A schedule delay early warning and correction system based on project management data

Abstract

This invention discloses a schedule delay early warning and correction system and method based on project management data, belonging to the field of engineering project management technology. The system includes: a schedule planning and data management module for establishing and associating structured schedule plans with multi-dimensional data; a schedule monitoring and event triggering module for real-time monitoring and generating delay events when deviations exceed thresholds; a multi-dimensional impact coupling analysis engine for synchronously propagating schedule delay events, quantifying costs, and assessing risks, and outputting a comprehensive report; and an intelligent deduction and decision-making module for correction schemes, automatically generating multiple correction schemes based on the report and performing simulations, using visual comparisons to assist decision-making. This invention achieves multi-dimensional automatic coupling analysis and intelligent decision support for schedule, cost, and risk, forming a complete closed loop from early warning and analysis to scheme optimization, significantly improving the initiative and scientific nature of project management.

Description

Technical Field

[0001] This invention relates to the field of engineering project management technology, specifically to a schedule delay early warning and correction system based on project management data. Background Technology

[0002] Project schedule control is one of the core tasks of engineering project management, spanning the entire process from project planning, design, procurement, construction, and acceptance. Effective early warning and correction of schedule delays are crucial for ensuring project success in engineering project management. Currently, relevant technical solutions each have their own focus, but all have certain limitations.

[0003] One common practice is node-based management based on standard templates. For example, this involves dividing the entire project process into multiple stages such as preliminary planning, design, construction, and acceptance, and pre-setting numerous key milestones for tracking. The advantage of this approach is that it provides a clear management framework and makes tasks and responsibilities explicit. However, its inherent drawback is that the management logic is relatively static: when a milestone is delayed, the system can usually only issue status indicators and notifications, and cannot automatically and quantitatively analyze the cascading impact of the delay on the overall project (such as the total project duration and subsequent tasks), nor can it dynamically assess its related impact on project costs and risks. The decision-making process heavily relies on the manager's experience and judgment. Another type of technology focuses on the informatization of workflows, such as the workflow control system disclosed in patent document CN103177307A. Its advantage lies in promoting the structuring of processes and information. However, its weakness lies in its relatively weak ability to quantitatively analyze schedule deviations and assess cross-dimensional impacts.

[0004] In addition, there are dynamic management technologies that focus on single dimensions such as risk or cost, such as the risk warning method in patent document CN110516963A and the benefit dynamic evaluation model in CN121032306A. These technologies achieve refined analysis in their respective fields, but they often operate independently of the overall project schedule and lack real-time, automatic data correlation and logical linkage with specific schedule nodes, which can easily lead to "data silos".

[0005] In summary, the core problem with existing technologies lies in the disconnect between key dimensions such as project schedule, cost, and risk management. When schedule delays occur, there is a lack of a system capable of performing the following actions instantly and automatically: (1) quantifying the propagation effect of delays in the schedule network based on project data structures (such as task dependencies); (2) dynamically calculating the cost increase caused by changes in schedule; (3) assessing and providing early warnings of potentially triggered associated risks; and (4) intelligently generating and comparing multiple feasible correction schemes based on the above global analysis. This results in a passive and one-sided response to schedule delays, and decision-making lacks comprehensive data support. Summary of the Invention

[0006] The technical problem to be solved by this invention is to overcome the shortcomings of existing technologies, such as the fragmentation of various dimensions of project management and the lack of quantitative analysis and intelligent decision support for responses to schedule delays. This invention provides a schedule delay early warning and correction system and method that can deeply integrate schedule, cost and risk data to achieve dynamic impact analysis, scheme simulation and collaborative decision-making.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a progress delay early warning and correction system based on project management data, comprising: The schedule planning and data management module is used to manage the structured schedule of a project. The schedule includes multiple task nodes, logical dependencies between nodes, planned time and responsible entity information for each node; and multi-dimensional management data is associated and stored for each node. The multi-dimensional management data includes at least: node cost budget information, resource allocation information, pre-associated risk event information, and cost sensitivity coefficient for quantitative analysis. The progress monitoring and event triggering module is used to monitor the actual completion status of each task node. When it is detected that the actual progress of a node deviates from its planned value to a preset threshold, node delay event information is generated. The multi-dimensional impact coupling analysis engine, as the core analysis unit of the system, responds to the node delay event information and is connected to the schedule planning and data management module. The engine is configured to synchronously execute the following analyses: (a) Schedule impact propagation analysis: Based on the logical dependencies between the nodes and network planning techniques, the impact of the delay on subsequent dependent nodes and the total project duration is recursively calculated, and an updated schedule forecast is output. (b) Cost impact quantification analysis: Based on the cost sensitivity coefficient associated with the affected nodes, cost budget information, and updated schedule forecast, the preset cost impact calculation model is invoked to quantify the direct and indirect cost impact values ​​caused by the schedule change; (c) Dynamic risk association assessment: Based on the pre-associated risk event information, assess the changes in the probability or degree of impact of the delayed event on the associated risk. The engine outputs a comprehensive impact report that integrates the results of schedule, cost, and risk impact analysis; The intelligent deduction and decision-making module for the revised scheme is connected to the multi-dimensional influence coupling analysis engine, and the module is configured as follows: Based on the comprehensive impact report and the current project constraints, at least two candidate schedule revision schemes will be automatically generated. For each candidate solution, its execution process is simulated in a simulation environment, and the comprehensive impact of implementing the solution on the total project duration, total cost, resource requirements and overall risk level is deduced and predicted. The system presents the results of each candidate solution in a visual format, comparing and contrasting them side by side. It also allows users to set decision preference weights based on project goals, calculate the comprehensive score and ranking of the solutions based on the weights, and output recommended solutions and decision support dashboards.

[0008] As a preferred embodiment of this application, the schedule planning and data management module supports loading a full-process schedule node template based on the project type. The template divides the life cycle of the project into multiple stages, including preliminary planning, design, bidding, construction, and acceptance, and presets multiple key control nodes and node status early warning rules in each stage.

[0009] As a preferred embodiment of this application, the node status early warning rule includes a multi-level response mechanism; the multi-dimensional impact coupling analysis engine dynamically determines and triggers the corresponding level of response mechanism based on the severity of the progress and cost impact calculated by it, wherein the high-level response mechanism automatically associates with the emergency communication or meeting initiation process, and pushes the comprehensive impact report and decision support dashboard as core materials.

