Highway bid construction progress simulation system based on BIM technology

Abstract

This application relates to the field of BIM application technology, and in particular to a highway section construction progress simulation system based on BIM technology. The system includes: a data acquisition module for acquiring geographic information, design parameters, and impact information of the road section to be constructed; a model building module for constructing a 3D BIM model and dividing it into several sub-intervals; a mapping module for establishing a dynamic mapping between actual construction parameters and the BIM model; a requirement generation module for generating construction requirements based on impact information; a strategy customization module for constructing a construction strategy containing planned progress indicators and dividing it into time-based sub-cycles; a simulation module for simulating construction progress based on resource allocation parameters; and a verification module for calculating the comprehensive deviation, determining whether the simulation results are qualified, and analyzing the reasons for non-qualification. This application achieves accurate simulation, automatic verification, and adaptive optimization of construction progress through dynamic mapping between BIM and construction parameters and multi-dimensional deviation weighted verification.

Description

Technical Field

[0001] This application relates to the field of BIM application technology, and in particular to a highway section construction progress simulation system based on BIM technology. Background Technology

[0002] In the construction of highway sections, construction schedule management is a crucial link in ensuring on-time project delivery, controlling costs, and optimizing resource allocation. Traditional construction schedule management methods mainly rely on manual experience, Gantt charts, or simple project management software, lacking the comprehensive modeling capabilities for complex geographical environments and dynamic construction parameters, and failing to accurately reflect changes in actual construction conditions. With the gradual promotion of BIM technology, some construction management has begun to introduce 3D visualization models to assist in schedule display; however, most existing systems remain at the level of static modeling and passive display, lacking effective verification mechanisms and automatic optimization capabilities for construction schedule projection results. Specifically, existing technologies struggle to dynamically map actual construction parameters to BIM models, leading to a disconnect between the projection process and the actual site conditions; furthermore, when deviations exist between projection results and construction requirements, there is no systematic analysis of the causes of these deviations, and a lack of adaptive optimization strategies for different causes is also lacking.

[0003] Chinese Patent Application No. 202310040556.2 discloses a BIM-based automatic optimization method for railway bridge construction progress and resource allocation. Existing railway bridge design methods cannot achieve automatic optimization of construction organization. This method constructs a bridge BIM model, assigning it bridge site information and engineering information; establishes a corresponding database of general construction progress indicators and a database of resource allocation, and assigns them to the bridge BIM model; calculates the total construction period under the initial resource allocation conditions according to the sequence of beam-pier-foundation; checks whether the total construction period meets the requirements of the planned total construction period; if not, selects the process with the minimum resource allocation, increases the resource allocation, and recalculates the total construction period; repeats the calculation until the requirements of the planned total construction period are met. This method uses a customizable database as the basis for deduction, achieving the lowest construction resource cost while meeting the construction period requirements, and realizing automatic optimization design of construction organization.

[0004] However, existing technologies still have the following problems:

[0005] There is a lack of multi-dimensional comprehensive deviation verification of the construction progress projection results of highway sections, as well as the ability to automatically classify and adaptively optimize the reasons for non-compliance. Summary of the Invention

[0006] To address this, the present invention provides a highway section construction progress simulation system based on BIM technology, which overcomes the problem in the prior art of lacking multi-dimensional comprehensive deviation verification of highway section construction progress projection results and automatic classification and adaptive optimization capabilities for non-compliance reasons.

[0007] To achieve the above objectives, this invention provides a highway section construction progress simulation system based on BIM technology. It includes:

[0008] The data acquisition module is used to obtain relevant information about the road section to be constructed, including geographical information, design parameters, and impact information.

[0009] The model building module, connected to the acquisition module, is used to build a three-dimensional BIM model based on the geographic information in the relevant information, and to divide the road section to be constructed into several sub-sections according to spatial location.

[0010] The mapping module, which is connected to the acquisition module and the model building module respectively, is used to establish a dynamic mapping relationship between actual construction parameters and the three-dimensional BIM model.

[0011] A requirement generation module, connected to the acquisition module, is used to generate construction requirements based on the impact information in the relevant information.

[0012] The strategy customization module, connected to the requirement generation module, is used to construct a construction strategy including planned progress indicators based on the construction requirements, and to divide the total construction period into several sub-construction periods according to time.

[0013] The simulation module is connected to the model building module, the mapping module and the strategy customization module respectively, and is used to simulate the construction progress under each sub-construction cycle based on preset construction resource configuration parameters to obtain the simulation results;

[0014] The verification module is connected to the simulation module and the requirement generation module respectively. It is used to calculate the comprehensive deviation between the simulation result and the construction requirement, determine whether the simulation result is qualified based on the comprehensive deviation, and analyze the reasons for the failure when the simulation result is unqualified.

[0015] An optimization module, connected to the verification module, is used to determine the corresponding optimization strategy based on the reasons for non-compliance and to adjust the corresponding parameters.

[0016] Furthermore, the verification module calculates the overall deviation, wherein,

[0017] The verification module obtains the simulated cumulative completion percentage at the end of each sub-construction cycle in the simulation results, compares it with the planned cumulative completion percentage of the corresponding sub-construction cycle in the construction requirements, calculates the absolute value of the progress deviation between the two, and calculates the average progress deviation under all sub-construction cycles as the first deviation component.

[0018] The verification module obtains the absolute value of the time difference between the simulated completion time and the planned completion time of each sub-interval, sums them up for all sub-intervals and divides by the total number of sub-intervals to obtain the second deviation component.

[0019] The verification module obtains the hypothetical impact information set in the deduction module, calculates the normalized deviation between it and the standard impact information defined in the construction requirements, and uses it as the third deviation component.

[0020] The verification module calculates the weighted sum of the first deviation component, the second deviation component, and the third deviation component to obtain the comprehensive deviation degree.

[0021] Furthermore, the verification module determines whether the deduction result is qualified based on the comprehensive deviation degree, wherein,

[0022] If the overall deviation is less than or equal to the preset deviation threshold, the verification module determines that the deduction result is qualified;

[0023] If the overall deviation is greater than the preset deviation threshold, the verification module determines that the deduction result is unqualified and analyzes the reasons for the unqualification.

