Visual process management method and device and storage medium

Abstract

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of process management, and in particular, to a visual process management method, apparatus, and storage medium. Background Art

[0002] In complex project management scenarios, changes in task status, deadlines, or resource allocation often trigger multi-level chain reactions. For example, the early completion of a certain task may lead to adjustments in the start times of subsequent tasks, thereby affecting resource allocation and the overall progress. Traditional management methods require the system to be able to quickly analyze the scope of influence of task changes, predict potential decision points, and visually present the propagation path of the influence through visualization means. In addition, the system needs to have a closed-loop feedback ability to continuously track the execution effect of decisions to ensure the accuracy and timeliness of project dynamic adjustment.

[0003] In the prior art, process management technologies mostly use tools such as Gantt charts, Critical Path Method (CPM), or Agile Kanban to display task dependencies through static charts. Some systems introduce graph theory algorithms (such as breadth-first search) to identify direct influences between tasks and use matrix calculations to evaluate time offsets. For example, some tools construct a dependency graph through a task network model and mark key nodes using a threshold warning mechanism. For decision support, some systems use manually set weights or a rule engine based on historical data to generate priority suggestions. However, traditional methods rely on static charts, cannot update the influence propagation path in real time, and lack the ability to dynamically adjust the layout, making it difficult to intuitively reflect the real-time changes in the task network.

[0004] In summary, the prior art has the problem of insufficient dynamic layout adjustment ability. Summary of the Invention

[0005] The present invention provides a visual process management method, apparatus, and storage medium to achieve real-time dynamic layout adjustment of process management.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a visual process management method, including: Obtaining input data on task status changes; Performing model initialization processing according to the input data to obtain a task network model; Performing path search using a graph traversal algorithm according to the task network model to obtain a set of influence paths; Performing an influence matrix construction operation according to the set of influence paths to obtain an influence degree matrix; Performing dynamic influence path mapping according to the influence degree matrix and the task network model to obtain a dynamic propagation path graph; Based on the influence degree matrix and the dynamic propagation path diagram, key nodes are extracted to obtain a set of decision points; Based on the set of decision points, priority calculation and visual identification processing are performed to obtain the deviation node identification result; Based on the deviation node identification result, model optimization processing is performed to obtain an optimized task network model.

[0007] As an optional implementation manner, the performing model initialization processing based on the input data to obtain a task network model includes: Performing duplicate value merging, missing value imputation, and outlier filtering processing on the input data to obtain regular task data; Based on the regular task data, an association matrix construction process is performed using an association rule mining algorithm to generate a task association matrix; Based on the task association matrix, a dependency condition extraction process is performed to construct a task dependency graph with node weights; When the node weights of the task dependency graph are not defined, a random initialization method is used to perform network parameter setting processing to obtain an initial task network model.

[0008] As an optional implementation manner, the performing path search using a graph traversal algorithm based on the task network model to obtain a set of influence paths includes: Based on the task network model, a graph traversal algorithm process is performed to generate a set of task nodes including directly influencing nodes and indirectly influencing nodes; Based on the set of task nodes, a node type discrimination process is performed to generate a labeled node type list; Based on the node type list, a path search process is performed to obtain the complete influence paths from each task node to the root node; Based on the complete influence paths, a duplicate path merging and redundant node removal process is performed to obtain an optimized set of influence paths.

[0009] As an optional implementation manner, the performing an influence matrix construction operation based on the set of influence paths to obtain an influence degree matrix includes: Based on the set of influence paths, a time offset calculation and a resource demand change amount calculation process are performed to obtain a two-dimensional influence numerical set; Based on the two-dimensional influence numerical set, a matrix construction algorithm process is performed to obtain an initial influence matrix including a time-resource coupling relationship; For the initial influence matrix, a preset threshold judgment process is performed, and when the node value exceeds the preset threshold, a warning label generation instruction is triggered; Generate an instruction according to the warning identifier, perform node clustering and priority classification processing, and obtain an impact degree matrix.

[0010] As an optional implementation manner, performing dynamic impact path mapping according to the impact degree matrix and the task network model to obtain a dynamic propagation path diagram includes: Based on the impact degree matrix and the task network model, perform node-path association processing using a multi-dimensional parameter mapping algorithm to generate an initial dynamic propagation path diagram; According to the initial dynamic propagation path diagram, perform visual hierarchical rendering processing to generate an enhanced path diagram; According to the enhanced path diagram, perform target node data extraction to obtain a structured impact analysis data set; According to the structured impact analysis data set, perform outlier detection and trend prediction processing to obtain node prediction parameters; According to the node prediction parameters, perform dynamic update processing of the path diagram to obtain a dynamic propagation path diagram.

