An Adaptive Risk Assessment Method and System for Information System Engineering Supervision Projects

Abstract

This application provides a risk adaptive assessment method and system for information system engineering supervision projects. Among them, the engineering management solution forms a dynamic knowledge graph by constructing a data model that integrates building information models and geographic information systems. It uses an ultra-wideband positioning device array to capture the three-dimensional coordinate trajectories of construction machinery in real time, and adopts multi-path interference suppression technology to generate the spatial occupancy hot zone distribution. Based on digital twin technology, it establishes an operation simulation environment and creates a spatio-temporal risk topology network to warn of path conflicts and safety distance problems. According to the network node density characteristics, it dynamically adjusts the weights of risk assessment parameters, and evaluates the equipment collision probability and construction process conflict index. Finally, based on the composite risk level result, it feedback-adjusts the stress threshold and signal sampling frequency to optimize construction management and safety guarantee. The technical solution provided by this application can improve the efficiency and safety of risk adaptive assessment for information system engineering supervision projects.

Description

Technical Field

[0001] This application relates to the technical field of risk adaptive assessment for information system engineering supervision projects, and particularly to a risk adaptive assessment method and system for information system engineering supervision projects. Background Art

[0002] In modern large-scale construction projects, the construction process is complex and changeable, involving the coordination of multiple professional fields and technical parameters. To ensure the safe and efficient progress of the project, a technical solution that can integrate building information models with geographic information systems is required to effectively manage multi-dimensional risk factors such as pre-buried pipelines, stress thresholds of load-bearing structures, and equipment installation specifications. In addition, the construction site changes dynamically and frequently, and real-time monitoring of the position and movement of construction machinery is crucial for avoiding collisions and optimizing the operation process. At the same time, by digitally simulating and evaluating potential risks in the construction process in advance, the overall management level and safety of the project can be significantly improved.

[0003] Currently, some advanced construction projects have adopted a real-time monitoring system based on the Internet of Things sensor network to track the position and status of construction machinery. This system uses the global positioning system and wireless communication network to transmit the real-time position data of construction machinery to the central management system and combines it with the building information model for preliminary spatial conflict detection. In this way, project managers can identify potential spatial occupancy conflicts and safety clearance problems at an early stage, so as to adjust the construction plan and resource allocation in a timely manner and reduce the possibility of on-site accidents.

[0004] Although the real-time monitoring system based on the Internet of Things sensor network provides a certain degree of spatial conflict detection ability, there are still several obvious limitations. First, the global positioning accuracy is limited in indoor or urban dense areas, which may lead to large positioning deviations and affect the actual application effect. Second, this system mainly relies on static building information models for conflict detection and lacks the real-time response ability to dynamic changes in the construction site, unable to fully cover all potential risk factors. Finally, due to the lack of integration of ultra-wideband positioning technology or multi-path interference suppression algorithms, its ability to accurately capture the trajectory of mobile devices in complex environments is limited, and it is difficult to generate reliable spatial occupancy heat zone distribution data of mobile devices, thus limiting the potential for further risk analysis and optimization. Summary of the Invention

[0005] This application provides a risk adaptive assessment method and system for information system engineering supervision projects to solve the problems of low efficiency and insufficient security in the risk adaptive assessment of information system engineering supervision projects in the prior art.

[0006] In the first aspect, this application provides a risk adaptive assessment method for information system engineering supervision projects, including:

[0007] Construct an engineering management data model that integrates Building Information Modeling (BIM) and Geographic Information System (GIS), integrate a multi-dimensional risk factor set including pipeline embedded parameters, stress threshold values of load-bearing structures, and equipment installation specification requirements, and form a dynamic knowledge graph containing spatial constraint conditions and construction logic rules;

[0008] Deploy an ultra-wideband positioning device array to capture the three-dimensional coordinate trajectories of construction machinery in real time, eliminate positioning deviations through multi-path interference suppression technology, and generate mobile device space occupancy hot zone distribution data;

[0009] Establish a digital twin-driven operation simulation environment, dynamically couple the risk factors in the dynamic knowledge graph with the mobile device space occupancy hot zone distribution data, and generate a spatio-temporal risk topology network including early warnings of network equipment installation path conflicts and safety distance warnings;

[0010] Based on the node density distribution characteristics of the spatio-temporal risk topology network, dynamically adjust the weight allocation strategy of the construction machinery trajectory risk assessment parameters, and form a composite risk level assessment result including equipment collision probability and construction process conflict index;

[0011] According to the composite risk level assessment result, reversely adjust the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array.

