Digital collaborative management method and system based on BIM (Building Information Modeling)

Abstract

The invention relates to the technical field of collaborative management, in particular to a BIM-based digital collaborative management method and system, and the method comprises the following steps: collecting the terrain and climate data of a high-altitude water conservancy project through a geographic information system, analyzing the geological characteristics and hydrological conditions of a project coverage area through the geographic data, and generating a project basic data set. According to the invention, key data of a high-altitude water conservancy project is integrated to a BIM (Building Information Model) platform, so that efficient management of the whole life cycle of the project is realized, material consumption and project progress are recorded and analyzed in time, project management is enabled to be more rapid in response, a construction strategy can be adjusted in time, and project management efficiency is improved. In addition, the digital collaborative management of the BIM also strengthens the risk management process, and through systematically identifying, recording and evaluating risk points in construction, the risk prevention and response efficiency is improved, the project execution and resource utilization efficiency is optimized, and the overall success rate and sustainability of the project are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of collaborative management, and particularly relates to a BIM-based digital collaborative management method and system. BACKGROUND

[0002] The technical field of collaborative management mainly involves multiple teams or organizations effectively sharing information, resources and responsibilities in the same project to optimize project results. This management method emphasizes communication, coordination and cooperation among multiple stakeholders, aiming to improve decision-making efficiency and execution quality. In project management, collaborative management ensures that all participants can access real-time data and resources by implementing unified standards and processes, achieving higher transparency and lower operational risk. The latest information technology, such as cloud computing and artificial intelligence, is constantly used to enhance team interaction and information flow.

[0003] Among them, a BIM-based digital collaborative management method refers to using building information modeling (BIM) technology to realize digital management and collaboration of data in high-altitude water conservancy projects. The use is to integrate project information through BIM technology to realize the whole life cycle management of construction projects, including design, construction, operation and maintenance, etc. This method is particularly suitable for high-altitude environments, as construction and operation are usually more challenging, requiring more precise resource management and efficient collaboration processes. Through the implementation of BIM, the project team can better predict and solve problems unique to high altitudes, such as extreme weather and difficult material transportation, improving the success rate and safety of the project.

[0004] The existing collaborative management technology faces many challenges in the application of high-altitude water conservancy projects, lacking the ability to respond to complex terrain and harsh climate conditions in real time, and multiple teams or organizations lack the ability to fully utilize and dynamically update real-time data in information sharing and resource management. In high-altitude areas, this is particularly prominent, as construction and operation challenges are greater, such as extreme weather and difficult material transportation. Without in-depth geological and hydrological data analysis, existing technologies are difficult to effectively predict and respond to these challenges. The existing project management method uses static resource allocation and construction strategies, lacking flexibility, leading to resource waste and construction delays in rapidly changing construction environments. For example, failure to update construction paths and resource allocation in real time will result in unnecessary cost increases and safety risks. The shortcomings are particularly evident in emergency situations, when plans need to be quickly adjusted to respond to unexpected events, and existing methods cannot provide sufficient support, affecting the efficiency and safety of the entire project. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and propose a BIM-based digital collaborative management method and system.

[0006] In order to achieve the above object, the application adopts the following technical scheme, a digital collaborative management method based on BIM, comprising the following steps:

[0007] S1: Adopting a geographic information system, collecting topographic and climatic data of high-altitude water conservancy projects, analyzing geological characteristics and hydrological conditions of the project coverage area through geographic data, and generating a project base data set;

[0008] S2: Analyzing the road accessibility and construction area safety of high-altitude through the project base data set, adjusting the path weight according to real-time weather information, updating the resource allocation logic, and obtaining an adjusted network model;

[0009] S3: Based on the adjusted network model, using BIM digital collaborative management to identify redundant paths in resource allocation, configure water conservancy construction equipment and material flow, and generate a resource optimization configuration table;

[0010] S4: Using the resource optimization configuration table, monitoring the real-time data of the construction site, recording the material consumption speed and project progress, and analyzing the deviation reason, adjusting the construction strategy, and generating a construction feedback analysis result;

[0011] S5: Using the construction feedback analysis result, through BIM digital collaborative management, identifying risk points in construction, recording risk level, evaluating influence range and potential loss, and generating a risk assessment result;

[0012] S6: According to the risk assessment result, analyze the risk change trend in construction, combine with real-time monitoring data, adjust construction plan and resource allocation strategy, implement risk mitigation measures, and obtain a dynamic management scheme.

[0013] As a further scheme of the application, the project base data set includes the terrain height map, rainfall distribution map and groundwater flow condition of the high-altitude area, the adjusted network model includes construction path weight adjustment, resource allocation scheme and construction safety area mapping, the resource optimization configuration table includes optimized deployment of construction equipment, adjustment scheme of material flow and identification result of redundant paths, the construction feedback analysis result includes material consumption speed, project progress, deviation reason analysis and strategy adjustment scheme, the risk assessment result includes risk level record, influence range evaluation and potential loss prediction, and the dynamic management scheme includes implementation of risk mitigation measures and update schedule of construction plan.

[0014] As a further scheme of the application, the geographic information system is used to collect topographic and climatic data of high-altitude water conservancy projects, and the project base data set is generated by analyzing the geological characteristics and hydrological conditions of the project coverage area through geographic data.

[0015] S101: Adopting geographic information system, selecting high altitude area to scan and collect terrain data, setting climate monitoring site, recording climate data of air temperature and wind speed in real time, obtaining terrain climate initialization data set;

[0016] S102: According to the terrain climate initialization data set, adjusting the spatial resolution of terrain data, setting the climate data sampling frequency to once per hour, screening and verifying the data quality, generating the processed climate data set;

[0017] S103: Using the processed climate data set, integrating existing geological and hydrological data, analyzing the spatial correspondence of terrain and hydrological characteristics in high altitude area, recording potential water source area and geological stability area, generating project basic data set.

[0018] As a further scheme of the application, by the project basic data set, the road accessibility and construction area safety of high altitude are analyzed, the path weight is adjusted according to real-time weather information, the resource allocation logic is updated, and the adjusted network model is obtained. The steps are as follows:

[0019] S201: Based on the project basic data set, the key road nodes of high altitude water conservancy project are identified, the slope and curvature between differentiated nodes are recorded, and the width and material of the road are classified and evaluated, and the road accessibility analysis table is generated;

[0020] S202: Using the road accessibility analysis table, using linear weighted sum method, adjusting the driving safety index of differentiated road section according to real-time weather information, recording rainfall and snowfall, and recalibrating risk area, generating path weight adjustment table;

[0021] S203: According to the path weight adjustment table, the flow demand of personnel and materials is evaluated, the construction vehicles and material storage sites are optimized, the construction equipment is reasonably deployed, and the adjusted network model is generated.

[0022] As a further scheme of the application, the formula of the linear weighted sum method is as follows:

[0023]

[0024] Wherein, W i is the weight of road section i, S i is the safety baseline index based on road accessibility data, e is the base of natural logarithm, R i is the risk coefficient adjusted according to rainfall and snowfall, T i is the road traffic flow index, V i is the sight distance factor, a i , b i and c i are adjustment coefficients.

[0025] As a further scheme of the present application, based on the adjusted network model, the step of identifying redundant paths in resource allocation, configuring water conservancy construction equipment and material flow, and generating a resource optimization configuration table using BIM digital collaborative management is specifically:

[0026] S301: Using the adjusted network model, using BIM digital collaborative management, analyzing construction routes and resource flow charts, calibrating construction paths and detecting reused routes, verifying redundant paths that need to be optimized and deleted, and generating a redundant path identification chart;

[0027] S302: According to the redundant path identification chart, adjust the flow of key resources, including cement and steel, optimize storage location and use time, and reconfigure the layout and scheduling plan of construction equipment, and generate a resource flow optimization record;

[0028] S303: Based on the resource flow optimization record, integrate data in water conservancy construction and develop resource allocation schemes, optimize construction efficiency by adjusting the configuration plan and construction queue of multiple resources and equipment, and generate a resource optimization configuration table.

[0029] As a further scheme of the present application, using the resource optimization configuration table, monitoring real-time data of the construction site, recording material consumption speed and project progress, and analyzing deviation reasons, adjusting construction strategies, and generating construction feedback analysis results, the steps are specifically:

[0030] S401: Based on the resource optimization configuration table, deploy monitoring equipment at key construction nodes, collect material usage and project progress in real time, and synchronize information to the central monitoring, and generate real-time monitoring data records;

[0031] S402: According to the real-time monitoring data records, use data comparison and analysis to identify the stage of deviation between material consumption speed and expected consumption, and identify the reasons for the deviation, including logistics delay and construction efficiency problems, and generate deviation reason analysis results;

[0032] S403: Using the deviation reason analysis results, adjust the construction strategy of high-altitude water conservancy projects, optimize the construction process by adjusting the material supply speed and rearranging the working hours of workers, and verify the optimization of resource utilization, and generate construction feedback analysis results.