[0024] Furthermore, the verification module analyzes the reasons for non-compliance, wherein,

[0025] The verification module calculates the difference between the simulated construction period and the corresponding planned construction period for each sub-interval, which is denoted as the period interval deviation, and calculates the variance of all period interval deviations.

[0026] If the variance is less than a preset variance threshold, the reason for non-compliance is determined to be a systematic factor;

[0027] If the variance is greater than or equal to the preset variance threshold, the reason for non-compliance is determined to be a local factor.

[0028] Furthermore, for systemic factors, the verification module further distinguishes,

[0029] The verification module calculates the correlation coefficient between the average schedule deviation and the resource allocation sufficiency rate of each sub-interval.

[0030] If the absolute value of the correlation coefficient is greater than the preset correlation coefficient threshold, the reason for non-compliance is determined to be insufficient resource allocation;

[0031] If the absolute value of the correlation coefficient is less than or equal to the preset correlation coefficient threshold, the reason for non-compliance is determined to be parameter mismatch in the inference model.

[0032] Furthermore, for localized factors, the verification module further distinguishes,

[0033] The verification module acquires the time series of external environmental data and calculates the time alignment between the intensity of environmental disturbance and the progress deviation of the sub-interval.

[0034] If the time alignment of at least two consecutive sub-intervals is greater than the preset alignment threshold, the reason for non-compliance is determined to be external environmental shock.

[0035] Otherwise, the reason for non-compliance is determined to be abnormal local construction conditions.

[0036] Furthermore, the optimization module determines the corresponding optimization strategy based on the cause of non-compliance, wherein,

[0037] When the reason for failure is the mismatch of the inference model parameters, the optimization module generates a model retraining instruction, increases the training sample size of the prediction model in the inference module, and then retrains it.

[0038] When the reason for non-compliance is insufficient resource allocation, the optimization module generates a resource increase instruction to improve at least one parameter in the construction resource allocation parameters.

[0039] Furthermore, the optimization module determines the corresponding optimization strategy based on the cause of non-compliance, wherein,

[0040] When the reason for non-compliance is external environmental impact, the optimization module generates an environmental model correction instruction to correct the parameters of the environmental disturbance prediction sub-model in the inference module.

[0041] When the reason for non-compliance is abnormal local construction conditions, the optimization module generates a local correction instruction, updates the construction difficulty coefficient of the corresponding sub-interval in the 3D BIM model, and only re-performs the local simulation for that sub-interval and subsequent adjacent sub-intervals.

[0042] Furthermore, the verification module is also used to re-acquire the inference results and perform verification after the optimization module executes the optimization strategy, wherein,

[0043] If the overall deviation after re-verification is still greater than the preset deviation threshold, the verification module upgrades and determines that the reason for non-compliance is an abnormal coupling of multiple factors, and outputs a manual intervention alarm signal.

[0044] Furthermore, the system also includes:

[0045] The visualization module, which is connected to the simulation module and the verification module respectively, is used to synchronously display the comparison between the planned progress and the simulation progress in the three-dimensional BIM model, in units of the sub-intervals.

[0046] Compared with existing technologies, the beneficial effects of this invention are that by constructing a comprehensive deviation score composed of three parts—schedule deviation, timing deviation, and assumption fit—this invention can comprehensively evaluate the closeness of the simulation results to the construction requirements from three independent aspects: the overall schedule deviation in the time dimension, the degree of dispersion of the completion time in the spatial dimension, and the degree of fit between the simulation assumptions and the actual influencing conditions. This provides a unified quantitative decision-making basis for the feasibility of the construction simulation results. A low comprehensive deviation score can directly determine that the simulation results are highly consistent with the requirements, while a high comprehensive deviation score automatically triggers cause analysis or process optimization, thereby effectively improving the accuracy and reliability of the construction simulation.

[0047] Furthermore, this invention introduces the variance of the periodic interval deviation as a discrimination index for the cause of non-compliance, and determines a preset variance threshold by combining historical data. This enables the automatic differentiation between systematic and local factors that lead to the deduction of non-compliance causes, providing a quantitative basis for the selection of subsequent optimization measures and effectively improving the pertinence and efficiency of construction deduction and correction.

[0048] Furthermore, this invention introduces the correlation coefficient between average schedule deviation and resource allocation adequacy rate as a further distinguishing indicator. Combined with the correlation coefficient threshold determined by historical data, it achieves automated and accurate differentiation between insufficient resource allocation and model parameter mismatch among the causes of systemic non-compliance. This provides clear directional guidance for subsequent optimization measures, effectively avoids ineffective adjustments caused by misjudgment of causes, and significantly improves the diagnostic accuracy and optimization efficiency of the construction simulation system.

[0049] Furthermore, by introducing the time alignment index of environmental disturbance intensity and sub-interval progress deviation, combined with the continuous sub-interval judgment rules and preset alignment threshold, this invention achieves automated and accurate differentiation between external environmental impact and local construction condition anomalies in the causes of local non-compliance. This provides clear directional guidance for subsequent optimization measures, effectively avoids ineffective adjustments caused by misjudgment of causes, and significantly improves the diagnostic accuracy and adaptability of the construction simulation system under complex working conditions.

[0050] Furthermore, by establishing a clear mapping relationship between the causes of non-compliance and optimization strategies, this invention triggers model retraining and resource increase instructions for two different causes: parameter mismatch and insufficient resource allocation. When resources are increased, the bottleneck resource type is further identified and the adjustment range is quantified, thereby realizing the automated and precise execution of optimization measures and significantly improving the closed-loop optimization capability and iteration efficiency of the construction simulation system.

[0051] Furthermore, this invention formulates differentiated optimization strategies for two different causes: external environmental shocks and abnormal local construction conditions. When there is an environmental shock, the environmental disturbance prediction sub-model is corrected and a global re-analysis is performed. When there are abnormal local conditions, only the construction difficulty coefficient of the corresponding sub-interval in the BIM model is updated and a local re-analysis is performed. This achieves accurate matching of optimization measures and efficient utilization of computing resources, effectively avoids the computational overhead caused by global re-analysis, and significantly improves the response speed and adaptability of the construction simulation system under abnormal working conditions. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of a highway section construction progress simulation system based on BIM technology, as described in this application embodiment.