[0012] Optionally, the dynamically coupling the risk factors in the dynamic knowledge graph with the mobile device space occupancy hot zone distribution data to generate a spatio-temporal risk topology network including early warnings of network equipment installation path conflicts and safety distance warnings includes:

[0013] Based on the spatial coordinate set of the pipeline embedded parameters in the dynamic knowledge graph and the safety distance threshold value of the equipment installation specification requirements, establish a risk factor association model;

[0014] According to the real-time motion vectors in the mobile device space occupancy hot zone distribution data, calculate the three-dimensional trajectory envelopes of each mobile device within a preset time window, and generate equipment motion trend projections through interpolation prediction;

[0015] Perform spatio-temporal grid discretization analysis on the spatio-temporal influence domain in the risk factor association model and the equipment motion trend projection, and calculate the network equipment installation path conflict probability value for each grid cell. The network equipment installation path conflict probability value is obtained by weighting the area ratio of the equipment trajectory penetrating the pipeline influence domain and the time overlap coefficient;

[0016] Construct a dynamic safety distance threshold function based on the stress threshold parameter of the load-bearing structure, and adjust the safety distance threshold in real time according to the equipment mass parameter and motion acceleration;

[0017] Fuse the network device installation path conflict probability value and the safety distance threshold to construct a spatio-temporal correlation matrix, and generate a spatio-temporal risk topology network including network device installation path conflict warnings and safety distance alarms.

[0018] Optionally, the spatio-temporal grid discretization analysis of the spatio-temporal influence domain in the risk factor association model and the projection of the device movement trend includes calculating the network device installation path conflict probability value for each grid cell, including:

[0019] Establish a spatio-temporal grid division mechanism aligned with the coordinate system of the risk factor association model, discretize the three-dimensional space coordinate axes into cube units, and divide the time axis into time windows according to the construction progress to obtain spatio-temporal grid cells;

[0020] Traverse the spatio-temporal grid cells, extract the trajectory segments of the projection of the device movement trend, calculate the geometric intersection volume of the trajectory segments, and use the ratio of the geometric intersection volume to the total volume of the pipeline influence domain as the spatial permeability;

[0021] Calculate the intersection duration of the trajectory segment with the activated period of the pipeline influence domain within the time window, and use the ratio of the intersection duration to the preset time window length as the time coverage;

[0022] Obtain the preset spatial penetration weight factor and time coverage weight factor according to the pipeline type parameter, perform a linear combination of the spatial permeability and the time coverage, and generate a dynamic probability value reflecting the pipeline collision risk;

[0023] When the projection of the device movement trend involves a multi-pipeline crossing area, superimpose and calculate the dynamic probability values of the pipelines in the same spatio-temporal grid cell to generate the network device installation path conflict probability value.

[0024] Optionally, the linear combination of the spatial permeability and the time coverage to generate a dynamic probability value reflecting the pipeline collision risk includes:

[0025] Establish a mapping relation table of the pipeline type parameter, the spatial penetration weight factor, and the time coverage weight factor, set the reference values of the spatial permeability and the time coverage for the engineering attribute characteristics, and dynamically correct the reference values according to the current construction stage parameters;

[0026] According to the corrected reference values, construct a two-dimensional weight vector, perform homogeneous coordinate transformation on the spatial permeability and the time coverage, and generate a risk vector including spatio-temporal characteristics;

[0027] Calculate the risk coupling coefficient according to the pipeline type parameter and the construction machinery type parameter, and perform a tensor product operation on the risk vector and the risk coupling coefficient to obtain the initial probability tensor;

[0028] The electromagnetic interference intensity parameter of the pipeline intersection area is introduced as an environmental correction factor, and the initial probability tensor is nonlinearly mapped to generate a dynamic probability value reflecting the pipeline collision risk.