[0010] As a preferred embodiment of this application, the cost impact calculation model used in the multi-dimensional impact coupling analysis engine is as follows: , in, This represents the cost impact value of node i. Represents the direct cost baseline for node i. This represents the cost sensitivity coefficient of node i. This represents the change in the project duration at node i. This represents the original planned construction period for node i. This indicates the project's average daily indirect cost rate. This represents the risk impact coefficient of the k-th associated node i. This represents the sum of the associated risk coefficients.

[0011] As a preferred embodiment of this application, the system further includes a self-learning optimization module connected to the schedule planning and data management module and the multi-dimensional impact coupling analysis engine, used to: continuously collect actual impact data of node delay events in historical projects; compare the actual data with the predicted data of the analysis engine; and optimize the cost sensitivity coefficient and risk impact parameters through algorithms based on the comparison differences to improve the system's prediction accuracy.

[0012] As a preferred embodiment of this application, the progress monitoring and event triggering module further includes a predictive early warning unit, which analyzes the current execution efficiency trend of nodes to predict the probability and degree of future delays, and triggers an early warning and analysis process before the delay actually occurs.

[0013] Secondly, the present invention provides a method for early warning and correction of progress delays based on the above system.

[0014] Thirdly, the present invention provides an electronic device and a computer-readable storage medium.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Automated coupled analysis based on structured data models and intelligent event-driven approaches was achieved: By constructing a machine-readable, atomic, and relationally explicit "structured schedule," and associating nodes with multi-dimensional management data, the system possesses deep data correlation capabilities. The progress monitoring module uses a rule engine to intelligently adjudicate deviations, generating "executable intelligent event packages" containing execution instructions. This drives the "multi-dimensional impact coupled analysis engine" to synchronously and automatically execute progress impact propagation, cost quantification calculation, and risk assessment, breaking down "data silos" between various management dimensions and enabling real-time, quantitative analysis from delayed events to global impacts.

[0016] 2. A complete intelligent closed loop has been constructed, encompassing early warning, analysis, simulation, and decision recommendation: The system not only provides accurate early warnings but also, through the "Intelligent Simulation and Decision-Making Module for Corrective Solutions," automatically generates multiple candidate corrective solutions based on the comprehensive impact report and project constraints. It then simulates the future comprehensive impact of each solution on schedule, cost, resources, and risks in a simulation environment. Multiple solutions can be compared through a visual decision dashboard, and users can set decision preference weights. The system automatically calculates recommended solutions, elevating project management from passive response to proactive decision-making and solution optimization based on data simulation.

[0017] 3. Possesses continuous self-optimization and adaptive capabilities: Through the "self-learning optimization module," the system continuously collects actual impact data of delayed events in historical projects, compares it with predicted values, and uses machine learning algorithms to dynamically optimize key model parameters such as cost sensitivity coefficient and risk impact coefficient. This mechanism enables the system's prediction accuracy and solution derivation reliability to continuously evolve with project practice, forming a virtuous cycle of "becoming smarter with use."

[0018] 4. Significantly improves management efficiency and the scientific and collaborative nature of decision-making: The system reduces manual intervention and improves response speed through automated processes (such as multi-level response mechanisms that automatically trigger communication). At the same time, its quantitative impact reports, simulation results, and visualized decision support help managers comprehensively and intuitively weigh the advantages and disadvantages of different solutions under complex constraints such as schedule, cost, resources, and risks, and make better decisions that align with overall goals. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall system architecture provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the specific implementation of the schedule planning and data management module in one embodiment of the present invention; Figure 3 This is a flowchart illustrating the specific implementation of the progress monitoring and event triggering module in one embodiment of the present invention; Figure 4 This is a flowchart of the multi-dimensional influence coupling analysis engine in one embodiment of the present invention; Figure 5 This is a flowchart illustrating the specific implementation of the intelligent deduction and decision-making module for corrective schemes in one embodiment of the present invention. Figure 6 This is a schematic diagram of the system composition of another embodiment of the present invention (application in municipal road renovation projects); Figure 7 The above is a flowchart of a progress delay warning and correction method provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0022] Example 1 like Figure 1 As shown, this invention provides a schedule delay early warning and correction system based on project management data, including a schedule planning and data management module, a schedule monitoring and event triggering module, a multi-dimensional impact coupling analysis engine, and a correction scheme intelligent deduction and decision-making module that are interconnected. The schedule planning and data management module is used to manage the structured schedule of the project and associate multi-dimensional management data with each node. In this embodiment of the invention, the schedule includes multiple task nodes, logical dependencies between nodes, planned time and responsible entity information for each node, etc.; the multi-dimensional management data includes at least: node cost budget information, resource allocation information, pre-associated risk event information, and cost sensitivity coefficients for quantitative analysis, etc. It's important to note that in project management, a "schedule" refers to the pre-arrangement and design of the start and end times, sequence, required resources, and responsible parties for all project activities in order to achieve project objectives. It is a dynamic management benchmark and communication tool, its core function being to answer the questions of "when, by whom, and what work to accomplish." Traditionally, schedules can be represented by Gantt charts, milestone charts, or simple task lists.

[0023] It should be noted that "structured schedule planning" is an enhancement and deepening of traditional schedule planning in this system. It specifically refers to a machine-readable, parsable digital plan model with clearly defined logical relationships between its internal elements. Its "structured" nature is mainly reflected in the following three levels, fundamentally different from traditional paper-based or simple chart-based plans: Element atomization and attribute generation: Decompose project work into indivisible or smallest manageable units called "task nodes". Each node is not a simple text description, but a data object with a series of predefined attribute fields (such as ID, name, planned start / end time, duration, responsible person, status, etc.).

[0024] Relationships are made explicit and digitized: Logical dependencies between nodes (such as Complete-Start FS, Start-Start SS) are explicitly defined and stored, forming a directed network graph (usually an Active Node Network Graph AON). This allows computers to automatically perform critical path calculations, float time analysis, and impact propagation simulations.

[0025] Data association: Each task node acts as a "data hub," linked to management data across other dimensions such as cost, resources, and risk through unique identifiers (such as node IDs). This association is not a loose reference, but rather a data relationship built into the system that can be automatically queried and calculated by the engine.

[0026] It is this "structuring" that transforms the schedule from a static "planning document" into a dynamic "time skeleton of the project digital twin," laying the data foundation for subsequent automated analysis.

[0027] In an optional embodiment of the present invention, the schedule planning and data management module supports loading a full-process schedule node template based on the project type. The template divides the life cycle of the project into multiple stages, including preliminary planning, design, bidding, construction, and acceptance, and presets multiple key control nodes and node status early warning rules in each stage.

