A method and system for assessing progress of an engineering project

Abstract

The present application relates to the technical field of project progress evaluation, and discloses a project progress evaluation method and system, which comprises the following steps: collecting on-site abnormal reports marked with geographic coordinates and time stamps, diagnosing potential systemic risks by using spatial clustering and time series analysis, and then marking key construction tasks affected by the systemic risks as blocked states; thereafter, the system quantifies the blocked total value of all subsequent task chains caused by the blocked states, determines an initial uncertainty coefficient, and finally, according to the effective reduction of the uncertainty coefficient by the response work, the corresponding share of the blocked value is unlocked and counted into the total project progress; in the dilemma of the failure of the initial plan benchmark, the core logic of progress evaluation is changed from following the physical plan to measuring the unlocking ability of the subsequent engineering value, which is the key intellectual labor of the project team to reduce uncertainty during the crisis response period.

Description

Technical Field

[0001] This invention relates to a method and system for evaluating the progress of engineering projects, belonging to the field of engineering project progress evaluation technology. Background Technology

[0002] In the management practice of large-scale engineering projects, it is an industry consensus to ensure the orderly progress of projects and cost control by using a work breakdown structure to manage tasks hierarchically based on a pre-established detailed engineering plan. This provides a clear and stable reference system for the quantitative assessment of project status by comparing the actual physical workload completed with the planned baseline.

[0003] However, when such projects, especially the construction of critical infrastructure, enter the implementation phase that involves direct interaction with complex natural environments, such as the foundation construction of deep-water bridge piers or tunnel excavation through complex geological areas, a long-accepted implicit cost emerges. This is because the core data model of the project management system is based on the static assumption that the site conditions and exploration reports are basically consistent. Once the site encounters an unforeseen major geological change, this assumption collapses instantly, causing the original engineering plan and design scheme to not only become inapplicable but also fundamentally lose its effectiveness as an evaluation benchmark. At this point, the crucial intellectual labor that the project team must invest in to ensure project safety, such as supplementary exploration, emergency redesign, and demonstration, cannot be identified as an effective progress contribution in the existing evaluation system because it was not defined in the original plan. Instead, it is merely manifested as the stagnation and delay of the original physical construction tasks. This creates a serious logical discrepancy between the data in the progress report and the key efforts made by the project team to solve the core problems.

[0004] To address the challenges of such plan failures, a seemingly straightforward improvement path is to initiate a process for plan change or re-benchmarking. However, such processes are essentially a delayed administrative confirmation, failing to provide real-time, objective quantification of the value of work done during the crucial crisis response period before the plan change is finally approved. In other words, they do not address a deeper, inherent contradiction: when the plan itself, serving as the evaluation benchmark, has failed, the system loses its ability to measure the value of all actions, especially those aimed at reshaping the benchmark. Specifically, existing technologies suffer from the following shortcomings: 1. When physical construction stalls and intellectual work becomes crucial for resolving core project bottlenecks, the evaluation system lacks a mechanism to quantify such non-physical work as equivalent progress contributions; 2. Faced with a field problem of unknown origin, uncertain scope of impact, and potential for continuous evolution, the evaluation system cannot measure the project team's progress in improving problem awareness and reducing uncertainty; 3. When systemic risks manifest as geographically dispersed and temporally asynchronous local anomalies, task-based anomaly reporting mechanisms cannot proactively identify and diagnose the deep connections and common roots behind these phenomena. Therefore, the technical problem to be solved by this invention is how to establish a new evaluation mechanism that can dynamically identify and objectively quantify the real value and progress of various unplanned efforts invested in responding to crises and solving core problems when the initial planning baseline is fundamentally invalidated due to major unforeseen factors, and can proactively diagnose potential systemic risks from scattered field information. Summary of the Invention

[0005] This invention provides a method and system for evaluating the progress of engineering projects. Its main purpose is to address the problem of how to objectively quantify the actual progress of unplanned work invested in solving core issues and proactively diagnose potential systemic risks when the initial engineering plan fails due to major unforeseen factors.

[0006] To achieve the above objectives, the present invention provides a method for evaluating the progress of engineering projects, comprising:

[0007] Provides a geographic information map covering the entire project area, and receives and aggregates multiple on-site anomaly reports marked with geographic coordinates and timestamps on the geographic information map;

[0008] Multiple on-site anomaly reports are processed. When the density of anomaly reports exceeds a defined clustering threshold within a defined geographical area and time window, the area is identified as an anomalous hotspot.

[0009] Analyze the timestamp sequence of all abnormal reports within the abnormal hotspot. When the timestamp sequence shows synchronicity, a systemic risk is diagnosed, and all critical construction tasks affected by this systemic risk are marked as obstructed.

[0010] After marking key construction tasks as blocked, identify and quantify the total planned value of all subsequent task chains blocked due to the blocked status, and determine the initial uncertainty coefficient based on the initial information of the diagnosed systemic risks.

