Project progress management system based on double-time-mark code name network diagram

Abstract

The invention discloses a project progress management system based on a double-time-mark code name network diagram, and the system defines a non-critical task time difference as a dynamic elastic interval, dynamically adjusts an interval boundary through combining upstream delay, resource conflicts and external interference data, and outputs real-time elastic interval data; by calculating an elastic interval width attenuation rate, when the attenuation rate exceeds a safety baseline threshold value, triggering early warning of a corresponding path, and outputting a buffer health index and an early warning level; constructing a time difference consumption chain model, inputting local progress deviation, then simulating time difference loss of subsequent tasks, displaying paths and high-risk areas on a double-time-scale network diagram in a pulse animation and thermodynamic diagram manner, outputting a cascade risk report, and screening out high-consumption-rate paths; and traversing tasks of which the elastic interval does not reach an early warning threshold in the path, generating a translation scheme, and after logic and resource constraint detection, outputting a feasibility scheduling instruction and a corresponding risk mitigation rate.

Description

Technical Field

[0001] This invention belongs to the field of project management technology, and in particular relates to a project progress management system based on a dual-time-scaled symbol network diagram. Background Technology

[0002] Dual-timescaled network diagrams are a widely used visualization tool for project schedule management. They clearly display the planned start and end times of tasks on a time axis and precisely describe the dependency order between tasks using logical relationship lines, thus integrating the project's time attributes and logical structure into a single view. In current project schedule management practices based on dual-timescaled network diagrams, identifying the critical path and calculating the float of non-critical path tasks are core steps. Existing technologies generally treat task float as a static, independent numerical attribute, its main function being to determine the delayable time range of a single task and provide auxiliary reference for critical path analysis. Mainstream project management methods and tools are all built upon this static float model.

[0003] However, the float of non-critical paths is essentially a crucial buffer resource for projects to cope with uncertainty. In actual project execution, the available float of non-critical tasks continuously changes dynamically due to factors such as upstream task delays, resource allocation conflicts, or external environmental interference. This change propagates along the path through logical dependencies between tasks, creating a chain reaction. Existing methods have significant limitations: on the one hand, they cannot quantitatively assess the real-time consumption rate of non-critical path buffer capacity; on the other hand, they are difficult to predict the systematic erosion of subsequent non-critical task buffer pools and even the overall project buffer resources through the consumption chain of local schedule deviations. This leads managers to rely primarily on alerts after actual task delays occur for passive responses, lacking a proactive identification and early warning mechanism for the hidden depletion of non-critical path buffer capacity and its chain risks, thus becoming a potential source of project schedule loss of control.

[0004] Therefore, it is necessary to design a project schedule management system based on a dual-time-scaled symbol network diagram to solve the above problems. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a project schedule management system based on a dual-time-scaled symbol network diagram. It aims to solve the problems that existing technologies cannot quantitatively evaluate the real-time consumption rate of non-critical path buffer capacity, and it is difficult to predict the systematic erosion of subsequent non-critical task buffer pools and even the overall project buffer resources through the consumption chain of local schedule deviations. This improves the ability to predict project schedule risks and the efficiency of resource scheduling, and provides more scientific and dynamic technical support for the schedule management of complex projects.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A project schedule management system based on a dual-time-scaled symbol network diagram includes: The elastic interval dynamic modeling module is used to define the float of non-critical tasks as a dynamic elastic interval. The upper limit of the interval is the initial free float, and the lower limit is the compressible float. Non-critical tasks are those in the double-code network diagram whose total float is greater than zero and are not on the critical path, as determined by the total float calculation. The flexible interval dynamic modeling module dynamically compresses and restores the interval boundary based on upstream task delays, resource conflicts, and external interference data, combined with a preset dependency strength coefficient, and outputs real-time flexible interval data that reflects the project progress buffer status. The buffer monitoring module is used to calculate the elastic interval width decay rate. When the decay rate exceeds the safety baseline threshold trained on historical data, it triggers an early warning for the corresponding path and outputs the buffer health index and early warning level. The cascade simulation module is used to construct a time difference consumption chain model and define the inter-task dependency transmission coefficient. After receiving the input local progress deviation, the cascade simulation module simulates and calculates the time difference loss value of subsequent tasks. At the same time, it displays the consumption path with pulse animation on the dual time-scaled network diagram and marks the high-risk area of ​​buffer depletion with a heat map, and outputs a cascade risk report. The scheduling optimization module is used to filter out high-consumption paths from the cascading risk report, traverse the tasks in the elastic interval of the high-consumption path that have not reached the warning threshold, generate a shift plan within the time difference range, and output a feasible scheduling instruction and the corresponding risk mitigation rate after the plan is checked to ensure that there is no conflict between the logical dependency relationship between the plan and the task and meets the resource constraints.

[0007] Preferably, the elastic interval dynamic modeling module includes: The basic parameter parsing unit is configured to perform the following steps: The basic parameter analysis unit extracts the initial free float and compressible float of non-critical tasks from the dual-time-scaled symbol network diagram, and establishes an initial interval model with the initial free float as the upper limit of the dynamic elastic interval and the compressible float as the lower limit of the dynamic elastic interval. The compressible time difference is a preset minimum allowable time under the premise of ensuring the completion of the core tasks of the mission. Its value is preset based on project management requirements and the limits of task resource allocation. Simultaneously, the logical dependencies between tasks are analyzed. By identifying the arrow connection relationship between task nodes in the dual-time-scaled network diagram, the dependency type represented by the arrows is distinguished. The identifiers of the preceding and following connection nodes of each task node are recorded. The association list containing the list of preceding and following tasks of each task is formed in the order of task number. The dependency types include end-start, start-start, end-end, and start-end; the preceding task list lists the identifiers and dependency types of all directly preceding tasks; the following task list lists the identifiers and dependency types of all directly following tasks; wherein the task identifier includes the task number, task name, and time coordinate information in the dual-timescaled network diagram; The influencing factor quantification unit is configured to perform the following steps: The influencing factor quantification unit converts upstream task delay data into delay duration quantification values ​​and resource conflict data into resource contention intensity coefficients. The resource contention intensity coefficient is an indicator characterizing the degree of resource competition between tasks, with a value range of [0, 1], where a value of 0 indicates no resource conflict and a value of 1 indicates that the resource is completely occupied. External interference data is converted into interference impact weights, with a value range of [0, 1], where a value of 0 indicates no interference and a value of 1 indicates that interference prevents the task from progressing. The influence value of each factor on the current task elasticity range is generated by weighted calculation based on the preset dependency strength coefficient; the dependency strength coefficient is a value of [0, 1] set based on the importance of task association, and the formula is: influence value = (delay time × a + resource contention strength coefficient × b + interference influence weight × c) × dependency strength coefficient; where a, b, and c are preset weight coefficients, and a + b + c = 1; The interval dynamic adjustment unit is configured to perform the following steps: The interval dynamic adjustment unit performs interval boundary updates based on the impact value, and the update strategy is as follows: When the impact value is positive, it indicates that there is an adverse impact. The upper limit of the interval is compressed according to the impact value. The compressed upper limit is equal to the difference between the current upper limit and the impact value, and the compressed upper limit is not lower than the lower limit of the interval. When the impact value is negative, it means that the adverse effect has been eliminated. The upper limit of the interval is restored according to the absolute value of the impact value. The restored upper limit is equal to the sum of the current upper limit and the absolute value of the impact value, and the restored upper limit does not exceed the initial free float. Synchronously record the timestamp, influencing factors, and range change for each adjustment, and output real-time elastic range data.

