchical division on all task nodes according to the dependency relationship, and determining the hierarchical position of each task node in the partial sequence; constructing upper bound operation and lower bound operation rules of the partial sequence; And packaging the hierarchical division result and the lattice operation rule into a partial lattice structure, wherein each node stores a task unique identifier, a hierarchical code, a predecessor node set, a successor node set and a node attribute dictionary.
  4. 4. The project full life cycle progress visualization method of claim 2, wherein the calculation flow of the time margin parameter is: Performing forward traversal on the partial sequence structure, and calculating the earliest starting time and earliest finishing time of each task node layer by layer from the first level; Performing reverse traversal on the partial sequence structure, and calculating the latest completion time and the latest starting time of each task node layer by layer from the highest level; a total float time is calculated for each task node, the total float time being defined as the difference between the latest start time and the earliest start time of the task.
  5. 5. The project full life cycle progress visualization method of claim 2, wherein the key metric value calculation method is as follows: For each task node, acquiring the total floating time and the subsequent task number; The calculation rule of the key metric value is that when the total floating time is greater than zero, the key metric value is equal to the product of the reciprocal of the total floating time and the number of subsequent tasks plus one; And adding the key metric value as a weight attribute into a node attribute dictionary of a corresponding task node in the partial sequence structure to form a weighted key partial sequence.
  6. 6. A method of visualizing a full lifecycle progress of an item as recited in claim 1, the method is characterized in that the flow for generating the deviation conduction matrix comprises the following steps: Based on the weighted key partial sequence, collecting resource configuration data of each task, and calculating a resource redundancy index; extracting all task pairs with reachable relations and a conduction path set thereof based on the weighted key partial sequence; Calculating a single-hop conductivity coefficient and a total conductivity coefficient of the paths on each conductive path based on the conductive path set; Based on the total path conductivity, a biased conductivity matrix is constructed.
  7. 7. The project full life cycle progress visualization method of claim 6, wherein the conductivity calculation flow is: for each pair of adjacent task nodes in the conduction path, calculating a single-hop conduction coefficient from the preceding task to the following task; carrying out standardization processing on three factors of buffer time occupation ratio, resource redundancy and dependence intensity; Calculating a single-hop conduction coefficient by adopting a weighted geometric average method; For each conductive path, the single-hop conductivity coefficients of all adjacent task pairs on the path are multiplied to obtain the total path conductivity coefficient of the path.
  8. 8. The method for visualizing a full lifecycle schedule of an item as recited in claim 1, wherein the generating a risk stratification quantitative simulation comprises: collecting real-time deviation data of each task and constructing a deviation vector; Calculating an accumulated conduction influence value vector through matrix operation based on the deviation conduction matrix and the deviation vector; Calculating the risk score of each task by combining the deviation vector, the accumulated conduction influence value vector and the key measurement value; and constructing a risk layering quantitative pseudo-array based on the dependency relationship between the risk score vector and the weighted key partial sequence.
  9. 9. The project full life cycle progress visualization method of claim 8, wherein the risk stratification quantization pseudo-array construction flow is as follows: defining a base set of the quasi-array as a set of all task nodes in the weighted key partial sequence; Defining a judging rule of an independent set; Adopting a greedy algorithm to solve the maximum independent set decomposition of the quasi-array; arranging all the maximum independent sets in descending order according to the average risk scores of the tasks in the sets, and distributing a hierarchical priority label for each maximum independent set; and packaging the basic set, the independent set group, the hierarchical division result and the quasi-array operation interface into a risk hierarchical quantization quasi-array.