[0053] Figure 2 This is a flowchart illustrating the determination of whether the simulation results are qualified in the highway section construction progress simulation system based on BIM technology, as described in this application embodiment. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] like Figures 1-2 As shown, Figure 1 This is a schematic diagram of the highway section construction progress simulation system based on BIM technology in this application; Figure 2 This is a flowchart for determining whether the simulation results are qualified in the highway section construction progress simulation system based on BIM technology in this application.

[0057] This application's embodiment of a highway section construction progress simulation system based on BIM technology includes:

[0058] The data acquisition module is used to obtain relevant information about the road section to be constructed, including geographical information, design parameters, and impact information.

[0059] The model building module, connected to the acquisition module, is used to build a three-dimensional BIM model based on the geographic information in the relevant information, and to divide the road section to be constructed into several sub-sections according to spatial location.

[0060] The mapping module, which is connected to the acquisition module and the model building module respectively, is used to establish a dynamic mapping relationship between actual construction parameters and the three-dimensional BIM model.

[0061] A requirement generation module, connected to the acquisition module, is used to generate construction requirements based on the impact information in the relevant information.

[0062] The strategy customization module, connected to the requirement generation module, is used to construct a construction strategy including planned progress indicators based on the construction requirements, and to divide the total construction period into several sub-construction periods according to time.

[0063] The simulation module is connected to the model building module, the mapping module and the strategy customization module respectively, and is used to simulate the construction progress under each sub-construction cycle based on preset construction resource configuration parameters to obtain the simulation results;

[0064] The verification module is connected to the simulation module and the requirement generation module respectively. It is used to calculate the comprehensive deviation between the simulation result and the construction requirement, determine whether the simulation result is qualified based on the comprehensive deviation, and analyze the reasons for the failure when the simulation result is unqualified.

[0065] An optimization module, connected to the verification module, is used to determine the corresponding optimization strategy based on the reasons for non-compliance and to adjust the corresponding parameters.

[0066] In this embodiment of the invention, the data acquisition process of the acquisition module is as follows: First, by connecting with the engineering survey and design system, Beidou / GPS positioning equipment, meteorological service interface, and IoT sensors at the construction site, relevant information about the road section to be constructed is acquired in real time or periodically. Geographic information includes, but is not limited to, topographic elevation data, geological stratification data, underground pipeline distribution, river system location, and boundaries of existing roads and surrounding buildings; design parameters include, but are not limited to, roadbed width, pavement structure layer thickness, bridge and tunnel location coordinates, design elevation, horizontal and vertical curve elements, and slope ratio; influencing information includes, but is not limited to, meteorological data, traffic control periods, material supply cycles, environmental protection construction windows, local policy work stoppage requirements, and historical construction efficiency statistics. The acquisition module timestamps and aligns the spatial coordinates of the above multi-source heterogeneous data before uniformly outputting it to the model building module and the requirement generation module.

[0067] In this embodiment of the invention, after receiving the geographic information output by the acquisition module, the model building module first uses a Revit or Civil 3D platform to build a 3D BIM model of the road section to be constructed. This model includes terrain surfaces, road centerlines, cross-sectional templates, and structural components. Then, the road section to be constructed is divided into several sub-sections every 200 meters or according to the points of change in engineering properties such as bridges, tunnels, and roadbeds, and each sub-section is assigned a unique identifier and spatial boundary coordinates. The mapping module connects the acquisition module and the model building module respectively, and dynamically associates the actual construction parameters obtained by the acquisition module (such as real-time machinery fuel consumption, team attendance, and daily completed work volume) with the corresponding sub-section components in the BIM model through the API interface, so as to realize real-time synchronization between construction progress and model attributes. The requirements generation module calculates the planned completion percentage and allowable period fluctuation range of each sub-section based on the impact information provided by the acquisition module (such as the number of rainy days and the probability of material supply disruption), forming construction requirements. The strategy customization module formulates construction strategies based on construction needs, including milestone nodes and planned progress indicators (such as the daily amount of roadbed filling to be completed), and divides the total construction cycle into several sub-construction cycles by month or week. The simulation module connects to the model building module, mapping module, and strategy customization module respectively. Taking preset construction resource configuration parameters (such as 5 excavators, 10 dump trucks, and 30 workers) as input, it uses discrete event simulation or a neural network model trained based on historical data to simulate the construction progress of each sub-interval within each sub-construction cycle. Finally, it outputs the cumulative completion percentage at the end of each sub-cycle and the estimated completion time of each sub-interval as the simulation result.

[0068] Specifically, the verification module calculates the overall deviation, where,

[0069] The verification module obtains the simulated cumulative completion percentage at the end of each sub-construction cycle in the simulation results, compares it with the planned cumulative completion percentage of the corresponding sub-construction cycle in the construction requirements, calculates the absolute value of the progress deviation between the two, and calculates the average progress deviation under all sub-construction cycles as the first deviation component.

[0070] The verification module obtains the absolute value of the time difference between the simulated completion time and the planned completion time of each sub-interval, sums them up for all sub-intervals and divides by the total number of sub-intervals to obtain the second deviation component.

[0071] The verification module obtains the hypothetical impact information set in the deduction module, calculates the normalized deviation between it and the standard impact information defined in the construction requirements, and uses it as the third deviation component.

[0072] The verification module calculates the weighted sum of the first deviation component, the second deviation component, and the third deviation component to obtain the comprehensive deviation degree.

[0073] In this embodiment of the invention, firstly, the simulated cumulative completion percentage at the end of each sub-construction cycle is obtained from the simulation module, and the planned cumulative completion percentage for the corresponding sub-construction cycle is obtained from the demand generation module. The absolute value of the difference between the two is calculated one by one, and then the average value is calculated for all sub-construction cycles to obtain the first deviation component. This component reflects the overall progress deviation in the time dimension. Secondly, the simulated completion time of each sub-interval is obtained from the simulation module and compared with the planned completion time of each sub-interval defined in the construction requirements. The absolute value of the difference in completion time for each sub-interval is calculated, and the sum is divided by the total number of sub-intervals to obtain the second deviation component. This component reflects the degree of dispersion of the completion time sequence in the spatial dimension. Thirdly, the hypothetical impact information set by the simulation module during the simulation process (such as the hypothetical rainfall frequency and the number of days of material supply delay) is extracted and normalized item by item with the standard impact information defined in the construction requirements (such as the local average annual rainfall frequency and the material supply cycle stipulated in the contract). That is, the relative deviation of each impact factor is calculated first, and then the Euclidean distance or weighted average is taken to obtain the third deviation component. This component reflects the degree of consistency between the simulation assumptions and the actual impact conditions. Finally, the verification module calculates the weighted sum to obtain the overall deviation. The lower the overall deviation, the closer the simulation results are to the construction requirements; conversely, the higher the deviation, the greater the deviation, requiring further analysis of the causes or triggering of the optimization process.