[0033] As a further scheme of the present application, using the construction feedback analysis results, through BIM digital collaborative management, identifying risk points in construction, and recording risk levels, evaluating the scope of influence and potential losses, and generating risk assessment results, the steps are specifically:

[0034] S501: Based on the construction feedback analysis results, through BIM digital collaborative management, integrate construction data, and identify risks, including safety hazards and construction mistakes, mark the location and nature of differentiated risk points, and generate risk point identification records;

[0035] S502: Use the risk point identification records to analyze the potential impact of each risk point, estimate and predict the loss degree using similar cases, identify the risk level, prioritize, and generate a risk level evaluation table;

[0036] S503: According to the risk level evaluation table, evaluate the affected range of high-altitude water conservancy projects, refer to the potential cost of personnel safety and project delay, and develop response strategies to generate risk assessment results.

[0037] As a further scheme of the present application, according to the risk assessment results, analyze the risk change trend in construction, combine real-time monitoring data, adjust construction plan and resource allocation strategy, implement risk mitigation measures, and obtain the steps of dynamic management scheme:

[0038] S601: Based on the risk assessment results, analyze the change trend of differentiated risk points, update the risk state using real-time monitoring data, identify the change of risk factor number, and generate risk trend analysis results;

[0039] S602: Use the risk trend analysis results to re-evaluate the construction plan and resource allocation, adjust the allocation of manpower and materials according to the priority of risk level, optimize the construction schedule and material use strategy, and generate a construction plan adjustment scheme;

[0040] S603: Use the construction plan adjustment scheme to implement targeted risk mitigation measures, optimize safety training and adjust work area layout, check construction safety and progress, and generate a dynamic management scheme.

[0041] A BIM-based digital collaborative management system for executing the above-mentioned BIM-based digital collaborative management method, the system comprising:

[0042] The terrain analysis module uses geographic information system to collect terrain and climate data in high-altitude areas, records geological characteristics and hydrological conditions, and generates basic geographic data sets;

[0043] The road analysis module uses geological characteristic analysis and real-time weather information adjustment based on the basic geographic data set to calculate the accessibility of roads and safety indicators of construction areas, and generates path safety evaluation results;

[0044] The resource allocation module optimizes construction resource allocation using BIM digital technology based on the path safety assessment results, avoids redundant paths, and adjusts the flow of construction equipment and materials, generating a resource optimization configuration table;

[0045] The risk management module monitors real-time data of the construction site, identifies risk points, records risk levels, assesses the impact range and potential loss, and generates a risk assessment result based on the resource optimization configuration table;

[0046] The dynamic adjustment module uses the risk assessment result in combination with real-time monitoring data to analyze the risk change trend in construction, optimizes safety training and adjusts the work area layout, and generates a dynamic management scheme.

[0047] Compared with the prior art, the advantages and positive effects of the present application are:

[0048] In the present application, by integrating key data of high-altitude water conservancy projects into the BIM (Building Information Modeling) platform, efficient management of the entire project life cycle is achieved. Not only the accuracy and access speed of data are improved, but also the project team can make construction plans and resource allocation with unprecedented precision. By monitoring the construction site with real-time data, timely recording and analyzing material consumption and project progress, project management becomes more responsive, and construction strategies can be adjusted in real time to respond to changes in actual conditions. The digital collaborative management of BIM also strengthens the risk management process, systematically identifies, records and assesses risk points in construction, improving the efficiency of risk prevention and response. High-altitude water conservancy projects can maintain high safety standards under extreme conditions, optimize project execution and resource utilization efficiency, and significantly improve the overall success rate and sustainability of the project. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The workflow diagram of the present application;

[0050] Figure 2 The S1 refinement flowchart of the present application;

[0051] Figure 3 The S2 refinement flowchart of the present application;

[0052] Figure 4 The S3 refinement flowchart of the present application;

[0053] Figure 5 The S4 refinement flowchart of the present application;

[0054] Figure 6 The S5 refinement flowchart of the present application;

[0055] Figure 7 The S6 refinement flowchart of the present application;

[0056] Figure 8 System flowchart of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0058] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0059] Referring to Figure 1 The present application provides a technical solution, a digital collaborative management method based on BIM, comprising the following steps:

[0060] S1: using a geographic information system, collecting topographic, climatic data and original project plans of high-altitude water conservancy projects, analyzing the geological characteristics and hydrological conditions of the project coverage area through geographic data, and generating a project base data set;

[0061] S2: analyzing the road accessibility and construction area safety of high-altitude areas through the project base data set, adjusting the path weight according to real-time weather information, updating the resource allocation logic, and obtaining an adjusted network model;

[0062] S3: based on the adjusted network model, using BIM digital collaborative management to identify redundant paths in resource allocation, configure water conservancy construction equipment and material flow, optimize storage and transportation plans of materials, and generate a resource optimization configuration table;

[0063] S4: using the resource optimization configuration table, monitoring real-time data of the construction site, recording material consumption speed and project progress, and analyzing deviation reasons, adjusting construction strategies, and generating construction feedback analysis results;

[0064] S5: using the construction feedback analysis results, through BIM digital collaborative management, analyzing risk points in construction, including construction delay and resource shortage, checking the risk level through probability analysis, evaluating the influence range and potential loss, and generating risk assessment results;

[0065] S6: Based on the risk assessment results, analyze the changes in risks during the construction of the water conservancy project, adjust the construction plan and resource allocation strategy in combination with real-time monitoring data, implement risk mitigation measures, and obtain a dynamic management scheme.

[0066] The project base data set includes topographic height maps, rainfall distribution maps, and groundwater flow conditions in high-altitude areas. The adjusted network model includes construction path weight adjustment, resource allocation scheme, and construction safety area mapping. The resource optimization allocation table includes optimized deployment of construction equipment, adjustment scheme of material flow direction, and identification result of redundant path. The construction feedback analysis result includes material consumption speed, project progress, deviation cause analysis, and strategy adjustment scheme. The risk assessment result includes risk level record, impact range evaluation, and potential loss prediction. The dynamic management scheme includes implementation of risk mitigation measures and update schedule of the construction plan.

[0067] Please refer to Figure 2 , using geographic information systems, collect topographic and climatic data of high-altitude water conservancy projects, analyze geological characteristics and hydrological conditions of the project coverage area through geographic data, and generate the steps of the project base data set as follows:

[0068] S101: Use geographic information systems to scan and collect topographic data in high-altitude areas, and set up climate monitoring stations to record real-time climate data such as temperature and wind speed. The execution process of the topographic and climatic initialization data set is as follows:

[0069] Use geographic information systems to scan and collect topographic data in high-altitude areas. The specific operation includes selecting a specific area to set the scanning range and parameters. Use high-precision scanning equipment to execute at least 100 data points per square kilometer, including the height, slope, and landform type of the terrain. At the same time, set up climate monitoring stations in the area, including at least a thermometer and an anemometer, ensuring at least one monitoring point per 10 square kilometers. Real-time record of temperature and wind speed climate data, data updated every 15 minutes, get the topographic and climatic initialization data set, using the formula:

[0070]

[0071] Where D init represents the climate initialization data index, T a,j and V a,j represent the temperature and wind speed values of the a-th inspiration point, k a,j is the flexibility weighting coefficient of the inspiration point, p a,j is the solid weighting coefficient of the inspiration point, and g a is the adjustment coefficient.

