INTELLIGENT METHOD AND SYSTEM FOR TASK DISTRIBUTION AND PROGRESS MONITORING FOR CROSS-DEPARTMENT PROJECT COLLABORATION

Description

2. The tracking of individual tasks, from departmental summaries to project-wide overviews, is prone to discrepancies and delays due to inconsistent tracking standards, asynchronous updates, and complex association relationships. As a result, managers are frequently confronted with distorted or outdated information at critical decision points.5 Therefore, accurate, real-time, and consistently correlated tracking and representation of the multi-stage progress of cross-departmental tasks—from the specific work items through the entire departmental level to the project-wide overview—becomes a crucial challenge for ensuring efficient collaboration in cross-departmental projects.10. CONTENT OF THE PRESENT INVENTION The present invention proposes an intelligent method and system for tracking tasks distribution and progress monitoring for cross-departmental project collaboration,to solve the problems existing in the state of the art.15 To achieve the above-mentioned objectives, this invention provides an intelligent method for task allocation and progress monitoring for cross-departmental project collaboration, which includes the following: processing heterogeneous data by applying a standardized conversion protocol based on the fine-grained progress data collected by executors from different departments, to obtain uniformly formatted individual task progress data; grouping task dependencies by means of a hierarchical clustering algorithm based on the uniformly formatted individual task progress data, and recursively merging nested hierarchical data,to determine 25 summary progress data at the department level; detect and update timestamp deviations in the summary progress data at the department level using a time series analysis algorithm based on the summary progress data at the department level, and trigger a synchronization mechanism to adjust delayed data in order to obtain real-time synchronized progress data of the departments; Constructing a project-wide overview using a mapping function based on the real-time synchronized progress data of the departments, and iteratively correcting divergent links in the association paths to obtain consistent global progress association data; 35 BE2026 / 7104 3 Extracting multi-level nested features based on the consistent global progress association data, and unfolding a nested structure and filling in missing links through an ecursive traversal,to obtain a consistent multi-level progress view; generating dynamic charts using its visualization rendering engine based on the consistent multi-level progress view, and injecting tracking markers to update non-real-time dependencies to determine accurately represented progress information; and distributing the accurately represented progress information to project management endpoints via a feedback loop module, and performing traceability and consistency checks for discrepancies between distributed data and the original aggregation to obtain finally validated cross-departmental collaboration progress data. Optionally, processing heterogeneous data includes applying a standardized conversion protocol based on the fine-grained progress data collected by the executing departments to obtain uniformly formatted individual task progress data.The following: traversing distributed nodes to obtain an original execution log sequence containing executor identifiers, work time granularity values, and fingerprint information for the deliverable; analyzing the original execution log sequence and marking inconsistent data fragments as heterogeneous fields if their data structure differs from a predefined metadata base value; indexing the heterogeneous fields based on their type attributes to the corresponding mapping rules and performing a format conversion to generate standardized fields; and combining the standardized fields with non-heterogeneous data and outputting a normalized task log to obtain the uniformly formatted individual task progress data. Optionally, the grouping of task dependencies using a hierarchical clustering algorithm based on uniformly formatted individual task progress data is included.and the recursive merging of nested hierarchical data to determine summary progress data at the department level, the following: creating a task dependency tree diagram based on the individual task progress data; BE2026 / 7104 4 traversing a task dependency tree diagram, marking nodes with a nested hierarchy and a parent-child node assignment relationship when an internal distance of a branch node satisfies predefined conditions; performing a bottom-to-top recursive traverse based on the parent-child node assignment relationship, merging progress values ​​of the five subordinate nodes with their corresponding parent nodes to generate fused progress data; and mapping the fused progress data to organizational units,to determine the summary progress data at the department level. Optionally includes detecting and updating timestamp deviations in the summary progress data at the department level using a time series analysis algorithm based on the summary progress data at the department level, and triggering a synchronization mechanism to adjust delayed data in order to obtain real-time synchronized department progress data, following: Analyze work logs captured from the department progress source to generate independent data fragments and multidimensional timestamp sequences with task identifiers; Inputting multidimensional timestamp sequences into an inset window time series model to determine a standard reference time, and calculating a delay deviation between a write time of the independent data fragments and the standard reference time; and generating a forced update command,to obtain a latest progress value when the delay variance exceeds a preset threshold, and updating the independent data fragments with the latest progress value to obtain the real-time synchronized progress data of the departments. Optionally includes constructing a project-wide overview using the mapping function based on the real-time synchronized progress data of the departments, and iteratively correcting divergent links in the association paths to obtain consistent global progress association data. This includes capturing the real-time