[0028] In an optional embodiment of the present invention, the node status early warning rule includes a multi-level response mechanism; the multi-dimensional impact coupling analysis engine dynamically determines and triggers the corresponding level of response mechanism according to the severity of the progress and cost impact calculated by it, wherein the high-level response mechanism automatically associates with the emergency communication or meeting initiation process, and pushes the comprehensive impact report and decision support dashboard as core materials.

[0029] In an optional embodiment of the present invention, such as Figure 2 As shown, the specific implementation process of the schedule planning and data management module includes: Step 1: Create a structured schedule network based on the template. The core of this step is to quickly build the project skeleton using a standardized template and embed early warning rules, including: Template Loading and Adaptation: Users select the appropriate full-process progress node template from the system template library based on the project type (e.g., "water environment remediation" or "educational building"). This template pre-divides the project lifecycle into several stages, such as "preliminary planning, design, bidding, construction, and acceptance," and pre-sets industry-standard key control nodes for each stage (e.g., "scheme review" and "construction drawing output" in the design stage). After the system loads the template, it generates an initial list of task nodes and stage divisions.

[0030] Personalized Project Decomposition and Node Creation: Based on the framework provided by the template, users can refine and adjust the specific project Work Breakdown Structure (WBS). The system creates each work package as an independent "task node" data object, inheriting some attributes (such as type identifier) ​​from the corresponding node in the template. Each node is assigned a unique ID and categorized into the corresponding stage.

[0031] Logical relationship definition and network construction: Users establish dependencies (such as FS, SS) between nodes based on template-suggested logical relationships or according to the actual situation of the project, through a graphical interface. The system internally builds and stores the project network diagram.

[0032] Schedule estimation and baseline schedule generation: Users assign planned durations and times to each node. The system automatically uses the Critical Path Method (CPM) to calculate the entire network and generate a structured baseline schedule that includes the critical path and float time.

[0033] Early warning rule initialization: In this stage, the system automatically associates the preset node status early warning rules in the template with specific nodes. These rules define the trigger thresholds (e.g., delay days) and visual identifiers (e.g., colors) for multi-level response mechanisms (e.g., Level 1 alert, Level 2 warning, Level 3 alarm, Level 4 critical event). For example, the template might specify that "a Level 2 (yellow) warning is automatically triggered when a critical path node is delayed by more than 2 days."

[0034] Step 2: Link multi-dimensional management attributes with dynamic response anchors. This step injects "intelligent genes" into the nodes and dynamically links the response mechanism with consequence analysis, including: Cost dimension correlation: Direct cost budget: Enter detailed direct cost estimates for each node, such as estimates of the direct costs of labor, materials, machinery, etc. required to complete the node.

[0035] Cost sensitivity coefficient ( The system can recommend initial values ​​from templates or historical databases based on node type (e.g., α may be lower for earthwork projects, and higher for high-tech processes), which users can calibrate. This coefficient will be used by the subsequent analysis engine and is key to quantifying cost impact.

[0036] Resource dimension association: Define the human and equipment resource requirements of a node in a structured way, such as specifying the types and quantities of personnel, equipment types and shifts required to complete the node.

[0037] Risk dimension correlation: From the project's global risk database, pre-associate potential risk events for nodes (e.g., associating the "construction" node with the "severe weather" risk).

[0038] Set the risk impact coefficient for each associated risk ( ): For each associated risk event, assess its impact coefficient on node cost or schedule.

[0039] Dynamic response mechanism anchoring: In this step, the system not only links static data, but more importantly, it pre-defines the subsequent automated processing flow for each level of the early warning rule's response mechanism. For example, in the configuration interface, the system allows users to bind the process of "automatically initiating an emergency meeting and generating a report" to the response level of "Level 4 (Red) Severe Incident". This means that when this level of early warning is triggered, the subsequent communication and decision-making actions have been pre-defined.

[0040] Step 3: Integrate and maintain the holographic dynamic data volume. The system backend integrates all information into a project holographic data object with node ID as its core. This object not only contains plans and multi-dimensional attributes, but also embeds the definitions of early warning rules and response processes. This module is also responsible for the continuous maintenance of this data volume (version management, progress updates) to ensure that it always reflects the latest baseline status of the project.

[0041] In embodiments of the present invention, the schedule planning and data management module is not only a repository of static data, but also a configuration center that integrates a standardized framework, embeds intelligent response rules, and manages dynamic benchmarks. Through template-based startup and multi-dimensional data association, it ultimately provides the entire system with a "data heart" that combines standardization, predictability, and action orientation.

[0042] The progress monitoring and event triggering module monitors the actual completion status of each task node. When it detects that the actual progress of a node deviates from its planned value to a preset threshold, it generates node delay event information. This module serves as the sensing hub and trigger switch connecting "planning" and "intelligent analysis." Its core mission is to continuously sense the real-world progress of the project, compare it with the planned baseline in the digital world, and precisely trigger subsequent intelligent analysis processes when significant deviations are detected. Its implementation is a multi-step automated closed-loop process, deeply reliant on the structured data foundation provided by the progress planning and data management module.

[0043] In an optional embodiment of the present invention, the progress monitoring and event triggering module further includes a predictive early warning unit, which analyzes the current execution efficiency trend of the node, predicts the probability and degree of future delays, and triggers an early warning and analysis process before the delay actually occurs.

[0044] like Figure 3 As shown, the specific implementation process of the progress monitoring and event triggering module includes: Step 1: Rules and baselines are loaded synchronously. When this module starts, it first performs in-depth data integration with the schedule and data management module, synchronously loading three core elements: Structured baseline plan: Obtain a complete digital network model of the project, including task nodes, logical relationships, and time parameters, as the sole source of fact for schedule comparison.

[0045] Node alert rule base: Retrieves multi-level response mechanism rules pre-configured for each node. This includes trigger thresholds for each level (e.g., levels one to four), visual identifiers (colors), and special judgment logic that may apply to each node based on its type and location (e.g., whether it is on the critical path).

[0046] Response action instruction templates: Obtain the definition of preset automated processing procedures bound to early warning mechanisms at all levels, such as "automatically notify department managers and create analysis tasks for level 3 alarms" and "automatically initiate emergency meetings, generate report drafts, and copy senior management for level 4 critical events".

[0047] Step 2: Multi-source data acquisition and fusion. The module collects the actual status of task nodes through multiple channels to ensure comprehensive and timely data. Automatic sensing: By integrating IoT sensors, mobile applications, BIM platforms, etc. through APIs, it can automatically acquire actual start / end times, work hour records, equipment operation data, etc.