[0011] As an optional implementation manner, performing key node extraction according to the impact degree matrix and the dynamic propagation path diagram to obtain a set of decision points includes: According to the impact degree matrix, perform coupling analysis processing to generate a candidate key node set; Based on the dynamic propagation path diagram, perform node impact propagation depth calculation processing to obtain a multi-dimensional feature vector of the node including path level weights; According to the candidate key node set and the multi-dimensional feature vector of the node, perform fusion processing using a dynamic decision tree algorithm to obtain a set of decision points.

[0012] As an optional implementation manner, performing priority calculation and visual identification processing according to the set of decision points to obtain a deviation node identification result includes: According to the set of decision points, perform matrix construction processing to obtain a weight assignment result of the target correlation degree; According to the weight assignment result, perform multi-dimensional weight fusion processing to obtain a decision point priority list; According to the decision point priority list, perform a progressive color mapping algorithm processing to obtain a visual node color identification; According to the visual node color identification, perform dynamic threshold comparison processing to generate a deviation detection result set; Based on the deviation detection result set, perform spatial coordinate positioning and symbol coding processing to obtain a deviation node identification result.

[0013] As an alternative implementation, the model optimization process based on the deviation node identification result to obtain an optimized task network model includes: Performing clustering analysis and association rule mining processing based on the deviation node identification result to obtain an updated influence path set; Performing multi-dimensional influence parameter recalculation processing based on the updated influence path set to obtain an optimized influence degree matrix; Performing dynamic adjustment processing of node space coordinates based on the optimized influence degree matrix to obtain the node layout of the optimized visualization flowchart; Performing task network model parameter update processing based on the node layout of the visualization flowchart to obtain an optimized task network model.

[0014] In a second aspect, the present invention provides a visualization process management device, including: A data acquisition module for acquiring input data on task status changes; A model initialization module for performing model initialization processing based on the input data to obtain a task network model; A path search module for performing path search using a graph traversal algorithm based on the task network model to obtain an influence path set; A matrix construction module for performing an influence matrix construction operation based on the influence path set to obtain an influence degree matrix; A path mapping module for performing dynamic influence path mapping based on the influence degree matrix and the task network model to obtain a dynamic propagation path diagram; A node extraction module for performing key node extraction based on the influence degree matrix and the dynamic propagation path diagram to obtain a decision point set; A visualization identification module for performing priority calculation and visualization identification processing based on the decision point set to obtain a deviation node identification result; A model optimization module for performing model optimization processing based on the deviation node identification result to obtain an optimized task network model.

[0015] In a third aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the visualization process management method described in any one of the above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention processes the many-to-many task relationships through an association rule mining algorithm and constructs a dynamic task network model, enabling comprehensive identification of direct and indirect dependencies between tasks, avoiding the defect of missing multi-level chain effects in traditional methods, and improving the coverage and accuracy of impact path analysis.

[0017] (2) The present invention uses graph traversal algorithms (depth-first and breadth-first search) to quickly generate a set of impact paths, combined with the visual mapping of a dynamic impact propagation path graph, supporting real-time updates of task status changes and their propagation paths. Through hierarchical rendering and color coding techniques, it intuitively displays the impact degree and priority of nodes, enhancing the user's dynamic monitoring ability of complex task networks.

[0018] (3) The present invention dynamically generates a priority list of decision points based on the analytic hierarchy process and assigns weights in combination with multi-dimensional data, significantly reducing the subjectivity of manual intervention, ensuring the objectivity of decision point identification and priority ranking, and improving decision-making efficiency.

[0019] (4) The present invention locates the source of deviation through a deviation analysis algorithm, dynamically updates the set of impact paths and the impact degree matrix, and adjusts the visual flow chart in combination with a node layout optimization algorithm to form a closed-loop feedback mechanism. This mechanism can continuously optimize the task network model, enhance the system's adaptability to dynamic changes, and ensure the continuous improvement of project management strategies.

[0020] (5) The present invention utilizes the threshold warning mechanism of the impact degree matrix to timely identify over-limit task nodes, combines the dynamic matching of resource allocation data to optimize resource allocation, reduces resource conflicts and waste, and simultaneously gives early warnings of potential risks.

[0021] (6) The present invention helps users quickly locate the source of problems, clarify the responsible nodes, and promote collaborative management across departments and links through a structured impact analysis data set and a traceable deviation analysis chain. Brief Description of the Drawings

[0022] Figure 1 is a schematic flowchart of a visual process management method provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a visual process management device provided by an embodiment of the present invention. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] Reference Figure 1 , the first embodiment of the present invention provides a visual process management method, including the following steps: S11, obtain input data of task status change; S12, perform model initialization processing according to the input data to obtain a task network model; S13, perform path search using a graph traversal algorithm according to the task network model to obtain an influence path set; S14, perform an influence matrix construction operation according to the influence path set to obtain an influence degree matrix; S15, perform dynamic influence path mapping according to the influence degree matrix and the task network model to obtain a dynamic propagation path graph; S16, perform key node extraction according to the influence degree matrix and the dynamic propagation path graph to obtain a decision point set; S17, perform priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; S18, perform model optimization processing according to the deviation node identification result to obtain an optimized task network model.