[0008] Preferably, the buffer monitoring module includes: The attenuation rate calculation unit is configured to perform the following steps: The attenuation rate calculation unit receives real-time elastic interval data output by the elastic interval dynamic modeling module, and calculates the attenuation rate of the elastic interval width according to a preset period, based on the time axis of the dual-timescale network diagram. The calculation formula is: Attenuation rate = (initial interval width of the current period - end interval width of the current period) / initial interval width of the current period × 100%; where the interval width is the difference between the upper and lower limits of the dynamic elastic interval, a positive attenuation rate indicates that the interval is in a compressed state, and a negative attenuation rate indicates that the interval is in a recovery state. The threshold comparison unit is configured to perform the following steps: The threshold comparison unit calls the elastic interval decay rate data of the same type of path in the historical project progress database, and generates a safety baseline threshold through statistical analysis. The safety baseline threshold represents the maximum acceptable decay rate of the same type of project within the normal risk range. The currently calculated decay rate is compared with the safety baseline threshold. When the decay rate is greater than the safety baseline threshold, the corresponding path is determined to have an abnormal buffer resource consumption rate. The warning output unit is configured to perform the following steps: The early warning output unit calculates the buffer health index based on the difference between the decay rate and the safety baseline threshold. The buffer health index is a quantitative indicator representing the current health status of project schedule buffer resources, with a value range of [0,1]. The calculation formula is: Buffer Health Index = 1 - (Current Decay Rate / Safety Baseline Threshold). The closer the index is to 1, the healthier the buffer status; the closer the index is to 0, the more depleted the buffer resources are. A first threshold and a second threshold are set as the basis for classifying early warning levels, with the first threshold being greater than the second threshold. When the buffer health index ≥ the first threshold, there is no early warning; at this time, buffer resources are sufficient, the decay rate is within the normal range, and the project schedule is stable. No special intervention is required; only routine monitoring is needed. When the second threshold ≤ buffer health index < first threshold, it is a Level 1 warning, indicating that the rate of buffer resource consumption is accelerating. Although it has not exceeded the safety baseline, there is a potential risk. This warning triggers the cascading simulation module to conduct key simulation analysis of the risk transmission path. When the buffer health index < second threshold, it is a Level 2 warning, indicating that the rate of buffer resource consumption has become significantly abnormal and is approaching the depletion threshold. This warning will directly drive the scheduling optimization module to activate the emergency adjustment mechanism, prioritizing the generation and verification of scheduling schemes for tasks on high-risk paths. The warning level, buffer health index, and corresponding path identifier are output in association.

[0009] Preferably, the cascaded simulation module includes a consumption chain modeling unit, configured to perform the following steps: The consumption chain modeling unit constructs a time difference consumption chain model based on the task logic relationships in the dual-time-scaled symbol network graph: Extract the node information of all tasks and the logical dependencies between tasks from the dual-time-scaled network graph. Treat each task as an independent node and construct directed edges based on the logical dependencies between tasks to form a complete transmission path from the source task of the deviation to the task that ultimately affects it. Simultaneously define the inter-task dependency transitivity coefficient. , represents the degree of influence of the schedule deviation of the preceding task i on the time difference of the subsequent task j, and its value range is [0,1]; the rules for determining the coefficient value are as follows: Based on the logical dependency type between tasks, basic values ​​are assigned. The base value of the end-start dependency relationship is set to the highest level, the start-start dependency relationship to the second highest level, the end-end dependency relationship to the medium level, and the start-end dependency relationship to the lowest level. The base values ​​of each level are distributed in an arithmetic sequence within the interval [0,1]. The adjustments are made based on the degree of inter-task correlation. The degree of correlation is determined by a comprehensive assessment of the number of sub-projects jointly participated in by the tasks, the proportion of resource sharing, and the historical collaboration deviation rate. The specific process is as follows: The weighting percentage of the number of sub-projects jointly involved in the task is as follows: Let the number of overlapping sub-items be m, and the total number of sub-items be M. The score for this item is Q1 = m / M. The weighting of resource sharing ratio is as follows: Let the number of shared resources be r, and the total amount of resources required for task i be R. i The total resources required for task j is R. j The score for this item is Q2 = 2r / (R). i +R j ); The weighting of historical coordination deviation rate is And satisfy Let p be the number of times the two tasks in the historical project deviated together, and P be the total number of times they collaborated. The score for this item is Q3 = 1 - p / P. The formula for calculating the correction factor T is: ; Where T0 is the base value of the correction coefficient. Adjustment range for correction coefficients; Through constraints Ensure that the correction factor is within Within the range; Ultimately dependent on transitivity Furthermore, the truncation process ensures that the corrected coefficients remain within the [0,1] range, and the closer the coefficient is to 1, the more significant the impact.

