Mould project progress lag risk early warning method

Abstract

The invention discloses a mold project progress lag risk early warning method, which is applied to the technical field of risk early warning, and comprises the following steps: defining a key monitoring node of an automobile mold, and constructing a risk judgment rule base containing a threshold value, a key index and a task dependency relationship; the plan data and the multi-source actual feedback data are integrated, the plan data and the actual execution data are compared in real time through an intelligent linkage engine, a progress deviation value is calculated, and an early warning node is recognized according to a threshold value in a risk judgment rule base; based on the dependency relationship in the risk decision rule base, the identified early warning node is evaluated, the influence path of the early warning node is simulated, and a visual data flow diagram is generated; and triggering a grading early warning notification according to the risk grade in the evaluation result. According to the method, various risk factors are fused into the comprehensive risk index through the dynamic aggregation algorithm, so that the accuracy and reliability of risk early warning are effectively improved.

Description

Technical Field

[0001] This application relates to the field of risk warning technology, and more specifically, to a method for early warning of risks associated with delays in the progress of mold projects. Background Technology

[0002] Automotive molds are tools and equipment used in the automotive manufacturing process to produce parts. They are usually made of high-strength materials such as steel and aluminum, and are processed into the required automotive parts shapes through processes such as die casting, injection molding, and stamping. Molds are crucial in automotive production because they ensure the precision, quality, and production efficiency of parts. Each mold corresponds to the production of one or more automotive parts and usually requires multiple stages such as design, processing, and verification.

[0003] Early warning of delays in mold project schedules aims to prevent delays in mold design, manufacturing, and testing from negatively impacting the overall automotive production plan. Mold production has a long cycle and involves multiple complex processes; therefore, issues such as design changes, technical problems, and resource shortages may arise during project progress, leading to delays. By promptly identifying and warning of delay risks, project managers can take appropriate measures to reduce the impact of delays on subsequent production, thereby ensuring the project is completed on time, reducing the risk of production line shutdowns or increased costs, and maintaining efficient production processes and quality control.

[0004] Existing methods may only focus on a single schedule deviation, failing to consider multi-dimensional factors such as time deviation, C-value impact factor, and customer priority. This results in an inability to comprehensively and accurately identify and assess potential risks. In addition, the dependencies between tasks in a project are often not clear enough, making it impossible to accurately track the risk propagation path between tasks. As a result, the impact of delayed tasks on subsequent tasks may not be identified in a timely manner. Furthermore, traditional assessment methods usually ignore the complex chain effects between tasks, resulting in a failure to fully analyze the impact of delay risks on the overall project schedule.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] To overcome the above problems, this application aims to propose a method for early warning of delay risks in mold projects. The purpose is to address the issue that the dependencies between tasks in a project are often not clear enough, making it impossible to accurately track the risk propagation path between tasks. This results in the inability to identify the impact of delayed tasks on subsequent tasks in a timely manner. Furthermore, traditional assessment methods often ignore the complex chain effects between tasks, leading to a failure to fully analyze the impact of delay risks on the overall project schedule.

[0007] Therefore, the specific technical solution adopted in this application is as follows: A method for early warning of project schedule delays in mold making, the method comprising: S1. Define the key monitoring nodes for automotive molds and construct a risk judgment rule base that includes thresholds, key indicators, and task dependencies; S2. Integrate planned data with multi-source actual feedback data, use the intelligent linkage engine to compare planned data with actual execution data in real time, calculate the progress deviation value, and identify early warning nodes based on the threshold in the risk judgment rule base. S3. Based on the dependencies in the risk assessment rule base, evaluate the identified early warning nodes, simulate their impact paths, and generate a visualized data flow diagram; trigger graded early warning notifications according to the risk level in the assessment results.

[0008] Optionally, a risk assessment rule base is constructed, including thresholds, key indicators, and task dependencies, including: Based on the standard process flow of automotive molds, key nodes are extracted from the planning rule base as risk monitoring points, and risk judgment indicators are defined for each key node to obtain a list of risk monitoring elements. Based on the list of monitoring elements, a risk level judgment threshold is configured for each node, and combined with the C value, a node risk judgment rule set is constructed. A risk transmission model is constructed based on the nodes and dependencies in the risk monitoring element list; Integrate the monitoring element list, node risk assessment rule set, and dynamic impact coefficient to construct a risk assessment rule base.