[0072] The formula aims to construct an index for evaluating the quality of initial climate data collected in high altitude areas, called the Climate Initialization Data Index (D init ), whose derivation process is based on the combined analysis of multiple geographical and meteorological factors. The overall idea is to integrate terrain scanning information with real-time data such as temperature and wind speed at inspiration points (monitoring points), and to perform weighted adjustments based on the dynamic and static importance of each inspiration point to form a comprehensive index that reflects the value of the candidate monitoring point data;

[0073] Based on high-precision geographic information collection, the data of the monitoring points include temperature T a,j With wind speed V a,j , are two key meteorological variables that determine the representativeness of the initial climate data. In order to uniformly process the contributions of the two variables, they are added together to form a temperature wind superposition term (T a,j +V a,j ), the differences in spatial distribution, terrain complexity and response capabilities of different monitoring points need to be weighted by the flexibility coefficient k a,j To adjust, multiply the temperature wind term by k a,j , to highlight monitoring points with stronger response capabilities in certain complex terrain areas;

[0074] In order to balance the fixed influence of monitoring points, a fixed weighted term p is introduced a,j ,The contribution of inspiration points is evaluated from the perspectives of stability, long-term data representativeness, etc., and is subtracted from the previous term during the processing, reflecting the inhibitory effect of the fixed weight of inspiration points on flexibility regulation, which reflects the model's preference for assigning high initial values ​​to monitoring points with "stronger dynamic capabilities and weaker fixed attributes";

[0075] In order to prevent the impact of the magnitude difference of different indicators, the normalization adjustment factor g is introduced. a , divide the whole by this factor to keep the result within a reasonable scale range. In order to prevent negative or zero values ​​from causing abnormal subsequent calculations, add 1 to the result of the entire fraction to form a complete exponential structure;

[0076] The formula constructs a climate initialization indicator that integrates real-time meteorological data and point characteristics through flexible adjustment of the temperature-wind composite term, suppression adjustment of fixed weights, and normalization and offset processing. This structure takes into account both the physical properties of the data and the spatial and point differences, and is a typical application of multi-factor fusion evaluation.

[0077] S102: Initialize the dataset based on the terrain and climate, adjust the spatial resolution of the terrain data, set the climate data sampling frequency to once per hour, screen and verify the data quality, and generate the processed climate dataset. The execution process is as follows;

[0078] According to the terrain climate initialization data set, the spatial resolution of the terrain data is adjusted, the original scanning data is spatially interpolated using GIS software to improve the resolution of the data in complex terrain areas, and the target is set to at least 1000 data points per square kilometer. The sampling frequency of climate data is set to once per hour, and the sampling of the monitoring site is programmed to adjust to ensure the consistency and timeliness of data collection. The data quality is screened, including temperature and wind speed data anomaly detection and elimination, to generate the processed climate data set, using the formula:

[0079]

[0080] Where D processed represents the processed climate data set, clean(T b,i ) and clean(V b,i ) represent the cleaned bth temperature and wind speed data, respectively, and n b represents the total number of cleaned data points.

[0081] The formula aims to construct an index representing the quality of the processed climate data set, called D processed , which measures the overall performance of meteorological data in spatial accuracy and data stability after sampling, interpolation and cleaning. The derivation process mainly considers the following key steps and technical logic:

[0082] When collecting climate data in complex terrain areas, the spatial distribution density of data points must be high enough to ensure that the details of the regional characteristics are accurately captured. Therefore, GIS software is used to spatially interpolate the original terrain scanning data to fill in the gaps in low-density data, thereby improving spatial resolution. The target is set to include at least 1000 sampling points per square kilometer, which requires high quality of data post-processing;

[0083] In the time dimension, meteorological data is required to be collected at a frequency of once per hour, and the sampling device is uniformly programmed and frequency controlled to ensure the consistency and synchronization of data in the time dimension, which is the premise of data comparability and subsequent processing efficiency;

[0084] In the data quality screening stage, data quality directly affects the scientific nature of subsequent analysis, so temperature and wind speed data collected need to be detected and removed for abnormal values. The data cleaning functions clean(T b,i ) and clean(V b,i ) are introduced in the formula, which represent the results of temperature and wind speed data in the bth group of climate data after abnormal value correction. Cleaning operations can include extreme value removal, outlier replacement or interpolation to ensure the reasonableness of input data;

[0085] In order to measure the overall data set, the arithmetic mean is used to average all processed data samples, and an index D representing the overall quality of the processed climate data set is constructed processed The mathematical expression of the index is:

[0086]

[0087] Where n b is the total number of data samples, and by summing and normalizing, the index is not affected by the sample size, facilitating horizontal comparison in different regions and different time windows;

[0088] The formula integrates the technical requirements of spatial resolution interpolation, time synchronization sampling, and outlier removal, reflecting the important role of data preprocessing in climate modeling. The index not only reflects the spatial integrity of the data, but also embodies its stability and cleanliness, providing a solid foundation for subsequent modeling and analysis.

[0089] S103: Using the processed climate data set, integrate existing geological and hydrological data, and analyze the topography and hydrological characteristics of high-altitude areas. Record potential water source areas and geological stability areas, and generate the project basic data set as follows:

[0090] Using the processed climate data set, integrate existing geological and hydrological data, and spatially correspond geological data and hydrological data with climate data. Use GIS tools to match data points according to coordinates. Analyze the topography and hydrological characteristics of high-altitude areas, and identify potential water source areas. At the same time, use the geological stability index to evaluate the geological stability of the area, record the geological stability area, and generate the project basic data set, using the formula:

[0091]

[0092] Where D base represents the basic geological sediment data index, G c,i , W c,i and H c,i represent the address data, hydrological data and data elevation value of the cth region, s c,i is the standardization coefficient of the region, m c,i is the adjustment coefficient, and n c represents the total number of regions considering geological and hydrological data;

[0093] The formula aims to construct a comprehensive index D base, which is used to evaluate the geological sediment stability of the region, support subsequent water source identification and geological zoning analysis. The core idea is to integrate geological data, hydrological data and climate data, and combine spatial matching, normalization and weighting processing to evaluate the regional geological stability from multiple dimensions, and obtain the geological foundation data index;

[0094] The geological and hydrological characteristics of each region are spatially integrated. The geological and hydrological data are spatially matched with the processed climate data according to the coordinates by using GIS tools, ensuring that different types of data can be superimposed and analyzed in a unified spatial pattern. The topography and hydrological characteristics of high-altitude areas are analyzed in depth to identify potential water source areas and combine with the geological stability index to demarcate stable areas.

[0095] For each region c (a total of n c regions), its stability evaluation depends on three aspects: geological index G c,i , hydrological index W c,i , and data elevation value H c,i , where G c,i and W c,i reflect the direct influence of geological-hydrological factors such as sediment type, water flow speed, and hydrological recharge in the region. The sum of the two constitutes the preliminary influence score.

[0096] To adjust the differences between regional index values, a standardization coefficient s c,i is introduced to weaken the deviation of excessively large or small indicators on the result. This item is subtracted from the overall influence item to form the corrected influence expression G c,i +W c,i ―s c,i , the data elevation value H c,i and the control coefficient m c,i are introduced, respectively representing the elevation influence and data quality adjustment factor of the region. After multiplication, add 1 to form the complete denominator structure H c,i ·m c,i +1. Such design can alleviate the excessive influence of high-altitude areas or incomplete data areas on the overall score.

[0097] The corrected scores of all regional index values are combined in the form of average to obtain the overall foundation data sediment index.

[0098] The formula takes into account the weighted influence (numerator), normalization adjustment (denominator), and average aggregation (external summation / normalization) in structure, reflecting the direct effect of geological and hydrological characteristics on regional sediment stability, and reasonably introducing standardization and adjustment mechanisms. It is a complex evaluation model suitable for complex terrain and multi-source data analysis. This index can not only be used for geological zoning, but also can provide decision support for basic engineering.

[0099] Please refer toFigure 3 The steps of adjusting the path weight according to real-time meteorological information, updating the resource allocation logic, and obtaining the adjusted network model through the project basic data set are as follows:

[0100] S201: Based on the project basic data set, identify the key road nodes of the high-altitude water conservancy project, record the slope and curvature between the differentiated nodes, and classify and evaluate the width and material of the road, and the execution process of generating the road accessibility analysis table is as follows:

[0101] Based on the project basic data set, identify the key road nodes of the high-altitude water conservancy project, including using GIS tools to determine the key geographic coordinate points, which represent the intersection or turning points of the road. Record the slope and curvature between each node, which involves detailed mathematical analysis of the terrain data to determine the actual physical properties of the road. At the same time, classify and evaluate the width and material of the road, use quantitative scoring methods to evaluate the durability and adaptability of each material, generate a road accessibility analysis table, and the formula is as follows:

[0102]

[0103] where D access represents the road accessibility score, P d,i , C d,i , W d,i and M d,i represent the slope, curvature, width and material scores between the d d th node, and n access represents the total number of nodes;

[0104] The formula is used to derive the road accessibility evaluation index D access , which aims to quantify the traffic accessibility of key road nodes in high-altitude water conservancy projects. This evaluation model considers the geometric properties, geological conditions and material durability of the road, and through the analysis of key geographic coordinate points (such as road intersection points or turning points), it quantitatively evaluates the road traffic difficulty from multiple dimensions;