[0029] Optionally, the multi-dimensional risk factor set integrating pipeline pre-buried parameters, load-bearing structure stress thresholds and equipment installation specification requirements forms a dynamic knowledge graph containing space constraints and construction logic rules, including:

[0030] Based on the pipeline pre-buried parameters, a spatial collision rule is generated through the minimum clearance constraint between pipelines, wherein the spatial collision rule includes a three-dimensional buffer zone parameter of the pipeline intersection area;

[0031] Analyze the time-varying parameters of the stress threshold of the load-bearing structure, establish a stress propagation path model based on the topological relationship of the support points, and generate dynamic constraint conditions including the maximum allowable load based on the mechanical transfer characteristics between the support points;

[0032] Convert positioning tolerance, tightening torque and heat dissipation spacing parameters in equipment installation specification requirements into a construction logic rule chain, wherein the construction logic rule chain includes process execution priority parameters and mutually exclusive trigger conditions for parallel operations;

[0033] Based on the spatial coordinate system of the building information model, the spatial collision rules, dynamic constraints and construction logic rule chains are topologically encoded, and a dynamic knowledge graph is formed by defining the spatial interference relationship between pipelines and load-bearing structures, and the logical dependency relationship between construction procedures and equipment trajectories.

[0034] Optionally, the step of converting the positioning tolerance, tightening torque and heat dissipation spacing parameters in the equipment installation specification requirements into a construction logic rule chain includes:

[0035] Establish a mapping relationship table between equipment types and installation parameters in equipment installation specification requirements, and generate equipment in-place verification rules based on the horizontal deviation threshold and vertical settlement tolerance in the positioning tolerance parameters. The equipment in-place verification rules include linkage constraints on the allowable deviation range and the spacing between adjacent equipment.

[0036] Analyze the functional relationship between the tightening torque and the equipment quality parameters, establish a torque gradient adjustment model, and generate installation strength rules that include the torque graded loading strategy and the tightening sequence dependency;

[0037] Based on the thermal radiation attenuation coefficient in the heat dissipation spacing parameters and the equipment power parameters, the dynamic safe heat dissipation distance is calculated to generate the thermal field mutual exclusion triggering rules when the equipment cluster is deployed;

[0038] The equipment placement verification rules, installation strength rules and thermal field mutually exclusive triggering rules are arranged in time sequence according to the construction process stages to obtain a construction logic rule chain.

[0039] Optionally, the reverse adjustment of the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array according to the composite risk level evaluation result includes:

[0040] Based on the equipment collision probability value in the composite risk level evaluation result, establish a dynamic mapping relationship between the collision probability threshold and the stress threshold of the load-bearing structure, and generate the stress threshold parameter;

[0041] Analyze the time distribution characteristics of the construction process conflict index, divide the construction period into peak periods and intermittent periods in combination with the construction stage parameters, increase the sampling frequency of the ultra-wideband positioning device array according to the predicted deviation amplitude of the mobile device trajectory during the peak period, and reduce the sampling frequency proportionally according to the change rate of the hot zone distribution of the mobile device space occupancy during the intermittent period;

[0042] Establish the coupling constraint conditions between the stress threshold parameter and the sampling frequency, and when the stress threshold parameter is triggered to be lowered and the sampling frequency is increased at the same time, ensure that the adjustment of the stress threshold parameter does not exceed the structural safety boundary;

[0043] Inject the adjusted stress threshold parameter into the load-bearing structure node attributes of the engineering management data model in reverse, and at the same time send the coupling constraint conditions to the signal sampling frequency configuration of the ultra-wideband positioning device array.

[0044] In a second aspect, the present application provides an information system engineering supervision project risk adaptive evaluation system, including:

[0045] A construction module that constructs an engineering management data model integrating a building information model and a geographic information system, integrates a multi-dimensional risk element set of pipeline embedding parameters, load-bearing structure stress thresholds, and equipment installation specification requirements, and forms a dynamic knowledge graph including spatial constraint conditions and construction logic rules;

[0046] A capture module that deploys an ultra-wideband positioning device array to capture the three-dimensional coordinate trajectory of construction machinery in real time, eliminates positioning deviation through multipath interference suppression technology, and generates mobile device space occupancy hot zone distribution data;

[0047] A coupling module that establishes a job simulation environment driven by digital twins, dynamically couples the risk elements in the dynamic knowledge graph with the space occupancy hot zone distribution data, and generates a spatio-temporal risk topology network including early warnings of network equipment installation path conflicts and safety distance warnings;

[0048] A formation module, based on the node density distribution characteristics of the spatio-temporal risk topology network, dynamically adjusts the weight assignment strategy of the construction machinery trajectory risk assessment parameters to form a composite risk level assessment result including the equipment collision probability and the construction process conflict index;

[0049] An adjustment module, according to the composite risk level assessment result, reversely adjusts the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array.