[0025] In step S11, obtain input data of task status change.

[0026] It should be noted that the acquisition and preprocessing operation of task status change data refers to extracting task status change information from the project management system through a data collection interface and performing data cleaning and regularization processing. This operation can provide accurate basic data for subsequent task network model construction and influence path analysis. In the embodiment of the present invention, the acquisition and preprocessing operation of task status change data is based on multi-source heterogeneous data and is realized through steps such as data collection and cleaning. In project management, using data preprocessing technology can significantly improve the quality and usability of task status data and ensure the reliability of subsequent analysis.

[0027] Among them, the data collection operation refers to extracting task status change data from the project management system through an API interface or database connection. The data collection process includes extracting key fields such as task ID, status type, timestamp, resource allocation, etc., to ensure the integrity and consistency of the data. The data cleaning operation refers to performing duplicate removal, missing value filling, and outlier filtering processing on the collected task status change data. Through data cleaning, duplicate records can be removed, missing values can be filled, and abnormal data can be eliminated to ensure the accuracy of subsequent analysis.

[0028] In step S12, the performing model initialization processing according to the input data to obtain a task network model includes: Perform duplicate value merging, missing value imputation, and outlier filtering on the input data to obtain regularized task data; According to the regularized task data, use the association rule mining algorithm to perform association matrix construction processing to generate a task association matrix; According to the task association matrix, perform dependent condition extraction processing to construct a task dependency graph with node weights; When the node weights of the task dependency graph are undefined, use the random initialization method to perform network parameter setting processing to obtain an initial task network model.

[0029] It should be noted that the initialization process of the task network model refers to the process of cleaning, association analysis, and dependent condition extraction of task status data, constructing a task dependency graph, and initializing network parameters. This operation can provide a structured task network foundation for subsequent impact path analysis and decision support. In the embodiments of the present invention, the initialization process of the task network model is based on regularized task data and is implemented through steps such as association rule mining, dependent condition extraction, and random initialization. In project management, using the task network model can significantly improve the recognition efficiency and accuracy of the impact of task status changes and ensure the reliability of subsequent analysis.

[0030] Among them, the data regularization operation refers to performing duplicate value merging, missing value imputation, and outlier filtering on the input data. Through data regularization, redundant data can be removed, missing information can be filled, and outliers can be removed to ensure the integrity and consistency of task status data. The association matrix construction operation refers to using the association rule mining algorithm to process the regularized task data to generate a task association matrix. Through association rule mining, many-to-many relationships between tasks can be discovered, providing support for the construction of the task dependency graph. In the embodiments of the present invention, the association rule mining algorithm used is the FP-Growth algorithm. Of course, according to different actual application scenarios and user requirements, the association rule mining algorithm can also use the Apriori algorithm or other algorithms, and the present invention does not limit this. The dependent condition extraction operation refers to extracting the dependent conditions between tasks according to the task association matrix and the execution order. Through dependent condition extraction, a task dependency graph with node weights can be constructed, providing basic data for the initialization of the task network model. The network parameter initialization operation refers to using the random initialization method to set network parameters when the node weights of the task dependency graph are undefined. Through random initialization, initial parameter values can be provided for the task network model to ensure the stability and convergence of subsequent analysis. Through the above steps, the initialization process of the task network model can generate a structured task dependency graph and initial network parameters, ensuring the accuracy of subsequent impact path analysis and decision support, and providing reliable technical support for visual process management in project management.

[0031] In step S13, according to the task network model, a graph traversal algorithm is used to perform path search to obtain an influence path set, including: According to the task network model, perform graph traversal algorithm processing to generate a task node set including directly influenced nodes and indirectly influenced nodes; According to the task node set, perform node type discrimination processing to generate a labeled node type list; According to the node type list, perform path search processing to obtain complete influence paths from each task node to the root node; According to the complete influence paths, perform duplicate path merging and redundant node elimination processing to obtain an optimized influence path set.

[0032] It should be noted that the generation process of the influence path set refers to the process of searching the task network model through a graph traversal algorithm, identifying the direct and indirect nodes affected by the change of the task state, and generating an optimized influence path set. This operation can provide key path information for the subsequent construction of the influence degree matrix and the identification of decision points. In the embodiments of the present invention, the generation process of the influence path set is based on the task network model and is implemented through steps such as graph traversal, node type discrimination, path search, and path optimization. In project management, using a graph traversal algorithm can significantly improve the efficiency and accuracy of influence path identification and ensure the reliability of subsequent analysis.