[0074] In this embodiment of the invention, the method for determining the weighting coefficients is as follows: Based on historical construction data from at least three completed highway sections of the same type, principal component analysis or entropy weighting is used to calculate the objective weights of each deviation component. Specifically, the first deviation component, the second deviation component, the third deviation component, and label data indicating whether actual construction delays occurred for each historical section are collected. The contribution of each component to the final delay is fitted using a multiple linear regression or random forest model. The feature importance output by the model is normalized and used as the weighting coefficients. For example, the weight of the first deviation component is 0.45, the weight of the second deviation component is 0.35, and the weight of the third deviation component is 0.20.

[0075] This invention constructs a comprehensive deviation score consisting of three parts: schedule deviation, timing deviation, and assumption fit. This comprehensive score evaluates the approximation of the simulation results to the construction requirements from three independent aspects: the overall schedule deviation in the time dimension, the degree of dispersion of completion time in the spatial dimension, and the degree of fit between the simulation assumptions and the actual influencing conditions. It provides a unified quantitative decision-making basis for the feasibility of the construction simulation results. A low comprehensive deviation score directly indicates a high degree of fit between the simulation results and the requirements, while a high comprehensive deviation score automatically triggers root cause analysis or process optimization, thereby effectively improving the accuracy and reliability of the construction simulation.

[0076] Specifically, the verification module determines whether the deduction result is qualified based on the comprehensive deviation degree, wherein,

[0077] If the overall deviation is less than or equal to the preset deviation threshold, the verification module determines that the deduction result is qualified;

[0078] If the overall deviation is greater than the preset deviation threshold, the verification module determines that the deduction result is unqualified and analyzes the reasons for the unqualification.

[0079] In this embodiment of the invention, the preset deviation threshold is determined based on historical construction data from at least three completed highway sections of the same type. Specifically, the comprehensive deviation degree of each historical section and the corresponding actual construction results are collected, and the optimal classification threshold is determined through statistical analysis methods, so that the consistency between the judgment result of whether the historical section is qualified and the actual construction result is maximized at this threshold; or, a threshold optimization method in machine learning is used, with the judgment accuracy or F1 score as the objective function, and the comprehensive deviation degree value that maximizes the objective function is determined by traversal or grid search as the preset deviation threshold. Once this threshold is determined, it is used to determine the qualification of subsequent similar construction projects.

[0080] Specifically, the verification module analyzes the reasons for non-compliance, wherein,

[0081] The verification module calculates the difference between the simulated construction period and the corresponding planned construction period for each sub-interval, which is denoted as the period interval deviation, and calculates the variance of all period interval deviations.

[0082] If the variance is less than a preset variance threshold, the reason for non-compliance is determined to be a systematic factor;

[0083] If the variance is greater than or equal to the preset variance threshold, the reason for non-compliance is determined to be a local factor.

[0084] In this embodiment of the invention, the preset variance threshold is determined by the following method: First, historical construction data of at least five completed highway sections of the same type are collected. For each section, the deviation between the actual construction period and the planned construction period of each sub-section is recorded, and the variance of the deviation within each section is calculated. Simultaneously, the type of non-compliance reason ultimately confirmed for that section is recorded. Then, statistical analysis is performed on the data of each section to calculate the distribution range of variance in sections with systematic factors and the distribution range of variance in sections with local factors. The boundary value between the two distributions is taken as the preset variance threshold. Statistical analysis results show that when the variance is less than 2.5, all non-compliance reasons are systematic factors; when the variance is greater than or equal to 2.5 and less than 5.0, the non-compliance reasons are mainly local factors; when the variance is greater than or equal to 5.0, the non-compliance reason is clearly a localized sudden event. Considering both the accuracy and the misjudgment rate, the preset variance threshold is determined to be 2.5. In actual operation, if a simulation result fails to meet the requirements, the verification module calculates the variance of the periodic interval deviation for each sub-interval. When the variance is 1.8, the failure is determined to be due to systemic factors, indicating a need to adjust the overall construction plan or resource allocation. When the variance is 3.2, the failure is determined to be due to local factors, indicating a need to investigate abnormal conditions in specific sub-intervals. This threshold can be periodically calibrated based on the accumulation of subsequent engineering data, with a calibration cycle of once every five sections completed.

[0085] This invention introduces the variance of periodic interval deviation as a discrimination index for the cause of non-compliance, and determines a preset variance threshold by combining historical data. This enables the automatic differentiation between systematic and local factors that lead to the deduction of non-compliance causes, providing a quantitative basis for the selection of subsequent optimization measures and effectively improving the pertinence and efficiency of construction deduction and correction.

[0086] Specifically, for systemic factors, the verification module further distinguishes,

[0087] The verification module calculates the correlation coefficient between the average schedule deviation and the resource allocation adequacy rate of each sub-interval.

[0088] If the absolute value of the correlation coefficient is greater than the preset correlation coefficient threshold, the reason for non-compliance is determined to be insufficient resource allocation;

[0089] If the absolute value of the correlation coefficient is less than or equal to the preset correlation coefficient threshold, the reason for non-compliance is determined to be parameter mismatch in the inference model.