[0048] Human feedback: Provides a convenient interface for those in charge to update task progress percentages, submit deliverables, and report issues.

[0049] All collected actual data are precisely correlated with the baseline node in the upstream module through the node ID, forming a "plan-actual" data pair.

[0050] Step 3: Intelligent Deviation Adjudication Driven by a Rules Engine. This module has a built-in rules engine that adjudicates the status of each node in real time. The adjudication process is not a simple comparison of values, but rather a comprehensive application of upstream configured rules and real-time context. Basic deviation calculation: Calculate basic indicators such as time deviation (ΔT) and schedule deviation percentage.

[0051] Dynamic rule adjudication: The rule engine performs multi-level judgments based on the attributes of the current node and the global state of the project. Threshold matching: The calculated deviation value is compared with the threshold of the warning rule specific to that node. For example, for a critical design node, a delay of 1 day may trigger a "level 2 warning"; while for a normal support task, the threshold may be 3 days.

[0052] Context adjustment: The engine checks whether the node is on the current critical path (path status is dynamically maintained by the schedule and data management module). If so, the adjudication result may automatically be upgraded by one warning level. Simultaneously, it checks whether the pre-associated risk events of the node are active, thereby performing a risk-weighted assessment.

[0053] Generate adjudication output: The engine ultimately outputs a structured adjudication result, explicitly containing: The level of alert triggered (e.g., "Level 3 Alert").

[0054] Judgment criteria (e.g., "delay of 3 days, triggering the node rule threshold; and located on the critical path, level +1").

[0055] The associated default response action code (e.g., "ACTION_LEVEL3_NOTIFY_AND_ANALYZE").

[0056] Step 4: Encapsulating and Generating an "Executable Smart Event Package". The module does not simply record a delay fact, but rather, based on the adjudication result, encapsulates and generates a highly structured executable smart event package (i.e., a node delay event information object). This event package contains: Event metadata: Event ID, timestamp, source node ID, and details (obtained from the upstream module).

[0057] The core elements of the ruling are: delay type, quantification deviation value, and the level of warning triggered.

[0058] Execution command: The preset response action code and required parameters (such as the list of meeting participants and the report template ID) corresponding to this trigger.

[0059] Decision context: associated responsible parties, risk labels, and critical path impact indicators.

[0060] Step 5: Automatic triggering and routing of the response chain. The generated event packet is immediately published to the system event bus, triggering two parallel automatic response chains: Predefined response action execution: The system's event handlers automatically execute predefined standard operations in the schedule and data management modules based on the "response action code" in the event package. For example, for a Level 4 event, it may automatically: a) display the node in red flashing on the project control room screen; b) push an instant message containing key information to the predefined emergency contact group; c) create an emergency meeting to be held one hour later in the scheduling system, with a link to the preliminary event report.

[0061] Intelligent analysis engine activation and empowerment: Event packets are synchronously and accurately pushed to the multi-dimensional impact coupling analysis engine. The rich contextual information in the event packets (such as node details, associated risks, and critical path status) provides the engine with the "fuel" for in-depth analysis, enabling it to perform accurate, multi-dimensional coupling impact calculations, rather than simply extrapolating based on the number of days of delay.

[0062] In summary, the progress monitoring and event triggering module strictly adheres to the "rule code" set by the progress planning and data management modules, continuously conducting "judicial review" of the project reality and generating intelligent events with "enforcement orders." This drives the entire system to achieve an efficient and precise closed loop from standardized rule configuration to automated intelligent response. This evolution is the core hub for the system to achieve intelligent project management.

[0063] Among them, the multi-dimensional impact coupling analysis engine, as the core analysis unit of the system, responds to the node delay event information and is connected to the schedule planning and data management module, such as... Figure 4 As shown, the engine is configured to perform the following analyses synchronously: (a) Schedule impact propagation analysis: Based on the logical dependencies between the nodes and network planning techniques, the impact of the delay on subsequent dependent nodes and the total project duration is recursively calculated, and an updated schedule forecast is output. (b) Cost impact quantification analysis: Based on the cost sensitivity coefficient associated with the affected nodes, cost budget information, and updated schedule forecast, the preset cost impact calculation model is invoked to quantify the direct and indirect cost impact values ​​caused by the schedule change; (c) Dynamic assessment of risk association: Based on the pre-association risk event information, combined with the specific characteristics of delayed events (such as the time of occurrence, quantitative deviation value, and critical path of impact), the changes in associated risks are dynamically assessed.

[0064] The engine outputs a comprehensive impact report that integrates the results of schedule, cost, and risk impact analysis.

[0065] In an optional embodiment of the present invention, the cost impact calculation model used in the multi-dimensional impact coupling analysis engine is as follows: , In the formula, This represents the expected increase in total cost (including direct and indirect costs and risk amplification effects) at node i due to changes in the project duration, expressed in monetary units.

[0066] This represents the direct cost baseline for node i, i.e., the direct cost budget required to complete the task, expressed in monetary units.

[0067] The cost sensitivity coefficient for node i is a dimensionless parameter, and its physical meaning is: for every 1% deviation of the project duration from the original planned duration (i.e., ... = 1%), direct costs will change accordingly. %. This coefficient comprehensively reflects the impact of factors such as task type, process complexity, and resource dependence on the elasticity of project duration and cost. For example, = 0.2 means that a 1% extension of the construction period will result in an average increase of 0.2% in direct costs.

[0068] This represents the change in the project duration at node i. Typically, a delay is represented by a positive value. >0), negative values ​​are used when rushing to complete a project ahead of schedule, and the unit is a time unit (e.g., day).

[0069] This represents the original planned duration of node i, expressed in time units (e.g., days).

[0070] This represents the project's average daily indirect cost rate, which is the amortization of fixed indirect costs incurred daily by the project, expressed in monetary units per day.

[0071] The coefficient of the k-th associated risk of node i is a dimensionless parameter, usually ranging from [0,1). It represents the additional percentage of the total cost of node i if the risk event is activated.

[0072] This represents the sum of the impact coefficients of all activated associated risks, reflecting the cumulative amplification effect of multiple risks.

[0073] The physical meaning and calculation logic of this formula are as follows: Part One It quantifies the changes in direct costs caused by variations in project duration. It reflects the elasticity of direct costs with changes in project duration, where... This reflects the proportion of the impact on the schedule.