[0011] Implement countermeasures to reduce the uncertainty factor, and calculate the unlocked value share of the total value of the blocked plan based on the amount of reduction in the uncertainty factor, and include the value share in the overall project schedule.

[0012] The preferred method for determining the uncertainty coefficient is as follows: decompose systemic risk into multiple technical risk factors, including solution maturity, resource availability, and external dependencies; for each technical risk factor, obtain its determined impact weight and its current state quantitative score; multiply the impact weight of each technical risk factor by its quantitative score to obtain the risk contribution value of that factor; and sum the risk contribution values ​​of all technical risk factors to obtain the uncertainty coefficient.

[0013] Preferably, multiple on-site anomaly reports are processed using a density-based spatial clustering algorithm; the determined clustering thresholds include a distance threshold for defining the neighborhood and a minimum number of reports required to form a core point; the timestamp sequence of all anomaly reports within the anomaly hotspot is analyzed, including: using Fourier transform or autocorrelation function to analyze the frequency components of the timestamp sequence to identify periodicity; and using cross-correlation function to calculate the correlation between the timestamps of different types of anomalies within the anomaly hotspot to determine synchronicity.

[0014] Preferably, after diagnosing the existence of systemic risks, the method further includes: determining the potential propagation path model of the systemic risks in geospatial space based on the type of systemic risks; retrieving from the project plan all future construction tasks whose geographical locations fall within the coverage of the potential propagation path model, as well as all subsequent construction tasks that logically depend on these tasks falling within the coverage; and generating an early warning list containing the future construction tasks and all subsequent construction tasks.

[0015] Preferably, after implementing response measures to reduce the uncertainty coefficient, the method further includes: recalculating the current uncertainty coefficient at a determined time frequency to form a time series; calculating the first-order rate of change of the uncertainty coefficient time series; and triggering an emergency assessment process for the response measures when the absolute value of the first-order rate of change exceeds a rate threshold.

[0016] Preferably, the rate threshold is dynamically adjusted according to the current life cycle stage of the project. Specifically, a lower rate threshold is set in the early stage of the project, and a higher rate threshold is set in the later stage of the project.

[0017] Preferably, after the uncertainty coefficient is reduced to a certain acceptable risk threshold for the project, the process also includes triggering a reassessment process for the total value of the blocked plan. Specifically, based on the finalized technical solution, the changes in the defined task content and resource requirements in the subsequent task chain are identified; based on the defined changes, the planned budget and planned man-hours of the affected subsequent tasks are recalculated, and the adjusted total value of the blocked plan is obtained.

[0018] Preferably, the response work includes designing a flexible foundation scheme that can adapt to continuous changes in site conditions; the reduction in the uncertainty coefficient is determined by mapping the extent to which the flexible foundation scheme can cover the range of changes in site conditions, and the extent to which the accuracy of predicting future changes in site conditions is improved, to a value representing a reduction in the uncertainty coefficient.

[0019] Preferably, after the uncertainty coefficient is reduced to a certain acceptable risk threshold for the project, the method further includes: creating a new project plan baseline version for key construction tasks and their subsequent task chains based on the final determined technical solution; in this new baseline version, the actual costs and hours consumed in performing the response work will be recorded as project risk response investment; thereafter, all project performance analyses will be conducted based on the new project plan baseline version.

[0020] A project schedule assessment system, comprising:

[0021] The report receiving module is used to provide a geographic information map covering the entire project area, and to receive and aggregate multiple on-site anomaly reports marked with geographic coordinates and timestamps on the geographic information map.

[0022] The risk diagnosis module is used to process multiple on-site anomaly reports. When the density of anomaly reports exceeds a defined clustering threshold within a defined geographical area and time window, the area is identified as an anomaly hotspot. The module analyzes the timestamp sequence of all anomaly reports within the anomaly hotspot. When the timestamp sequence is detected to exhibit periodicity or synchronicity, a systemic risk is diagnosed, and all critical construction tasks affected by this systemic risk are marked as obstructed.

[0023] The value quantification module is used to identify and quantify the total planned value of all subsequent task chains blocked by the blocked status after a critical construction task is marked as blocked.

[0024] The uncertainty assessment module is used to determine the initial uncertainty coefficient based on the initial information of the diagnosed systemic risk;

[0025] The schedule contribution conversion module is used to calculate the unlocked value share of the total value of the blocked plan based on the amount of reduction in uncertainty during the execution of response work to reduce uncertainty, and to include the value share in the total project schedule.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] 1. When a critical task in an engineering project cannot continue due to significant changes in on-site conditions, this method automatically traverses preset task dependencies, identifies all subsequent task chains blocked by them, and calculates the total planned value of these subsequent tasks. This objectively quantified value is then injected into a newly created work package designed to address the current problem. Subsequently, achieving the key milestones in this work package will directly trigger the conversion of the corresponding value into the project's total earned value. This mechanism shifts the core logic of project progress assessment from measuring adherence to a defunct physical construction plan to measuring the ability to unlock the value of subsequent engineering work. When the data foundation of the project plan no longer exists, it provides a new assessment basis for quantifying the actual progress of the project.