[0050] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an information system engineering supervision project risk adaptive assessment method as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium, storing a computer program, and when the computer program is executed by a computer, it implements an information system engineering supervision project risk adaptive assessment method as described in the first aspect.

[0052] In the embodiment of the present application, an engineering management data model integrating a building information model and a geographic information system is constructed, a multi-dimensional risk factor set integrating pipeline embedding parameters, load-bearing structure stress thresholds, and equipment installation specification requirements is integrated to form a dynamic knowledge graph including spatial constraint conditions and construction logic rules; an ultra-wideband positioning device array is deployed to capture the three-dimensional coordinate trajectory of construction machinery in real time, and positioning deviation is eliminated through multi-path interference suppression technology to generate mobile device space occupancy hot zone distribution data; a digital twin-driven operation simulation environment is established, and the risk factors in the dynamic knowledge graph are dynamically coupled with the mobile device space occupancy hot zone distribution data to generate a spatio-temporal risk topology network including network equipment installation path conflict warnings and safety distance warnings; based on the node density distribution characteristics of the spatio-temporal risk topology network, the weight assignment strategy of the construction machinery trajectory risk assessment parameters is dynamically adjusted to form a composite risk level assessment result including the equipment collision probability and the construction process conflict index; according to the composite risk level assessment result, the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array are reversely adjusted.

[0053] The technical solution of the present application has the following beneficial effects:

[0054] This application integrates pipeline embedding parameters, stress threshold values of load-bearing structures, and equipment installation specification requirements to form a dynamic knowledge graph, achieving comprehensive management of multi-dimensional risk factors. This step ensures precise control of spatial constraint conditions and construction logic rules during the construction process, improving the scientificity and systematicness of project management. Using ultra-wideband technology and multi-path interference suppression methods, the three-dimensional position information of construction machinery is accurately obtained to generate spatial occupancy heat zone distribution data. This step improves the accuracy of monitoring the positions of construction machinery on the construction site and effectively avoids potential safety hazards caused by positioning deviations. The risk factors in the dynamic knowledge graph are coupled with the spatial occupancy heat zone distribution data of mobile devices to create a spatio-temporal risk topology network, realizing path conflict warning and safety distance warning. This step provides an accurate risk prediction and timely warning mechanism for the construction process, enhancing the safety of on-site operations. According to the node density distribution characteristics of the spatio-temporal risk topology network, the weight allocation strategy of the risk assessment parameters of construction machinery trajectories is dynamically adjusted to form a composite risk level assessment result. This step realizes flexible response to complex situations on the construction site and optimizes the risk management strategy. According to the composite risk level assessment result, the stress threshold parameters and signal sampling frequency configuration are adjusted in reverse to continuously improve engineering management and safety guarantee measures. This final step ensures the efficient operation and safety improvement of the entire construction process.

[0055] Furthermore, this solution establishes a risk factor association model, combines it with the spatial occupancy heat zone distribution data of mobile devices, uses interpolation prediction to generate the projection of equipment movement trends, then calculates the path conflict probability value of network equipment installation paths through spatio-temporal grid discretization analysis, adjusts the safety distance based on the dynamic safety distance threshold function, and finally fuses these data to construct a spatio-temporal association matrix to generate a spatio-temporal risk topology network including path conflict warning and safety distance warning. The effect is to significantly improve the risk prediction ability and safety management efficiency on the construction site. Through accurate risk assessment and real-time warning mechanisms, potential safety accidents and construction conflicts are greatly reduced, ensuring the smooth progress of the project and the safety of personnel.