[0074] Part Two The extension of the construction period was quantified. The increased indirect costs (>0) are proportional to the change in the project duration.

[0075] Part Three It is a risk amplification factor. When a risk is activated ( When the value is greater than 0, the cost impact calculated in the first two parts will be proportionally amplified to quantify the potential additional losses caused by the risk event.

[0076] The specific implementation process of the multi-dimensional influence coupling analysis engine includes: Step 1: Parsing Event Packets and Data Preparation. After receiving the event packets, the engine first parses them: Extract trigger information: Obtain the source node ID, quantization deviation value (ΔT), delay type, and critical path impact identifier and risk label carried in the event packet.

[0077] Query holographic data: The engine immediately initiates a query to the schedule and data management module to obtain the complete holographic data object of the node based on the source node ID, including all its attributes, dependencies, associated multidimensional data (cost sensitivity coefficient, direct cost budget, pre-associated risk list, etc.) and the current project network diagram in which the node is located.

[0078] Step 2: Schedule Impact Propagation Analysis (Dynamic Network Recalculation). This is the engine's foundational analysis layer, designed to quantify the "ripple effect" of delays across the project timeline, including: Network status update: The engine uses ΔT in the event packet as the initial perturbation to update the actual or predicted completion time of the source node.

[0079] Recursive forward propagation calculation: Based on the latest network graph obtained from the schedule and data management module, which contains the logical dependencies of all nodes, the engine applies the Critical Path Method (CPM) or Program Review and Evaluation Technique (PERT) to perform recursive calculations.

[0080] Starting from the source node, proceed forward along all its subsequent dependency paths.

[0081] For each affected subsequent node, recalculate its earliest start time and earliest finish time.

[0082] This process continues until it reaches all affected end nodes or the final milestone of the project.

[0083] Critical Path Reorientation and Total Project Duration Update: After the forward propagation is completed, the engine recalculates the entire network, determines the new critical path and the new total project duration, and identifies nodes that have become or left the critical path due to the delay.

[0084] Output schedule forecast update: Generate an updated project timeline, clearly indicating the list of affected nodes and the change in start / end time for each node. ), and the difference between the new total project duration and the original total project duration (ΔT) total ).

[0085] Step 3: Cost Impact Quantification Analysis (Model-Based Dynamic Calculation). This step is conducted concurrently with the schedule analysis and aims to translate time impact into economic impact, including: Determine the set of affected nodes: based on all affected subsequent nodes (including the source node) identified in step 2.

[0086] Node-by-node cost impact calculation: For each node i in the affected node set, a pre-defined cost impact quantification model is invoked for calculation. The model is as described above: , Parameter injection: (Direct cost baseline) (Cost Sensitivity Coefficient) (Original planned construction period) obtained from the holographic data of the nodes; Obtained from the schedule impact results in step 2; IR (project daily indirect cost rate) is obtained from the project global parameters.

[0087] Risk coefficient aggregation: (Risk Impact Coefficient) Based on the risk events pre-associated with the node, and combined with the current risk status (obtained from the event package or risk database), it dynamically determines whether to include them in the calculation. ).

[0088] Total cost impact summary: calculated for all affected nodes Summing these values ​​yields the estimated total cost impact of this delay on the project (Σ). ).

[0089] Output Cost Impact Report: Generate a detailed table listing the cost impact components (direct, indirect, and risk amplification) and total value for each affected node.

[0090] Step 4: Dynamic Risk Correlation Assessment (Situation Simulation). This step assesses the secondary impact of delay events on the project's risk landscape, including: Risk event activation determination and probability adjustment: The system has a built-in risk event-condition mapping table. For example, the preset activation condition for the "rainy season construction" risk is "the overlap between the actual execution time window of the task and the rainy season (such as June-August) in historical meteorological data exceeds a threshold". When the delay causes the new predicted time of the "pipeline relocation" node to enter this window, the system determines that the risk is activated and increases the probability of the risk occurrence according to the overlap using a preset linear or nonlinear function (e.g., from 10% to 60%).

[0091] Risk Impact Chain Analysis: The system maintains a directed graph of risk impact relationships. When a risk is activated, the engine traverses the graph to identify all possible secondary risks. For example, the risk node "critical equipment delay" is connected to "subsequent process quality risk" and "contract default risk" in the graph. Based on the strength of the relationships, the engine automatically calculates the increment of the trigger probability of secondary risks.

[0092] Risk Index Quantification Calculation: The overall or partial risk index R of a project is calculated using the following formula: R = Σ(P k × I k ) Among them, P k I represents the probability of the kth activated risk occurring (after adjustment by the above steps). k This represents the overall impact coefficient (between 0 and 1) of the risk on project cost or schedule. The engine adjusts the probability P based on preset values ​​in the risk database and real-time adjustments. k The risk index R is dynamically recalculated and output as a numerical value or a level (such as low, medium, high).

[0093] Output risk assessment update: Generate a dynamic risk assessment report, which not only lists the newly activated risks, but also clearly provides their adjusted probability value, impact coefficient, calculated risk index and change.

[0094] Step 5: Generate and output the comprehensive impact report. The engine aggregates, cross-analyzes, and formats the analysis results from the three dimensions mentioned above to generate the final comprehensive impact report. This report includes at least: Executive Summary: This section outlines the delay events, the triggering warning levels, and the overall impact (e.g., the total project duration is expected to be delayed by X days, the total cost is expected to increase by Y yuan, and Z new high-risk items are added).

[0095] Detailed analysis: Update the project's Gantt chart or network diagram, highlighting the scope of the impact.

[0096] Cost impact details and summary.

[0097] Risk dynamic assessment list and risk heat map.

[0098] Severity rating: The engine uses a preset algorithm (e.g., based on ΔT) to rate the impact severity. total , Σ The weighted score of the risk index increment is used to rate the severity of the overall impact of this delayed event (e.g., "mild", "moderate", "severe", "catastrophic"). This rating can serve as an important input for subsequent decision support.

[0099] Related data and context: Include the source event package ID, analysis timestamp, model version used, and other information.

[0100] In summary, the multi-dimensional impact coupling analysis engine transforms an isolated node delay event into a comprehensive, quantitative, and actionable integrated impact report by simulating its propagation in the schedule network, its transformation in the economic model, and its turbulence in the risk field. This report not only describes "what happened," but more importantly, predicts "what will happen," thus providing a solid basis for the downstream intelligent deduction and decision-making modules for corrective measures. It is the core intelligent component that enables the entire system to leap from "perceiving the problem" to "understanding the severity of the problem."