[0028] 2. In response to the complex situation where the on-site problem itself has a continuously evolving nature, this method introduces an uncertainty assessment of the total potential value of the hindered project and defines the response work as a series of specific tasks aimed at reducing this uncertainty, such as reducing the prediction error to a specific range. The project team's progress reports on these tasks are directly linked to the dynamic adjustment of the uncertainty assessment parameters. As a result, the accumulation of earned value of the project is given a new generation path, namely, it originates from the reduction of uncertainty. This allows the intellectual labor that the project team puts into improving cognition, enhancing prediction and adaptability when dealing with an evolving problem without a clear end point to be systematically identified and quantified as a contribution to the certainty of the entire project, and thus recorded in the effective project schedule.

[0029] 3. This method changes the traditional way of binding on-site anomaly information with project tasks. All anomaly reports are first assigned precise geographic coordinates and timestamps and aggregated into a unified geospatial information database. The system continuously uses spatial clustering analysis to identify geographic clusters of anomaly information and analyzes the temporal patterns of information within these clusters. This process of first aggregating in geospatial space and then performing pattern recognition in the temporal dimension enables the system to proactively discover and construct early signals of potential systemic risks with common spatiotemporal characteristics from a large number of scattered, varied, and initially seemingly unrelated local anomaly reports, avoiding the lag in cognition of major risks due to information fragmentation.

[0030] 4. After identifying anomalous hotspots with specific spatiotemporal patterns, the system will determine the corresponding systemic risk type based on a preset set of diagnostic rules. Combining the impact propagation model of this risk type with the geographical distribution and logical dependencies of project tasks, the system will automatically identify all construction areas and task units that may be affected by this systemic risk in the future, thereby triggering an early warning that matches the scope of the risk impact. This series of actions transforms the handling of anomaly reports from a localized and task-oriented post-event response to a global and proactive early warning process targeting potential risk propagation paths. This enables project decision-makers to make more timely responses based on a data-driven systemic risk profile that goes beyond the content of a single report. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the risk diagnosis process of the present invention;

[0032] Figure 2 This is a schematic diagram illustrating how the uncertainty coefficient of the present invention changes with the stage of the response work;

[0033] Figure 3 This is a diagram of the functional modules and data flow architecture of the present invention.

[0034] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0036] This application provides a method and system for project schedule assessment. Its architecture is built upon a unified geospatial information database and consists of a report receiving module, a risk diagnosis module, a value quantification module, an uncertainty assessment module, and a schedule contribution conversion module. The report receiving module collects on-site anomaly reports marked with geographic coordinates and timestamps. The risk diagnosis module performs spatial clustering and temporal analysis on the collected data to diagnose potential systemic risks and mark affected key construction tasks. Subsequently, the value quantification module and the uncertainty assessment module work together; the former identifies and quantifies the total planned value of all subsequent task chains blocked due to the obstructed state, while the latter determines an initial uncertainty coefficient based on the diagnosed risk information. Finally, the schedule contribution conversion module calculates the unlocked value share from the total blocked planned value based on the reduction in the uncertainty coefficient caused by the response work and includes this share in the overall project schedule.

[0037] In the implementation of large-scale engineering projects, on-site anomalies often present geographically dispersed, temporally asynchronous, and with varying appearances, making it difficult for traditional reporting mechanisms based on individual tasks to identify their inherent connections. To address this situation, the report receiving module of this invention provides a geographic information map covering the entire project area. This map integrates a Geographic Information System (GIS) kernel, registering and overlaying the project's Building Information Model (BIM) or Computer-Aided Design (CAD) drawings with the actual geographic coordinate system. On-site personnel can automatically obtain and submit the geographic coordinates of anomalies using the GPS function of their mobile terminals, or manually mark them on the map interface when automatic location is not possible. Simultaneously, the system automatically records a timestamp accurate to the second for each report. All reports are stored in a database that supports geospatial queries. Its data structure is defined as a record containing a unique anomaly identifier, geographic point data type, precise timestamp, and a JSONB field for storing structured dynamic observation data. In this way, all anomaly reports are given spatiotemporal attributes and aggregated in a unified information database, laying a data foundation for subsequent correlation analysis.