synchronized progress data of the departments and building an initial project view with a task node topology structure using the mapping function. BE2026 / 7104 5 Scanning the initial project view to calculate a path deviation value, and locking a set of association paths that includes anomalous link points,when the path deviation value exceeds a threshold; calculate an iterative correction factor based on the assignment path set and insert the iterative correction factor into the anomalous link points to perform a logistic regression to generate a corrected association table; and reorganize the task nodes using the corrected association table to determine the consistent global progress association data. Optionally include extracting multi-level nested features based on the consistent global progress association data, and unfolding a nested structure and filling in missing links through an italic traversal to obtain a consistent multi-level progress view. This includes analyzing metadata on parent-child dependencies between tasks from the consistent global progress association data.to extract independent features with multi-layered nested relationships; performing a depth search on the multi-layered, nested features using a recursive traversal algorithm to obtain a shallow task node sequence, and calculating a logical predecessor-successor degree of similarity between neighboring nodes in the sequence; if the degree of similarity indicates that transformation conditions are missing between the neighboring nodes, generating a virtual link object with transition logic and inserting it in place of the missing link in the task node sequence; and obtaining the continuous multi-level progress view based on the completed task node sequence. Optional features include generating dynamic charts using a visualization rendering engine based on the continuous multi-level progress view, and injecting trace markers to update non-real-time dependencies.To determine the precisely displayed progress information, the following: generating visualization rendering commands based on the multi-level progress view, creating a dynamic diagram object and extracting a dependency relationship matrix; 35 BE2026 / 7104 6 comparing the dependency relationship matrix with a real-time status snapshot and, if logical discrepancies are detected, generating a tracking marker code; injecting the tracking marker code into the dynamic diagram object, locating primitives that do not respond to changes, generating a rendering update queue and setting local redraw areas; and updating the local redraw areas to determine the precisely displayed progress information. Optionally, the distribution of precisely displayed progress information to 10 project management end devices includes a feedback loop module.and performing traceability and consistency checks in case of discrepancies between distributed data and original aggregation, in order to obtain finally validated cross-departmental collaboration progress data, the following: capturing an original aggregate data set generated by cleaning and reorganizing real-time task logs15, and forwarding it to the project management endpoints after processing by a feedback loop module; Capturing progress data displayed on the terminal devices and their non-conformity with the original aggregate data set, locking transmission path nodes and extracting code conversion protocols to a consistency comparison in order to locate data distortion segments; and retrieving redundant backups of a source page for error correction and reconstruction according to the data distortion segments,to obtain the finally validated, cross-departmental collaboration progress data. The present invention proposes, in addition to an intelligent system for task distribution and progress monitoring for cross-departmental project collaboration, comprising: a data acquisition and standardization module that serves to acquire and standardize fine-grained progress data from different departments in order to obtain uniformly formatted individual task progress data; a task dependency grouping and merging module that serves to perform hierarchical clustering of the task dependencies based on the uniformly formatted individual task progress data and to recursively merge nested data in order to obtain summary progress data at the department level; a time series synchronization adjustment module that serves toTo detect and synchronize timestamp deviations in the summary progress data at the department level in order to obtain real-time synchronized progress data for the departments; a global association mapping correction module that serves to construct a project-wide overview based on the real-time synchronized progress data of the departments and iteratively correct divergent links in the association paths in order to obtain consistent global progress association data; a module for unfolding and populating multi-level features that serves to unfold a multi-level nested structure from the global progress association data and to populate missing links in order to obtain a consistent multi-level progress view; A module for updating the dynamic visualization rendering, which serves to generate dynamic diagrams based on the multi-level progress view and to update dependency relationships through tracking markers.to obtain precisely15 presented progress information; and a module for feedback distribution and traceability verification, which serves to distribute the progress information to project management terminals and, in case of data discrepancies, to trace and verify them in order to obtain finally validated cross-departmental collaboration progress data.20 Compared to the prior art, the present invention offers the following advantages and technical effects. The present invention describes a technical solution for cross-departmental collaboration progress management. It addresses the challenges of heterogeneous progress data, complex dependencies, difficulties with real-time synchronization, and insufficient consistency of the overall view in cross-departmental collaboration and enables efficient management through innovative methods. First, the present invention processes heterogeneous data using a standardized conversion protocol,to ensure a uniform format for the individual task progress data; subsequently, hierarchical clustering algorithms and recursive