[0009] Alternatively, the method for constructing a risk transmission model is as follows: Analyze the risk monitoring element list, define dependencies based on the preconditions between each node, and construct a project management topology diagram; Based on the project management topology diagram, specific assessment rules are defined for each risk transmission path, forming a transmission rule set; The C value is integrated into the transmission rules set, and parameters are set according to the resource conflict handling rules to construct a dynamic impact coefficient for risk transmission; A risk transmission model is constructed based on the transmission rule set and dynamic influence coefficient.

[0010] Optionally, the schedule deviation value is calculated and the warning node is identified based on the threshold in the risk assessment rule base, including: Integrate planned data with actual execution data from multiple sources of feedback; The intelligent linkage engine is used to compare each set of planned and actual data in real time and calculate the progress deviation value of each monitoring node. The schedule deviation value is compared with the threshold in the risk assessment rule base, and the risk level of the node is determined.

[0011] Alternatively, the method for determining the risk level of a node is as follows: Receive the progress deviation value and corresponding context information from each monitoring node. Based on the risk assessment rule base, multidimensional risk factors including time deviation factor, C-value influence factor and customer priority factor are calculated respectively. A dynamic aggregation algorithm is used to integrate multi-dimensional risk factors into a comprehensive risk index for early warning nodes; Based on the preset level mapping rules, the comprehensive risk index is determined as the corresponding node risk level.

[0012] Optionally, the expression for the comprehensive risk index is: ; In the formula, This represents the overall risk index; This represents the global operating condition correction factor; This represents the entropy weighting correction term; This represents the amplification factor for extreme risks; This indicates an indicator function triggered by extreme risks; Indicates the first Risk factors; This indicates the total number of risk factors; Indicates the first One risk factor; Indicates the first One risk factor; Indicates the first Dynamic weights of class factors; Indicates the first Risk factors; Indicates the first The nonlinear amplification factor of the class factor.

[0013] Optionally, the identified early warning nodes are evaluated, including: Receive early warning nodes and their corresponding progress deviation values ​​and initial risk levels; Based on the task dependencies in the risk assessment rule base, a risk transmission impact subgraph is constructed with the early warning node as the starting point. Based on the preset transmission rules, calculate the chain effect value of each node in the influence subgraph; Aggregate analysis based on calculated cascading impact values ​​assesses the overall impact on the project's critical path and milestones, generating a risk assessment report that includes a list of affected nodes, total delay forecasts, and a comprehensive risk level.

[0014] Optionally, the expression for the cascading effect value is: ; In the formula, Indicates task The cascading impact value or risk impact value; Indicates task Schedule deviation value; Indicates task For the task The positive influence weight; Indicates task and tasks The influence transmission coefficient between them; Indicates task Keyness weight; Indicates task Weighting coefficients; Indicates task For the task The reverse feedback weight.

[0015] Optionally, its impact path is simulated and a visual data flow graph is generated, including: Extract core data on the risk impact path from the risk assessment report and associate them with the corresponding levels, including the data layer, business layer, and decision-making layer, according to the field mapping relationship; The visualization rendering component of the intelligent linkage engine is invoked to generate a data flow diagram of the risk impact path and display it in the visualization interface of the decision-making level; Based on the comprehensive risk level in the risk assessment report, and combined with the permission isolation rules, the hierarchical notifications in the early warning rule base are matched to generate an early warning notification containing multi-level risk information. The intelligent linkage engine publishes early warning notifications to an asynchronous message queue, sends tiered early warning notifications via mobile push notifications, and records the sending status and notification logs.