[0038] To identify potential systemic risk signals from scattered local anomaly reports, the risk diagnosis module is configured to continuously run a density-based spatial clustering algorithm (DBSCAN) to identify geographic clusters of anomaly information, i.e., anomaly hotspots; the algorithm has two core parameters that define the distance threshold of the neighborhood ( ) and the minimum number of reports required to constitute the core point ( Its value is determined through a standardized offline calibration process, specifically: at the beginning of the project, based on the Work Structure Breakdown (WBS) data, the average distance between all geographically adjacent lowest-level construction task units is calculated. and distance threshold Set as The minimum number of reports threshold This is determined by analyzing the average density of anomaly reports within a unit spatiotemporal window in a historical database of similar projects. For example, if historical data shows that 2 to 3 anomalies frequently occur within a 500-meter radius over 72 hours during a specific construction phase, then... It can be set to 3, when a new report makes it The total number of reports within the neighborhood reached When a hotspot is identified, the area is immediately identified as an abnormal hotspot. Furthermore, once a hotspot is identified, the module immediately extracts the timestamp sequence of all reports within it, uses Fourier transform or autocorrelation function to analyze its frequency components to identify periodicity, and uses cross-correlation function to calculate the correlation between the timestamps of different types of anomalies to determine synchronicity. When the intensity of periodicity or synchronicity exceeds a preset threshold, such as when the absolute value of the cross-correlation coefficient is greater than 0.7, the system diagnoses the existence of systemic risk and marks all critical construction tasks affected by it as obstructed.

[0039] When critical construction tasks are hindered due to systemic risks, causing the original planned baseline to become invalid, this method addresses the issue that the intellectual labor involved in risk response cannot be reflected as an effective progress contribution in traditional evaluation systems. It shifts the logic of progress evaluation from following a failed physical plan to measuring the ability to unlock the value of subsequent projects. Its value quantification module is configured as follows: after a task is marked as hindered, based on a pre-defined task dependency graph in the project plan, a graph traversal algorithm automatically identifies all subsequent task chains triggered by the hindered state and calculates the sum of the planned budgets for these tasks, using this sum as the total value of the hindered plan. Meanwhile, the uncertainty assessment module determines an initial uncertainty coefficient based on the initial information of the diagnosed systemic risks. The calculation process for this coefficient is as follows: first, the systemic risks are decomposed into multiple technical risk factors such as solution maturity, resource availability, and external dependencies. Each factor is assigned an influence weight determined at the beginning of the project using the Analytic Hierarchy Process (AHP). ), and obtain a quantitative score based on its current status within a preset scoring standard (e.g., level 1 to 5). Finally, the initial uncertainty coefficient is obtained by summing the risk contributions of all technical risk factors. The formula is: Uncertainty Coefficient = .

[0040] To measure the project team's progress in improving awareness and reducing uncertainty, this method defines coping efforts as a set of tasks aimed at reducing uncertainty. Its progress contribution conversion module is configured to, during the execution of coping efforts, convert the progress contribution based on the reduction in the uncertainty coefficient (…). ), calculate the share of the total value of the blocked plan that has been unlocked ( This share will be included in the overall project schedule, and the calculation formula is as follows: For example, when an effort aimed at improving the maturity of a solution is completed, causing the quantitative score of the corresponding technology risk factor to decrease from 5 to 2, if the factor's influence weight is 0.04, the uncertainty coefficient will decrease by 0.04 accordingly. (5-2)=0.12. If the total value of the blocked plan is 800 million yuan, then the value share unlocked this time is... The 96 million yuan was then included in the project's earned value. Through this calculation, the reduction in uncertainty caused by the response work was directly quantified and included in the overall project schedule, so that the intellectual labor put into the team to deal with the evolutionary problem could be reflected as a contribution to the certainty of the project.

[0041] To achieve dynamic management of the risk response process, the system recalculates the current uncertainty coefficient at a defined time frequency, forming a time series, and calculates the first-order rate of change of this series. When the absolute value of the first-order rate of change exceeds a preset rate threshold, the system automatically triggers an emergency assessment process for the response work. It should be noted that this rate threshold is dynamically adjusted according to the project's life cycle stage; a lower rate threshold is set in the early stages of the project, and a higher rate threshold is set in the later stages. When the uncertainty coefficient decreases to the project's acceptable risk threshold, the system will trigger a reassessment process for the total value of the blocked plan. Based on the final determined technical solution, the system clarifies the changes in the content and resource requirements of subsequent task chains, and recalculates the planned budget and man-hours of the affected tasks accordingly, obtaining the adjusted total value of the blocked plan. At the same time, a new project plan baseline version is created for the key construction tasks and their subsequent task chains, and the actual costs and man-hours consumed in executing the response work are recorded as project risk response investment. All subsequent project performance analyses are based on this new baseline version.