[0056] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1The flowchart of a method for adaptively evaluating risks of an information system engineering supervision project provided by the present application is shown;

[0059] Figure 2 The structural schematic diagram of a system for adaptively evaluating risks of an information system engineering supervision project provided by the present application is shown;

[0060] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. Detailed implementation manners

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

[0062] In some processes described in the specification and claims of the present application and the above-mentioned accompanying drawings, a plurality of operations that appear in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0063] The present solution constructs an engineering management data model that integrates a building information model and a geographic information system, integrates a multi-dimensional risk factor set of pipeline embedded parameters, load-bearing structure stress thresholds, and equipment installation specification requirements, and forms a dynamic knowledge graph that includes spatial constraint conditions and construction logic rules. By integrating different types of engineering data into a unified data model, the comprehensive management and precise control of the complex situations at the construction site are realized, ensuring the scientific and reasonable application of various parameters and rules during the construction process.

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0065] Figure 1 For the flowchart of a method for adaptively evaluating risks of an information system engineering supervision project provided by an embodiment of the present application, as Figure 1 shown, the method includes:

[0066] 101. Construct an engineering management data model that integrates building information modeling and geographic information systems, integrating a multi-dimensional risk factor set of pipeline embedded parameters, load-bearing structure stress thresholds, and equipment installation specification requirements, and forming a dynamic knowledge graph containing spatial constraint conditions and construction logic rules;

[0067] In this step, the dynamic knowledge graph includes a multi-dimensional risk factor set of pipeline embedded parameters, load-bearing structure stress thresholds, and equipment installation specification requirements.

[0068] Building information modeling is a digital tool used to create and manage data on the physical and functional characteristics of building projects. It includes information such as all the geometries of a building, material properties, construction progress, etc. It is used to optimize decision-making in the design, construction, and operation processes, and improve the efficiency and quality of projects.

[0069] Geographic information systems is a system used to capture, store, analyze, and display all types of geographic data. It includes maps, satellite images, terrain data, etc., and is used to support various application fields such as urban planning, environmental management, and disaster response.

[0070] The engineering management data model integrates various engineering-related data into a unified data structure, including a multi-dimensional risk factor set such as pipeline embedded parameters, load-bearing structure stress thresholds, and equipment installation specification requirements. This model is used to comprehensively manage and precisely control various complex situations at the construction site.

[0071] The multi-dimensional risk factor set includes data in many aspects such as pipeline embedded parameters, load-bearing structure stress thresholds, and equipment installation specification requirements. These data are used to identify and evaluate potential risk factors during the construction process to ensure construction safety and efficiency.

[0072] Spatial constraint conditions refer to the spatial limitations and rules that must be observed during the construction process, such as the minimum clear distance between pipelines and the safety distance for equipment installation. These conditions are used to prevent physical collisions and potential problems during construction.

[0073] Construction logic rules are a series of standards and specifications that guide the execution sequence and dependency relationships of construction processes, such as parameters like positioning tolerances, tightening torques, and heat dissipation distances. These rules ensure that the construction process complies with standards and avoids operation errors.

[0074] Pipeline embedded parameters include the minimum clear distance constraint between pipelines and three-dimensional buffer zone parameters, which are used to prevent physical collisions between pipelines.

[0075] The load-bearing structure stress threshold establishes a stress propagation path model by analyzing time-varying parameters and generates dynamic constraint conditions for the maximum allowable load.

[0076] The equipment installation specification requirements are transformed into a construction logic rule chain, including parameters such as positioning tolerance, fastening torque, and heat dissipation spacing, to ensure that the equipment installation process complies with the standards.

[0077] In the embodiments of the present application, first, spatial collision rules are generated based on pipeline embedding parameters, a stress propagation path model is established in combination with the topological relationship of support points, and dynamic constraint conditions for the maximum allowable load are defined. Then, the equipment installation specifications are transformed into a construction logic rule chain, and topological association coding is performed in the spatial coordinate system of the building information model. Finally, a dynamic knowledge graph is formed, integrating spatial constraint conditions and construction logic rules to achieve comprehensive management and precise control of complex situations at the construction site.

[0078] In a large commercial complex project, first, all pipeline embedding parameters are collected, including the positions and sizes of water pipes, cables, etc., to generate spatial collision rules. At the same time, the stress thresholds of the load-bearing structure are analyzed, and a stress propagation path model is established to ensure the maximum bearing capacity of each pillar. According to the equipment installation specification requirements, a detailed construction logic rule chain is formulated, such as the positioning tolerance and fastening torque of elevator equipment. These data are integrated into a unified data model to form a dynamic knowledge graph, providing basic data support for subsequent steps.