[0105] In the formula, p d,i and c d,i represent the slope and curvature evaluation values of the road node, respectively reflecting the changes in the vertical and horizontal directions of the road. Large slope or sharp curvature changes will have a serious impact on traffic, so these two items constitute the basic indicators of road traffic difficulty. Adding these two items forms the numerator, which represents the total amount of geometric deformation affecting accessibility;

[0106] The denominator part introduces two key adjustment factors: W d,i and M d,i, respectively represent the evaluation value of road node width and material durability, the greater the width, the stronger the road capacity; the more stable and durable the material, the better its adaptability in harsh environments, therefore, multiplying the width and material index can be understood as a weighted amplification term of road capacity, which is used to balance the adverse effects brought by geometric deformation;

[0107] The entire formula is summed up for all key nodes, and the total number of nodes n d is normalized to obtain the average road accessibility index;

[0108] The structure has obvious practical significance: when the road slope and curvature are large, the numerator increases, indicating that the difficulty of passing increases; while the road is wide enough and the material is good, the denominator expands, which can reduce the weight influence of the passing difficulty, making the overall evaluation more reasonable;

[0109] This formula has high applicability in engineering practice, especially for road construction projects in complex terrain areas such as plateaus and mountains. Through systematic evaluation of road geometric properties and physical structure, it can help decision-makers identify traffic bottleneck sections, optimize water conservancy construction paths, and improve construction efficiency and traffic safety. In summary, this formula establishes a comprehensive evaluation model, providing a scientific and quantifiable basis for road passability evaluation.

[0110] S202: Adopt the road accessibility analysis table, use the linear weighted sum method to adjust the driving safety index of the differentiated road section according to the real-time meteorological information, record the rainfall and snowfall, and recalibrate the risk area, the execution process of the path weight adjustment table is as follows:

[0111] The road accessibility analysis table is used, and the linear weighted sum method is used to adjust the driving safety index of the differentiated road section according to the real-time meteorological information. Specifically, it includes real-time recording of rainfall and snowfall, and recalibration of risk areas according to wind speed. The safety index of each road section is calculated by linear weighting according to meteorological parameters, and the weight reflects the driving risk under different weather conditions, and a path weight adjustment table is generated.

[0112] The formula of the linear weighted sum method is as follows:

[0113]

[0114] Where, W i is the weight of road section i, S i is the safety baseline index based on road accessibility data, e is the base of natural logarithm, R i is the risk coefficient adjusted according to rainfall and snowfall, T i is the road traffic flow index, V i is the sight distance factor, a i , bi and c i is the adjustment factor.

[0115] The execution process is as follows:

[0116] S i Obtained from the road accessibility analysis table, considering factors such as road width, slope, and material, R i Calculated based on real-time weather station data, specifically considering the impact of rainfall and snow on road conditions, T i Calculated from the data provided by the traffic monitoring system, considering the traffic density during peak and non-peak periods, V i The visibility is evaluated based on the visibility measured by environmental monitoring equipment. The risk factor is increased under low visibility conditions. i 、b i and c i The value of is obtained through historical data analysis and regression testing to ensure that the prediction accuracy of the model is consistent with the actual situation;

[0117] The derivation process is as follows:

[0118] The weights of road segments are modeled using a linear weighted sum approach. The essence of this approach is to comprehensively consider multiple factors related to traffic safety, and to perform reasonable normalization and adjustments to reflect the impact of roads on driving safety under different weather and traffic conditions.

[0119] The weight of a road segment should be related to its traffic safety risk. Traffic safety risk can be composed of two main factors: one is the basic safety level of the road itself, and the other is the risk change affected by external environmental changes (such as meteorological conditions). Therefore, a basic safety index S is used. i , which represents the inherent risk of the road without external influences; and then introduces a modified risk factor R that is closely related to the weather. i , represents the impact of weather changes such as rain and snow on road conditions;

[0120] In order to unify the two factors into a unified model, the adjustment coefficient a is introduced i and b i , to S i and R i The importance of the weighted processing is carried out to construct the basic weighting factor: a i ·S i +b i ·R i ;

[0121] It is necessary to consider the amplification effect of the actual traffic flow on the road on the risk. Even if a road itself is relatively safe, if the density of vehicles passing through it is extremely high, the risk will also increase. Therefore, the traffic volume coefficient T is introduced. i and adjustment coefficient Ci , both multiplied and square rooted, to construct a dynamic adjustment factor Amplify or reduce the above weighting results;

[0122] Considering the influence of distance on risk perception, a distance factor V is introduced i , in the model It can avoid the risk index from being amplified meaningless due to the excessive path distance, so as to realize more stable and reasonable normalization;

[0123] The formula realizes the multi-dimensional modeling of road section traffic risk through the combination of basic safety index, weather risk correction factor, traffic flow adjustment term and distance suppression function, and has strong practical applicability and logical rationality.

[0124] S203: According to the path weight adjustment table, evaluate the flow demand of personnel and materials, optimize the arrangement of construction vehicles and material storage sites, check the reasonable deployment of construction equipment, and generate the execution process of the adjusted network model as follows:

[0125] According to the path weight adjustment table, evaluate the flow demand of personnel and materials, which involves calculating the optimal path to reduce the consumption of resources and time. Optimize the arrangement of construction vehicles and material storage sites, taking into account the distance, cost and safety factors to ensure construction efficiency. Check the reasonable deployment of construction equipment, including the analysis of the position, state and usage frequency of machinery to ensure the project is completed on time, generate the adjusted network model, and use the formula:

[0126]

[0127] Where, D network is the network efficiency index, E g,i is the effective output of the gth period, P g,i is the performance index, T g,i is the input time, ΔT g,i is the time adjustment amount, C g,i is the cost coefficient, ΔC g,i is the cost adjustment amount, K g,i is the adjustment coefficient, and n g is the number of evaluation groups;

[0128] The formula constructs a comprehensive index for evaluating the running efficiency of a system or engineering network, called network efficiency index D network , whose derivation process is based on the relationship between system resource input and output, considering multiple key factors such as time, cost, performance, etc., and introducing dynamic adjustment coefficient to ensure that the index has good responsiveness and universality under different running conditions;

[0129] The essence of efficiency is the ratio of output to input, in this model, the output part is composed of two variables: the effective output E g,i and performance index P g,i , where E g,i represents the useful output generated by the system in a certain period, which can cover quantitative achievements such as data processing volume, task completion number or energy utilization; P g,i measures the quality or performance level of these outputs, and their product is then square rooted to form an enhanced output term, indicating that the system is more efficient under the condition of "high output and high quality";

[0130] The input considers three aspects of resource consumption: time, cost and adjustment difficulty. Specifically, T g,i represents the input time, and ΔT g,i is the additional adjustment time consumed in the actual operation process; the sum of the two reflects the time cost of operation, and the cost dimension is represented by C g,i and its adjustment amount ΔC g,i , covering the original input and additional cost, introducing a control factor K g,i as a correction term for the complexity of system scheduling, optimization, etc. This product term represents the comprehensive input of the system operation;

[0131] After adding up the efficiency values of all evaluation groups and dividing by the total number n g , the average network efficiency index is obtained;

[0132] This formula has high universality and is suitable for efficiency analysis of various engineering systems, information networks or management processes. It measures "output benefit" through the numerator and depicts "resource input and adjustment complexity" through the denominator, which is a composite evaluation method that meets the needs of real-world applications. This model not only reflects the current efficiency level, but also has good scalability and sensitivity, which is of great significance for identifying bottlenecks and optimizing allocation.

[0133] Please refer to Figure 4 , based on the adjusted network model, using BIM digital collaborative management, identifying redundant paths in resource allocation, configuring water conservancy engineering construction equipment and material flow, and generating resource optimization allocation table steps are as follows:

[0134] S301: Using the adjusted network model, using BIM digital collaborative management, analyzing construction route and resource flow chart, marking construction path and detecting repeated use of route, verifying the need to optimize and delete redundant paths, and generating redundant path identification chart execution process as follows:

[0135] Using the adjusted network model, BIM digital collaborative management is used to analyze the construction route and resource flow chart, including using BIM software to simulate the route, calibrate each construction path, and detect the reuse of the route within the project cycle. Identify and analyze the intersection and parallel section of the path in order to identify inefficient or overlapping parts. For the identified redundant path, perform a verification process to confirm the parts that need to be optimized and deleted, and generate a redundant path identification chart.