[0042] Example 1: During the deep-water pier foundation construction of a large cross-sea bridge project, when the foundation pit was excavated according to the initial plan, the on-site team discovered that the geological conditions at the design elevation differed from the previous exploration report. This revealed an unexplored weak interlayer whose apparent characteristics changed with the tidal cycle. This situation rendered the original enlarged foundation plan unsuitable, and construction was immediately suspended. Simultaneously, sporadic minor seepage and unexpected settlement began to appear in the tunnel excavation area several kilometers away from the pier. Faced with this situation, the on-site engineers used the report receiving module provided by this invention to submit the dynamic changes of the weak soil layer observed at the pier, as well as the seepage and settlement points in the tunnel area, as on-site anomaly reports with precise geographical coordinates and timestamps to the system's geospatial information database. The risk diagnosis module then processed these seemingly isolated reports in time and space. Its built-in density-based spatial clustering algorithm, based on multiple geographically adjacent received reports, identified a cluster within a preset distance threshold. With minimum report quantity threshold Under the constraints, these anomalies are identified as an anomaly hotspot. Furthermore, the module performs time-series analysis on the timestamp sequence of all anomaly reports within the hotspot. The cross-correlation function calculation results show that there is a correlation coefficient exceeding 0.7 between the changes in the soil properties of the bridge piers and the peak value of seepage in the tunnel area. Moreover, the Fourier transform reveals that the sequence has a 12-hour periodicity synchronized with the local tides. Based on this, the system diagnoses a systemic risk caused by deep groundwater activity, with an impact spanning multiple construction areas, and marks all construction tasks falling within the scope of this risk, including bridge pier foundation construction, as obstructed.

[0043] After a task is blocked, the project evaluation no longer tracks the physical progress of the failed plan, but instead measures the ability to unlock the value of subsequent works. This triggers the value quantification module of the method in this invention, which identifies all subsequent construction task chains that depend on the task by traversing the task dependency graph and calculates the total value of the blocked plan. The initial investment was 800 million RMB. Meanwhile, based on the initial information of the diagnosed systemic risks, the uncertainty assessment module weighted multiple technical risk factors, including solution maturity, resource availability, and external dependencies, determining an initial unspecified coefficient of 0.8. The project team then established a dedicated response unit, whose tasks included conducting supplementary hydrogeological exploration, establishing a groundwater dynamic model, and designing a flexible foundation scheme adaptable to continuous geological changes. Once this unit completed the supplementary exploration and established a preliminary groundwater dynamic prediction model, the quantitative score of the solution maturity risk factor decreased accordingly, causing the overall unspecified coefficient to drop from 0.8 to 0.6. Based on this change, the progress contribution conversion module immediately calculated and unlocked [the relevant data / mechanisms]. The value share is included in the total earned value of the project.

[0044] The application of this mechanism avoids the assessment interruption and data breakage that would inevitably occur under traditional assessment methods due to the failure of the planned baseline. Even during the phase of physical construction suspension, the intellectual effort put into reducing uncertainty by the project team can be continuously quantified as effective project progress through the reduction of the uncertainty coefficient. As the response work deepens, the flexible foundation solution is finally approved, and the uncertainty coefficient drops below the acceptable risk threshold for the project. At this point, the system triggers a re-benchmarking of the project plan, generating new construction tasks and dependencies based on the new flexible foundation solution. The earned value accumulated previously due to the reduction of uncertainty is then linked to the earned value calculation under the new planned baseline. In this way, the project management team obtains a decision-making basis that reflects the true progress during the crisis and ultimately guides the project to continue under the updated planned baseline.

[0045] Example 2: To quantitatively evaluate the effectiveness of the method of the present invention in diagnosing systemic risks and assessing project progress, an experimental platform based on discrete event simulation was constructed. This platform, based on historical data from a completed urban subway tunnel project, reproduced its work breakdown structure, task dependencies, and planned budget. An anomaly injection module was configured, which can inject on-site anomaly reports to specified geographic coordinates at specific nodes on the simulation timeline according to a preset script. This experiment included a control group and an experimental group, both running on the same simulation platform and receiving consistent anomaly injection sequences. The control group used a traditional project management information system based on earned value management. The project progress assessment relied on manual anomaly marking for individual tasks; the experimental group integrated the project progress assessment method of this invention; the total simulation time was set to 1000 hours, and the anomaly injection script was designed to simulate a systemic risk scenario caused by deep geological tectonic activity. The injection rules were as follows: between 200 and 450 hours of simulation time, at non-uniform time intervals, a total of 8 independent local anomaly reports with the appearance of minor water seepage or millimeter-level settlement were injected in three geographically dispersed construction areas. Subsequently, at the 500-hour node, a major geological mutation anomaly that caused the task to be unable to continue was injected on the path of the critical tunnel excavation task.