[0016] Compared with the prior art, this application has the following beneficial effects: 1. This application, by constructing a risk assessment rule base and a risk transmission model, can achieve comprehensive monitoring and dynamic evaluation of key monitoring nodes and task dependencies, promptly identify potential risks, and combine planned data with actual feedback data. By using an intelligent linkage engine for real-time comparison and deviation calculation, it can accurately identify early warning nodes and generate multi-dimensional risk factors, ensuring a detailed and comprehensive analysis of project schedule delays. By simulating impact paths and generating visual data flow diagrams, it helps project managers intuitively understand the risk propagation between nodes, optimize the decision-making process, and effectively reduce the impact of delays by dynamically aggregating risk factors and generating tiered early warning notifications.

[0017] 2. This application constructs a risk assessment rule base, combining key nodes, risk assessment indicators, and task dependencies to comprehensively monitor and evaluate the risk of project schedule delays; by extracting key nodes from standard processes, defining risk assessment indicators, and configuring risk level assessment thresholds for each node, the accuracy of risk identification is ensured; the risk transmission model built based on the project management topology diagram clearly shows the dependencies between tasks, effectively tracks the propagation path of risks in the project, dynamically adjusts transmission rules, and optimizes resource conflict handling.

[0018] 3. This application utilizes an intelligent linkage engine for real-time comparison, which can accurately calculate the schedule deviation value and identify early warning nodes based on the threshold in the risk judgment rule base. It not only considers schedule deviation, but also incorporates multiple dimensions such as time deviation, C-value influence factor and customer priority into the risk assessment. Through a dynamic aggregation algorithm, various risk factors are integrated into a comprehensive risk index, which effectively improves the accuracy and reliability of risk warning.

[0019] 4. This application constructs a risk transmission impact subgraph starting from the early warning node, and based on task dependencies and transmission rules, it can calculate the chain impact value of each node in detail, thereby accurately identifying the risk propagation path. The introduction of the chain impact value formula allows the risk of each node to be dynamically assessed by combining multiple dimensions such as task progress deviation, impact weight, and transmission coefficient, ensuring the comprehensiveness and accuracy of risk analysis. The generated risk assessment report provides a list of affected nodes, total delay prediction, and comprehensive risk level, providing strong data support for decision-making. Attached Figure Description

[0020] The above-mentioned features, characteristics, and advantages of this application, as well as their implementation methods, will become clearer and more understandable in conjunction with the following description of the embodiments, which are illustrated in detail with reference to the accompanying drawings. Schematic diagrams are shown here: Figure 1 This is a flowchart of the method for early warning of the risk of delay in the progress of mold projects in the embodiments of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0022] According to an embodiment of this application, a method for early warning of risks of delays in mold project progress is provided.

[0023] This application, by constructing a risk assessment rule base and a risk transmission model, enables comprehensive monitoring and dynamic evaluation of key monitoring nodes and task dependencies. It promptly identifies potential risks, combines planned data with actual feedback data, and utilizes an intelligent linkage engine for real-time comparison and deviation calculation. This allows for accurate identification of early warning nodes and the generation of multi-dimensional risk factors, ensuring a detailed and comprehensive analysis of project delays. By simulating impact paths and generating visualized data flow diagrams, it helps project managers intuitively understand the risk propagation between nodes, optimize decision-making processes, and effectively reduce the impact of delays through dynamic aggregation of risk factors and generation of tiered early warning notifications. Figure 1 As shown, the mold project schedule delay risk warning method according to an embodiment of this application includes: S1. Define the key monitoring nodes of the automotive mold and build a risk judgment rule base that includes thresholds, key indicators and task dependencies.

[0024] Preferably, a risk assessment rule base is constructed, including thresholds, key indicators, and task dependencies, comprising: Based on the standard process flow of automotive molds, key nodes are extracted from the planning rule base as risk monitoring points, and risk judgment indicators are defined for each key node to obtain a list of risk monitoring elements. Based on the list of monitoring elements, a risk level judgment threshold is configured for each node, and combined with the C value, a node risk judgment rule set is constructed. A risk transmission model is constructed based on the nodes and dependencies in the risk monitoring element list; Integrate the monitoring element list, node risk assessment rule set, and dynamic impact coefficient to construct a risk assessment rule base.