[0033] Among them, the graph traversal algorithm processing operation refers to traversing the task network model using a search algorithm to generate a set of task nodes that includes directly affected nodes and indirectly affected nodes. Through the graph traversal algorithm, all nodes affected by the change in task status can be quickly located, providing basic data for subsequent path analysis. In the embodiment of the present invention, the search algorithm adopted is the depth-first search algorithm. Of course, according to different actual application scenarios and user requirements, other algorithms such as the breadth-first search algorithm can also be adopted, and the present invention does not limit this. The node type discrimination processing operation refers to classifying the set of task nodes according to the relationship between the task nodes and the state change source node to generate a node type list with direct / indirect identifiers. Through node type discrimination, the primary influence and multi-level chain influence can be clearly distinguished, facilitating the subsequent adoption of targeted measures. The path search processing operation refers to processing the task nodes in the node type list to generate a complete influence path from each task node to the root node. Through path search, the influence propagation path of the task status change can be traced, providing support for influence degree analysis. In the embodiment of the present invention, the path search algorithm adopted is the backtracking algorithm. Of course, according to different actual application scenarios and user requirements, other algorithms such as the shortest path algorithm can also be adopted, and the present invention does not limit this. The path optimization processing operation refers to performing duplicate path merging and redundant node elimination processing on the complete influence path to generate an optimized set of influence paths. Through path optimization, duplicate paths and redundant nodes can be removed, improving the accuracy and readability of the set of influence paths.

[0034] In step S14, the operation of constructing an influence matrix according to the set of influence paths to obtain an influence degree matrix includes: Calculating the time offset and the change amount of resource requirements according to the set of influence paths to obtain a two-dimensional influence numerical set; Based on the two-dimensional influence numerical set, performing matrix construction algorithm processing to obtain an initial influence matrix including time-resource coupling relationships; Performing a preset threshold judgment process on the initial influence matrix, and triggering a warning flag generation instruction when the node value exceeds the preset threshold; According to the warning flag generation instruction, performing node clustering and priority classification processing to obtain an influence degree matrix.

[0035] It should be noted that the construction process of the impact degree matrix refers to the process of generating a two-dimensional impact value set by calculating the time offset and the change in resource requirements of task nodes, and constructing a matrix containing the time-resource coupling relationship based on this. This operation can provide quantitative impact degree data for subsequent decision point identification and priority calculation. In the embodiments of the present invention, the construction process of the impact degree matrix is implemented through steps such as time offset calculation, resource requirement change calculation, matrix construction, threshold judgment, and node clustering based on the impact path set. In project management, using the impact degree matrix can significantly improve the quantitative analysis ability of the impact of task status changes and ensure the accuracy of subsequent decision-making support.

[0036] Among them, the two-dimensional impact value calculation operation refers to calculating the time offset and the change in resource requirements according to the task nodes in the impact path set to generate a two-dimensional impact value set. Through the time offset calculation, the impact of task status changes on the time schedule can be quantified; through the calculation of the change in resource requirements, the impact of task status changes on resource allocation can be quantified. The matrix construction algorithm processing operation refers to generating an initial impact matrix containing the time-resource coupling relationship by using the matrix construction algorithm based on the two-dimensional impact value set. Through matrix construction, the time offset and the change in resource requirements can be mapped into the matrix to form quantitative impact degree data. The threshold judgment processing operation refers to performing a preset threshold judgment on the node values in the initial impact matrix, and triggering a warning flag generation instruction when the node value exceeds the preset threshold. Through threshold judgment, task nodes with a greater impact degree can be quickly identified to provide support for subsequent warnings and decisions. In the embodiments of the present invention, the preset threshold is 80. Of course, according to different actual application scenarios and user requirements, it can also be set to other values such as 70 and 90. The present invention does not limit this. The node clustering and priority classification processing operation refers to classifying the task nodes by using a clustering algorithm according to the warning flag generation instruction to generate an impact degree matrix. Through node clustering, the task nodes can be divided into high, medium, and low impact levels; through priority classification, the critical task nodes and non-critical task nodes can be determined. In the embodiments of the present invention, the hierarchical clustering algorithm is used as the clustering algorithm. Of course, according to different actual application scenarios and user requirements, other clustering algorithms such as the K-means clustering algorithm can also be used. The present invention does not limit this. Through the above steps, the construction process of the impact degree matrix can generate quantitative impact degree data, ensure the accuracy of subsequent decision point identification and priority calculation, and provide reliable technical support for the visual process management in project management.

[0037] In step S15, the dynamic impact path mapping according to the impact degree matrix and the task network model to obtain a dynamic propagation path diagram includes: Based on the influence degree matrix and the task network model, a multi-dimensional parameter mapping algorithm is adopted to perform node-path association processing to generate an initial dynamic propagation path map; According to the initial dynamic propagation path map, perform visual hierarchical rendering processing to generate an enhanced path map; According to the enhanced path map, perform target node data extraction to obtain a structured influence analysis data set; According to the structured influence analysis data set, perform outlier detection and trend prediction processing to obtain node prediction parameters; According to the node prediction parameters, perform dynamic update processing of the path map to obtain a dynamic propagation path map.