[0101] The intelligent deduction and decision-making module for the modified scheme is connected to the multi-dimensional influence coupling analysis engine, and the module is configured as follows: Based on the comprehensive impact report and the current project constraints, at least two candidate schedule revision schemes will be automatically generated. For each candidate solution, its execution process is simulated in a simulation environment, and the comprehensive impact of implementing the solution on the total project duration, total cost, resource requirements and overall risk level is deduced and predicted. The system presents the results of each candidate solution in a visual format, comparing and contrasting them side by side. It also allows users to set decision preference weights based on project goals, calculate the comprehensive score and ranking of the solutions based on the weights, and output recommended solutions and decision support dashboards.

[0102] like Figure 5 As shown, the specific implementation process of the intelligent inference and decision-making module for the revised scheme includes: Step 1: Activation and conditional input of the solution generation strategy. After the module starts, it first parses the "Comprehensive Impact Report" to extract key decision inputs, including: Problem Diagnosis: Identify the core issues that need to be addressed, such as reducing the total project duration by ΔT_total days, controlling the cost increase to within X yuan, and mitigating the high risks associated with item Y.

[0103] Current constraints: Obtain real-time project constraints from the schedule and data management module, including available resource pools (personnel, equipment), remaining budget, contract terms and conditions, and the floating time distribution of the current network diagram.

[0104] Preset Strategy Library: This library calls upon pre-built or user-defined correction strategy knowledge bases within the system. It contains various typical corrective measures and their applicable conditions and impact models, such as: "Increase resource input (rush work)", "Adjust task logic relationships (rapid follow-up)", "Reduce project scope (remove non-core deliverables)", "Adopt alternative technologies or processes", and "Make business changes (such as claims or extension requests)".

[0105] Step 2: Automatic generation and initialization of candidate solutions. Based on input conditions (the project duration to be compressed ΔT_target, the cost control upper limit C_max, the available resource pool, etc.), the module mainly uses an improved genetic algorithm to automatically generate an initial population of candidate solutions. The specific steps are as follows: Gene encoding: A candidate solution is encoded as a chromosome (individual). A chromosome consists of multiple gene segments, each corresponding to an adjustable task node. The gene value represents the type and strength of the corrective action taken at that node. For example, gene values ​​can be encoded using real numbers, where the integer part represents the action type (0: no adjustment, 1: increase resources to expedite completion, 2: adjust logical relationships for rapid follow-up, 3: reduce the scope, etc.), and the decimal part represents the strength parameter (e.g., percentage increase in resources).

[0106] Initial population generation: A certain number of individuals (e.g., 50) are randomly generated as the initial population. Simultaneously, heuristic rules are incorporated: for example, nodes on newly identified critical paths are preferentially assigned the "rush" type, with the strength set within a reasonable range based on the node's float time and resource availability.

[0107] Fitness Function Design: The fitness function is used to evaluate the performance of an individual. Its calculation is based on simulation results of the individual (solution), and the formula is as follows: Fitness = w1 × f(T) + w2 × f(C) + w3 × f(R), where f(T) is the schedule satisfaction function, f(C) is the cost control satisfaction function, f(R) is the risk control effect function, and w1, w2, w3 are weights set according to decision preferences.

[0108] in: f(T) = max(0, 1 - |ΔT_sim - ΔT_target| / ΔT_target), is used to measure the degree to which the project schedule target is met. ΔT_sim is the simulated schedule deviation, and ΔT_target is the target schedule reduction.

[0109] f(C) = max(0, 1 - ΔC_sim / C_max), is used to measure the degree to which cost control objectives are met. ΔC_sim represents the simulated cost impact, and C_max is the upper limit of cost control. f(R) = 1 - R_sim / R_base, which measures the effectiveness of risk control (R_sim is the risk index after the implementation of the plan, and R_base is the benchmark risk index).

[0110] Genetic operations: performing selection (roulette wheel selection), crossover (single-point crossover), and mutation (random perturbation of random gene loci) operations to iteratively evolve the population.

[0111] Solution output: When the maximum number of iterations is reached or the fitness converges, the individuals with the highest fitness are decoded into specific correction candidate solutions.

[0112] Each plan is a set of specific adjustment instructions to the project plan. For example: Option A (Aggressive Acceleration): Increase resource allocation by 20% for all nodes on the new critical path identified in the report.

[0113] Option B (Robust Optimization): Utilize the float time of non-critical paths to rearrange some non-critical tasks; at the same time, only increase resources by 10% for a few bottleneck tasks.

[0114] Option C (Scope Compromise): Similar to Option B, but with the additional proposal to remove a low-priority deliverable in exchange for time.

[0115] Each scheme is defined as a set of executable operations and corresponding parameters.

[0116] Step 3: Based on simulation, multi-dimensional effect deduction. For each candidate solution, the module starts a discrete event simulation environment. This environment uses the current holographic project data model (containing all nodes, relationships, and multi-dimensional attributes) maintained by the schedule planning and data management module as the baseline, and applies the adjustment instructions defined by the solution.

[0117] Simulation Execution: The simulation engine simulates the project execution process after the solution is implemented. It takes into account resource conflicts, new dependencies, and uncertainties that the solution itself may introduce (such as efficiency degradation due to rushing).

[0118] Multidimensional Indicator Prediction: After the simulation, the engine outputs a prediction of the project's future state under this scheme. Key indicators include: New project total duration The new total cost (based on the updated project duration, recalculates the cost impact, and invokes a model that is consistent with the multi-dimensional impact coupling analysis engine) Peak and Balance of Resource Load Project risk index after implementation of the plan (assessing whether the plan introduces new risks or mitigates existing risks). Generate a scheme simulation file: Each scheme corresponds to a complete simulation result file.

[0119] Step 4: Multi-objective decision analysis and visualization. The module aggregates and compares the results of all proposed solutions and inputs them into the multi-objective decision analyzer, including: Construct a decision matrix: Form a matrix in which rows represent candidate solutions and columns represent various evaluation indicators (project period, cost, resource load, risk, etc.).

[0120] Weighting and Overall Scoring: The system provides an interactive interface that allows decision-makers (project managers) to set the weights of each indicator based on the project's current strategic priorities (e.g., 0.6 for labor options, 0.3 for costs, and 0.1 for risks). The decision analyzer calculates the overall utility score for each option based on these weights.