[0079] 102. Deploy an ultra-wideband positioning device array to capture the three-dimensional coordinate trajectories of construction machinery in real time, eliminate positioning deviations through multi-path interference suppression technology, and generate mobile device space occupancy hot zone distribution data;

[0080] In this step, the ultra-wideband positioning device array is a set of high-precision positioning devices that capture the three-dimensional coordinate trajectories of objects in real time by sending and receiving ultra-wideband signals. These devices are used to accurately monitor the positions of construction machinery at the construction site to avoid collisions and optimize the operation process.

[0081] The three-dimensional coordinate trajectory refers to a dataset that records the movement path of an object in three-dimensional space. It includes position information in three directions and is used to accurately describe the movement trajectory of construction machinery or equipment.

[0082] The multi-path interference suppression technology is a technology used to reduce errors caused by multi-path effects during wireless signal transmission. This technology processes the received signals through algorithms to eliminate positioning deviations and improve positioning accuracy.

[0083] The positioning deviation refers to the difference between the actual measured position and the true position. At the construction site, positioning deviations may lead to misoperations or collision risks of construction machinery, so they need to be corrected through technical means.

[0084] The mobile device space occupancy hot zone distribution data shows the space occupancy of construction machinery at different time points, which helps to identify potential collision risks.

[0085] In the embodiments of the present application, an ultra-wideband positioning device array is deployed at the construction site, and its high-precision positioning ability is utilized to capture the three-dimensional coordinate trajectories of construction machinery. The multi-path interference suppression technology is used to process signals to improve the positioning accuracy. The captured data is analyzed to generate the spatial occupancy heat zone distribution data of the construction machinery, showing the activity range and density distribution of the equipment in each time period.

[0086] Continuing with the above commercial complex project, an ultra-wideband positioning device array is deployed on-site to monitor the movement trajectories of heavy machinery such as tower cranes and excavators in real time. Through the multi-path interference suppression technology, it is ensured that the positioning error is less than 10 centimeters. The generated spatial occupancy heat zone distribution data shows the activity hot spots of each construction machinery in different time periods, providing key data for the next risk assessment.

[0087] 103. Establish a digital twin-driven operation simulation environment, dynamically couple the risk factors in the dynamic knowledge graph with the spatial occupancy heat zone distribution data of the mobile device to generate a spatio-temporal risk topology network including early warnings of network device installation path conflicts and safety distance warnings;

[0088] In this step, digital twin-driven is a technology that uses a virtual model to simulate real-world objects. In the construction environment, digital twin-driven simulates the changes in the construction site through real-time data feedback to help identify and solve potential problems.

[0089] The operation simulation environment is a virtual construction scenario built based on digital twin technology, used to simulate and predict various situations during the construction process. It includes functions such as equipment movement trajectories and risk warnings to help optimize the construction plan and resource allocation.

[0090] Risk factors refer to factors that may have a negative impact on the construction process, such as pipeline embedding parameters, load-bearing structure stress thresholds, equipment installation specification requirements, etc. These factors are used to evaluate and prevent potential risks in construction.

[0091] The early warning of installation path conflicts is an early warning mechanism generated based on the spatio-temporal risk topology network, used to identify potential conflict points in the equipment installation path in advance and issue warnings to adjust the construction plan in a timely manner.

[0092] The safety distance warning is an early warning mechanism generated based on the dynamic safety distance threshold function, used to detect whether the distance between equipment or between equipment and fixed structures meets the safety standards and issue warnings when the requirements are not met.

[0093] The spatio-temporal risk topology network includes early warnings of network device installation path conflicts and safety distance warnings, helping to identify and avoid potential risks in construction in advance.

[0094] In the embodiments of the present application, in the digital twin environment, first, the risk factors in the dynamic knowledge graph are dynamically coupled with the spatial occupancy hot zone distribution data of mobile devices. Through spatio-temporal grid discretization analysis, the network device installation path conflict probability value of each grid cell is calculated. Based on the load-bearing structure stress threshold parameter, a dynamic safety distance threshold function is constructed to generate a spatio-temporal correlation matrix, and finally a spatio-temporal risk topology network including path conflict warnings and safety distance warnings is formed.