[0038] It should be noted that the generation process of the dynamic propagation path map refers to the process of associating the influence degree matrix with the task network model through a multi-dimensional parameter mapping algorithm to generate an initial dynamic propagation path map, and generating the final dynamic propagation path map through steps such as visual hierarchical rendering, target node data extraction, and trend prediction. This operation plays an important role in visual process management and can provide an intuitive visual display for subsequent decision support and deviation analysis. In the embodiments of the present invention, the generation process of the dynamic propagation path map is based on the influence degree matrix and the task network model and is realized through steps such as multi-dimensional parameter mapping, visual hierarchical rendering, outlier detection, and trend prediction. In project management, using the dynamic propagation path map can significantly improve the intuitiveness and operability of the impact of task status changes and ensure the accuracy and efficiency of subsequent analysis.

[0039] Among them, the multi-dimensional parameter mapping algorithm processing operation refers to performing node-path association processing based on the influence degree matrix and the task network model using the multi-dimensional parameter mapping algorithm to generate an initial dynamic propagation path graph. Through multi-dimensional parameter mapping, parameters such as time offset, resource demand change amount, and node weight can be mapped into the task network model to form an initial dynamic propagation path graph. The visual hierarchical rendering processing operation refers to performing hierarchical rendering processing on the initial dynamic propagation path graph to generate an enhanced path graph. Through visual hierarchical rendering, information such as influence intensity, urgency, and node type can be marked with different colors, shapes, and sizes to improve the readability and intuitiveness of the path graph. The target node data extraction operation refers to extracting detailed influence data of the target node from the enhanced path graph according to user interaction behavior to generate a structured influence analysis data set. Through target node data extraction, basic data can be provided for subsequent outlier detection and trend prediction. The outlier detection and trend prediction processing operation refers to performing outlier detection and trend prediction processing on the structured influence analysis data set to generate node prediction parameters. Through outlier detection, task nodes with large deviations can be identified; through trend prediction, the future influence trend of the node can be predicted to provide support for dynamic update of the path graph. The path graph dynamic update processing operation refers to performing real-time update processing on the dynamic propagation path graph according to the node prediction parameters to generate a final dynamic propagation path graph. Through path graph dynamic update, the influence propagation trend of task state changes can be reflected in real time to provide intuitive visual support for project management decisions.

[0040] In step S16, the extracting of key nodes according to the influence degree matrix and the dynamic propagation path graph to obtain a decision point set includes: Performing coupling analysis processing according to the influence degree matrix to generate a candidate key node set; Performing node influence propagation depth calculation processing based on the dynamic propagation path graph to obtain a node multi-dimensional feature vector including path level weights; Performing fusion processing on the candidate key node set and the node multi-dimensional feature vector using a dynamic decision tree algorithm to obtain a decision point set.

[0041] It should be noted that the generation process of the decision point set refers to the process of extracting key nodes from the influence degree matrix and the dynamic propagation path diagram through coupling analysis, influence propagation depth calculation, and dynamic decision tree algorithm, and generating the decision point set. This operation plays an important role in visual process management and can provide key node information for subsequent priority calculation and decision support. In the embodiment of the present invention, the generation process of the decision point set is based on the influence degree matrix and the dynamic propagation path diagram, and is realized through steps such as coupling analysis, influence propagation depth calculation, and dynamic decision tree algorithm. In project management, using the decision point set can significantly improve the recognition efficiency and accuracy of key nodes and ensure the reliability of subsequent decision support.

[0042] Among them, the coupling analysis processing operation refers to performing coupling analysis processing according to the time offset and resource demand change amount in the influence degree matrix to generate a candidate key node set. Through coupling analysis, task nodes with relatively large time offset and resource demand change amount can be identified, providing candidate data for subsequent decision point extraction. The influence propagation depth calculation processing operation refers to calculating the influence propagation depth of nodes based on the dynamic propagation path diagram to generate a node multi-dimensional feature vector including path level weights. Through the influence propagation depth calculation, the influence range of nodes in the task network can be quantified, providing multi-dimensional feature data for decision point extraction. The dynamic decision tree algorithm processing operation refers to using the dynamic decision tree algorithm to perform fusion processing on the candidate key node set and the node multi-dimensional feature vector to generate a decision point set. Through the dynamic decision tree algorithm, factors such as time offset, resource demand change amount, and influence propagation depth can be comprehensively considered to extract key decision points, providing support for subsequent priority calculation and decision support.