[0025] Preferably, the method for constructing the risk transmission model is as follows: Analyze the risk monitoring element list, define dependencies based on the preconditions between each node, and construct a project management topology diagram; Based on the project management topology diagram, specific assessment rules are defined for each risk transmission path, forming a transmission rule set; The C value is integrated into the transmission rules set, and parameters are set according to the resource conflict handling rules to construct a dynamic impact coefficient for risk transmission; A risk transmission model is constructed based on the transmission rule set and dynamic influence coefficient.

[0026] It should be explained that the risk assessment rule base is the core basis for achieving "automatic plan generation", covering node definition, schedule logic, dependency relationship, priority strategy and other content; The build process is as follows: Identify typical project types: Establish a standardized template library based on historical project classifications (such as single-operation dies, multi-station dies, drawing dies, trimming and punching dies, etc.).

[0027] Define standard plan nodes: For each type of mold, set a common sequence of planning nodes, for example: 10. Process Design - Process Review and Approval; 15. Structural Design - Structural Review and Approval; Formal drawings were issued on the 30th. 40. Material Procurement - Castings Arrival; 50 Machining - Semi-finished Fixtures; 60 Assembly - Full Process; 70 Debugging - OTS Certification Passed; 80 Pre-acceptance - Shipment; 90 Final Acceptance - Goods arrived at the buyer's site and passed the initial acceptance inspection; 100% warranty; Configure logical relationships between nodes: Dependencies are defined using "preconditions," for example: "Official drawing issuance" can only be started after "structural review" is completed; "processing" can only begin after "materials arrive". Configure standard time parameters: set a default time (unit: days) for each node, and support floating according to mold complexity: for example, the basic time for "assembly-full sequence parts" is 15 days. If the C value is > 20,000, it will be extended to 20 days. Set priority and resource conflict handling rules: When multiple projects compete for the same resource (such as a test bench), schedule them according to their C value and customer level. Versioned management rule base: It supports the creation of multiple rule versions (such as V1: basic version; V2: new energy vehicle-specific version), each applicable to different types of projects; Testing, Validation, and Deployment: Run typical projects in a simulated environment to verify the accuracy of the generated plan; Once published, only administrators can modify it; ordinary users can only reference it.

[0028] S2. Integrate planned data with multi-source actual feedback data, use the intelligent linkage engine to compare planned data and actual execution data in real time, calculate the progress deviation value, and identify early warning nodes based on the threshold in the risk judgment rule base.

[0029] Preferably, calculating the schedule deviation value and identifying the early warning node based on the threshold in the risk assessment rule base includes: Integrate planned data with actual execution data from multiple sources of feedback; The intelligent linkage engine is used to compare each set of planned and actual data in real time and calculate the progress deviation value of each monitoring node. The schedule deviation value is compared with the threshold in the risk assessment rule base, and the risk level of the node is determined.

[0030] Preferably, the method for determining the risk level of a node is as follows: Receive the progress deviation value and corresponding context information from each monitoring node. Based on the risk assessment rule base, multidimensional risk factors including time deviation factor, C-value influence factor and customer priority factor are calculated respectively. A dynamic aggregation algorithm is used to integrate multi-dimensional risk factors into a comprehensive risk index for early warning nodes; Based on the preset level mapping rules, the comprehensive risk index is determined as the corresponding node risk level.

[0031] Preferably, the expression for the comprehensive risk index is: ; In the formula, This represents the overall risk index; This represents the global operating condition correction factor; This represents the entropy weighting correction term; This represents the amplification factor for extreme risks; This indicates an indicator function triggered by extreme risks; Indicates the first Risk factors; This indicates the total number of risk factors; Indicates the first One risk factor; Indicates the first One risk factor; Indicates the first Dynamic weights of class factors; Indicates the first Risk factors; Indicates the first The nonlinear amplification factor of the class factor.

[0032] It needs to be explained that the actual progress data is being collected: Users can manually enter or select the actual start / completion date on the "Mold Project Feedback" page.

[0033] Automatic deviation detection: Compare the "planned time" with the "actual time" to calculate the number of days of delay: Triggering Chain Reaction Assessment: If the current node is delayed, it will automatically determine whether its downstream nodes are affected: For example: "Structural review" delayed by 3 days → "Official drawings issued" delayed by 3 days.