[0095] In the above project, digital twin technology is used to simulate the dynamic changes on the construction site, and the pipeline embedding parameters and equipment installation specification requirements are combined with the movement trajectories of actual construction machinery. Through spatio-temporal grid discretization analysis, potential equipment installation path conflict points are identified and warnings are issued. For example, during the pouring of a certain floor slab, the system discovers in advance that the boom of the crane may conflict with the already laid pipeline, and adjusts the construction plan in time to avoid accidents.

[0096] 104. Based on the node density distribution characteristics of the spatio-temporal risk topology network, dynamically adjust the weight allocation strategy of the construction machinery trajectory risk assessment parameters to form a composite risk level assessment result including the equipment collision probability and the construction process conflict index;

[0097] In this step, the node density distribution characteristics refer to the occurrence frequency and density of risk events at each node in the spatio-temporal risk topology network. These characteristics are used to dynamically adjust the weight allocation strategy of the construction machinery trajectory risk assessment parameters.

[0098] The construction machinery trajectory risk assessment parameters include factors such as the mass, movement speed, and acceleration of the equipment, and are used to evaluate the collision probability and construction process conflict index of the construction machinery at different time periods.

[0099] The weight allocation strategy refers to a method of dynamically adjusting the importance coefficients of each risk assessment parameter according to the node density distribution characteristics. This method is used to optimize the risk management strategy and improve the safety of the construction site.

[0100] The equipment collision probability refers to the possibility of collision between construction machinery or between construction machinery and fixed structures calculated based on the spatio-temporal risk topology network. This probability is used to evaluate the risk level during the construction process.

[0101] The construction process conflict index refers to the time and space conflict degree between different construction processes calculated based on the spatio-temporal risk topology network. This index is used to evaluate the coordination and efficiency during the construction process.

[0102] The composite risk level assessment result is a comprehensive risk assessment report generated by combining the equipment collision probability and the construction process conflict index. This report is used to guide subsequent risk management and construction scheduling.

[0103] In the embodiments of the present application, the node density distribution characteristics of the spatio-temporal risk topology network are analyzed to determine the risk levels of each node. According to the node density, the weight allocation strategy of the risk assessment parameters of the construction machinery trajectory is adjusted, the equipment collision probability and the construction process conflict index are calculated, and a composite risk level assessment result is formed. These results are used to guide subsequent risk management and construction scheduling.

[0104] In the subsequent construction stage of the commercial complex project, by analyzing the node density of the spatio-temporal risk topology network, it is found that the construction machinery activities in some areas are frequent and there is a high collision risk. The system automatically adjusts the weights of the risk assessment parameters in these areas and increases the key monitoring intensity. For example, during the construction of the basement parking lot, the system increases the risk assessment weights of the concrete pump truck and the steel bar transporter, effectively preventing multiple potential collision accidents.

[0105] 105. According to the composite risk level assessment result, reversely adjust the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array.

[0106] In this step, the stress threshold parameter refers to the maximum stress value that the load-bearing structure can withstand at different construction stages. This parameter is used to ensure the safety and stability of the structure during the construction process.

[0107] The signal sampling frequency configuration refers to the frequency setting of the ultra-wideband positioning device array for collecting data at different time periods. Increasing the sampling frequency during the peak period can improve the monitoring accuracy, while reducing the sampling frequency during the intermittent period can save resources.

[0108] In the embodiments of the present application, based on the equipment collision probability value in the composite risk level assessment result, a dynamic mapping relationship between the collision probability threshold and the stress threshold of the load-bearing structure is established. Analyze the time distribution characteristics of the construction process conflict index and adjust the sampling frequency of the ultra-wideband positioning device array. Set the coupling constraint conditions for the stress threshold parameter and the sampling frequency to ensure that the adjusted parameters do not exceed the structural safety boundary.

[0109] When the project is approaching the end, the system appropriately reduces the stress threshold parameter of some load-bearing structures according to the composite risk level assessment result, and increases the sampling frequency of the ultra-wideband positioning device array during the peak period to improve the monitoring accuracy. For example, during the installation stage of the top-layer steel structure, the system detects a high collision risk and temporarily increases the stress threshold and the sampling frequency, ensuring the construction safety and the smooth progress of the schedule.