merging techniques are used to create summary data at the department level; time series analysis and synchronization mechanisms ensure the real-time capability of the data; a consistent overall view of progress is created using association mapping and iterative correction; finally, precise, multi-level progress information is generated through recursive traversal, missing link filling, and dynamic visualization rendering and, after consistency checking, distributed to the project management terminals via feedback loops. The core innovation of the present invention consists in the To solve the challenges of complexity and consistency in cross-departmental progress management through multi-stage data processing and real-time synchronization mechanisms,which significantly improves the transparency and efficiency of project management and provides reliable technical support for large-scale, cross-departmental collaboration. DESCRIPTION OF THE DRAWINGS The attached drawings, which form part of this application, serve to facilitate a better understanding of the present application. The exemplary embodiments and descriptions of the present application serve to explain this application and do not constitute an inadmissible limitation of this application. In the drawings: Fig. 1 is a flow diagram of a process according to an embodiment of the present invention; and Fig. 2 is a system structure diagram of an embodiment of the present invention. DETAILED DESCRIPTION It should be noted that the forms of execution and features described in this application are combinable unless otherwise stated. The application is now described in detail with reference to the attached drawings and forms of execution. It should be noted thatthat the steps shown in the flowchart in the accompanying drawings can be executed in a computer system like a series of computer-executable instructions, and although a logical sequence is shown in the flowchart, the steps shown or described may in some cases be executed in a different order than the one shown here. Implementation 1 As shown in Figure 1, this implementation provides an intelligent method 30 and system for task allocation and progress monitoring for cross-departmental project collaboration, including: processing heterogeneous data by applying a standardized conversion protocol based on the fine-grained progress data collected by executors from different departments,to obtain uniformly formatted individual task progress data; BE2026 / 7104 9 Grouping of task dependencies using a hierarchical clustering algorithm based on the uniformly formatted individual task progress data, and recursively merging nested hierarchical data to determine summary progress data at the department level; Detecting and updating timestamp deviations in the summary progress data at the department level using a time series analysis algorithm based on the summary progress data at the department level, and triggering a synchronization mechanism to adjust delayed data to obtain real-time synchronized department progress data; Constructing a project-wide overview using a mapping function based on the real-time synchronized progress data of the departments, and iteratively correcting deviating links in the association paths,to obtain consistent global progress association data; extracting multi-level nested features based on the consistent global progress association data, and unfolding a nested structure and 15 filling in missing links by ecursive traversal to obtain a consistent multi-level progress view; generating dynamic charts using its visualization rendering engine based on the consistent multi-level progress view, and injecting tracking markers to update dependencies not mapped in real time, 20 determining precisely represented progress information; and distributing the precisely represented progress information to project management endpoints via a feedback loop module, and performing traceability and consistency checks on inconsistencies between distributed data and original aggregation,to obtain finally validated cross-departmental collaboration progress data. Furthermore, processing heterogeneous data by applying a standardized conversion protocol based on the fine-grained progress data collected by executors from different departments, to obtain uniformly formatted individual task progress data, includes the following: 30 traversing distributed nodes to obtain an original execution log sequence containing executor identifiers, work time granularity values, and fingerprint information for the deliverable; analyzing the original execution log sequence and marking inconsistent data fragments as heterogeneous fields if their data structure deviates from a 35 predefined metadata base value; BE2026 / 7104 10 Indexing heterogeneous fields based on their type characteristics to the corresponding mapping rules and performing a format conversion,to generate standardized fields; and combining standardized fields with non-heterogeneous data and outputting a normalized task log to obtain uniformly formatted individual task progress data. In a distributed task management system, for example, traversing distributed nodes to determine executor IDs, work time granularity values, and deliverable fingerprint information is a critical step. Suppose that in a software development project, several development nodes are distributed across different regions, each responsible for developing a specific module. The executor ID can be a unique developer ID such as "DEV-001"; the work time granularity value the work time recorded in hours, for example, 8 hours; and the deliverable fingerprint information is the hash value of the code submission.which serves to uniquely identify the submitted content. Through the 15 traversing nodes, the system generates a raw protocol sequence that captures the aforementioned information for each node and thus forms a data stream with features from multiple sources. This step ensures the completeness of the data sources and lays the foundation for the subsequent analysis. In this implementation, the system indexes the corresponding mapping rules20 based on the type features of heterogeneous fields. For heterogeneous fields representing units of working time, the mapping rule could, for example, consist of converting minutes to hours, e.g., 480 minutes divided by 60 = 8 hours. After these rules are executed, the