[0034] Automatically adjust subsequent plans: Update the "Planned Start Date" and "Planned Completion Date" of the affected nodes to maintain logical consistency; Adjust the "OT percentage" (i.e., the percentage of completed tasks) in sync. Generate early warning notification: If the delay exceeds the threshold (e.g., ≥5 days), send a reminder to the project manager; Optionally, you can choose whether to automatically initiate the "extension application process"; Preserve traces of the original plan: All changes are recorded in the log, forming a "version history" for easy tracking; Supports manual intervention and confirmation: Users can choose to accept or reject the suggested corrections.

[0035] S3. Based on the dependencies in the risk assessment rule base, evaluate the identified early warning nodes, simulate their impact paths, and generate a visualized data flow diagram; trigger graded early warning notifications according to the risk level in the assessment results.

[0036] Preferably, the identified early warning nodes are evaluated, including: Receive early warning nodes and their corresponding progress deviation values ​​and initial risk levels; Based on the task dependencies in the risk assessment rule base, a risk transmission impact subgraph is constructed with the early warning node as the starting point. Based on the preset transmission rules, calculate the chain effect value of each node in the influence subgraph; Aggregate analysis based on calculated cascading impact values ​​assesses the overall impact on the project's critical path and milestones, generating a risk assessment report that includes a list of affected nodes, total delay forecasts, and a comprehensive risk level.

[0037] Preferably, the expression for the cascading effect value is: ; In the formula, Indicates task The cascading impact value or risk impact value; Indicates task Schedule deviation value; Indicates task For the task The positive influence weight; Indicates task and tasks The influence transmission coefficient between them; Indicates task Keyness weight; Indicates task Weighting coefficients; Indicates task For the task The reverse feedback weight.

[0038] Preferably, simulating its impact path and generating a visualized data flow graph includes: Extract core data on the risk impact path from the risk assessment report and associate them with the corresponding levels, including the data layer, business layer, and decision-making layer, according to the field mapping relationship; The visualization rendering component of the intelligent linkage engine is invoked to generate a data flow diagram of the risk impact path and display it in the visualization interface of the decision-making level; Based on the comprehensive risk level in the risk assessment report, and combined with the permission isolation rules, the hierarchical notifications in the early warning rule base are matched to generate an early warning notification containing multi-level risk information. The intelligent linkage engine publishes early warning notifications to an asynchronous message queue, sends tiered early warning notifications via mobile push notifications, and records the sending status and notification logs. It needs to be explained that the data hierarchy structure is defined as follows: L1: The underlying data layer includes raw fields such as project number, mold number, C value, customer information, and bill of materials; L2: Middle business layer such as planned nodes, implementation units, completion progress, OT percentage and other derived fields; L3: Aggregated indicators for high-level decision-making, such as overall project progress, risk warning, and resource utilization.

[0039] Establish field mapping relationships: Clearly define the field binding relationships between different levels, for example: "Project Number" → Associated with "Project List", "Mold Details", and "Plan Feedback"; "Mold Number" → Associated with "C Value Calculation", "Work Hour Allocation", and "Actual Progress"; Develop an intelligent linkage engine: The engine listens for key field change events (such as "Actual Completion Date" being filled in); Trigger a pre-defined rule chain to automatically update related fields; Implement cross-module linkage logic: Example 1: When a new mold is added to the "Mold Details" → "C Value Calculation" is automatically triggered → "Plan Generation" is affected → "Project Progress Feedback" is updated; Example 2: When a node in the "Project Progress Feedback" is completed, the overall progress bar in the "Project List" will be automatically advanced, triggering a refresh of the "Large Screen Dashboard".

[0040] Supports asynchronous message queue mechanism: Use message middleware (such as RabbitMQ / Kafka) to decouple modules and ensure stable operation under high concurrency; Visualized linkage path tracing: Provides a "data flow graph" function to show how changes in a certain field propagate to other modules.

[0041] Access control and security: Different roles can only access data at their corresponding levels to prevent unauthorized operations.