[0090] In this embodiment of the invention, the correlation coefficient is calculated using the Pearson correlation coefficient method. Specifically, given n sub-intervals, the average schedule deviation and resource allocation adequacy ratio of each sub-interval are first calculated. Then, the total average of the average schedule deviations and the total average of the resource allocation adequacy ratios of all sub-intervals are calculated. Next, for each sub-interval, the difference between its average schedule deviation and the total average, and the difference between its resource allocation adequacy ratio and the total average are calculated. These two differences are multiplied and summed over all sub-intervals to obtain the numerator. The denominator is calculated by taking the square root of the difference between the average schedule deviation and the total average for each sub-interval, and the sum of the square root of the difference between the resource allocation adequacy ratio and the total average for each sub-interval. Finally, the numerator is divided by the denominator to obtain the correlation coefficient. The correlation coefficient ranges from -1 to +1, where a negative value indicates a negative correlation between the average schedule deviation and the resource allocation adequacy ratio, and the closer the absolute value is to -1, the stronger the correlation. The absolute value of the correlation coefficient is compared with a preset correlation coefficient threshold. If the absolute value is greater than the threshold, it is determined that the resource allocation is insufficient; otherwise, it is determined that the parameters of the inference model are mismatched.

[0091] In this embodiment of the invention, the preset correlation coefficient threshold is determined by the following method: First, historical construction data of at least six completed highway sections of the same type are collected. For each section, the average progress deviation sequence and resource allocation adequacy rate sequence of each sub-section are recorded, and the Pearson correlation coefficient between the two is calculated. Simultaneously, the types of systematic non-compliance reasons confirmed by on-site verification for that section are recorded. Statistical analysis shows that the absolute value of the correlation coefficient for sections that are non-compliant due to insufficient resource allocation is greater than 0.7; the absolute value of the correlation coefficient for sections that are non-compliant due to model parameter mismatch is less than 0.3. Considering both the differentiation effect and the safety margin, the preset correlation coefficient threshold is determined to be 0.5. In actual operation, if a simulation result is deemed unqualified due to systemic factors, and the verification module calculates a correlation coefficient of -0.82, with an absolute value of 0.82 greater than 0.5, then the reason for unqualification is insufficient resource allocation, indicating the need to increase the supply of personnel, equipment, or materials. If the correlation coefficient is 0.15, with an absolute value of 0.15 less than 0.5, then the reason for unqualification is parameter mismatch in the simulation model, indicating the need to calibrate the construction efficiency parameters or influencing factor weights in the model.

[0092] This invention introduces the correlation coefficient between average schedule deviation and resource allocation adequacy rate as a further distinguishing indicator. Combined with the correlation coefficient threshold determined by historical data, it achieves automated and accurate differentiation between insufficient resource allocation and model parameter mismatch among the causes of systemic non-compliance. This provides clear directional guidance for subsequent optimization measures, effectively avoids ineffective adjustments caused by misjudgment of causes, and significantly improves the diagnostic accuracy and optimization efficiency of the construction simulation system.

[0093] Specifically, for localized factors, the verification module further distinguishes,

[0094] The verification module acquires the time series of external environmental data and calculates the time alignment between the intensity of environmental disturbance and the progress deviation of the sub-interval.

[0095] If the time alignment of at least two consecutive sub-intervals is greater than the preset alignment threshold, the reason for non-compliance is determined to be external environmental shock.

[0096] Otherwise, the reason for non-compliance is determined to be abnormal local construction conditions.

[0097] In this embodiment of the invention, the external environmental data includes meteorological data (such as rainfall, wind speed, and temperature), geological data (such as groundwater level changes and rock strata anomalies), and supply chain data (such as material supply disruptions and transportation interruptions) during the construction period of each sub-interval. The environmental disturbance intensity is defined as a comprehensive index obtained by weighting and synthesizing the above data after normalization, with a value ranging from 0 to 1. The larger the value, the more severe the environmental disturbance. The calculation method for the time alignment is as follows: First, the construction time range of each sub-interval is divided into several time windows of equal length (e.g., in days); then, the average environmental disturbance intensity within each time window and the actual progress deviation within that window (the difference between the simulated construction period and the planned construction period) are calculated; next, the Pearson correlation coefficient between the environmental disturbance intensity sequence and the progress deviation sequence within that sub-interval is calculated. The absolute value of this correlation coefficient is the time alignment, ranging from 0 to 1. The closer the value is to 1, the stronger the temporal synchronization between the environmental disturbance and the progress deviation.

[0098] In this embodiment of the invention, the preset alignment threshold is determined by the following method: Historical construction data from at least five completed highway sections of the same type are collected. For each section, the environmental disturbance intensity sequence, progress deviation sequence, and the types of local non-compliance reasons confirmed by on-site verification are recorded for each sub-section. Statistical analysis shows that for sections that are non-compliant due to external environmental shocks, the time alignment of their consecutive sub-sections is greater than 0.7; for sections that are non-compliant due to abnormal local construction conditions, the time alignment is generally less than 0.4. Considering both reliability and the risk of misjudgment, the preset alignment threshold is set to 0.6. In actual operation, if a simulation result is deemed unqualified due to local factors, the verification module calculates the time alignment of each sub-interval. If there are at least two consecutive sub-intervals with time alignment of 0.75 and 0.82 respectively, both greater than 0.6, the reason for the unqualification is determined to be an external environmental shock, prompting a check of the meteorological records or supply chain disruptions during that time period. If the time alignment of each sub-interval is less than 0.6, or only a single sub-interval exceeds the threshold but is discontinuous, the reason for the unqualification is determined to be abnormal local construction conditions, prompting a check of the equipment status, personnel operation, or temporary material supply issues within the specific sub-interval.

[0099] This invention introduces the time alignment index of environmental disturbance intensity and sub-interval progress deviation, combined with continuous sub-interval judgment rules and preset alignment thresholds, to achieve automated and accurate differentiation between external environmental impact and local construction condition anomalies in the causes of local non-compliance. This provides clear directional guidance for subsequent optimization measures, effectively avoids ineffective adjustments caused by misjudgment of causes, and significantly improves the diagnostic accuracy and adaptability of the construction simulation system under complex working conditions.

[0100] Specifically, the optimization module determines the corresponding optimization strategy based on the reasons for non-compliance, wherein,

[0101] When the reason for failure is the mismatch of the inference model parameters, the optimization module generates a model retraining instruction, increases the training sample size of the prediction model in the inference module, and then retrains it.

[0102] When the reason for non-compliance is insufficient resource allocation, the optimization module generates a resource increase instruction to improve at least one parameter in the construction resource allocation parameters.