[0046] After the trial run, the two groups showed differences in risk identification capabilities. At the 380-hour mark of the simulation, after receiving the 6th local anomaly report, the experimental group's risk diagnosis module, through spatial clustering and temporal analysis, identified the inherent correlation between these scattered anomalies, diagnosed systemic risks, and triggered an early warning. In contrast, in the control group, until the end of the simulation, these 8 local anomalies were consistently recorded as independent low-priority events, and the system failed to perform any correlation diagnosis or generate any early warnings. Furthermore, during the 300-hour response period following the occurrence of a major anomaly at 500 hours, the two groups exhibited significant differences in their progress quantification methods. Due to the stagnation of physical work, the earned value of the control group was zero, and the project's Schedule Performance Index (SPI) remained consistently low at 0.55, failing to reflect any work undertaken by the team to resolve the issue during this period. In contrast, the experimental group's value quantification module identified the total planned value of the blocked subsequent task chains due to the disruption. The initial uncertainty coefficient was set at 0.9 for a project value of 500 million yuan. As the simulation script achieved the target milestones, the uncertainty coefficient was gradually reduced to 0.5. The progress contribution conversion module accordingly calculated and accumulated the corresponding contributions. The value share of the project is used as the earned value, which gradually restores the SPI from 0.55 to 0.75. The experimental results show that, under the same input conditions, the method of the present invention can identify the systemic risks hidden under scattered local anomalies earlier than the traditional method, and can provide an effective progress assessment basis for the project team's efforts to solve problems and reduce uncertainty during the crisis response period when the original baseline fails.

[0047] Example 3: This example combines Figures 1 to 3 This paper describes the implementation of a method and system for evaluating the progress of engineering projects. Figure 1 As shown: The process begins with the risk diagnosis module sending an anomaly report set to the spatial clustering algorithm module. The spatial clustering algorithm then performs DBSCAN density analysis and identifies anomalous hotspots by checking distance thresholds and minimum report counts. Once an anomalous hotspot is discovered, the risk diagnosis module extracts its internal timestamp sequence and sends it to the time series analyzer. The time series analyzer analyzes periodicity using Fourier transform and synchronicity using cross-correlation functions, then returns the time series features to the risk diagnosis module. The risk diagnosis module matches the returned features with the diagnostic rule set to confirm the type of systemic risk and finally notifies the task management system to mark the affected tasks as blocked. If no anomalous hotspots are found, the completion status is directly updated. The entire process forms a closed loop with the continued monitoring actions of the risk diagnosis module.

[0048] like Figure 2 As shown: the horizontal axis represents the response phases from the initial stage T0 to the eighth stage T8, and the vertical axis represents the magnitude of the uncertainty coefficient. The uncertainty coefficient, represented by the solid line in the figure, starts from an initial value of about 0.9 in stage T0 and decreases monotonically as the response work progresses. The acceptable risk threshold, represented by the dashed line in the figure, remains at a constant level of 0.4. The uncertainty coefficient curve intersects the acceptable risk threshold line after stage T7 and reaches about 0.35, which is below the threshold, in stage T8. This indicates that the systemic risk has been reduced to within the acceptable range of the project in this stage.

[0049] like Figure 3As shown: On-site anomaly reports are received as input data by the report receiving module, which is responsible for collecting anomaly reports with spatiotemporal attributes and writing the structured data into a unified geospatial information database. The risk diagnosis module reads data from this database to perform the functions of identifying anomaly hotspots and diagnosing systemic risks. Its diagnosis results drive the value quantification module to quantify the total value of the blocked plan, and drive the uncertainty assessment module to determine the initial uncertainty coefficient. Subsequently, the outputs of the value quantification module and the uncertainty assessment module are jointly fed into the schedule contribution conversion module. This module calculates the unlocking value and includes it in the overall project schedule, ultimately forming the overall project schedule report as the output.

[0050] Example 4: In a large-scale high-altitude railway tunnel project with a planned construction period of four years, in order to deploy the project progress assessment method of the present invention, before the project officially starts, it is necessary to perform a standardized offline calibration of the key parameters in its core algorithm module so that the method can adapt to the geological environment, technical complexity and management requirements of this specific project; firstly, the influence weight of each technical risk factor in the uncertainty assessment module ( To determine the risk level, the project's core management team and domain experts formed an evaluation group. Using the analytic hierarchy process (AHP), the group assessed three technical risk factors: solution maturity, resource availability, and external dependencies. They compared the relative importance of each factor to the systemic risk of the high-altitude tunnel project pairwise, using a scale of 1 to 9 to form a judgment matrix. When comparing solution maturity and resource availability, the former was assigned a score of 3, while the latter was assigned a score of 1 / 3. After completing pairwise comparisons of all factors, the influence weight of each technical risk factor was obtained by calculating the largest eigenvalue of the judgment matrix and its corresponding normalized eigenvector. The results are as follows: Solution Maturity Resource availability external dependencies These weight values ​​are then fixed in the uncertainty assessment module of the system.