[0010] Preferably, the cascaded simulation module further includes a time difference loss calculation unit, configured to perform the following steps: The time difference loss calculation unit is based on local deviation data. Cascade calculations are performed; the local schedule deviation data represents the degree of deviation between the actual progress and the planned progress of a single task. A positive value indicates a task delay, with the actual progress lagging behind the plan; a negative value indicates a task ahead of schedule, with the actual progress exceeding the plan; the absolute value is the deviation duration. The local schedule deviation data is obtained by collecting the actual start time, current completion percentage, and resource input timeliness data of the task through a project execution tracking system, comparing them with the planned time parameters in the dual-timescale network diagram, and using the formula: = Actual time elapsed - Planned time elapsed is calculated; In addition, when the buffer monitoring module outputs a level 1 or higher warning, it automatically extracts the task corresponding to the first task that triggered the anomaly in the warning path. The value is used as the initial input for the cascade calculation; local schedule deviation data is used. Substituting these values ​​as initial inputs into the time difference loss chain model, the time difference loss values ​​for each subsequent task are calculated sequentially according to the path order: The initial deviation task is set to S0, and the local schedule deviation data is as follows: For the subsequent nth task S n Its time difference loss value is: ; Where k∈[0,n-1], This represents the actual deviation value for the k-th task. Let be the dependency transitivity coefficient between the k-th task and the n-th task. This represents the schedule deviation value generated by the nth task itself. Actual deviation value The calculation method is to add the actual deviation of the previous task after it is affected by the dependency transitivity coefficient to the deviation of the current task itself, so as to form the actual deviation value of the current task: ; The self-scheduled deviation value ΔSn' is the deviation of this task independent of the effects of preceding events, which is obtained through real-time monitoring data; This represents the actual deviation value for the (k-1)th task. is the dependency transitivity coefficient between the (k-1)th task and the kth task.

[0011] Preferably, the cascaded simulation module further includes: The visualization unit is configured to perform the following steps: The visualization unit maps the time difference loss calculation process to a dual-timescale network diagram, dynamically displaying the deviation propagation process along the consumption chain path through pulse animation. The pulse frequency f of the animation is correlated with the time difference loss value L. n Related, satisfying: ; in Based on the base frequency, This represents the frequency coefficient; simultaneously, it uses a heatmap to indicate the buffer depletion risk level of each task, with heat values... The remaining elasticity interval width W and the cumulative time difference loss value of the task The ratio is determined by the following formula: ; in This is the risk amplification factor; the smaller the ratio Lt / W, the higher the thermal value. The higher the value, the higher the risk threshold. When the heat value exceeds the risk threshold, it is determined to be a high-risk area, including the cumulative time difference loss value. This is the sum of the time difference losses for this task and all preceding related tasks; The report generation unit is configured to perform the following steps: The report generation unit summarizes the consumption chain path information, the time difference loss value of each task, the distribution of high-risk areas and the risk diffusion trend to form a cascaded risk report. The cascaded risk report includes risk source identification, a list of main transmission paths, key affected tasks and their time difference loss quantitative data, task node coordinates of buffer depletion warning areas and risk diffusion prediction cycle.

[0012] Preferably, the risk diffusion prediction period is the estimated time required from the current deviation state to the complete depletion of buffer resources in the high-risk area, calculated as follows: First, obtain the current cumulative time difference loss value for tasks within the high-risk area. The remaining elasticity interval width W, and the time difference loss growth v over the most recent 3 statistical periods: ; in For the duration of the cycle, This indicates the cumulative time difference loss incurred by the task within a statistical period; By fitting a linear regression model to the historical growth rate data, a trend line predicting future loss growth rates is obtained: ; in This represents the projected rate of increase in time difference loss at time t. To accelerate growth, The initial growth rate is given, where t is a time variable with units consistent with the statistical period; based on the remaining buffer capacity. Combined with the predicted growth rate, the theoretical exhaustion time is calculated. Introducing a risk amplification factor and the average of all path dependency strength coefficients for this path. After adjustments, the final prediction period is: ; When the prediction period Y is less than a single statistical period, it is automatically corrected to the set minimum warning unit.

[0013] Preferably, the scheduling optimization module includes a task filtering unit, a scheme generation unit, a constraint detection unit, and an instruction output unit.

[0014] Preferably, the task screening unit receives the cascading risk report and the early warning information output by the buffer monitoring module, extracts the high consumption rate path identifier, traverses the real-time elasticity interval data of all tasks on the path, filters out the tasks whose elasticity interval has not reached the early warning threshold, and forms an adjustable task list; wherein, the high consumption rate path refers to the task logical dependency path marked in the cascading risk report, which must simultaneously meet two characteristics: first, the elasticity interval width decay rate of the path has exceeded the preset standard; second, the cumulative growth rate of the time difference loss value transmitted along the path exceeds the preset multiple of the average growth rate of all paths in the project.

[0015] Preferably, the scheme generation unit generates a task translation scheme with the real-time elastic range of the adjustable task as the time constraint boundary; the translation scheme includes the adjustment amount of the task start time and end time, the value range of the adjustment amount does not exceed the width of the current elastic range of the task, and the adjustment direction is to delay backward to release resources or advance forward to make up for the schedule deviation. The constraint detection unit performs dual constraint verification on the translation scheme: first, logical dependency detection, which checks whether the time overlap relationship between the adjusted task and its predecessor and successor tasks conforms to the logical constraints in the dual-time-scaled network diagram; second, resource constraint detection, which verifies whether the resource demand period of the adjusted task overlaps or conflicts with the resource occupation period of other tasks. The conflict judgment criterion is that the same resource is occupied by multiple tasks in the same time unit. Based on the constraint detection results, the instruction output unit calculates the risk mitigation rate for the translation scheme that has passed dual verification. The risk mitigation rate is the decrease in the time difference loss rate of the high-consumption path after the scheme is implemented. Risk mitigation rate = (time difference loss rate of the high-consumption path before adjustment - time difference loss rate of the high-consumption path after adjustment) / time difference loss rate of the high-consumption path before adjustment × 100%. Wherein, time difference loss rate = increment of time difference loss within a certain period / length of that period. The time difference loss rate of the high-consumption path after adjustment is re-simulated and calculated by the cascaded simulation module. The executable translation scheme is converted into a scheduling instruction. The scheduling instruction includes the task identifier, the adjusted start and end times, resource allocation requirements, and the corresponding risk mitigation rate, and is output to the project management terminal.