[0121] Generate decision support dashboards: The system automatically generates intuitive visual dashboards (such as parallel coordinate graphs, radar charts, and bar charts) that display the performance and overall scores of each solution across different dimensions. The dashboards clearly identify the recommended solution (usually the one with the highest overall score) and support "hypothesis analysis," allowing users to dynamically adjust weights and observe changes in the solution ranking in real time.

[0122] Step 5: Solution Output and Execution Integration. The module's final output includes a decision recommendation report ranking the recommended solutions and an interactive decision support dashboard. This output will: Automatically pushed to relevant responsible persons as core material for decision-making meetings.

[0123] If a decision-maker adopts a plan, the specific adjustment instructions contained in that plan can be converted into system instructions and fed back to the schedule and data management module for formally updating the project baseline plan, thus forming a closed loop of "analysis-decision-execution".

[0124] This module relies heavily on the quantitative description of the problem provided by the upstream multi-dimensional impact coupling analysis engine, uses the real-time, structured project model provided by the schedule planning and data management module as the simulation baseline, and finally feeds the decision results back to this module to update the plan, thus achieving a global closed loop.

[0125] In a preferred embodiment of the present invention, the system further includes a self-learning optimization module connected to the schedule planning and data management module and the multi-dimensional impact coupling analysis engine, used to: continuously collect actual impact data of node delay events in historical projects; compare the actual data with the predicted data of the analysis engine; and optimize the cost sensitivity coefficient and risk impact parameters through algorithms based on the comparison differences to improve the prediction accuracy of the system.

[0126] The specific implementation process of the self-learning optimization module includes: Step 1: Data Collection and Alignment. The module runs continuously in the background, listening for and collecting two types of key historical data: Predictive Data Snapshot: Whenever the multi-dimensional impact coupling analysis engine completes an analysis and generates a "Comprehensive Impact Report," the module automatically captures and stores a copy of the report, particularly the predicted values ​​for schedule delays (ΔT_total_pred), cost increases (ΣΔC_i_pred), and risk evolution. Simultaneously, it records the ID of the "Executable Smart Event Package" that triggered this analysis.

[0127] Actual Impact Data: After project completion or closure of relevant phases, the module extracts the actual total duration impact (ΔT_total_act) and actual total cost impact (ΣΔC_i_act) of the corresponding delayed events from the final state of the schedule planning and data management module, or through integrated business systems (such as financial settlement systems and completion reports). Precise alignment between predicted and actual data is achieved through event package IDs.

[0128] Step 2: Bias Calculation and Sample Library Construction For each aligned prediction-actual data pair, the module calculates the prediction bias: Project duration prediction deviation: ΔT_error = ΔT_total_pred - ΔT_total_act Cost prediction error: ΔC_error = ΣΔC_i_pred - ΣΔC_i_act Qualitative risk assessment: Compare the predicted risks with the actual risks that occur.

[0129] These deviation data are related to the corresponding node type, project characteristics, and initial parameters (such as...). , Together, they form a training sample and are stored in the system's historical sample database.

[0130] Step 3: Model parameter optimization and knowledge update. Periodically (or when a certain number of samples have accumulated), the module initiates the machine learning training process. Feature engineering and training: Using historical samples as the training set, features such as node type, task complexity, and resource environment, and labels such as cost prediction bias, train regression models or use optimization algorithms (such as gradient descent).

[0131] Parameter calibration: Parameter inversion is performed using algorithms such as gradient descent or Bayesian optimization. During training, cost sensitivity coefficients and risk impact coefficients are used as trainable parameters of the model. The actual cost impact ΔC_act and actual construction period impact ΔT_act from historical samples are used as training objectives. By minimizing the loss function (such as mean squared error) between predicted and actual values, the error is backpropagated and these parameters are updated. For example, after training, the conclusion might be: "For all 'mechanical and electrical installation' task nodes in historical data, adjust the common prior value of their cost sensitivity coefficient α from 0.18 to 0.207," thus achieving parameter calibration.

[0132] Rule Extraction: When a significant pattern of prediction bias is found under a specific combination of features (such as "task type = earthwork project" and "season = rainy season"), the system uses decision tree or association rule mining algorithms to automatically extract correction rules in the form of "IF-THEN" from a large number of historical samples. For example, the rule might be extracted as: "IF Task type = earthwork project AND execution month IN [6,7,8] THEN The project duration impact factor is suggested to be corrected to the original value × 1.3". These extracted rules are stored in the rule base as empirical knowledge.

[0133] Knowledge Update: The calibrated new parameter values ​​and refined new rules are pushed and updated to the global parameter and rule libraries of the schedule and data management module. When configuring similar nodes for a new project or performing similar impact predictions, the system will prioritize using these optimized parameters and rules.

[0134] Step 4: Validation and Iteration The system tracks the accuracy of the next round of predictions after parameter updates, forming a continuous improvement loop of "prediction -> collection of actual data -> calculation of deviation -> optimization of parameters -> re-prediction".

[0135] This module closely learns from the predicted output of the multi-dimensional influence coupling analysis engine and the final baseline data of the schedule and data management module, and directly feeds the learning results back to the underlying parameter library of the management module, thereby optimizing the input quality of future analysis of the entire system. It is a key loop link for the system to achieve intelligence and self-adaptation.

[0136] Example 2 This embodiment describes in detail the specific application of the system of Embodiment 1 of the present invention in a "municipal road renovation project".

[0137] like Figure 6 As shown, this system includes: a schedule planning and data management module, a schedule monitoring and event triggering module, a multi-dimensional impact coupling analysis engine, an intelligent deduction and decision-making module for correction schemes, and a self-learning optimization module.

[0138] Step 1: Plan Initialization and Data Association In the schedule and data management module, load the project plan. The plan includes multiple task nodes such as "Construction Drawing Design", "Traffic Diversion Scheme Approval", and "Pipeline Relocation Construction", and sets "Design Completion" as a prerequisite for "Approval" and "Relocation". Associate the following data with the "Pipeline Relocation Construction" node: planned construction period of 20 days, cost budget (labor and machinery) of 300,000 yuan, cost sensitivity coefficient of 0.18, and pre-associated risk "Inconsistent underground pipeline data".

[0139] Step Two: Progress Monitoring and Event Triggering The progress monitoring and event triggering module acquires data through the on-site daily report system. When it detects that the "Construction Drawing Design" node is delayed by 5 days due to changes, the module generates an event message "Design Node Delayed by 5 Days".