[0043] In step S17, the priority calculation and visual identification processing are performed according to the decision point set to obtain a deviation node identification result, including: According to the decision point set, matrix construction processing is performed to obtain a weight allocation result of the target correlation degree; According to the weight allocation result, multi-dimensional weight fusion processing is performed to obtain a decision point priority list; According to the decision point priority list, progressive color mapping algorithm processing is performed to obtain a visual node color identification; According to the visual node color identification, dynamic threshold comparison processing is performed to generate a deviation detection result set; Based on the deviation detection result set, spatial coordinate positioning and symbol coding processing are performed to obtain a deviation node identification result.

[0044] It should be noted that the generation process of the deviation node identification result refers to the process of calculating the priority and visual identification of the decision point set through steps such as matrix construction, multi-dimensional weight fusion, progressive color mapping, and dynamic threshold comparison, so as to generate the deviation node identification result. This operation plays an important role in the visual process management and can provide key node information for subsequent deviation analysis and closed-loop adjustment. In the embodiment of the present invention, the generation process of the deviation node identification result is based on the decision point set and is implemented through steps such as matrix construction, multi-dimensional weight fusion, progressive color mapping, and dynamic threshold comparison. In project management, using the deviation node identification result can significantly improve the efficiency and accuracy of deviation detection and ensure the reliability of subsequent closed-loop adjustment.

[0045] Among them, the matrix construction processing operation refers to constructing a judgment matrix using the analytic hierarchy process (AHP) according to the decision point set to generate the weight distribution result of the target correlation degree. Through matrix construction, the correlation degree between the decision point and the project goal can be quantified, providing basic data for priority calculation. The multi-dimensional weight fusion processing operation refers to performing weight fusion processing according to the weight distribution result in combination with multi-dimensional parameters such as time sensitivity, resource constraints, and influence propagation depth to generate a decision point priority list. Through multi-dimensional weight fusion, multiple influencing factors can be comprehensively considered to improve the accuracy of priority calculation. The progressive color mapping algorithm processing operation refers to performing color identification processing on the nodes using the progressive color mapping algorithm according to the decision point priority list to generate a visual node color identification. Through progressive color mapping, the importance degree of the decision point can be intuitively displayed, facilitating managers to quickly identify key nodes. The dynamic threshold comparison processing operation refers to performing dynamic threshold comparison on the progress value and resource value in the task execution data stream according to the visual node color identification to generate a deviation detection result set. Through dynamic threshold comparison, task nodes with large deviations can be quickly identified, providing support for subsequent deviation analysis. The spatial coordinate positioning and symbol coding processing operation refers to performing spatial coordinate positioning and symbol coding processing on the deviation nodes based on the deviation detection result set to generate the deviation node identification result. Through spatial coordinate positioning, the position of the deviation node can be accurately identified; through symbol coding, the type and degree of the deviation node can be intuitively displayed.

[0046] In step S18, the model optimization processing is performed according to the deviation node identification result to obtain an optimized task network model, including: Performing clustering analysis and association rule mining processing according to the deviation node identification result to obtain an updated influence path set; Based on the updated influence path set, performing multi-dimensional influence parameter recalculation processing to obtain an optimized influence degree matrix; According to the optimized impact degree matrix, perform dynamic adjustment processing on the node space coordinates to obtain the optimized node layout of the visual flow chart; According to the node layout of the visual flow chart, perform parameter update processing on the task network model to obtain an optimized task network model.

[0047] It should be noted that the generation process of the optimized task network model refers to the process of processing the deviation node identification result through steps such as clustering analysis, association rule mining, multi-dimensional impact parameter recalculation, and dynamic adjustment of node space coordinates to generate an optimized task network model. This operation plays an important role in visual flow management and can provide an optimized task network basis for subsequent reconstruction of the dynamic impact propagation path and closed-loop adjustment. In the embodiments of the present invention, the generation process of the optimized task network model is based on the deviation node identification result and is implemented through steps such as clustering analysis, association rule mining, multi-dimensional impact parameter recalculation, and dynamic adjustment of node space coordinates. In project management, using the optimized task network model can significantly improve the dynamic adjustment ability of the impact of task status changes and ensure the accuracy and efficiency of subsequent closed-loop adjustment.

[0048] Among them, the clustering analysis and association rule mining processing operation refers to classifying and associating the deviation nodes using a clustering algorithm and an association rule mining algorithm according to the deviation node identification result to generate an updated impact path set. Through clustering analysis, the common characteristics of the deviation nodes can be identified; through association rule mining, the implicit relationships between the deviation nodes can be discovered to provide support for impact path update. The multi-dimensional impact parameter recalculation processing operation refers to recalculating multi-dimensional impact parameters such as time offset, resource demand change amount, and node weight based on the updated impact path set to generate an optimized impact degree matrix. Through multi-dimensional impact parameter recalculation, the impact degree of the deviation nodes on the task network can be quantified to provide basic data for node layout optimization. The dynamic adjustment processing operation of node space coordinates refers to dynamically adjusting the node space coordinates using a force-directed graph algorithm or a hierarchical layout algorithm according to the optimized impact degree matrix to generate the optimized node layout of the visual flow chart. Through dynamic adjustment of node space coordinates, the layout and link relationship of task nodes can be optimized, and the readability and intuitiveness of the visual flow chart can be improved. The parameter update processing operation of the task network model refers to updating parameters such as node weight, link relationship, and impact propagation path of the task network model according to the optimized node layout of the visual flow chart to generate an optimized task network model. Through parameter update of the task network model, the consistency between the task network model and the visual flow chart can be ensured to provide support for subsequent closed-loop adjustment.