[0103] In this embodiment of the invention, for cases of parameter mismatch in the inference model, the optimization module executes the following specific process: First, the construction project data corresponding to the current inference result is added as new samples to the original training dataset of the prediction model in the inference module. Then, a sample increment strategy is determined: if the original training sample size is less than 100 sets, at least 20 new samples are added at once; if the original training sample size is greater than or equal to 100 sets, a sliding window mechanism is used to replace the earliest 10 sets of data with the 10 most recently completed construction data sets, maintaining the total amount of training data while improving data timeliness. After the sample expansion is completed, the model retraining process is triggered, using the same algorithm architecture as the original model, with resource configuration parameters, environmental disturbance intensity, and planned construction cycle of each sub-interval as input features, and actual progress deviation as output label, to retrain the prediction model.

[0104] After retraining, the new model is deployed to the inference module to replace the original model, and the model version number and update date are recorded. For cases of insufficient resource allocation, the optimization module executes the following specific process: First, it analyzes the correlation coefficient between the average schedule deviation of each sub-interval and the resource allocation sufficiency rate calculated during the non-compliance cause determination process, identifying the resource type with the strongest negative correlation to schedule deviation. Then, it generates targeted resource increase instructions: if the bottleneck is personnel resources, the number of construction personnel in the corresponding sub-interval is increased by 10% to 25% based on the original plan, with the increase positively correlated with the absolute value of the correlation coefficient (for every 0.1 increase in the absolute value of the correlation coefficient, the increase increases by 2 percentage points); if the bottleneck is equipment resources, the number of equipment shifts is increased or the daily working hours are extended; if the bottleneck is material resources, the frequency of material supply batches is increased or the quantity of each batch is increased.

[0105] The resource increase instruction also includes a priority indicator, prioritizing the increase of bottleneck resource types with the highest absolute values ​​of correlation coefficients. For example, if correlation coefficient analysis during a certain operation shows that the correlation coefficient for personnel resources is -0.82, for equipment resources it is -0.45, and for materials resources it is -0.20, then the generated instruction will increase the number of personnel by 19% (calculated as a basic increase of 10% plus 0.82, corresponding to 16 percentage points, totaling 26%, but the upper limit is controlled within 25%, so 25% is used), increase equipment shifts by 10%, and leave material supply unchanged for the time being. After the resource adjustment is completed, the optimization module will feed back the updated resource configuration parameters to the demand generation module for regenerating construction requirements or as the baseline configuration for the next round of simulation.

[0106] This invention establishes a clear mapping relationship between the causes of non-compliance and optimization strategies. It triggers model retraining and resource increase instructions for two different causes: parameter mismatch and insufficient resource allocation in the simulation model. When resources are increased, it further identifies the bottleneck resource type and quantifies the adjustment range, thereby realizing the automated and precise execution of optimization measures and significantly improving the closed-loop optimization capability and iteration efficiency of the construction simulation system.

[0107] Specifically, the optimization module determines the corresponding optimization strategy based on the reasons for non-compliance, wherein,

[0108] When the reason for non-compliance is external environmental impact, the optimization module generates an environmental model correction instruction to correct the parameters of the environmental disturbance prediction sub-model in the inference module.

[0109] When the reason for non-compliance is abnormal local construction conditions, the optimization module generates a local correction instruction, updates the construction difficulty coefficient of the corresponding sub-interval in the 3D BIM model, and only re-performs the local simulation for that sub-interval and subsequent adjacent sub-intervals.

[0110] In this embodiment of the invention, for situations involving external environmental shocks, the optimization module executes the following specific process: First, it extracts the time alignment data between the environmental disturbance intensity and the progress deviation of the sub-intervals calculated during the non-compliance cause determination process, and identifies the time periods corresponding to continuous sub-intervals where the time alignment exceeds a preset alignment threshold (0.6). Then, it acquires the actual external environmental records within this time period (including measured rainfall, wind speed, and temperature data from meteorological stations, groundwater level change data from geological survey reports, and material supply interruption data recorded by the logistics system), and compares them item by item with the predicted values ​​of the environmental disturbance prediction sub-model in the deduction module for this time period, calculating the prediction deviation ratio of each type of environmental factor (actual value minus predicted value divided by predicted value). Next, it corrects the parameters of the environmental disturbance prediction sub-model according to the prediction deviation ratio: if the rainfall prediction is consistently low (actual rainfall is on average more than 30% higher than the predicted value), the rainfall intensity distribution parameters (such as rainfall probability, average rainfall, and rainfall duration) in the sub-model are updated using Bayesian methods based on the measured data; if the predicted material supply interruption frequency is insufficient, the supply chain interruption probability parameter is adjusted. After correction, the updated environmental disturbance prediction sub-model is deployed to the simulation module, and the model is used to re-simulate the remaining sub-intervals of the current construction project to generate a corrected construction plan. For example, if the time alignment of three consecutive sub-intervals in a certain operation is 0.75, 0.82, and 0.71 respectively, and the actual cumulative rainfall during this period is 180mm while the model prediction is only 90mm, resulting in a rainfall prediction deviation of +100%, then the optimization module will correct the monthly average rainfall parameter in the environmental disturbance prediction sub-model from the original 90mm to 150mm (considering a certain safety margin, taking the weighted average of the measured and predicted rainfall), and the rainfall probability parameter from 0.3 to 0.6, triggering a re-simulation of the remaining sub-intervals.