[0051] Next, we will discuss the distance threshold of the density-based spatial clustering algorithm in the risk diagnosis module. ) and minimum reporting threshold ( The calibration process utilizes a virtual anomaly report dataset generated from geological exploration data along the railway project route. This dataset simulates the distribution density of anomaly reports from similar historical projects in different geological units. The calibration team employs the k-distance plot method, calculating the distance from each point in the dataset to its k-th nearest neighbor (where k is set to 4), sorting these distance values, and plotting them. The corresponding points are then identified at the inflection points of the resulting curves. The value is 250 meters; then, fixed. It is 250 meters, through adjustment The values ​​of [value] are selected and the clustering results are observed to determine the final [value]. Under this value, the proportion of noise points in the clustering results is less than 5%, and the main geological structures can be identified as independent clusters.

[0052] Furthermore, a dynamic adjustment rule is configured for the rate threshold of the first-order rate of change of the uncertainty coefficient time series. To ensure that this threshold can adapt to changes in risk tolerance at different stages of the project's life cycle, its value is set as a percentage of the project's completed physical progress. The relevant piecewise function; when When the rate threshold is set to a baseline value of 0.05, when... When the threshold is calculated, the formula is: ;when At that time, the threshold is locked at the upper limit of 0.15; this functional relationship is configured in the system, so that the adjustment process of the rate threshold is automated and deterministic. After the above procedures are completed, each core parameter of the method of the present invention is given an initial value with specific engineering basis and data support, and the system can be put into use in the project.

[0053] Example 5: Before deploying the method of the present invention, an offline data mining and model training procedure needs to be executed to establish the systematic risk diagnosis rule set required for the risk diagnosis module. This procedure uses a database containing historical data of multiple completed large-scale engineering projects. The database stores the original on-site anomaly reports throughout the entire project lifecycle, as well as the final conclusions of systematic risk events associated with these anomalies confirmed by root cause analysis. During the training process, the system uses the spatiotemporal distribution characteristics of historical anomaly reports, including the density, radius, combination of anomaly types, and periodicity of the time series of anomaly hotspots, as the input feature vector. The systematic risk type confirmed after the fact, in this case, is the influence of deep groundwater activity or the weak creep of hidden fault zones, as the output label. Supervised learning is performed using a decision tree algorithm to construct a classification model that can map specific spatiotemporal anomaly patterns to specific systematic risk types. This model is finally solidified into the diagnostic rule set of the risk diagnosis module.

[0054] To ensure that field engineers can provide highly consistent input on dynamically changing anomalies and uncertainties when using the report receiving module, a baseline calibration must be performed on all operators before project commencement. This procedure presents operators with a series of pre-set animations or video clips simulating different on-site anomaly evolution processes, requiring them to submit independent anomaly reports for each simulation scenario via a mobile terminal using the method described in this invention. The system backend automatically compares the submitted report content, including a structured description of the anomaly's dynamic characteristics and the selection of uncertainty levels, with the pre-set benchmark calibration data for that simulation scenario, and calculates the inter-evaluator reliability coefficient (using the Coen Kappa coefficient). This process is repeated until the consistency of all operators' reports reaches a pre-set threshold of a Coen Kappa coefficient greater than 0.8. After completing this procedure, operators can provide on-site anomaly reports with a consistent benchmark for the project.

[0055] Example 6: Before applying the method of the present invention to a new engineering project, a preliminary baseline model construction procedure needs to be performed. The purpose is to establish a specific systemic risk impact propagation model for the project and use it as the basis for subsequent online risk response. This procedure defines the propagation path model in geospatial space for each type of systemic risk in the constructed diagnostic rule set, based on its physical attributes and the specific geological survey data of the current project. Taking the systemic risk type of deep groundwater activity impact as an example, its propagation path model is defined as a composite model consisting of a main influence vector and a lateral attenuation function. The direction and length of the main influence vector are determined based on the main flow direction and average permeability of groundwater as specified in the initial geological survey report of the project, while the lateral attenuation function is used to define the width boundary of the impact range in the direction perpendicular to the main influence vector.

[0056] In constructing this model, the system first uses a virtual anomalous hotspot centroid as the starting point on the project's geographic information map, and generates the main path for impact propagation along the determined groundwater mainstream vector. Then, along this main path, the system calculates the geographic polygon boundary of the affected area based on the lateral attenuation function, and identifies all future construction tasks in the project plan whose geographic construction scope intersects with this polygon area as geographic exposure tasks. Next, using this list of geographic exposure tasks as input, the system performs a forward graph traversal in the project's work breakdown structure and task dependency graph, identifying all subsequent construction tasks that logically depend directly or indirectly on any geographic exposure task. Finally, the system merges the list of geographic exposure tasks with the list of logically dependent tasks to form a complete list of affected tasks. The logic for generating this list is solidified into the propagation path model corresponding to this type of systemic risk and stored in the system for online runtime invocation.