[0016] The beneficial effects of this invention are as follows: This invention dynamically models the time difference of non-critical tasks in a dual-code network diagram as an elastic interval, dynamically adjusts the boundary based on multiple factors to accurately reflect the buffer state, and captures anomalies in elastic interval decay in real time through a buffer monitoring module. It also links a cascaded simulation module to construct a time difference consumption chain model, quantifies the deviation transmission path and time difference loss, and visually presents the risk diffusion trend. Finally, relying on a scheduling optimization module, it generates logical and resource-constrained translation schemes for high-consumption paths, forming a closed-loop management system of dynamic modeling, risk monitoring, simulation early warning, and intelligent scheduling. This system breaks through the limitations of static time difference control in traditional project schedule management. It achieves refined tracking of buffer resources through dynamic elastic intervals, improves the accuracy of deviation transmission prediction by leveraging dependency transmission coefficients and cascaded calculations, and combines visualization technology and intelligent scheduling algorithms. This not only identifies high-risk areas in advance but also outputs feasible optimization solutions, significantly improving the ability to predict project schedule risks and resource scheduling efficiency, providing more scientific and dynamic technical support for the schedule management of complex projects. Attached Figure Description

[0017] Figure 1 It is a system module diagram of a project schedule management system based on a dual-time-scaled symbol network diagram. Detailed Implementation

[0018] Example 1: like Figure 1 As shown, a project schedule management system based on a dual-time-scaled symbol network diagram includes: The elastic interval dynamic modeling module is used to define the float of non-critical tasks as a dynamic elastic interval. The upper limit of the interval is the initial free float, and the lower limit is the compressible float. Non-critical tasks are those in the double-code network diagram whose total float is greater than zero and are not on the critical path, as determined by the total float calculation. The flexible interval dynamic modeling module dynamically compresses and restores the interval boundary based on upstream task delays, resource conflicts, and external interference data, combined with a preset dependency strength coefficient, and outputs real-time flexible interval data that reflects the project progress buffer status. The buffer monitoring module is used to calculate the elastic interval width decay rate. When the decay rate exceeds the safety baseline threshold trained on historical data, it triggers an early warning for the corresponding path and outputs the buffer health index and early warning level. The cascade simulation module is used to construct a time difference consumption chain model and define the inter-task dependency transmission coefficient. After receiving the input local progress deviation, the cascade simulation module simulates and calculates the time difference loss value of subsequent tasks. At the same time, it displays the consumption path with pulse animation on the dual time-scaled network diagram and marks the high-risk area of ​​buffer depletion with a heat map, and outputs a cascade risk report. The scheduling optimization module is used to filter out high-consumption paths from the cascading risk report, traverse the tasks in the elastic interval of the high-consumption path that have not reached the warning threshold, generate a shift plan within the time difference range, and output a feasible scheduling instruction and the corresponding risk mitigation rate after the plan is checked to ensure that there is no conflict between the logical dependency relationship between the plan and the task and meets the resource constraints.

[0019] Preferably, the elastic interval dynamic modeling module includes: The basic parameter parsing unit is configured to perform the following steps: The basic parameter analysis unit extracts the initial free float and compressible float of non-critical tasks from the dual-time-scaled symbol network diagram, and establishes an initial interval model with the initial free float as the upper limit of the dynamic elastic interval and the compressible float as the lower limit of the dynamic elastic interval. The compressible time difference is a preset minimum allowable time under the premise of ensuring the completion of the core tasks of the mission. Its value is preset based on project management requirements and the limits of task resource allocation. Simultaneously, the logical dependencies between tasks are analyzed. By identifying the arrow connection relationship between task nodes in the dual-time-scaled network diagram, the dependency type represented by the arrows is distinguished. The identifiers of the preceding and following connection nodes of each task node are recorded. The association list containing the list of preceding and following tasks of each task is formed in the order of task number. The dependency types include end-start, start-start, end-end, and start-end; the preceding task list lists the identifiers and dependency types of all directly preceding tasks; the following task list lists the identifiers and dependency types of all directly following tasks; wherein the task identifier includes the task number, task name, and time coordinate information in the dual-timescaled network diagram; The influencing factor quantification unit is configured to perform the following steps: The influencing factor quantification unit converts upstream task delay data into delay duration quantification values ​​and resource conflict data into resource contention intensity coefficients. The resource contention intensity coefficient is an indicator characterizing the degree of resource competition between tasks, with a value range of [0, 1], where a value of 0 indicates no resource conflict and a value of 1 indicates that the resource is completely occupied. External interference data is converted into interference impact weights, with a value range of [0, 1], where a value of 0 indicates no interference and a value of 1 indicates that interference prevents the task from progressing. The influence value of each factor on the current task elasticity range is generated by weighted calculation based on the preset dependency strength coefficient; the dependency strength coefficient is a value of [0, 1] set based on the importance of task association, and the formula is: influence value = (delay time × a + resource contention strength coefficient × b + interference influence weight × c) × dependency strength coefficient; where a, b, and c are preset weight coefficients, and a + b + c = 1; The interval dynamic adjustment unit is configured to perform the following steps: The interval dynamic adjustment unit performs interval boundary updates based on the impact value, and the update strategy is as follows: When the impact value is positive, it indicates that there is an adverse impact. The upper limit of the interval is compressed according to the impact value. The compressed upper limit is equal to the difference between the current upper limit and the impact value, and the compressed upper limit is not lower than the lower limit of the interval. When the impact value is negative, it means that the adverse effect has been eliminated. The upper limit of the interval is restored according to the absolute value of the impact value. The restored upper limit is equal to the sum of the current upper limit and the absolute value of the impact value, and the restored upper limit does not exceed the initial free float. Synchronously record the timestamp, influencing factors, and range change for each adjustment, and output real-time elastic range data.