[0111] For situations involving abnormal local construction conditions, the optimization module executes the following specific process: First, it locates the abnormal sub-intervals identified during the non-compliance cause determination process (i.e., the specific sub-interval number corresponding to the local factor, such as the third sub-interval). Then, it obtains the abnormal information recorded within this sub-interval (such as equipment failure type and duration, personnel operation error records, specific materials and shortage duration of temporary materials), and determines the adjustment range of the construction difficulty coefficient based on the type of abnormal information: If it is an equipment failure, the equipment construction efficiency coefficient of the corresponding sub-interval in the 3D BIM model is reduced by 15% to 30%, with the reduction range proportional to the failure duration (reduced by 3 percentage points per day of failure, with a maximum of 30%); if it is a personnel operation error, the personnel efficiency coefficient is reduced by 10% to 20%; if it is a temporary material shortage, the material supply delay coefficient is increased by 5 to 15 days. After the construction difficulty coefficient is updated, the optimization module does not perform a global re-analysis. Instead, it only performs a local simulation on the abnormal sub-interval and its subsequent adjacent sub-intervals (e.g., if the abnormal sub-interval is the third sub-interval, then the third, fourth, and fifth sub-intervals are re-analyzed). The updated construction difficulty coefficient is used during the simulation, while the completed sub-intervals before the abnormal sub-interval (the first and second sub-intervals) retain their original simulation results. After the local simulation is completed, the newly generated simulation results are combined with the simulation results of the original unaffected sub-intervals to form a revised complete construction plan. For example, if the third sub-interval is determined to have abnormal local construction conditions during a certain operation, specifically due to three consecutive days of paving equipment failure within that sub-interval, the optimization module lowers the equipment construction efficiency coefficient of the third sub-interval in the 3D BIM model from 1.0 to 0.91 (a 3% reduction per day of failure, for a total reduction of 9%). Then, it only performs a local simulation on the third, fourth, and fifth sub-intervals. After the simulation is completed, the newly generated results for the third to fifth sub-intervals are combined with the original results for the first, second, and sixth and subsequent sub-intervals to generate a revised complete construction plan. After the local simulation is completed, the system records the correction information for the abnormal sub-interval for reference in subsequent simulations of similar working conditions.

[0112] This invention develops differentiated optimization strategies for two different causes: external environmental shocks and abnormal local construction conditions. When environmental shocks occur, the environmental disturbance prediction sub-model is corrected and a global re-analysis is performed. When local conditions are abnormal, only the construction difficulty coefficient of the corresponding sub-interval in the BIM model is updated and a local re-analysis is performed. This achieves precise matching of optimization measures and efficient utilization of computing resources, effectively avoids the computational overhead caused by global re-analysis, and significantly improves the response speed and adaptability of the construction simulation system under abnormal conditions.

[0113] Specifically, the verification module is further configured to re-acquire the inference results and verify them after the optimization module executes the optimization strategy, wherein,

[0114] If the overall deviation after re-verification is still greater than the preset deviation threshold, the verification module upgrades and determines that the reason for non-compliance is abnormal coupling of multiple factors, and outputs a manual intervention alarm signal.

[0115] In this embodiment of the invention, after the optimization module completes strategy execution, the verification module automatically triggers a new round of simulation and verification closed loop. The specific process is as follows: First, the verification module sends a "re-simulate after optimization" instruction to the simulation module, and the simulation module regenerates the simulation results based on the parameters updated by the optimization module. Then, the verification module recalculates the comprehensive deviation of the new simulation results according to the preset comprehensive deviation calculation method and compares it with the preset deviation threshold. If the comprehensive deviation after re-verification is less than or equal to the preset deviation threshold, it indicates that the optimization strategy is effective, the system determines that the simulation result is qualified, the current optimization process ends, and the optimized parameters are solidified as the benchmark parameters for subsequent simulations. If the comprehensive deviation after re-verification is still greater than the preset deviation threshold, it indicates that single-factor optimization has failed to solve the problem. At this time, the verification module upgrades the cause of non-compliance from the previously determined single cause to a multi-factor coupling anomaly and immediately generates a manual intervention alarm signal. This alarm signal is output through the system's human-computer interaction interface, and the content includes at least: the current comprehensive deviation value, the preset deviation threshold, the historical record of the executed optimization strategy, and the comparison between the current value and the target value of each deviation component. For example, if an initial simulation fails due to parameter mismatch in the simulation model, and the optimization module retrains the model and re-simulates, the overall deviation of the re-verification is 0.42, which is still greater than the preset deviation threshold of 0.35. The verification module then determines this as an anomaly caused by multi-factor coupling and outputs an alarm message: "Overall deviation exceeds 0.42; model retraining optimization was ineffective; manual verification is recommended to check for coupling effects between resource allocation and environmental impacts." While outputting the alarm, the system retains the current simulation data and optimization records for review during manual analysis.

[0116] Specifically, the system also includes:

[0117] The visualization module is connected to the simulation module and the verification module respectively, and is used to synchronously display the comparison difference between the planned progress and the simulation progress in the three-dimensional BIM model, in units of the sub-intervals.

[0118] In this embodiment of the invention, the visualization module is integrated into the front-end display interface of the system, and communicates with the simulation module and the verification module in real time. This module uses a 3D BIM model as the display carrier, and displays the comparison between the planned progress and the simulated progress synchronously, organized by sub-intervals (each sub-interval corresponds to an independent construction section in the BIM model, distinguished by different colors or textures). The specific display method is as follows: For each sub-interval, the visualization module obtains the planned cumulative completion percentage sequence output by the demand generation module and the simulated cumulative completion percentage sequence output by the simulation module, calculates the progress deviation (simulated value minus planned value) at the end of each sub-construction cycle, and identifies it using different colors based on the sign and magnitude of the deviation: sub-intervals with progress ahead of schedule (deviation greater than +5%) are highlighted in blue, those with normal progress (deviation between -5% and +5%) are displayed in green, those with progress lagging behind (deviation less than -5%) are displayed in yellow, and those with severe delays (deviation less than -15%) are displayed in flashing red. Meanwhile, the visualization module overlays key information cards above each sub-section of the BIM model. These cards include: sub-section number, planned completion time, simulated completion time, overall deviation, and the result of the non-compliance determination (if any). Users can click on any sub-section to expand a detailed comparison view, displaying a superimposed comparison of the planned and simulated progress curves for each sub-construction cycle within that sub-section. Furthermore, the visualization module supports a timeline sliding function, allowing users to drag the timeline slider to dynamically view the three-dimensional distribution of progress completion status for each sub-section at different construction time points. When the verification module determines the simulation result to be non-compliant and outputs the reason for non-compliance, the visualization module automatically highlights the abnormal sub-sections (such as sub-sections corresponding to local factors or sub-sections with the highest correlation coefficient among systematic factors) with a red border and displays a pop-up explanation of the reason on the interface. For example, when the reason for non-compliance is abnormal local construction conditions in the third sub-section, the visualization module automatically marks the third sub-section in the BIM model with a flashing red border. Clicking on it displays a pop-up message: "The third sub-section is 12% behind schedule due to equipment failure for 3 consecutive days, triggering local correction and optimization." When the verification module outputs a manual intervention alarm signal, the visualization module displays an alarm banner at the top of the interface and marks all relevant sub-areas with purple borders, prompting manual intervention for verification.