[0140] Step 3: Multi-dimensional influence coupling analysis The multi-dimensional impact coupling analysis engine is triggered, and the analysis includes: Impact on schedule: Based on task dependencies, the engine calculated that the start times of the two subsequent nodes, "traffic diversion approval" and "pipeline relocation construction," will both be delayed by 5 days. Critical path analysis confirmed that this will result in an estimated 5-day delay in the overall project duration.

[0141] Cost Impact: Calculated using the model. For the "Pipeline Relocation Construction" node, the direct cost impact = 300,000 × 0.18 × (5 / 20) = 13,500 yuan. With an average daily indirect cost rate of 1,500 yuan, the indirect cost impact is 7,500 yuan. Preliminary estimate suggests this delay will result in a cost increase of approximately 21,000 yuan.

[0142] Risk assessment: Delays increase the probability of pipeline relocation occurring during the rainy season, and the system automatically raises the relevant construction risk level.

[0143] The engine outputs the "Comprehensive Impact Report on Design Delays": Total construction period increased by 5 days, related costs increased by 21,000 yuan, and construction risks increased during the rainy season.

[0144] Step 4: Scheme Demonstration and Intelligent Decision Making After receiving the report, the intelligent deduction and decision-making module for the revised scheme generates two candidate schemes: Option A: Add a construction team to work in parallel for the "pipeline relocation" project. The estimated additional cost is 15,000 yuan, but the project duration for this node can be shortened by 3 days, thereby reducing the total project duration delay to 2 days.

[0145] Option B: Optimize the "traffic diversion" plan, shortening its approval and implementation time by 2 days. This will not increase direct costs but carries certain technical risks. The overall project duration will be delayed by 3 days after the simulation.

[0146] Module 104 simulates and extrapolates the proposed solutions, presenting the results (schedule, cost, and risks) on the decision dashboard. Figure 3 Based on the urgency of the project's objective of "alleviating traffic congestion," the project manager assigned a weight of 0.6 to work-related options and 0.4 to cost. After automatic calculation, the system prioritized option A.

[0147] Step 5: System Self-Learning After the project is completed, the self-learning optimization module collects actual data (the actual construction period was affected by 4 days, and the actual cost increased by 18,000 yuan). After comparing it with the predicted value, it automatically fine-tunes the cost sensitivity coefficient of "relocation" tasks and optimizes the future prediction model.

[0148] Example 3 This embodiment provides a method for progress delay warning and correction based on the system described in Embodiments 1 and 2, such as... Figure 7 As shown, the steps include: S1: Manage project schedules and associate multi-dimensional data with nodes; S2: Monitor node progress and identify delay events; S3: Perform multi-dimensional impact coupling analysis on delayed events and generate a comprehensive impact report; S4: Generate multiple corrective measures based on the report and perform effect simulation; S5: Compare and display the results of the proposed solutions, and output the recommended solutions.

[0149] The multi-dimensional impact coupling analysis includes simultaneous calculations of the propagation of schedule impacts, quantification of cost impacts, and risk correlation assessments.

[0150] The specific implementation process of this method is the same as that of the module in Embodiment 1 or 2, and will not be repeated here.

[0151] Example 4 The present invention also provides an electronic device, including: a processor, a transmitting device, an input device, an output device, and a memory. The processor may be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory may be implemented using a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), and is used to store computer program code. The computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any of the above possible implementation methods.

[0152] Example 5 The present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to perform a method as described in any of the above possible implementations.

[0153] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0154] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A schedule delay early warning and correction system based on project management data, characterized in that, include: The schedule planning and data management module is used to manage the structured schedule of the project and associate multi-dimensional management data with the task nodes in the plan. The multi-dimensional management data includes at least cost budget information, pre-associated risk information, and cost sensitivity coefficient. The progress monitoring and event triggering module is used to monitor the actual status of task nodes and generate node delay event information when the progress deviation exceeds the threshold. The multi-dimensional impact coupling analysis engine, responding to the node latency event information, is used for synchronous execution: (a) Calculate the impact of delays on the overall project duration based on task logical dependencies; (b) Quantify the cost impact value based on the cost sensitivity coefficient and schedule impact; (c) Assess the changes in associated risks caused by the delay; And output a comprehensive impact report; The intelligent deduction and decision-making module for corrective schemes is used for: At least two revised candidate solutions are generated based on the comprehensive impact report; The overall impact of each proposed solution on the project schedule, cost, and risks will be analyzed. The simulation results are compared and presented, and recommended solutions are output based on decision preferences.

2. The system according to claim 1, characterized in that, The schedule planning and data management module supports loading full-process schedule node templates based on project type. The templates divide the project into multiple stages and preset key nodes and early warning rules.

3. The system according to claim 2, characterized in that, The early warning rules include a multi-level response mechanism; the multi-dimensional impact coupling analysis engine dynamically triggers the corresponding level of response mechanism based on the analysis results, and the higher-level response mechanism is associated with the emergency communication process.

4. The system according to claim 1, characterized in that, In the multi-dimensional impact coupling analysis engine, the cost impact value of the affected node i is calculated using the following model: ， in, This represents the expected increase in total cost at node i due to the change in project duration. Represents the direct cost baseline for node i. This represents the cost sensitivity coefficient of node i. For every 1% deviation of the project duration from the original planned duration, the direct cost will change accordingly. %, This represents the change in the project duration at node i. This represents the original planned construction period for node i. This indicates the project's average daily indirect cost rate. Let represent the risk impact coefficient of the k-th associated node i, with a value range of [0, 1). This represents the sum of the impact coefficients of all activated associated risks.

5. The system according to claim 1 or 4, characterized in that, It also includes a self-learning optimization module, which collects the actual impact data of node latency, compares it with the predicted value, and optimizes the cost sensitivity coefficient and risk parameters through an algorithm.

6. The system according to claim 1, characterized in that, The progress monitoring and event triggering module includes a predictive early warning unit, which is used to predict future delays by analyzing node execution trends and trigger early warnings in advance.

7. A method for early warning and correction of progress delays based on the system according to any one of claims 1-6, characterized in that, include: S1: Manage project schedules and associate multi-dimensional data with nodes; S2: Monitor node progress and identify delay events; S3: Perform multi-dimensional impact coupling analysis on delay events and generate a comprehensive impact report; S4: Generate multiple correction schemes based on the report and perform effect simulation; S5: Compare and display the simulation results of the schemes and output the recommended scheme.

8. The method according to claim 7, characterized in that, The multi-dimensional impact coupling analysis includes simultaneous calculations of the propagation of schedule impacts, quantification of cost impacts, and risk correlation assessments.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method as described in claim 7 or 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in claim 7 or 8.

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