[0020] Preferably, the buffer monitoring module includes: The attenuation rate calculation unit is configured to perform the following steps: The attenuation rate calculation unit receives real-time elastic interval data output by the elastic interval dynamic modeling module, and calculates the attenuation rate of the elastic interval width according to a preset period, based on the time axis of the dual-timescale network diagram. The calculation formula is: Attenuation rate = (initial interval width of the current period - end interval width of the current period) / initial interval width of the current period × 100%; where the interval width is the difference between the upper and lower limits of the dynamic elastic interval, a positive attenuation rate indicates that the interval is in a compressed state, and a negative attenuation rate indicates that the interval is in a recovery state. The threshold comparison unit is configured to perform the following steps: The threshold comparison unit calls the elastic interval decay rate data of the same type of path in the historical project progress database, and generates a safety baseline threshold through statistical analysis. The safety baseline threshold represents the maximum acceptable decay rate of the same type of project within the normal risk range. The currently calculated decay rate is compared with the safety baseline threshold. When the decay rate is greater than the safety baseline threshold, the corresponding path is determined to have an abnormal buffer resource consumption rate. The warning output unit is configured to perform the following steps: The early warning output unit calculates the buffer health index based on the difference between the decay rate and the safety baseline threshold. The buffer health index is a quantitative indicator representing the current health status of project schedule buffer resources, with a value range of [0,1]. The calculation formula is: Buffer Health Index = 1 - (Current Decay Rate / Safety Baseline Threshold). The closer the index is to 1, the healthier the buffer status; the closer the index is to 0, the more depleted the buffer resources are. A first threshold and a second threshold are set as the basis for classifying early warning levels, with the first threshold being greater than the second threshold. When the buffer health index ≥ the first threshold, there is no early warning; at this time, buffer resources are sufficient, the decay rate is within the normal range, and the project schedule is stable. No special intervention is required; only routine monitoring is needed. When the second threshold ≤ buffer health index < first threshold, it is a Level 1 warning, indicating that the rate of buffer resource consumption is accelerating. Although it has not exceeded the safety baseline, there is a potential risk. This warning triggers the cascading simulation module to conduct key simulation analysis of the risk transmission path. When the buffer health index < second threshold, it is a Level 2 warning, indicating that the rate of buffer resource consumption has become significantly abnormal and is approaching the depletion threshold. This warning will directly drive the scheduling optimization module to activate the emergency adjustment mechanism, prioritizing the generation and verification of scheduling schemes for tasks on high-risk paths. The warning level, buffer health index, and corresponding path identifier are output in association.

[0021] Preferably, the cascaded simulation module includes a consumption chain modeling unit, configured to perform the following steps: The consumption chain modeling unit constructs a time difference consumption chain model based on the task logic relationships in the dual-time-scaled symbol network graph: Extract the node information of all tasks and the logical dependencies between tasks from the dual-time-scaled network graph. Treat each task as an independent node and construct directed edges based on the logical dependencies between tasks to form a complete transmission path from the source task of the deviation to the task that ultimately affects it. Simultaneously define the inter-task dependency transitivity coefficient. , represents the degree of influence of the schedule deviation of the preceding task i on the time difference of the subsequent task j, and its value range is [0,1]; the rules for determining the coefficient value are as follows: Based on the logical dependency type between tasks, basic values ​​are assigned. The base value of the end-start dependency relationship is set to the highest level, the start-start dependency relationship to the second highest level, the end-end dependency relationship to the medium level, and the start-end dependency relationship to the lowest level. The base values ​​of each level are distributed in an arithmetic sequence within the interval [0,1]. The adjustments are made based on the degree of inter-task correlation. The degree of correlation is determined by a comprehensive assessment of the number of sub-projects jointly participated in by the tasks, the proportion of resource sharing, and the historical collaboration deviation rate. The specific process is as follows: The weighting percentage of the number of sub-projects jointly involved in the task is as follows: Let the number of overlapping sub-items be m, and the total number of sub-items be M. The score for this item is Q1 = m / M. The weighting of resource sharing ratio is as follows: Let the number of shared resources be r, and the total amount of resources required for task i be R. i The total resources required for task j is R. j The score for this item is Q2 = 2r / (R). i +R j ); The weighting of historical coordination deviation rate is And satisfy Let p be the number of times the two tasks in the historical project deviated together, and P be the total number of times they collaborated. The score for this item is Q3 = 1 - p / P. The formula for calculating the correction factor T is: ; Where T0 is the base value of the correction coefficient. Adjustment range for correction coefficients; Through constraints Ensure that the correction factor is within Within the range; Ultimately dependent on transitivity Furthermore, the truncation process ensures that the corrected coefficients remain within the [0,1] range, and the closer the coefficient is to 1, the more significant the impact.

[0022] Preferably, the cascaded simulation module further includes a time difference loss calculation unit, configured to perform the following steps: The time difference loss calculation unit is based on local deviation data. Cascade calculations are performed; the local schedule deviation data represents the degree of deviation between the actual progress and the planned progress of a single task. A positive value indicates a task delay, with the actual progress lagging behind the plan; a negative value indicates a task ahead of schedule, with the actual progress exceeding the plan; the absolute value is the deviation duration. The local schedule deviation data is obtained by collecting the actual start time, current completion percentage, and resource input timeliness data of the task through a project execution tracking system, comparing them with the planned time parameters in the dual-timescale network diagram, and using the formula: = Actual time elapsed - Planned time elapsed is calculated; In addition, when the buffer monitoring module outputs a level 1 or higher warning, it automatically extracts the task corresponding to the first task that triggered the anomaly in the warning path. The value is used as the initial input for the cascade calculation; local schedule deviation data is used. Substituting these values ​​as initial inputs into the time difference loss chain model, the time difference loss values ​​for each subsequent task are calculated sequentially according to the path order: The initial deviation task is set to S0, and the local schedule deviation data is as follows: For the subsequent nth task S n Its time difference loss value is: ; Where k∈[0,n-1], This represents the actual deviation value for the k-th task. Let be the dependency transitivity coefficient between the k-th task and the n-th task. This represents the schedule deviation value generated by the nth task itself. Actual deviation value The calculation method is to add the actual deviation of the previous task after it is affected by the dependency transitivity coefficient to the deviation of the current task itself, so as to form the actual deviation value of the current task: ; The self-scheduled deviation value ΔSn' is the deviation of this task independent of the effects of preceding events, which is obtained through real-time monitoring data; This represents the actual deviation value for the (k-1)th task. is the dependency transitivity coefficient between the (k-1)th task and the kth task.