[0119] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A highway section construction progress simulation system based on BIM technology, characterized in that, include: The data acquisition module is used to obtain relevant information about the road section to be constructed, including geographical information, design parameters, and impact information. The model building module, connected to the acquisition module, is used to build a three-dimensional BIM model based on the geographic information in the relevant information, and to divide the road section to be constructed into several sub-sections according to spatial location. The mapping module, which is connected to the acquisition module and the model building module respectively, is used to establish a dynamic mapping relationship between actual construction parameters and the three-dimensional BIM model. A requirement generation module, connected to the acquisition module, is used to generate construction requirements based on the impact information in the relevant information. The strategy customization module, connected to the requirement generation module, is used to construct a construction strategy including planned progress indicators based on the construction requirements, and to divide the total construction period into several sub-construction periods according to time. The simulation module is connected to the model building module, the mapping module and the strategy customization module respectively, and is used to simulate the construction progress under each sub-construction cycle based on preset construction resource configuration parameters to obtain the simulation results; The verification module is connected to the simulation module and the requirement generation module respectively. It is used to calculate the comprehensive deviation between the simulation result and the construction requirement, determine whether the simulation result is qualified based on the comprehensive deviation, and analyze the reasons for the failure when the simulation result is unqualified. An optimization module, connected to the verification module, is used to determine the corresponding optimization strategy based on the reasons for non-compliance and to adjust the corresponding parameters.

2. The highway section construction progress simulation system based on BIM technology according to claim 1, characterized in that, The verification module calculates the overall deviation, where... The verification module obtains the simulated cumulative completion percentage at the end of each sub-construction cycle in the simulation results, compares it with the planned cumulative completion percentage of the corresponding sub-construction cycle in the construction requirements, calculates the absolute value of the progress deviation between the two, and calculates the average progress deviation under all sub-construction cycles as the first deviation component. The verification module obtains the absolute value of the time difference between the simulated completion time and the planned completion time of each sub-interval, sums them up for all sub-intervals and divides by the total number of sub-intervals to obtain the second deviation component. The verification module obtains the hypothetical impact information set in the deduction module, calculates the normalized deviation between it and the standard impact information defined in the construction requirements, and uses it as the third deviation component. The verification module calculates the weighted sum of the first deviation component, the second deviation component, and the third deviation component to obtain the comprehensive deviation degree.

3. The highway section construction progress simulation system based on BIM technology according to claim 2, characterized in that, The verification module determines whether the deduction result is qualified based on the comprehensive deviation degree, wherein, If the overall deviation is less than or equal to the preset deviation threshold, the verification module determines that the deduction result is qualified; If the overall deviation is greater than the preset deviation threshold, the verification module determines that the deduction result is unqualified and analyzes the reasons for the unqualification.

4. The highway section construction progress simulation system based on BIM technology according to claim 3, characterized in that, The verification module analyzes the reasons for non-compliance, among which... The verification module calculates the difference between the simulated construction period and the corresponding planned construction period for each sub-interval, which is denoted as the period interval deviation, and calculates the variance of all period interval deviations. If the variance is less than a preset variance threshold, the reason for non-compliance is determined to be a systematic factor; If the variance is greater than or equal to the preset variance threshold, the reason for non-compliance is determined to be a local factor.

5. The highway section construction progress simulation system based on BIM technology according to claim 4, characterized in that, For systemic factors, the verification module further distinguishes, The verification module calculates the correlation coefficient between the average schedule deviation and the resource allocation sufficiency rate of each sub-interval. If the absolute value of the correlation coefficient is greater than the preset correlation coefficient threshold, the reason for non-compliance is determined to be insufficient resource allocation; If the absolute value of the correlation coefficient is less than or equal to the preset correlation coefficient threshold, the reason for non-compliance is determined to be parameter mismatch in the inference model.

6. The highway section construction progress simulation system based on BIM technology according to claim 4, characterized in that, For localized factors, the verification module further distinguishes, The verification module acquires the time series of external environmental data and calculates the time alignment between the intensity of environmental disturbance and the progress deviation of the sub-interval. If the time alignment of at least two consecutive sub-intervals is greater than the preset alignment threshold, the reason for non-compliance is determined to be external environmental shock. Otherwise, the reason for non-compliance is determined to be abnormal local construction conditions.

7. The highway section construction progress simulation system based on BIM technology according to claim 5, characterized in that, The optimization module determines the corresponding optimization strategy based on the reasons for non-compliance, wherein, When the reason for failure is the mismatch of the inference model parameters, the optimization module generates a model retraining instruction, increases the training sample size of the prediction model in the inference module, and then retrains it. When the reason for non-compliance is insufficient resource allocation, the optimization module generates a resource increase instruction to improve at least one parameter in the construction resource allocation parameters.

8. The highway section construction progress simulation system based on BIM technology according to claim 6, characterized in that, The optimization module determines the corresponding optimization strategy based on the reasons for non-compliance, wherein, When the reason for non-compliance is external environmental impact, the optimization module generates an environmental model correction instruction to correct the parameters of the environmental disturbance prediction sub-model in the inference module. When the reason for non-compliance is abnormal local construction conditions, the optimization module generates a local correction instruction, updates the construction difficulty coefficient of the corresponding sub-interval in the 3D BIM model, and only re-performs the local simulation for that sub-interval and subsequent adjacent sub-intervals.

9. The highway section construction progress simulation system based on BIM technology according to claim 7, characterized in that, The verification module is further configured to re-acquire and verify the inference results after the optimization module executes the optimization strategy, wherein... If the overall deviation after re-verification is still greater than the preset deviation threshold, the verification module upgrades and determines that the reason for non-compliance is abnormal coupling of multiple factors, and outputs a manual intervention alarm signal.

10. The highway section construction progress simulation system based on BIM technology according to claim 1, characterized in that, The system also includes: The visualization module is connected to the simulation module and the verification module respectively, and is used to synchronously display the comparison difference between the planned progress and the simulation progress in the three-dimensional BIM model, in units of the sub-intervals.

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