[0136] S302: According to the redundant path identification chart, adjust the flow of key resources, including cement and steel, optimize the storage location and use time, and reconfigure the layout and scheduling plan of construction equipment, and the execution process of resource flow optimization record is as follows;

[0137] According to the redundant path identification chart, adjust the flow of key resources, including cement and steel, which involves re-evaluating storage locations and use times to reduce transportation costs and time delays. Use GIS and BIM tools to analyze the shortest and most economical paths for resources to reach the construction site, and reconfigure the layout and scheduling plan of construction equipment according to real-time construction progress, and generate resource flow optimization records.

[0138] S303: Based on the resource flow optimization record, integrate data in water conservancy construction and develop resource allocation scheme, optimize construction efficiency by adjusting the configuration plan and construction queue of multiple types of resources and equipment, and the execution process of resource optimization configuration table is as follows;

[0139] Based on the resource flow optimization record, integrate data in water conservancy construction and develop resource allocation scheme, including adjusting the configuration plan and construction queue of multiple types of resources and equipment to optimize construction efficiency. Consider the specific needs of the construction site, such as equipment usage frequency and resource consumption rate, and dynamically schedule and configure resources and equipment. By reducing waiting time and improving resource utilization efficiency, generate resource optimization configuration table, using the formula:

[0140]

[0141] where D optimize is the optimized performance index, E l,i represents the efficiency value of the lth period, U l,i represents the utilization rate, R l,i represents the resource consumption, V l,i represents the coefficient of variation of the ith period, k l,i is the cycle weight, n l represents the number of cycles evaluated.

[0142] The formula aims to build a resource optimization configuration performance index D optimize, which is used to evaluate the efficiency of resource and equipment allocation in water conservancy construction. The derivation is based on the resource flow record on the construction site. Through the integration of equipment utilization efficiency, resource utilization rate, consumption and volatility, the pros and cons of resource allocation in each period are quantitatively analyzed. From the perspectives of systematicness, multidimensionality and dynamic adjustment, a scientific resource optimization evaluation model is formed.

[0143] The output benefit part is composed of two key indicators: efficiency value E l,i and utilization rate U l,i , where E l,i reflects the effective output of resource use or equipment operation in the lth period, and U l,i represents the degree of full allocation and utilization of resources per unit time. After squaring the two items and adding them together, the comprehensive degree of resource use intensity and density is represented. The square root is used to restore the original dimension to form the initial performance item.

[0144] The combination of resource consumption and uncertainty factors is composed of three parts in the denominator: resource consumption rate R l,i , coefficient of variation V l,i , and volatility correction term The greater the resource consumption, the stronger the negative impact on performance, so it is placed in the denominator. The coefficient of variation measures the stability of resource use in each period. The larger the value, the more volatile it is, which means that the plan is difficult to implement and reduces performance, which is used to adjust the impact of volatility flexibly to make the model more stable.

[0145] The entire ratio is subjected to three square root operations to adjust the impact of extreme values on the model. Multiply by the period weight k l,i to highlight the importance of key periods (such as construction peak periods) in performance evaluation. The evaluation values of all periods are summed and averaged to obtain the comprehensive performance indicator.

[0146] The structure of this formula is clear and the logic is rigorous, taking into account efficiency, resource use intensity and uncertainty. It is a scientific basis for measuring the effectiveness of resource optimization allocation, especially suitable for complex engineering environments with resource shortages and tight schedules. It helps decision-makers adjust construction schedules and resource allocation strategies based on actual evaluation results to improve overall construction efficiency and resource use rationality.

[0147] Please refer to Figure 5 , which uses the resource optimization allocation table to monitor real-time data on the construction site, records material consumption speed and project progress, analyzes the causes of deviations, adjusts construction strategies, and generates construction feedback analysis results. The steps are as follows:

[0148] S401: Based on the resource optimization configuration table, deploy monitoring equipment at key construction nodes, collect material usage and project progress in real time, and synchronize information to the central monitoring system to generate real-time monitoring data records. The execution process is as follows:

[0149] Based on the resource optimization configuration table, deploy monitoring equipment at key construction nodes, including selecting key nodes such as important transfer points, concrete pouring areas and key structural parts. Install sensors and monitoring equipment at the nodes to collect material usage and project progress in real time. Data is synchronized in real time to the central monitoring system through a wireless network, so that the project management team can access and analyze data in real time to generate real-time monitoring data records using the formula:

[0150]

[0151] Where D monitor is the comprehensive score of the monitoring data record, M m,i is the measurement value of the a-th monitoring node, P m,i is the predicted value of the a-th monitoring node, β and α are the weight coefficients of the measurement and prediction values, L m,i is the noise level of the a-th monitoring node, is a small positive value to avoid division by zero, γ m,i is the adjustment index, n a is the total number of monitoring points.

[0152] The formula is used to construct the comprehensive monitoring evaluation index D monitor , which aims to fuse and analyze the measurement and prediction information of each monitoring node, and combine the noise interference level and weight factor to measure the performance quality of the overall monitoring system. The derivation process is based on the idea of multi-factor combination weighting, while introducing adjustment parameters and normalization processing to enhance the stability and practicality of the model.

[0153] In the monitoring system, the measurement value M m,i and the predicted value P m,i of the node are two key data sources. The measurement value represents the real-time data obtained by the sensor or device, while the predicted value represents the system's speculation of the target state. Since both are important indicators reflecting the system's running state, their information is fused by weighted superposition β·M m,i +α·P m,i , where β and α are the corresponding weight parameters. The purpose of weight setting is to differentiate the contribution of the two data according to data reliability or business priority.

[0154] To avoid signal noise interference with the index, the monitoring node noise level L m,iAs the denominator, a small positive value is added to prevent the denominator from being zero. It is reasonable to consider the noise level in the denominator: the larger the noise, the more unstable the node data, so the node's contribution to the overall index should be weakened;

[0155] To enhance the flexibility and responsiveness of the formula, an adjustment index γ is introduced m,i Exponentially amplify or reduce the results of each monitoring point to emphasize the influence of certain nodes (such as critical locations or risk areas) on the overall index. The results are processed by averaging the monitoring points to ensure the comparability of the index under different system scales.

[0156] The formula structure is clear and reasonable, which can comprehensively reflect the data quality of the monitoring system, and can introduce weights and adjustment coefficients according to local conditions, with good adaptability and promotional value. It is suitable for intelligent sensing, environmental monitoring, water conservancy project state evaluation and other scenes, and helps to improve the accuracy of data analysis and the scientificity of decision-making.

[0157] S402: According to the real-time monitoring data record, use data comparison and analysis to identify the stage of deviation between the material consumption speed and the expected consumption, and identify the deviation reasons, including logistics delay and construction efficiency problems. The execution process of deviation reason analysis result is as follows:

[0158] According to the real-time monitoring data record, use data comparison and analysis to identify the stage of deviation between the material consumption speed and the expected consumption. Analyze the comparison between the material consumption record and the predetermined plan, identify the time period when the consumption speed exceeds or is lower than the expected value, and identify the deviation reasons through further data mining, including logistics delay and construction efficiency problems. The process involves data clustering analysis of different time periods and deviation evaluation from the planned value, and generates deviation reason analysis result, using the formula:

[0159]

[0160] Where D deviation is the overall deviation analysis result, S o,i is the measured value, E o,i is the expected value, n d is the total number of data points, δ and γ are small amounts added to the measured value and the expected value, and α and β are adjustment coefficients;

[0161] The formula is used to construct a deviation analysis model D deviation , which aims to evaluate the difference between the actual data and the predicted plan in the material consumption process, and then provide quantitative basis for identifying construction efficiency problems, material management loopholes or progress control errors. Its derivation logic is based on the comparison and analysis of "measured value and expected value", and through logarithmic transformation, normalization processing and the introduction of adjustment parameters, the sensitivity and robustness of deviation identification are improved;

[0162] The deviation source in material consumption process mainly reflects the gap between the actual consumption value S o,i and the expected planned value E o,i , which can reflect the trend directly, but it is difficult to capture the relative difference in proportion in the case of large data fluctuations or significant magnitude changes. Therefore, the formula uses a logarithmic function, log(S o,i +δ) and log(E o,i +γ), to reflect the deviation degree of consumption trend through logarithmic difference, and introduces small constants δ and γ to avoid mathematical anomalies caused by zero values.

[0163] A standardization and adjustment term is constructed in the denominator part, which is in the form of α+β×(S o,i -E o,i ), where α is the basic adjustment coefficient, providing the basic deviation tolerance, and β controls the influence strength of the actual and predicted difference on the deviation evaluation. The existence of this term makes the model adaptive to the actual characteristics of different dimensions and projects, improving the comparability across projects or different stages.