[0023] Preferably, the cascaded simulation module further includes: The visualization unit is configured to perform the following steps: The visualization unit maps the time difference loss calculation process to a dual-timescale network diagram, dynamically displaying the deviation propagation process along the consumption chain path through pulse animation. The pulse frequency f of the animation is correlated with the time difference loss value L. n Related, satisfying: ; in Based on the base frequency, This represents the frequency coefficient; simultaneously, it uses a heatmap to indicate the buffer depletion risk level of each task, with heat values... The remaining elasticity interval width W and the cumulative time difference loss value of the task The ratio is determined by the following formula: ; in This is the risk amplification factor; the smaller the ratio Lt / W, the higher the thermal value. The higher the value, the higher the risk threshold. When the heat value exceeds the risk threshold, it is determined to be a high-risk area, including the cumulative time difference loss value. This is the sum of the time difference losses for this task and all preceding related tasks; The report generation unit is configured to perform the following steps: The report generation unit summarizes the consumption chain path information, the time difference loss value of each task, the distribution of high-risk areas and the risk diffusion trend to form a cascaded risk report. The cascaded risk report includes risk source identification, a list of main transmission paths, key affected tasks and their time difference loss quantitative data, task node coordinates of buffer depletion warning areas and risk diffusion prediction cycle.

[0024] Preferably, the risk diffusion prediction period is the estimated time required from the current deviation state to the complete depletion of buffer resources in the high-risk area, calculated as follows: First, obtain the current cumulative time difference loss value for tasks within the high-risk area. The remaining elasticity interval width W, and the time difference loss growth v over the most recent 3 statistical periods: ; in For the duration of the cycle, This indicates the cumulative time difference loss incurred by the task within a statistical period; By fitting a linear regression model to the historical growth rate data, a trend line predicting future loss growth rates is obtained: ; in This represents the projected rate of increase in time difference loss at time t. To accelerate growth, The initial growth rate is given, where t is a time variable with units consistent with the statistical period; based on the remaining buffer capacity. Combined with the predicted growth rate, the theoretical exhaustion time is calculated. Introducing a risk amplification factor and the average of all path dependency strength coefficients for this path. After adjustments, the final prediction period is: ; When the prediction period Y is less than a single statistical period, it is automatically corrected to the set minimum warning unit.

[0025] Preferably, the scheduling optimization module includes a task filtering unit, a scheme generation unit, a constraint detection unit, and an instruction output unit.

[0026] Preferably, the task screening unit receives the cascading risk report and the early warning information output by the buffer monitoring module, extracts the high consumption rate path identifier, traverses the real-time elasticity interval data of all tasks on the path, filters out the tasks whose elasticity interval has not reached the early warning threshold, and forms an adjustable task list; wherein, the high consumption rate path refers to the task logical dependency path marked in the cascading risk report, which must simultaneously meet two characteristics: first, the elasticity interval width decay rate of the path has exceeded the preset standard; second, the cumulative growth rate of the time difference loss value transmitted along the path exceeds the preset multiple of the average growth rate of all paths in the project.

[0027] Preferably, the scheme generation unit generates a task translation scheme with the real-time elastic range of the adjustable task as the time constraint boundary; the translation scheme includes the adjustment amount of the task start time and end time, the value range of the adjustment amount does not exceed the width of the current elastic range of the task, and the adjustment direction is to delay backward to release resources or advance forward to make up for the schedule deviation. The constraint detection unit performs dual constraint verification on the translation scheme: first, logical dependency detection, which checks whether the time overlap relationship between the adjusted task and its predecessor and successor tasks conforms to the logical constraints in the dual-time-scaled network diagram; second, resource constraint detection, which verifies whether the resource demand period of the adjusted task overlaps or conflicts with the resource occupation period of other tasks. The conflict judgment criterion is that the same resource is occupied by multiple tasks in the same time unit. Based on the constraint detection results, the instruction output unit calculates the risk mitigation rate for the translation scheme that has passed dual verification. The risk mitigation rate is the decrease in the time difference loss rate of the high-consumption path after the scheme is implemented. Risk mitigation rate = (time difference loss rate of the high-consumption path before adjustment - time difference loss rate of the high-consumption path after adjustment) / time difference loss rate of the high-consumption path before adjustment × 100%. Wherein, time difference loss rate = increment of time difference loss within a certain period / length of that period. The time difference loss rate of the high-consumption path after adjustment is re-simulated and calculated by the cascaded simulation module. The executable translation scheme is converted into a scheduling instruction. The scheduling instruction includes the task identifier, the adjusted start and end times, resource allocation requirements, and the corresponding risk mitigation rate, and is output to the project management terminal.

[0028] Example 2: The construction project of a 220kV substation covers multiple tasks such as site leveling, equipment installation, and line erection. The critical path is "foundation treatment → main transformer installation → busbar erection → system commissioning". The flexible interval dynamic modeling module targets the non-critical task "GIS equipment procurement (TS22)", where TS is the task identifier prefix. It extracts the initial free float of 6 days (upper limit) and compressible float of 2 days (lower limit) from the dual-timescale network diagram, forming the interval [2,6]. It analyzes that the task "equipment selection confirmation (TS18)" is "end-start" dependent. Combining the 2-day delay of TS18, the resource contention intensity coefficient of 0.4 with "cable procurement (TS23)", and the rainstorm interference impact weight of 0.2, the impact value of 1.12 is calculated through the dependency intensity coefficient of 0.7 (critical path association + shared core equipment). The upper limit of the interval is compressed to 6-1.12=4.88 days, and the real-time interval [2,4.88] is output. The buffer monitoring module calculates the interval decay rate of TS22 weekly: the initial width is 2.88 days, the interval width at the end of the cycle is 2.1 days, and the decay rate is (2.88-2.1) / 2.88×100%≈27%; the safe baseline threshold of 25% is obtained by calling historical data. Since 27%>25%, the buffer health index is calculated as 1-(27% / 25%)=0.08 (taken as 0), triggering a level 2 warning and outputting the path identifier TS18→TS22→TS25; The cascaded simulation module constructs a consumption chain TS18→TS22→TS25 based on dependencies, defining the dependency transitivity coefficient between TS18 and TS22 as follows: a base value of 0.8 for "end-start", combined with sub-project overlap of 0.6, resource sharing ratio of 0.5, and historical collaboration deviation rate of 0.1, resulting in a corrected μ. 18,22 =0.8 × 1.03 = 0.824; Input TS18 is delayed by 2 days, calculate TS22 actual deviation 2 × 0.824 + self-deviation 1 = 2.648 days, time difference loss value L 22 =2.648 days; the conduction path is shown through pulse animation, with the thermal value θ=1-e (-2×(2.648 / 2.1)) If the value is approximately 0.93 (exceeding the threshold of 0.7), TS22 is marked as a high-risk area, and a risk report is output predicting the buffer depletion period of 5 days. The scheduling optimization module extracts high-consumption paths from the report, filters out TS25 (health index 0.6) which was not warned in the elastic interval, and generates a shift plan 2 days in advance within its [1,4] interval. After testing, there is no conflict between the "end-start" relationship and resource requirements of TS25 and TS22 after adjustment. The risk mitigation rate is calculated as (0.7 / 7-0.2 / 7) / (0.7 / 7)×100%≈71%, and the scheduling instruction is output, which significantly reduces the path risk.