  10. 10. A project full lifecycle progress visualization system for implementing a project full lifecycle progress visualization method as recited in any one of claims 1-9, the system comprising: The weighted key partial sequence construction module is used for collecting the task dependency data of a plurality of items and real-time execution data, constructing a partial sequence structure of the task dependency relationship, calculating key metric values of each task based on the partial sequence structure and generating a weighted key partial sequence; the deviation conduction matrix generation module is used for extracting conduction paths among tasks along a partial sequence relation based on the weighted key partial sequence, and calculating a conduction coefficient by combining the redundancy of resources and the buffering time to generate a deviation conduction matrix; the risk layering quantization quasi-array generating module is used for receiving the deviation conduction matrix, collecting real-time deviation data of each task to construct a deviation vector, calculating the accumulated influence value of each task through matrix operation, and generating a risk layering quantization quasi-array by combining key metric values in the weighted key partial sequence; The differential early warning and visualization module is used for configuring a differential early warning strategy according to the priority of each risk level based on the risk layering quantitative simulated array, generating instant early warning for high risk level tasks and marking a conduction influence range, only recording an observation log for low risk level tasks, and rendering a visual early warning mark according to the level in a global progress view.

Description

Project full life cycle progress visualization method and system Technical Field The invention relates to the technical field of project management, in particular to a project full life cycle progress visualization method and system. Background In a multi-type project parallel management scene, a project full life cycle progress visualization system is generally adopted for progress tracking and abnormality early warning. The system is used for triggering early warning by comparing the time difference value by collecting the data of the task planning time and the actual completion time and assisting management personnel in monitoring project progress, and has the technical characteristics of real-time data collection and unified threshold early warning. In actual engineering management, engineers often face the situation that a system pushes a large amount of early warning information, and a large amount of time is required to check one by one, but potential delays of critical tasks may still be missed. This is because the existing system triggers an early warning only based on the time difference of a single task, does not distinguish between critical path and non-critical path tasks, and does not consider the conduction characteristics of progress deviation along the task dependency. The non-critical path task usually does not influence the overall progress due to the buffer time, but is frequently warned to occupy management effort, and the critical path task does not reach the time threshold but has the potential risk of deviation conduction trend, and the non-critical path task cannot be captured in time due to the lack of a dynamic recognition mechanism, so that the overall progress of the project is possibly delayed after the deviation is continuously accumulated. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides the following technical scheme: a project full life cycle progress visualization method, comprising: Collecting multi-project task dependent data and real-time execution data, constructing a partial sequence lattice structure of task dependent relation, calculating key metric values of each task based on the partial sequence lattice structure, and generating a weighted key partial sequence lattice; Based on the weighted key partial sequence, extracting conduction paths among tasks along the partial sequence relation, and calculating a conduction coefficient by combining the resource redundancy and the buffer time to generate a deviation conduction matrix; Receiving a deviation conduction matrix, collecting real-time deviation data of each task to construct a deviation vector, calculating an accumulated influence value of each task through matrix operation, and generating a risk layering quantization pseudo-array by combining key metric values in a weighted key partial sequence; Based on the risk layering quantitative simulation array, a differential early warning strategy is configured according to the priority of each risk level, instant early warning is generated for high-risk level tasks, a conduction influence range is marked, only observation logs are recorded for low-risk level tasks, and visual early warning identification is rendered according to the levels in a global progress view. Further, the process of constructing the weighted key partial sequence comprises the following steps: collecting multi-project task dependent data and real-time execution data, and constructing a multi-project original data set; Analyzing the dependency relationship among tasks based on the multi-project original data set, and constructing a partial sequence lattice structure; calculating time margin parameters of each task node based on the partial sequence structure; and calculating the key metric value of each task based on the time margin parameter and the partial sequence structure, and generating the weighted key partial sequence. Further, the construction flow of the partial lattice structure is as follows: Extracting all task nodes and the dependency relationships thereof from the multi-project original data set, taking each task node as an element of a partial sequence, and converting the predecessor and successor dependency relationships among tasks into a partial sequence relationship; Performing hierarchical division on all task nodes according to the dependency relationship, and determining the hierarchical position of each task node in the partial sequence; constructing upper bound operation and lower bound operation rules of the partial sequence; And packaging the hierarchical division result and the lattice operation rule into a partial lattice structure, wherein each node stores a task unique identifier, a hierarchical code, a predecessor node set, a successor node set and a node attribute dictionary. Further, the calculation flow of the time margin parameter is as follows: Performing forward traversal on the partial sequence structure, and