[0164] The deviation amount of data points is summed and divided by the total number of data points n d to obtain the global deviation analysis result.

[0165] This formula not only reflects the absolute difference, but also introduces the relative difference and proportional difference measurement mechanism, which is an analysis tool that considers both accuracy and scale adaptability. It is suitable for multiple scenarios such as construction projects, supply chain management, budget execution, etc., and can effectively identify abnormal trends, excessive use or delayed distribution in material consumption, providing a scientific basis for resource optimization and process improvement.

[0166] S403: Use the deviation cause analysis result to adjust the construction strategy of high-altitude water conservancy projects. Adjust the material supply speed and rearrange the working hours of workers to optimize the construction process and check the optimization of resource utilization. The execution process of the construction feedback analysis result is as follows:

[0167] Use the deviation cause analysis result to adjust the construction strategy of high-altitude water conservancy projects, including adjusting the material supply speed to ensure synchronization between material supply and demand, and rearranging the working hours of workers to optimize the construction process. The adjustment is based on the analysis result, aiming to reduce time and resource waste and improve construction efficiency. At the same time, check the optimization of resource utilization, generate construction feedback analysis results through continuous monitoring and adjustment, and use the formula:

[0168]

[0169] Where D feedbackR is the feedback efficiency index, R p,i E is the actual feedback response of the i th period, E p,i T is the expected feedback response of the p th period, T p,i σ is the adjustment factor, V p,i V is the feedback volatility of the p th period, V p,i λ is the weight coefficient, n p n is the number of evaluation periods;

[0170] The formula is used to derive the efficiency index D in construction feedback analysis feedback The core purpose is to evaluate the deviation degree between the actual feedback and the expected feedback in each construction period, and to comprehensively score the response efficiency of the feedback system by combining the response time and the feedback volatility factor. This formula can not only be used for feedback quality monitoring, but also provides important decision basis for construction resource scheduling and process optimization;

[0171] The main factor for evaluating feedback deviation is the difference between the actual feedback value R p,i and the expected feedback value E p,i The difference between the two reflects the degree of deviation from the expectation, so the absolute value is expressed in the numerator part

[0172] |R p,i -E p,i |, and then the square root is taken to alleviate the influence of extreme values and ensure symmetric response to positive and negative deviations;

[0173] The denominator part is the measurement of feedback response efficiency, which includes two key factors: feedback time T p,i and feedback volatility V p,i The former represents the time spent to achieve the expected feedback, and the shorter the time, the faster the response and the better the feedback effect. The latter reflects the stability of the feedback process, and the greater the fluctuation, the less consistent the feedback process, which affects trust and reliability. Therefore, the adjustment factor σ is multiplied by V p,i , and added to T p,i to form a composite feedback time expression, which more comprehensively reflects the efficiency of the feedback process;

[0174] In order to distinguish the weight influence of different periods in the overall feedback evaluation, the weight index λ p,i is added, which can set the weight according to the importance of each period task, the amount of resource input, or the urgency of feedback, so that the key period occupies a larger proportion in the feedback evaluation;

[0175] The evaluation value of the period is summed and divided by the number of periods n p to form the average index of feedback efficiency;

[0176] The formula integrates response bias, time efficiency, fluctuation adjustment and weight control in structure, has strong stability and adaptability, and can be widely applied to construction feedback analysis, equipment operation monitoring, construction progress response evaluation and other application scenarios in construction projects, providing data support and evaluation means for realizing continuous optimization and efficient management.

[0177] Please refer to Figure 6 , using the construction feedback analysis results, through BIM digital collaborative management, identifying the risk points in construction, recording the risk level, evaluating the influence range and potential loss, and generating the risk assessment results, the steps are as follows:

[0178] S501: Based on the construction feedback analysis results, through BIM digital collaborative management, integrate construction data, and conduct risk identification, including safety hazards and construction mistakes, calibrate the location and nature of differentiated risk points, and generate the execution process of risk point identification record as follows:

[0179] Based on the construction feedback analysis results, through BIM digital collaborative management, integrate construction data, and conduct risk identification, including identifying safety hazards and construction mistakes from the collected data. Using data analysis techniques such as fault tree analysis and root cause analysis, calibrate the location and nature of differentiated risk points, including unstable construction environment, equipment failure prone area and operation error frequent area. Each risk point records detailed information, including specific location, risk type and potential impact, generates risk point identification record, and uses the formula as follows:

[0180]

[0181] Among them, D ri represents the comprehensive score of risk points, H q,i represents the importance of the qth risk point, M q,i represents the score of risk point q, L q,i represents the influence length of risk point q, K is the adjustment coefficient, and n q is the total number of risk points;

[0182] The formula is used to construct the comprehensive evaluation model D ri of construction risk points, the goal of which is to identify and quantify the severity of various potential risk points in the construction process, so as to dynamically track, warn and optimize the management of them through digital management tools (such as BIM system), which integrates risk weight, occurrence probability, influence length and other dimensions, and is an important support means to realize intelligent evaluation of construction safety;

[0183] Risk points at construction sites include unstable environment, equipment failure tendency, and high-incidence areas of operational errors, which pose a major threat to construction efficiency and personnel safety. Therefore, it is crucial to establish a scientific quantitative system to identify high-risk areas. In this formula, the importance of risk points is represented by the indicator H. q,i It indicates that risk events can be graded and assessed based on their frequency, impact, or expert judgment during construction, with higher values ​​indicating more critical risks.

[0184] Risk point score M q,i It represents the performance score of the risk point during the assessment period, reflecting the degree of disturbance to the construction process. The two are multiplied by H q,i ·M q,i The weighted impact term of the risk point is formed as the numerator to reflect the direct intensity of the risk;

[0185] In the denominator, the influence length L is introduced q,i , that is, the scope of the risk point in time or space, combined with the constant K as a unified adjustment factor to form a standardized processing item It is used to reduce unnecessary interference of large-scale but minor risks on the assessment results. The denominator square root processing also has the effect of compressing extreme values ​​and improving the stability of the results.

[0186] The scores of all risk points are accumulated and averaged to form the overall risk assessment index of the construction site;

[0187] This model constructs a weighted scoring mechanism, taking into account risk severity, impact scope and data comparability, and realizes a structured assessment of multiple risk factors in complex construction sites. It can be widely used in pre-construction safety planning, process control and post-construction retrospective analysis, providing a scientific, quantitative and operational management tool for construction risk control.

[0188] S502: Using the risk point identification records, analyze the potential impact of each risk point, estimate and predict the extent of loss using similar cases, identify the risk level, prioritize, and generate a risk level assessment table. The execution process is as follows;

[0189] Using risk point identification records, analyze the potential impact of each risk point. The process involves using historical data and similar cases to estimate and predict the extent of loss caused by each risk point. Each risk point is assigned a risk level based on the potential loss level and probability of occurrence, and is prioritized. The risk level provides a basis for formulating risk mitigation measures and generates a risk level assessment table using the formula:

[0190]

[0191] Among them, D risk Indicates the total risk economic loss value, P r,irepresents the probability of the occurrence of the risk point q, L r,i represents the economic loss caused by the risk point q, n q represents the number of total risk points, and α and β are adjustment coefficients, and γ p is a global adjustment coefficient;

[0192] The formula aims to construct an index D risk that comprehensively quantifies the economic loss of construction risks, which is used to identify the potential impact of various construction risk points and provide scientific basis for risk level assessment and mitigation measures. Through the integration of historical data, experience estimation, and actual records, the model systematically evaluates the economic consequences of risk points in multiple dimensions, reflecting the intelligent and refined trend of modern engineering risk management.

[0193] The core of risk assessment is to measure the impact of risk events from two dimensions: one is the probability p r,i of the occurrence of the risk, and the other is the economic loss L r,i caused by the occurrence of the risk, where p r,i represents the probability of the occurrence of the risk point q in construction, which can be obtained through historical cases, sensor monitoring, or expert scoring; L r,i represents the economic impact caused by the occurrence of the risk point, such as rework cost, downtime loss, equipment maintenance cost, etc., and the product of these two values is the expected loss of the risk point;

[0194] In order to introduce adjustment mechanism and adapt to different local situations, the formula divides the loss value L r,i by the adjustment factor β, and then multiplies it by the global adjustment coefficient γ p , where β is used for standardization processing of loss evaluation methods of each project, and γ p reflects the adjustment of the sensitivity of the entire project area to risk, which can be flexibly set according to construction stage, geographical environment, or engineering importance. Local and global factors are jointly included in the model, improving the adaptability and practicality of the model;

[0195] The evaluation values of all risk points are accumulated and placed under the square root symbol to construct the total risk economic loss index;

[0196] The formula realizes the unified evaluation of multiple risk points, not only reflecting the damage impact of individual risk points, but also having systematization and flexibility, especially suitable for large-scale water conservancy projects, infrastructure construction, and other complex project scenarios that require real-time evaluation of multiple points. The results can be used to develop risk priority ranking, generate risk mitigation strategies, and design insurance models, providing data support and decision basis for risk control of engineering management.