[0057] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for evaluating the progress of an engineering project, characterized in that, include: Provides a geographic information map covering the entire project area, and receives and aggregates multiple on-site anomaly reports marked with geographic coordinates and timestamps on the geographic information map; Multiple on-site anomaly reports are processed. When the density of anomaly reports exceeds a defined clustering threshold within a defined geographical area and time window, the area is identified as an anomalous hotspot. Analyze the timestamp sequence of all abnormal reports within the abnormal hotspot. When the timestamp sequence is found to exhibit periodicity or synchronicity, a systemic risk is diagnosed, and all construction tasks affected by this systemic risk are marked as obstructed. After marking the construction task as blocked, identify and quantify the total value of the blocked plans for all subsequent task chains caused by the blocked state, and determine the initial uncertainty coefficient based on the initial information of the diagnosed systemic risks. Implement countermeasures to reduce the uncertainty factor, and calculate the unlocked value share of the total value of the blocked plan based on the amount of reduction in the uncertainty factor, and include the value share in the overall project schedule.

2. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, The uncertainty coefficient method specifically involves: decomposing systemic risk into multiple technical risk factors, such as solution maturity, resource availability, and external dependencies; and for each technical risk factor, obtaining its determined impact weight and its current state's quantitative score. The risk contribution value of each technology risk factor is obtained by multiplying its impact weight by its quantitative score. The uncertainty coefficient is obtained by summing the risk contribution values ​​of all technical risk factors.

3. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, Multiple on-site anomaly reports were processed using a density-based spatial clustering algorithm; the determined clustering thresholds included a distance threshold for defining neighborhoods and a minimum number of reports required to form a core point. Analyze the timestamp series of all anomaly reports within the anomaly hotspot, including: using Fourier transform or autocorrelation function to analyze the frequency components of the timestamp series in order to identify periodicity; By using cross-correlation functions, the correlation between the timestamps of different types of anomalies within anomaly hotspots is calculated to determine synchronicity.

4. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, After diagnosing the existence of systemic risks, the process also includes: determining the potential propagation path model of the systemic risks in geospatial space based on their type; retrieving from the project plan all future construction tasks whose geographical locations fall within the coverage of the potential propagation path model, as well as all subsequent construction tasks that logically depend on these tasks falling within the coverage area.

5. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, After implementing response measures to reduce the uncertainty coefficient, the process also includes: recalculating the current uncertainty coefficient at a defined time frequency to form a time series; calculating the first-order rate of change of the uncertainty coefficient time series; and triggering an emergency assessment process for the response measures when the absolute value of the first-order rate of change exceeds a rate threshold.

6. The method for evaluating the progress of an engineering project according to claim 5, characterized in that, The rate threshold is dynamically adjusted based on the current life cycle stage of the project. Specifically, a lower rate threshold is set in the early stages of the project, and a higher rate threshold is set in the later stages.

7. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, After the uncertainty coefficient is reduced to a certain acceptable risk threshold for the project, the process also includes triggering a reassessment of the total value of the blocked plan. Specifically, based on the finalized technical solution, the changes in the defined task content and resource requirements in the subsequent task chain are identified; based on the defined changes, the planned budget and planned man-hours of the affected subsequent tasks are recalculated, and the adjusted total value of the blocked plan is obtained.

8. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, The response includes designing a flexible foundation scheme that can adapt to continuous changes in site conditions; the reduction in the uncertainty coefficient is determined by the extent to which the flexible foundation scheme can cover the range of changes in site conditions, and the extent to which the accuracy of predicting future trends in site conditions is improved.

9. The method for evaluating the progress of an engineering project according to claim 1, characterized in that, After the uncertainty coefficient is reduced to a certain acceptable risk threshold for the project, the process also includes: creating a new project plan baseline version for the construction tasks and their subsequent task chains based on the final determined technical solution; in this new baseline version, the actual costs and hours consumed in performing the response work will be recorded as project risk response investment; thereafter, all project performance analyses will be based on the new project plan baseline version.

10. A project progress evaluation system, characterized in that, include: The report receiving module is used to provide a geographic information map covering the entire project area, and to receive and aggregate multiple on-site anomaly reports marked with geographic coordinates and timestamps on the geographic information map. The risk diagnosis module is used to process multiple on-site anomaly reports. When the density of anomaly reports exceeds a defined clustering threshold within a defined geographical area and time window, the area is identified as an anomaly hotspot. The module analyzes the timestamp sequence of all anomaly reports within the anomaly hotspot. When the timestamp sequence is detected to exhibit periodicity or synchronicity, a systemic risk is diagnosed, and all construction tasks affected by this systemic risk are marked as obstructed. The value quantification module is used to identify and quantify the total planned value of all subsequent task chains blocked by the blocked status after a construction task is marked as blocked. The uncertainty assessment module is used to determine the initial uncertainty coefficient based on the initial information of the diagnosed systemic risk; The schedule contribution conversion module is used to calculate the unlocked value share of the total value of the blocked plan based on the amount of reduction in uncertainty during the execution of response work to reduce uncertainty, and to include the value share in the total project schedule.

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