[0049] Refer to Figure 2 , the second embodiment of the present invention provides a visual flow management device, including: A data acquisition module for acquiring input data on task status changes; A model initialization module for performing model initialization processing based on the input data to obtain a task network model; A path search module for performing path search using a graph traversal algorithm based on the task network model to obtain a set of impact paths; A matrix construction module for performing an impact matrix construction operation based on the set of impact paths to obtain an impact degree matrix; A path mapping module for performing dynamic impact path mapping based on the impact degree matrix and the task network model to obtain a dynamic propagation path graph; A node extraction module for performing key node extraction based on the impact degree matrix and the dynamic propagation path graph to obtain a set of decision points; A visualization identification module for performing priority calculation and visualization identification processing based on the set of decision points to obtain a deviation node identification result; A model optimization module for performing model optimization processing based on the deviation node identification result to obtain an optimized task network model.

[0050] It should be noted that a visualized process management device provided by an embodiment of the present invention is used to execute all process steps of a visualized process management method in the above embodiment. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated herein.

[0051] Compared with the prior art, the present invention has the following beneficial effects: (1) By using an association rule mining algorithm to process many-to-many task relationships and combining the construction of a dynamic task network model, the present invention can comprehensively identify direct and indirect dependency relationships between tasks, avoid the defect of missing multi-level chain effects in traditional methods, and improve the coverage and accuracy of impact path analysis.

[0052] (2) The present invention uses a graph traversal algorithm (depth-first and breadth-first search) to quickly generate a set of impact paths, combined with the visual mapping of a dynamic impact propagation path graph, to support real-time updating of task status changes and their propagation paths. Through hierarchical rendering and color coding techniques, it intuitively displays the node impact degree and priority, enhancing the user's dynamic monitoring ability of complex task networks.

[0053] (3) Based on the analytic hierarchy process, the present invention dynamically generates a decision point priority list and combines multi-dimensional data for weight allocation, significantly reducing the subjectivity of manual intervention, ensuring the objectivity of decision point identification and priority ranking, and improving decision-making efficiency.

[0054] (4) The present invention locates the source of deviation through a deviation analysis algorithm, dynamically updates the influence path set and the influence degree matrix, and adjusts the visual flowchart in combination with a node layout optimization algorithm to form a closed-loop feedback mechanism. This mechanism can continuously optimize the task network model, improve the adaptability of the system to dynamic changes, and ensure the continuous improvement of project management strategies.

[0055] (5) The present invention utilizes the threshold warning mechanism of the influence degree matrix to timely identify over-limit task nodes, combines the dynamic matching of resource allocation data to optimize resource allocation, reduces resource conflicts and waste, and at the same time gives early warnings of potential risks.

[0056] (6) The present invention helps users quickly locate the source of problems, clarify the responsible nodes, and promote collaborative management across departments and links through a structured influence analysis dataset and a traceable deviation analysis chain.

[0057] An embodiment of the present invention also provides a terminal device. The terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a visual process management program. When the processor executes the computer program, it implements the steps in the above-mentioned embodiments of each visual process management method, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-mentioned system embodiments.

[0058] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the terminal device.

[0059] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the terminal device and do not constitute a limitation on the terminal device. It may include more or fewer components than the above, or combine some components, or different components. For example, the terminal device may further include input / output devices, network access devices, a bus, etc.

[0060] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device through various interfaces and lines.

[0061] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0062] Among them, if the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. 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-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or system, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0063] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the accompanying drawings of the system embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0064] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A visual process management method, characterized in that including: Obtaining input data on task status changes; Performing model initialization processing according to the input data to obtain a task network model; Performing path search using a graph traversal algorithm according to the task network model to obtain a set of influence paths; Performing an influence matrix construction operation according to the set of influence paths to obtain an influence degree matrix; Performing dynamic influence path mapping according to the influence degree matrix and the task network model to obtain a dynamic propagation path graph; Performing key node extraction according to the influence degree matrix and the dynamic propagation path graph to obtain a set of decision points; Performing priority calculation and visualization identification processing according to the set of decision points to obtain a deviation node identification result; Performing model optimization processing according to the deviation node identification result to obtain an optimized task network model.