[0042] It should be explained that, taking a certain automobile mold project as an example, each task in the automobile mold project has its own planned time and actual completion time; the project progress is affected by the interdependence between tasks, and delays will lead to the postponement of downstream tasks, creating a chain reaction. There are two key task nodes in the project: Task A: Mold design completed; Task B: Mold making; In the risk assessment rule base, each task has a corresponding threshold and risk factor; for example, the time deviation threshold for task A is ±5 days, the criticality weight for task B is 0.8, and the reverse feedback weight for task C is 0.7. Integrate planning data with feedback data: Task A is scheduled to start on January 1, 20XX, and complete on January 10, 2026. Task A's actual completion date: 20XX-01-12 (delayed by 2 days); Task B is scheduled to start on January 11, 20XX, and complete on January 20, 2026. Task B's actual completion date: 20XX-01-21 (delayed by 1 day); The schedule deviation for Task A is 2 days; The schedule deviation for Task B is 1 day; The progress deviations of both Task A and Task B exceeded the threshold of ±1 day, thus triggering the warning nodes. For task A, the risk assessment method compares its schedule deviation value with the threshold in the rule base and determines the risk level by calculating multidimensional risk factors; According to dependencies, a delay in task A will affect the start time of task B; Set the following parameters: the influence weight of task A on task B. =0.6, the transfer coefficient between task A and task B =0.5, the criticality weight of task B =0.8, the back feedback weight of task B =0.3, the weighting coefficient for task A =1.0; Based on the specific parameters mentioned above, calculate the cascading effect value of task A. C A =2.624, indicating that the delay of task A has had a significant impact on the overall project schedule; Based on the task dependencies in the risk assessment rule base, the impact path between task A and task B is constructed, and a data flow diagram is generated to show how the delay of task A affects task B and subsequent tasks. Data flow layer-level structure: L1 (Underlying Data Layer): Raw data such as project number, mold number, and customer information; L2 (Business Layer): Intermediate data such as planning milestones, progress, and OT percentage; L3 (Decision-making level): Risk assessment report, including aggregated indicators such as overall progress, risk warning, and resource utilization; Generate early warning notification: Based on the risk level and early warning rules of task A, trigger the corresponding early warning notification to remind the project manager of the task delay, and you can choose to automatically start the extension application process.

[0043] The demonstration uses specific numerical values ​​and formulas to illustrate how, based on the risk assessment rule base for automotive mold projects, planned and actual execution data can be integrated to calculate task schedule deviations and identify warning nodes. By simulating the impact paths of task dependencies, a chain reaction value (C-value) is generated, ultimately forming a risk assessment report and triggering corresponding warning notifications.

[0044] It should be noted that the calculation formulas and all parameters involved in the calculations in this application have been dimensionless beforehand. The process of dimensionless processing is well known in the industry and will not be described here.

[0045] Although the present application has disclosed the preferred embodiments above, the embodiments are merely examples for the purpose of illustration and are not intended to limit the present application. Those skilled in the art can make some modifications and refinements without departing from the spirit and scope of the present application. The scope of protection claimed by the present application should be determined by the claims.

Claims

1. A method for early warning of project schedule delays in mold making, characterized in that, The method includes: S1. Define the key monitoring nodes for automotive molds and construct a risk judgment rule base that includes thresholds, key indicators, and task dependencies; S2. Integrate planned data with multi-source actual feedback data, use the intelligent linkage engine to compare planned data with actual execution data in real time, calculate the progress deviation value, and identify early warning nodes based on the threshold in the risk judgment rule base. S3. Based on the dependencies in the risk assessment rule base, evaluate the identified early warning nodes, simulate their impact paths, and generate a visualized data flow diagram; trigger graded early warning notifications according to the risk level in the assessment results.

2. The method for early warning of project delay risks according to claim 1, characterized in that, The construction of the risk assessment rule base, which includes thresholds, key indicators, and task dependencies, includes: Based on the standard process flow of automotive molds, key nodes are extracted from the planning rule base as risk monitoring points, and risk judgment indicators are defined for each key node to obtain a list of risk monitoring elements. Based on the list of monitoring elements, a risk level judgment threshold is configured for each node, and combined with the C value, a node risk judgment rule set is constructed. A risk transmission model is constructed based on the nodes and dependencies in the risk monitoring element list; Integrate the monitoring element list, node risk assessment rule set, and dynamic impact coefficient to construct a risk assessment rule base.