heterogeneous fields are converted into standard fields that match the metadata baseline. This restructuring of the formatting ensures data consistency and facilitates subsequent uniform analyses.It reduces manual intervention and improves the efficiency of automated processing. For example, when standard fields are combined with non-heterogeneous data to output normalized task records, the system generates individual task progress data in a uniform format. Suppose the last record shows that "DEV-001" worked for 8 hours on a specific node with a specific hash value for its fingerprint to the deliverable, while another node, "DEV-002," worked for 6 hours with a different hash value for its fingerprint to the deliverable. This data is stored in a uniform format, for example, in a table, so that managers can easily see the task progress of each operator. This normalization process not only improves the readability of the data,but also provides a reliable basis for subsequent task planning and resource allocation. Furthermore, the grouping of task dependencies using a hierarchical clustering algorithm based on uniformly formatted individual task progress data, and the recursive merging of nested hierarchical data to determine summary progress data at the department level, includes the following: creating a task dependency tree diagram based on the individual task progress data; a traversing task dependency tree diagram; and marking nodes with a nested hierarchy and a parent-child node assignment relationship when an internal distance of a branch node meets predefined conditions. Performing a recursive bottom-to-top traverse based on the parent-child node assignment relationship, merging progress values ​​of the 15 subordinate nodes with their corresponding parent nodes,to generate fused progress data; and to map the fused progress data to organizational units in order to determine the summarizing progress data at the department level. Creating a task dependency tree diagram, for example, serves as a visual tool for representing the logical relationships between tasks. Suppose that in a department's software development project there are several task modules, such as requirements analysis, code writing, and testing and validation. Each module has clear dependencies; for example, code writing can only begin after the requirements analysis is completed, while testing and validation depends on the results of code writing. By organizing these tasks in a tree structure, the root node can represent the entire project.The subordinate nodes represent specific tasks, and the branches represent the dependencies between the tasks. This method intuitively illustrates the hierarchy and dependencies of the tasks and helps managers to quickly identify critical paths and potential bottlenecks. In the continuous diagram of the task dependency structure and when checking whether the internal distance between branch nodes fulfills a predefined condition, a threshold for time or resource consumption can be set, for example, to measure the dependency between the nodes. Assuming the predefined condition is that the difference in work cycles between the nodes is less than three days, then in the aforementioned software development project, if the interval between the completion of the requirements analysis and the start of code creation is two days, these two nodes will be marked as hierarchically nested.and code generation is a sub-node of requirements analysis. This tagging method helps to clarify the parent-child relationship of tasks and facilitate subsequent summarization and management of progress. Furthermore, detecting and updating timestamp deviations in the department-level summary progress data using a time-series analysis algorithm based on the department-level summary progress data, and triggering a synchronization mechanism to adjust delayed data to obtain real-time synchronized department progress data, includes the following: Analyze work logs captured from the department progress source to generate independent data fragments and multidimensional timestamp sequences with task identifiers; Input of multidimensional timestamp sequences into an introductory window time series model to determine a standard reference time,and calculating a delay variance between a write time of the independent data fragments and the standard reference time; and 20 generating a forced update command to obtain a latest progress value when the delay variance exceeds a preset threshold, and updating the independent data fragments with the latest progress value to obtain the departments' real-time synchronized progress data. 25 In a departmental progress management scenario, for example, parsing job logs and generating independent data fragments is a critical step. Job logs typically contain execution data of tasks, timestamps, and associated identification information. Parsing these logs allows the progress information of each task to be extracted and independent data-30 Create fragments with unique task identifiers. Assume that,A department has 10 tasks, and the log of each task records all operating times from start to finish. Parsing thus generates 10 independent data fragments, each corresponding to the progress information of a task. This approach facilitates subsequent individual analysis and processing and ensures that the data does not get mixed up. BE2026 / 7104 13 The processing of multidimensional timestamp sequences can be understood, for example, as recording task progress across different time dimensions, such as the planned start time, the actual start time, and the current update time of a task. Assume that the planned start time of a task is October 1, 2023, the actual start time is October 3, 2023, and the current update time is October 5, 2023; these timestamps form a multidimensional timestamp sequence. After inputting these timestamps into a sliding window time series model, the model determines a standard reference time based on historical data and temporal patterns.For example, October 2, 2023, which serves as a reference point for evaluating task progress. Setting the reference date contributes to the standardization of the evaluation criteria for different tasks. Furthermore, constructing a project-wide overview using the mapping function based on the real-time synchronized progress data of the departments, and iteratively correcting