[0197] S503: According to the risk level assessment table, assess the affected range of high-altitude water conservancy projects, refer to the potential cost of personnel safety and project delay, and develop response strategies. The execution process of generating risk assessment results is as follows:

[0198] According to the risk level assessment table, assess the affected range of high-altitude water conservancy projects, involving analyzing the impact range of each risk point and the potential cost of personnel safety and project delay, and developing response strategies such as resource reconfiguration, construction plan adjustment or emergency response measures. The strategy aims to mitigate or eliminate the impact of risk, generating risk assessment results.

[0199] Please refer to Figure 7 , according to the risk assessment results, analyze the risk change trend in construction, adjust the construction plan and resource allocation strategy combined with real-time monitoring data, implement risk mitigation measures, and get the dynamic management scheme steps are as follows:

[0200] S601: Based on the risk assessment results, analyze the change trend of differentiated risk points, update the risk state using real-time monitoring data, identify the change in the number of risk factors, and generate risk trend analysis results. The execution process is as follows:

[0201] Based on the risk assessment results, analyze the change trend of differentiated risk points, update the risk state using real-time monitoring data, including automatically extracting key indicators such as equipment failure rate, personnel safety accident frequency and construction delay events from monitoring equipment data. Use statistical analysis methods such as time series analysis to identify changes in the number of risk factors and development patterns, indicating the dynamic changes of risk points, and generate risk trend analysis results.

[0202] S602: Use risk trend analysis results to re-evaluate construction plans and resource allocation, adjust the allocation of manpower and materials according to the priority of risk levels, optimize construction schedule and material use strategies, and generate construction plan adjustment scheme execution process as follows:

[0203] Use risk trend analysis results to re-evaluate construction plans and resource allocation, the process includes using advanced project management software to dynamically adjust resource allocation, ensuring that resource allocation aligns with the priority of risk levels, and re-distributing human resources according to the risk rating of each construction area, allocating more manpower and supervision to high-risk areas. The allocation of materials is also optimized according to the risk rating, focusing on meeting the material needs of high-risk areas, optimizing construction schedule and material use strategies, minimizing delays and costs, generating construction plan adjustment scheme, using the formula:

[0204]

[0205] Where D adj represents the construction plan adjustment scheme, and R, P, L represent the resource reconfiguration, priority and risk level of the u-th resource point, respectively, n u represents the total number of resource points, e is the base of natural logarithm, k is the adjustment factor, C u is the critical value of resource allocation;

[0206] The formula is used to construct the construction plan adjustment scheme index D adj , which aims to dynamically optimize the construction plan and resource allocation according to the risk trend analysis results. The core idea is to realize the priority allocation of resources in high-risk areas by introducing the matching relationship between risk level and resource allocation priority, maximize construction efficiency and safety, and minimize delay and waste.

[0207] R u in the model represents the risk level of the u-th resource point, which is a numerical index assigned according to the risk identification, assessment and potential impact in the construction area; P u represents the priority of the resource point, reflecting the importance of the resource to the key construction task; L u represents the new configuration strength of the resource, which can be understood as the resource allocation intensity, or as the matching degree of the required resources for the construction task. The product R u ·P u ·L u constitutes the comprehensive demand pressure item of the resource in the current situation, which is the main part of the model;

[0208] The model uses a logical function as the adjustment item of the resource allocation restriction condition. The characteristic of the logical function is that when R u is significantly higher than the critical value C u , its value tends to 1, indicating that the resource should be prioritized; on the contrary, when R u approaches or is lower than C u , the value of this item tends to 0, indicating that the resource allocation priority is reduced. Here, k is the adjustment factor, which determines the slope of the logical function, i.e. the sensitivity of resource allocation to changes in risk level. C u is the boundary critical value of resource allocation, representing the default resource reallocation threshold of the system;

[0209] This model realizes the priority allocation of resources in high-risk construction areas by integrating risk level, resource priority and configuration strength, and introducing a nonlinear logical function as a dynamic control mechanism. It has high adaptability in construction progress adjustment, personnel scheduling, material support, etc. and can be widely applied in complex and variable construction environments. It is one of the typical tools for dynamic optimization of digital construction plan, and its results can guide project management personnel to develop adjustment strategies in real time, ensure smooth construction, rational use of resources and effective cost control.

[0210] S603: Adopting the construction plan adjustment scheme, implementing targeted risk mitigation measures, optimizing safety training and adjusting work area layout, checking construction safety and progress, and generating a dynamic management scheme. The execution process of the dynamic management scheme is as follows:

[0211] Adopting the construction plan adjustment scheme, implementing targeted risk mitigation measures, including reducing potential risks in the workplace through optimizing safety training and adjusting work area layout. Safety training covers operation safety procedures and emergency response skills, and work area layout adjustment ensures sufficient escape routes and safety warning signs. In the process of checking construction safety and progress, a dynamic management scheme is generated using a regular review and feedback mechanism, and the formula is:

[0212]

[0213] where D dyn represents the dynamic management scheme, and respectively represent the strength and quality assurance measures of the safety measures at the pth adjustment point, n p represents the total number of adjustment points;

[0214] The formula is used to construct the dynamic management scheme evaluation index D dyn , the purpose of which is to quantitatively analyze the implementation effect of risk mitigation measures during the construction plan adjustment process, ensuring the safety and controllability of the construction site. The model emphasizes the actual execution strength and the completion degree of quality assurance as the basis for evaluating the effectiveness of various safety measures in actual operation, supporting the optimization of dynamic scheduling, review mechanisms and risk feedback processes;

[0215] In the construction site, in order to reduce potential risks, specific safety measures such as safety training, sign placement, escape route setting, etc. need to be implemented. The measures are adapted to the local conditions and implemented at each "adjustment point". The adjustment point is the basic unit of model analysis. For each adjustment point, the safety measure strength at the pth point is represented by the variable , which represents the completeness, execution frequency or on-site implementation degree of the measure, such as employee training coverage, safety drill frequency, etc. Another variable represents the quality assurance progress of the safety measure, which represents the completion degree, compliance degree, etc. of the measure in the project plan;

[0216] In the formula, represents the square root processing of the safety measure strength, which on the one hand reduces the deviation caused by extreme values, and on the other hand also reflects the marginal effect of "measure strength improvement but effect tends to be stable"; while constitutes a reverse index, i.e. the higher the quality assurance completion degree ( The greater the risk suppression effect, the smaller the result value, reflecting that the risk is more effectively controlled under high-quality protection;

[0217] After the results of the adjustment points are summed up, the dynamic management scheme index of the entire construction site or project cycle is obtained. The lower the index value, the stronger and better the safety measures, and the better the overall management effect. Otherwise, attention should be paid to the weak or lagging links in individual execution;

[0218] The model is suitable for dynamic safety assessment scenarios, especially in high-risk or large-scale project construction processes, and can assist project managers in accurately identifying risk mitigation weak points, optimizing resource allocation and management schemes, and achieving a shift from "passive prevention" to "active regulation".

[0219] Please refer to Figure 8 A BIM-based digital collaborative management system, a BIM-based digital collaborative management system for executing the above-mentioned BIM-based digital collaborative management method, the system comprising:

[0220] The terrain analysis module uses geographic information systems to collect terrain and climate data in high-altitude areas, record geological characteristics and hydrological conditions, and generate a basic geographic data set;

[0221] The road analysis module uses geological characteristic analysis and real-time weather information adjustment based on the basic geographic data set to calculate the accessibility of the road and the safety index of the construction area, and generates a path safety assessment result;

[0222] The resource allocation module uses BIM digital technology to optimize construction resource allocation based on the path safety assessment result, avoids redundant paths, and adjusts the flow of construction equipment and materials, generating a resource optimization configuration table;

[0223] The risk management module monitors real-time data in the construction site based on the resource optimization configuration table, identifies risk points, records risk levels, assesses the impact range and potential losses, and generates a risk assessment result;

[0224] The dynamic adjustment module uses the risk assessment result in combination with real-time monitoring data to analyze the risk change trend in construction, optimizes safety training and adjusts the work area layout, and generates a dynamic management scheme.