3. The method for early warning of project delay risks according to claim 2, characterized in that, The method for constructing the risk transmission model is as follows: Analyze the risk monitoring element list, define dependencies based on the preconditions between each node, and construct a project management topology diagram; Based on the project management topology diagram, specific assessment rules are defined for each risk transmission path, forming a transmission rule set; The C value is integrated into the transmission rules set, and parameters are set according to the resource conflict handling rules to construct a dynamic impact coefficient for risk transmission; A risk transmission model is constructed based on the transmission rule set and dynamic influence coefficient.

4. The method for early warning of project delay risks according to claim 1, characterized in that, The calculation of the progress deviation value and the identification of early warning nodes based on the threshold in the risk assessment rule base include: Integrate planned data with actual execution data from multiple sources of feedback; The intelligent linkage engine is used to compare each set of planned and actual data in real time and calculate the progress deviation value of each monitoring node. The schedule deviation value is compared with the threshold in the risk assessment rule base, and the risk level of the node is determined.

5. The method for early warning of project delay risks according to claim 4, characterized in that, The method for determining the risk level of a node is as follows: Receive the progress deviation value and corresponding context information from each monitoring node. Based on the risk assessment rule base, multidimensional risk factors including time deviation factor, C-value influence factor and customer priority factor are calculated respectively. A dynamic aggregation algorithm is used to integrate multi-dimensional risk factors into a comprehensive risk index for early warning nodes; Based on the preset level mapping rules, the comprehensive risk index is determined as the corresponding node risk level.

6. The method for early warning of project delay risks according to claim 5, characterized in that, The expression for the comprehensive risk index is: ； In the formula, This represents the overall risk index; This represents the global operating condition correction factor; This represents the entropy weighting correction term; This represents the amplification factor for extreme risks; This indicates an indicator function triggered by extreme risks; Indicates the first Risk factors; This indicates the total number of risk factors; Indicates the first One risk factor; Indicates the first One risk factor; Indicates the first Dynamic weights of class factors; Indicates the first Risk factors; Indicates the first The nonlinear amplification factor of the class factor.

7. The method for early warning of project delay risks according to claim 1, characterized in that, The evaluation of the identified early warning nodes includes: Receive early warning nodes and their corresponding progress deviation values ​​and initial risk levels; Based on the task dependencies in the risk assessment rule base, a risk transmission impact subgraph is constructed with the early warning node as the starting point. Based on the preset transmission rules, calculate the chain effect value of each node in the influence subgraph; Aggregate analysis based on calculated cascading impact values ​​assesses the overall impact on the project's critical path and milestones, generating a risk assessment report that includes a list of affected nodes, total delay forecasts, and comprehensive risk levels.

8. The method for early warning of project delay risks according to claim 7, characterized in that, The expression for the cascading effect value is: ； In the formula, Indicates task The cascading impact value or risk impact value; Indicates task Schedule deviation value; Indicates task For the task The positive influence weight; Indicates task and tasks The influence transmission coefficient between them; Indicates task Keyness weight; Indicates task Weighting coefficients; Indicates task For the task The reverse feedback weight.

9. The method for early warning of project delay risks according to claim 8, characterized in that, The process of simulating its impact path and generating a visualized data flow graph includes: Extract core data on the risk impact path from the risk assessment report and associate them with the corresponding levels, including the data layer, business layer, and decision-making layer, according to the field mapping relationship; The visualization rendering component of the intelligent linkage engine is invoked to generate a data flow diagram of the risk impact path and display it in the visualization interface of the decision-making level; Based on the comprehensive risk level in the risk assessment report, and combined with the permission isolation rules, the hierarchical notifications in the early warning rule base are matched to generate an early warning notification containing multi-level risk information. The intelligent linkage engine publishes early warning notifications to an asynchronous message queue, sends tiered early warning notifications via mobile push notifications, and records the sending status and notification logs.

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