divergent links in the association paths to obtain consistent global progress association data, includes the following: capturing the real-time synchronized progress data of the departments and building an initial project view with a task node topology structure using the mapping function; Scanning the initial project view to calculate a path deviation value, and locking a set of association paths that includes anomalous link points of -20,when the path deviation value exceeds a threshold; calculate an iterative correction factor based on the assignment path set and insert the iterative correction factor into the anomalous link points to perform a logistic regression to generate a corrected association table; and 25 reorganize the task nodes using the corrected association table to determine the consistent global progress association data. In a real-time synchronization scenario for departmental progress data, for example, after receiving the latest progress information, a clear project view must be created that shows the dependencies between tasks.30 The creation of the mapping function can be understood as a graphical representation of task nodes and their logical relationships. Assuming a department has 10 task nodes, each representing a specific work step, the mapping function can generate an initial view that shows,which tasks are prerequisites and which require the completion of other tasks. Creating such a view facilitates an intuitive understanding of the overall project structure and forms the basis for subsequent analysis. When scanning the initial project view to calculate path deviation values, the focus can, for example, be on the dependency paths between task nodes. Suppose a path has an estimated completion time of 5 days, but the actual progress shows 7 days,which results in a deviation of 2 days and exceeds a preset threshold of 1 day. In this case, the system locks a path with anomalous connection points. For example, a critical task node on this path may be delayed due to insufficient resource allocation. Locking the path prevents the problem from propagating and allows resources to be focused on resolving the anomalous segment. Furthermore, extracting multi-level nested features based on the consistent global progress association data, and unfolding a nested structure and filling in missing links through a recursive traversal to obtain a consistent multi-level progress view, includes the following: analyzing metadata on parent-child dependencies between tasks from the consistent global progress association data,to extract independent features with multilayered nested relationships; 20 Performing a depth search on the multilayered, nested features using a recursive traversal algorithm to obtain a shallow task node sequence, and calculating a logical predecessor-successor degree of similarity between neighboring nodes in the sequence; if the degree of similarity indicates that 25 transformation conditions are missing between the neighboring nodes,Generating a virtual link object with transition logic and inserting it in place of the missing link in the task node sequence; and maintaining the continuous multi-level progress view based on the completed task node sequence.30 In project management, for example, analyzing the metadata on parent-child dependencies between tasks from the global progress association data is a crucial step. This parent-child dependency metadata refers to the structured description of hierarchical relationships between tasks. For example, if a large task is divided into several subtasks, this relationship must be precisely captured for subsequent analysis.35 Let us assume that in a large BE2026 / 7104 15 construction project, the main task is the construction of a bridge, while subtasks such as fun- The analysis of these dependencies clearly reveals that...which tasks represent prerequisites and which can only be carried out after the completion of other tasks. If, for example, independent, multi-stage, nested features are extracted that include hierarchical relationships, the task hierarchy within the aforementioned bridge project can be further refined. The foundation construction can comprise three smaller sub-tasks: surveying, excavation, and concreting. The pier construction, on the other hand, can include material procurement and on-site assembly. These multi-stage, nested features reflect the complexity and hierarchical depth of the tasks. In this form, these nested relationships can be represented by a tree structure.where each node represents a task and the subordinate nodes represent the specific work content after its decomposition. This contributes to a more intuitive understanding of the inclusion logic between tasks and forms the basis for subsequent processing. Furthermore, generating dynamic diagrams using a visualization rendering engine based on the continuous multi-level progress view, and injecting trace markers to update non-real-time dependencies in order to determine more precisely represented progress information, includes the following: generating visualization rendering commands based on the multi-level progress view, creating a dynamic diagram object, and extracting a dependency relationship matrix; Comparing dependency relationship matrix with a real-time status snapshot25 and, if logical discrepancies are found,Generating a tracking marker code; injecting a tracking marker code into the dynamic graph object, locating primitives that do not respond to changes, generating a rendering update queue, and setting local redraw areas; 30 and updating the local redraw areas to determine the accurately represented progress information. When visualizing multi-level progress data, one can, for example, first examine the hierarchical structure of the data to understand how its nested relationships affect the final representation. The capture of multi-level progress data serves to convert it into visualization instructions, the core of this step being to decompose complex hierarchical information into a series of instructions that can be processed by diagrams. Suppose that in a project management scenario the progress data contains several task levels, such as main tasks,Subtasks and further finer execution units.5 The data might, for example, contain 100 task nodes, of which 30 are main tasks and the rest are subtasks. By analyzing the dependencies and