2. The visualized process management method according to claim 1, wherein The performing model initialization processing according to the input data to obtain a task network model includes: Performing duplicate value merging, missing value imputation, and outlier filtering processing on the input data to obtain regular task data; Performing an association matrix construction process using an association rule mining algorithm according to the regular task data to generate a task association matrix; Performing a dependency condition extraction process according to the task association matrix to construct a task dependency graph with node weights; When the node weights of the task dependency graph are undefined, using a random initialization method to perform network parameter setting processing to obtain an initial task network model.

3. The visualized process management method according to claim 1, wherein The performing path search using a graph traversal algorithm according to the task network model to obtain a set of influence paths includes: Performing a graph traversal algorithm process according to the task network model to generate a task node set including direct influence nodes and indirect influence nodes; Performing a node type discrimination process according to the task node set to generate a labeled node type list; Performing a path search process according to the node type list to obtain complete influence paths from each task node to the root node; Performing duplicate path merging and redundant node removal processing according to the complete influence paths to obtain an optimized set of influence paths.

4. The visualized process management method according to claim 1, wherein The performing an influence matrix construction operation according to the set of influence paths to obtain an influence degree matrix includes: Performing time offset calculation and resource requirement change amount calculation processing according to the set of influence paths to obtain a two-dimensional influence numerical set; Performing a matrix construction algorithm process based on the two-dimensional influence numerical set to obtain an initial influence matrix including time-resource coupling relationships; Performing a preset threshold judgment process on the initial influence matrix, and triggering a warning label generation instruction when the node value exceeds the preset threshold; Performing node clustering and priority classification processing according to the warning label generation instruction to obtain an influence degree matrix.

5. The visualized process management method according to claim 1, wherein The performing dynamic influence path mapping according to the influence degree matrix and the task network model to obtain a dynamic propagation path graph includes: Performing node-path association processing using a multi-dimensional parameter mapping algorithm based on the influence degree matrix and the task network model to generate an initial dynamic propagation path graph; According to the initial dynamic propagation path diagram, perform visual hierarchical rendering processing to generate an enhanced path diagram; According to the enhanced path diagram, perform target node data extraction to obtain a structured impact analysis data set; According to the structured impact analysis data set, perform outlier detection and trend prediction processing to obtain node prediction parameters; According to the node prediction parameters, perform dynamic update processing of the path diagram to obtain a dynamic propagation path diagram.

6. The visualized process management method according to claim 1, wherein The step of performing key node extraction according to the impact degree matrix and the dynamic propagation path diagram to obtain a set of decision points includes: According to the impact degree matrix, perform coupling analysis processing to generate a candidate key node set; Based on the dynamic propagation path diagram, perform node impact propagation depth calculation processing to obtain a node multi-dimensional feature vector including path level weights; According to the candidate key node set and the node multi-dimensional feature vector, adopt a dynamic decision tree algorithm for fusion processing to obtain a set of decision points.

7. The visualized process management method according to claim 1, characterized in that The step of performing priority calculation and visual identification processing according to the set of decision points to obtain a deviation node identification result includes: According to the set of decision points, perform matrix construction processing to obtain a weight assignment result of the target correlation degree; According to the weight assignment result, perform multi-dimensional weight fusion processing to obtain a decision point priority list; According to the decision point priority list, perform a progressive color mapping algorithm processing to obtain a visual node color identification; According to the visual node color identification, perform dynamic threshold comparison processing to generate a deviation detection result set; Based on the deviation detection result set, perform spatial coordinate positioning and symbol coding processing to obtain a deviation node identification result.

8. The visualized process management method according to claim 1, wherein The step of performing model optimization processing according to the deviation node identification result to obtain an optimized task network model includes: According to the deviation node identification result, perform clustering analysis and association rule mining processing to obtain an updated impact path set; Based on the updated impact path set, perform multi-dimensional impact parameter recalculation processing to obtain an optimized impact degree matrix; According to the optimized impact degree matrix, perform dynamic adjustment processing of node spatial coordinates to obtain an optimized visual flowchart node layout; According to the visual flowchart node layout, perform task network model parameter update processing to obtain an optimized task network model.

9. A visual process management device, characterized in that Including: A data acquisition module for acquiring input data on task status changes; A model initialization module for performing model initialization processing according to the input data to obtain a task network model; A path search module for performing path search using a graph traversal algorithm according to the task network model to obtain an impact path set; A matrix construction module for performing impact matrix construction operations according to the impact path set to obtain an impact degree matrix; A path mapping module for performing dynamic impact path mapping according to the impact degree matrix and the task network model to obtain a dynamic propagation path diagram; A node extraction module for performing key node extraction according to the impact degree matrix and the dynamic propagation path diagram to obtain a set of decision points; A visualization identification module, configured to perform priority calculation and visualization identification processing according to the set of decision points, so as to obtain a deviation node identification result; A model optimization module, configured to perform model optimization processing according to the deviation node identification result, so as to obtain an optimized task network model.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the visualized process management method according to any one of claims 1 to 8.

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