[0225] The above is only a preferred embodiment of the present application, and does not limit the form of the present application. Any skilled person in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made in accordance with the technical essence of the present application to the above embodiments shall fall within the protection scope of the present application.

Claims

1. A digital collaborative management method based on BIM, characterized in that: The following steps are involved: Use geographic information systems to collect topographic and climate data for high-altitude water conservancy projects. Analyze the geological characteristics and hydrological conditions of the project coverage area through geographic data to generate a basic data set for the project. Using the project's basic dataset, we analyzed high-altitude road accessibility and construction area safety, adjusted path weights based on real-time meteorological information, and obtained an adjusted network model. Based on the adjusted network model, BIM digital collaborative management is used to identify redundant paths in resource allocation, configure the flow of water conservancy project construction equipment and materials, and generate a resource optimization configuration table; Using the resource optimization allocation table, monitor real-time data at the construction site, record material consumption rate and project progress, analyze causes of deviations, and generate construction feedback analysis results; Using the construction feedback analysis results, through BIM digital collaborative management, identify risk points in construction, record risk levels, and generate risk assessment results; Based on the risk assessment results, the risk change trend in construction is analyzed, the construction plan and resource allocation strategy are adjusted, and risk mitigation measures are implemented to obtain a dynamic management plan.

2. A BIM-based digital collaborative management method according to claim 1, characterized in that: The project basic data set includes terrain height maps, rainfall distribution maps and groundwater flow conditions in high-altitude areas; the adjusted network model includes construction path weight adjustment, resource allocation plan and construction safety area mapping; the resource optimization configuration table includes optimized deployment of construction equipment, material flow adjustment plan and redundant path identification results; the construction feedback analysis results include material consumption rate, project progress, deviation cause analysis and strategy adjustment plan; the risk assessment results include risk level records, impact range assessment and potential loss prediction; the dynamic management plan includes the implementation of risk mitigation measures and the update schedule of the construction plan.

3. The BIM-based digital collaborative management method according to claim 1, characterized in that: Using a geographic information system, we collected topographic and climate data for high-altitude water conservancy projects. We used this data to analyze the geological characteristics and hydrological conditions of the project's coverage area. The steps for generating the project's basic data set were as follows: Using a geographic information system, we selected high-altitude areas to scan and collect terrain data. At the same time, we set up climate monitoring stations to record temperature and wind speed data in real time to obtain a terrain climate initialization dataset. Initializing the terrain and climate dataset, adjusting the spatial resolution of the terrain data, setting the climate data sampling frequency to once per hour, screening and verifying the data quality, and generating a processed climate dataset; By using the processed climate dataset and integrating the existing geological and hydrological data, a spatial correspondence analysis of the topography and hydrological characteristics of high-altitude areas was conducted, potential water source areas and geologically stable areas were recorded, and the basic dataset of the project was generated.

4. The BIM-based digital collaborative management method according to claim 1, characterized in that: Using the project's basic dataset, we analyzed high-altitude road accessibility and construction area safety, adjusted path weights based on real-time meteorological information, and updated resource allocation logic to obtain the adjusted network model. The specific steps are: Based on the project's basic dataset, identify key road nodes for high-altitude water conservancy projects, record the slopes and curvatures between differentiated nodes, and classify and evaluate the width and material of roads to generate a road accessibility analysis table. Using the road accessibility analysis table, a linear weighted sum method is used to adjust the driving safety index of differentiated road segments according to real-time meteorological information, record rainfall and snowfall, and recalibrate risk areas to generate a path weight adjustment table; According to the path weight adjustment table, the flow needs of personnel and materials are evaluated, the construction vehicles and material storage locations are optimized, the reasonable deployment of construction equipment is checked, and an adjusted network model is generated.

5. The BIM-based digital collaborative management method according to claim 4, characterized in that: The formula of the linear weighted sum method is as follows: Among them, W i is the weight of road segment i, S i is the safety baseline index based on road accessibility data, e is the base of the natural logarithm, R i is the risk factor adjusted for rainfall and snowfall, T i is the road traffic flow index, V i is the viewing distance factor, a i 、b i and c i is the adjustment factor.

6. The BIM-based digital collaborative management method according to claim 1, characterized in that: Based on the adjusted network model, using BIM digital collaborative management, identifying redundant paths in resource allocation, configuring water conservancy project construction equipment and material flow, and generating a resource optimization allocation table are as follows: Utilizing the adjusted network model and BIM digital collaborative management, the construction route and resource flow diagram are analyzed, the construction path is calibrated and the reused routes are detected, the redundant paths that need to be optimized and deleted are verified, and a redundant path identification diagram is generated; Based on the redundant path identification diagram, the flow of key resources, including cement and steel, is adjusted to optimize storage locations and usage times, and the layout and scheduling of construction equipment are reconfigured to generate resource flow optimization records; Based on the resource flow optimization record, the data in the water conservancy project construction is integrated and a resource allocation plan is formulated. By adjusting the configuration plan and construction queue of multiple types of resources and equipment, the construction efficiency is optimized and a resource optimization configuration table is generated.

7. The BIM-based digital collaborative management method according to claim 1, characterized in that: The steps for using the resource optimization allocation table to monitor real-time data on the construction site, record material consumption rate and project progress, analyze the causes of deviations, adjust construction strategies, and generate construction feedback analysis results are as follows: Based on the resource optimization allocation table, monitoring equipment is deployed at key construction nodes to collect material usage and project progress in real time, and the information is synchronized to the central monitoring to generate real-time monitoring data records; Based on the real-time monitoring data records, using data comparison and analysis to identify the stages where the material consumption rate deviates from the expected consumption, and identify the causes of the deviation, including logistics delays and construction efficiency issues, and generate deviation cause analysis results; The deviation cause analysis results are used to adjust the construction strategy of the high-altitude water conservancy project. By adjusting the material supply speed and rearranging the working hours of the staff, the construction process is optimized, the resource utilization is checked, and the construction feedback analysis results are generated.

8. The BIM-based digital collaborative management method according to claim 1, characterized in that: Using the construction feedback analysis results, through BIM digital collaborative management, we can identify construction risk points, record risk levels, assess the scope of impact and potential losses, and generate risk assessment results in the following steps: Based on the construction feedback analysis results, through BIM digital collaborative management, construction data is integrated and risks are identified, including safety hazards and construction errors, the location and nature of differentiated risk points are calibrated, and risk point identification records are generated; Utilize the risk point identification records to analyze the potential impact of each risk point, estimate and predict the extent of losses using similar cases, identify risk levels, prioritize them, and generate a risk level assessment table; Based on the risk level assessment table, assess the affected scope of high-altitude water conservancy projects, refer to the potential costs of personnel safety and project delays, and formulate response strategies to generate risk assessment results.

9. The BIM-based digital collaborative management method according to claim 1, characterized in that: Based on the risk assessment results, analyze the risk trend during construction, combine it with real-time monitoring data, adjust the construction plan and resource allocation strategy, implement risk mitigation measures, and obtain a dynamic management plan. The specific steps are as follows: Based on the risk assessment results, analyze the changing trends of differentiated risk points, update the risk status using real-time monitoring data, identify changes in the number of risk factors, and generate risk trend analysis results; Using the risk trend analysis results, re-evaluate the construction plan and resource allocation, adjust the allocation of manpower and materials based on the priority of risk levels, optimize the construction schedule and material usage strategy, and generate a construction plan adjustment plan; Adopt the construction plan adjustment plan, implement targeted risk mitigation measures, optimize safety training and adjust the work area layout, check construction safety and progress, and generate a dynamic management plan.

10. A digital collaborative management system based on BIM, characterized in that: According to a BIM-based digital collaborative management method according to any one of claims 1 to 9, the system comprises: The terrain analysis module uses a geographic information system to collect topographic and climate data of high-altitude areas, record geological characteristics and hydrological conditions, and generate basic geographic data sets; The road analysis module calculates the accessibility of roads and the safety index of construction areas based on the basic geographic data set, using geological characteristics analysis and real-time meteorological information adjustment to generate a path safety assessment result; The resource allocation module uses BIM digital technology to optimize construction resource allocation based on the path safety assessment results, avoid redundant paths, and adjust the flow of construction equipment and materials to generate a resource optimization configuration table; The risk management module monitors the real-time data of the construction site based on the resource optimization allocation table, identifies risk points, records risk levels, assesses the scope of impact and potential losses, and generates risk assessment results; The dynamic adjustment module uses the risk assessment results in combination with real-time monitoring data to analyze the risk change trend during construction, optimize safety training and adjust the work area layout, and generate a dynamic management plan.

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