completion status of these tasks, rendering instructions are generated to ensure that the diagram intuitively represents the hierarchical relationships and progress. When creating a dynamic diagram object and extracting a dependency matrix, the dependencies between tasks can, for example, be represented in matrix form to simplify later comparisons. Suppose the matrix captures the relationships between Task A and Task B, and between Task C and Task A. If task B is not completed, this affects the status of task A. This matrix format allows for the rapid identification of critical paths and potential bottlenecks. After extracting the matrix, it is compared with real-time status snapshots. If it turns out thatSince the lack of an update to the status of Task B leads to an incorrect display of Task A, a trace marker code is generated to determine the cause of the problem. Furthermore, distributing the precisely displayed progress information to project management endpoints via a feedback loop module, and performing a trace and consistency check for discrepancies between distributed data and the original aggregation to obtain finally validated cross-departmental collaboration progress data, includes the following: capturing an original aggregate dataset generated by cleaning and reorganizing real-time task logs and forwarding it to the project management endpoints after processing by a feedback loop module; Recording the progress data displayed on the terminal devices, and their non-conformity with the original aggregate data set,Locking transmission path nodes and extracting code conversion logs to a consistency ver-30 equal to locate data distortion segments; and retrieving redundant backups of a source page for error correction and reconstruction according to the data distortion segments to obtain the finally validated cross-departmental collaboration progress data. In the area of ​​project management in engineering, for example, real-time task logs are cleaned and reorganized to create an original aggregate dataset. This dataset contains the work actually performed, records of resource consumption, and the status of key nodes, which are reported daily by each subcontractor. The original aggregate dataset then goes through the feedback loop module for processing. This mainly involves filtering out outliers, smoothing time series, and matching data from different sources to ensure data quality.before the data is transmitted to the project management endpoints for display. The data displayed on the project management endpoints includes, in particular, a dashboard showing the progress in percent, a list of milestone completion status, and a heatmap for resource allocation. 10 In one implementation, the system immediately locks the transmission path node from the data center to the endpoint when a manager determines that the construction progress of a bridge pile foundation is 85% complete, but the corresponding percentage in the original aggregate data set is only 78%. It should be noted that after the transmission nodes are locked, the system extracts the 15 code conversion protocols for the corresponding time period. The focus here is on comparing field value changes before and after JSON serialization, the application of compression algorithms, and the protocols of the character encoding format conversion. Stepwise backtracking determinesthat when processing a large number of floating-point progress values, a deviation of 0.07 due to precision truncation is amplified to a complete data offset by a specific intermediate proxy layer. Optionally, based on the identified data distortion segments—namely, the portion of the pile foundation completion field that was erroneously truncated during transmission—the system can automatically retrieve a copy of the original log from the redundant backup server on the source side. This copy contains the original report records without any transformation. After extracting the complete data for the appropriate batch, error correction and reconstruction are performed.where 78% of the exact values ​​are written back into the aggregation result.30 In this implementation, after reconstruction, the system resends the validated cross-departmental collaboration progress data to the manager's devices and simultaneously marks this error correction path as the priority transmission channel to ensure that subsequent similar data bypasses the problematic node. This corrects the overall progress displayed to the manager from the previously displayed deviation of 92% to the actual 89.4%. This significantly improves coordination and alignment between subcontractors and avoids erroneous decisions and incorrect resource allocation due to data distortions. This implementation form also proposes an intelligent system for task allocation and progress monitoring for cross-departmental project collaboration, which is used to implement the procedure and includes the following: a module for data collection and standardization,that serves to capture and standardize fine-grained progress data from different departments in order to obtain uniformly formatted individual task progress data; a module for grouping and merging task dependencies, which serves to perform hierarchical clustering of the task dependencies based on the uniformly formatted individual task progress data and to recursively merge nested data in order to obtain summary progress data at the department level; a module for adjusting the time series synchronization, which serves to detect and synchronize timestamp deviations in the summary progress data at the department level in order to obtain real-time synchronized progress data of the departments; a global association mapping correction module that serves to construct a project-wide overview based on the real-time synchronized progress data of the departments20,and to iteratively correct deviating links in the association paths in order to obtain consistent global progress association data; a module for unfolding and populating multi-level features, which serves to unfold a multi-level nested structure from the global progress association data and to populate missing links in order to obtain a consistent multi-level progress view; a module for updating the dynamic visualization rendering, which serves to generate dynamic charts based on the most,

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