Claims

1. A project schedule management system based on a dual-time-scaled symbolic network diagram, characterized in that, include: The elastic interval dynamic modeling module is used to define the time difference of non-critical tasks as a dynamic elastic interval, with the upper limit of the interval being the initial free time difference and the lower limit being the compressible time difference; Non-critical tasks are those in the double-code network diagram whose total float is greater than zero and are not on the critical path, as determined by total float calculation. The flexible interval dynamic modeling module dynamically compresses and restores the interval boundary based on upstream task delays, resource conflicts, and external interference data, combined with a preset dependency strength coefficient, and outputs real-time flexible interval data that reflects the project progress buffer status. The buffer monitoring module is used to calculate the elastic interval width decay rate. When the decay rate exceeds the safety baseline threshold trained on historical data, it triggers an early warning for the corresponding path and outputs the buffer health index and early warning level. The cascade simulation module is used to construct a time difference consumption chain model and define the inter-task dependency transmission coefficient. After receiving the input local progress deviation, the cascade simulation module simulates and calculates the time difference loss value of subsequent tasks. At the same time, it displays the consumption path with pulse animation on the dual time-scaled network diagram and marks the high-risk area of ​​buffer depletion with a heat map, and outputs a cascade risk report. The scheduling optimization module is used to filter out high-consumption paths from the cascading risk report, traverse the tasks in the elastic interval of the high-consumption path that have not reached the warning threshold, generate a shift plan within the time difference range, and output a feasible scheduling instruction and the corresponding risk mitigation rate after the plan is checked to ensure that there is no conflict between the logical dependency relationship between the plan and the task and meets the resource constraints.

2. The project progress management system based on a dual-time-scaled symbol network diagram according to claim 1, characterized in that, The elastic interval dynamic modeling module includes: The basic parameter parsing unit is configured to perform the following steps: The basic parameter analysis unit extracts the initial free float and compressible float of non-critical tasks from the dual-time-scaled symbol network diagram, and establishes an initial interval model with the initial free float as the upper limit of the dynamic elastic interval and the compressible float as the lower limit of the dynamic elastic interval. Simultaneously, the logical dependencies between tasks are analyzed. By identifying the arrow connection relationship between task nodes in the dual-time-scaled network diagram, the dependency type represented by the arrows is distinguished. The identifiers of the preceding and following connection nodes of each task node are recorded. The association list containing the list of preceding and following tasks of each task is formed in the order of task number. The influencing factor quantification unit is configured to perform the following steps: The influencing factor quantification unit converts upstream task delay data into delay duration quantification values, resource conflict data into resource contention intensity coefficients, and external interference data into interference impact weights, with a value range of [0, 1], where a value of 0 indicates no interference and a value of 1 indicates that interference prevents the task from progressing. The impact values ​​of each factor on the current task's elasticity range are generated by weighted calculation based on the preset dependency strength coefficient. The interval dynamic adjustment unit is configured to perform the following steps: The interval dynamic adjustment unit performs interval boundary updates based on the impact value, and the update strategy is as follows: When the impact value is positive, it indicates that there is an adverse impact. The upper limit of the interval is compressed according to the impact value. The compressed upper limit is equal to the difference between the current upper limit and the impact value, and the compressed upper limit is not lower than the lower limit of the interval. When the impact value is negative, it means that the adverse effect has been eliminated. The upper limit of the interval is restored according to the absolute value of the impact value. The restored upper limit is equal to the sum of the current upper limit and the absolute value of the impact value, and the restored upper limit does not exceed the initial free float. Synchronously record the timestamp, influencing factors, and range change for each adjustment, and output real-time elastic range data.

3. A project progress management system based on a dual-time-scaled symbol network diagram as described in claim 1, characterized in that, The buffer monitoring module includes: The attenuation rate calculation unit is configured to perform the following steps: The attenuation rate calculation unit receives real-time elastic interval data output by the elastic interval dynamic modeling module, and calculates the attenuation rate of the elastic interval width according to a preset period, based on the time axis of the dual time-scaled network diagram. The threshold comparison unit is configured to perform the following steps: The threshold comparison unit calls the elastic interval decay rate data of the same type of path in the historical project progress database, generates a safety baseline threshold through statistical analysis, compares the currently calculated decay rate with the safety baseline threshold, and determines that the buffer resource consumption rate of the corresponding path is abnormal when the decay rate is greater than the safety baseline threshold. The warning output unit is configured to perform the following steps: The early warning output unit calculates the buffer health index based on the difference between the attenuation rate and the safety baseline threshold. It sets a first threshold and a second threshold as the basis for classifying the early warning level, and the first threshold is greater than the second threshold. When the buffer health index is greater than or equal to the first threshold, there is no early warning. When the second threshold is less than or equal to the buffer health index and less than the first threshold, it is a level one early warning. When the buffer health index is less than the second threshold, it is a level two early warning. The early warning level, the buffer health index and the corresponding path identifier are associated and output.

4. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 1, characterized in that, The cascaded simulation module includes a consumption chain modeling unit, configured to perform the following steps: The consumption chain modeling unit constructs a time difference consumption chain model based on the task logic relationships in the dual-time-scaled symbol network graph: Extract the node information of all tasks and the logical dependencies between tasks from the dual-time-scaled network graph. Treat each task as an independent node and construct directed edges based on the logical dependencies between tasks to form a complete transmission path from the source task of the deviation to the task that ultimately affects it. Simultaneously define the inter-task dependency transitivity coefficient. The rules for determining the coefficient values ​​are as follows: Based on the logical dependency type between tasks, basic values ​​are assigned. The base value of the end-start dependency relationship is set to the highest level, the start-start dependency relationship to the second highest level, the end-end dependency relationship to the medium level, and the start-end dependency relationship to the lowest level. The base values ​​of each level are distributed in an arithmetic sequence within the interval [0,1]. The adjustments are made based on the degree of inter-task correlation. The degree of correlation is determined by a comprehensive assessment of the number of sub-projects jointly participated in by the tasks, the proportion of resource sharing, and the historical collaboration deviation rate. The specific process is as follows: The weighting percentage of the number of sub-projects jointly involved in the task is as follows: Let the number of overlapping sub-items be m, and the total number of sub-items be M. The score for this item is Q1 = m / M. The weighting of resource sharing ratio is as follows: Let the number of shared resources be r, and the total amount of resources required for task i be R. i The total resources required for task j is R. j The score for this item is Q2 = 2r / (R). i +R j ); The weighting of historical coordination deviation rate is And satisfy Let p be the number of times the two tasks in the historical project deviated together, and P be the total number of times they collaborated. The score for this item is Q3 = 1 - p / P. The formula for calculating the correction factor T is: ； Where T0 is the base value of the correction coefficient. Adjustment range for correction coefficients; Through constraints Ensure that the correction factor is within Within the range; Ultimately dependent on transitivity Furthermore, the truncation process ensures that the corrected coefficients remain within the [0,1] range, and the closer the coefficient is to 1, the more significant the impact.

5. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 4, characterized in that, The cascaded simulation module also includes a time difference loss calculation unit, configured to perform the following steps: The time difference loss calculation unit is based on local deviation data. Perform cascade calculations; combine local schedule deviation data Substituting these values ​​as initial inputs into the time difference loss chain model, the time difference loss values ​​for each subsequent task are calculated sequentially according to the path order: The initial deviation task is set to S0, and the local schedule deviation data is as follows: For the subsequent nth task S n Its time difference loss value is: ； Where k∈[0,n-1], This represents the actual deviation value for the k-th task. Let be the dependency transitivity coefficient between the k-th task and the n-th task. This represents the schedule deviation value generated by the nth task itself. Actual deviation value The calculation method is to add the actual deviation of the previous task after it is affected by the dependency transitivity coefficient to the deviation of the current task itself, so as to form the actual deviation value of the current task: ； The self-scheduled deviation value ΔSn' is the deviation of this task independent of the effects of preceding events, which is obtained through real-time monitoring data; This represents the actual deviation value for the (k-1)th task. is the dependency transitivity coefficient between the (k-1)th task and the kth task.

6. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 5, characterized in that, The cascaded simulation module also includes: The visualization unit is configured to perform the following steps: The visualization unit maps the time difference loss calculation process to a dual-timescale network diagram, dynamically displaying the deviation propagation process along the consumption chain path through pulse animation. The pulse frequency f of the animation is correlated with the time difference loss value L. n Related, satisfying: ； in Based on the base frequency, This represents the frequency coefficient; simultaneously, it uses a heatmap to indicate the buffer depletion risk level of each task, with heat values... The remaining elasticity interval width W and the cumulative time difference loss value of the task The ratio is determined by the following formula: ； in This is the risk amplification factor; the smaller the ratio Lt / W, the higher the thermal value. The higher the value, the higher the risk threshold. When the heat value exceeds the risk threshold, it is determined to be a high-risk area, including the cumulative time difference loss value. This is the sum of the time difference losses for this task and all preceding related tasks; The report generation unit is configured to perform the following steps: The report generation unit summarizes the consumption chain path information, the time difference loss value of each task, the distribution of high-risk areas and the risk diffusion trend to form a cascaded risk report. The cascaded risk report includes risk source identification, a list of main transmission paths, key affected tasks and their time difference loss quantitative data, task node coordinates of buffer depletion warning areas and risk diffusion prediction cycle.

7. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 6, characterized in that, The risk diffusion prediction period is the estimated time required from the current deviation state to the complete depletion of buffer resources in the high-risk area. The calculation method is as follows: First, obtain the current cumulative time difference loss value for tasks within the high-risk area. The remaining elasticity interval width W, and the time difference loss growth v over the most recent 3 statistical periods: ； in For the duration of the cycle, This indicates the cumulative time difference loss incurred by the task within a statistical period; By fitting a linear regression model to the historical growth rate data, a trend line predicting future loss growth rates is obtained: ； in This represents the projected rate of increase in time difference loss at time t. To accelerate growth, The initial growth rate is given, where t is a time variable with units consistent with the statistical period; based on the remaining buffer capacity. Combined with the predicted growth rate, the theoretical exhaustion time is calculated. Introducing a risk amplification factor and the average of all path dependency strength coefficients for this path. After adjustments, the final prediction period is: ； When the prediction period Y is less than a single statistical period, it is automatically corrected to the set minimum warning unit.

8. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 1, characterized in that, The scheduling optimization module includes a task selection unit, a scheme generation unit, a constraint detection unit, and an instruction output unit.

9. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 8, characterized in that, The task filtering unit receives the cascading risk report and the early warning information output by the buffer monitoring module, extracts the high consumption rate path identifier, traverses the real-time elastic range data of all tasks on the path, filters out the tasks whose elastic range has not reached the early warning threshold, and forms an adjustable task list.

10. A project progress management system based on a dual-time-scaled symbol network diagram according to claim 8, characterized in that, The scheme generation unit uses the real-time elastic range of the adjustable task as the time constraint boundary to generate a task shifting scheme. The shifting scheme includes the adjustment amount of the task start time and end time. The value range of the adjustment amount does not exceed the width of the current elastic range of the task, and the adjustment direction is to delay backward to release resources or advance forward to make up for the schedule deviation. The constraint detection unit performs dual constraint verification on the translation scheme: first, logical dependency detection, which checks whether the time overlap relationship between the adjusted task and its predecessor and successor tasks conforms to the logical constraints in the dual-time-scaled network diagram; second, resource constraint detection, which verifies whether the resource demand period of the adjusted task overlaps or conflicts with the resource occupation period of other tasks. The conflict judgment criterion is that the same resource is occupied by multiple tasks in the same time unit. Based on the constraint detection results, the instruction output unit calculates the risk mitigation rate for the translation scheme that has passed dual verification. The risk mitigation rate is the decrease in the time difference loss rate of the high consumption path after the scheme is implemented. The executable migration plan is converted into a scheduling instruction, which includes the task identifier, the adjusted start and end times, resource allocation requirements and the corresponding risk mitigation rate, and then output to the project management terminal.

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