[0110] In summary, steps 101 to 105 build an engineering management data model that integrates building information modeling and geographic information systems, deploy an ultra-wideband positioning device array, establish a digital twin-driven operation simulation environment, dynamically adjust the risk assessment parameters of construction machinery trajectories, and inversely adjust the stress threshold parameters in the engineering management data model according to the composite risk level assessment results. The entire solution realizes the comprehensive management and precise control of complex situations at the construction site. This method not only significantly improves the safety and efficiency of the construction site but also greatly reduces potential safety accidents and construction conflicts through accurate risk prediction and real-time warning mechanisms, ensuring the smooth progress of the project and the safety of personnel.

[0111] To solve the problems of risk prediction and management of complex situations at the construction site, this solution dynamically couples the risk elements in the dynamic knowledge graph with the spatial occupancy hot zone distribution data of mobile devices to generate a spatio-temporal risk topology network that includes early warnings of network device installation path conflicts and safety distance warnings. Based on the spatial coordinate set of pipeline embedding parameters and the safety distance threshold required by equipment installation specifications, a risk element association model is established, and spatio-temporal grid discretization analysis is performed in combination with the projection of equipment movement trends to calculate the probability value of network device installation path conflicts in each grid unit, thereby realizing the accurate prediction and real-time warning of potential risks at the construction site. In some embodiments, step 103 of dynamically coupling the risk elements in the dynamic knowledge graph with the spatial occupancy hot zone distribution data of mobile devices to generate a spatio-temporal risk topology network that includes early warnings of network device installation path conflicts and safety distance warnings includes:

[0112] 201. Based on the spatial coordinate set of pipeline embedding parameters in the dynamic knowledge graph and the safety distance threshold required by equipment installation specifications, establish a risk element association model;

[0113] In step 201, the pipeline embedding parameters include the specific three-dimensional coordinate positions and size information of all pipelines (such as water pipes, cables, etc.) embedded in the building. These data are used to determine the minimum clear distance between pipelines and avoid physical collisions. The spatial coordinate set refers to the set of specific coordinate points of these pipelines in three-dimensional space. The equipment installation specifications requirements include various standards and regulations that need to be followed during equipment installation, and the safety distance threshold is the minimum safety distance that needs to be maintained during equipment installation to prevent collisions between equipment or between equipment and fixed structures. The risk element association model is used to identify and evaluate potential risk factors during the construction process.

[0114] In the embodiments of the present application, first, pipeline embedded parameters in the dynamic knowledge graph are extracted to determine the specific coordinates and dimensions of each pipeline in the three-dimensional space. Combining with the safety distance threshold required by the equipment installation specifications, a risk factor association model is established. This model defines the minimum clear distance between pipelines and the safety distance during equipment installation to ensure that no physical collision occurs during the construction process. Finally, a detailed risk factor association model is generated through these parameters for risk assessment in subsequent steps.

[0115] 202. According to the real-time motion vectors in the mobile device space occupancy hot zone distribution data, calculate the three-dimensional trajectory envelopes of each mobile device within a preset time window, and generate a device motion trend projection through interpolation prediction.

[0116] In step 202, the real-time motion vectors describe a dataset of the motion direction and speed of the mobile device within a certain time window. The three-dimensional trajectory envelope is the spatial range that the device may occupy in the future period calculated according to the real-time motion vectors. Interpolation prediction uses mathematical methods (such as linear interpolation) to predict the future motion path of the device. The device motion trend projection is the prediction result of the future motion path of the device, using mathematical methods (such as linear interpolation) to predict the future motion path of the device.

[0117] In the embodiments of the present application, obtain the real-time motion vectors in the mobile device space occupancy hot zone distribution data, and within a preset time window, use the linear interpolation method to calculate the three-dimensional trajectory envelopes of each mobile device. By analyzing the current position and motion vectors of the device, predict its motion trend in the future period and generate a device motion trend projection. These data provide a basis for subsequent spatio-temporal grid discretization analysis.

[0118] 203. Perform spatio-temporal grid discretization analysis on the spatio-temporal influence domain in the risk factor association model and the device motion trend projection, and calculate the network device installation path conflict probability value for each grid cell. The network device installation path conflict probability value is obtained by weighting the area ratio of the device trajectory penetrating the pipeline influence domain and the time overlap coefficient.

[0119] In step 203, the spatio-temporal influence domain refers to the influence range of each risk factor in the risk factor association model in time and space. Spatio-temporal grid discretization analysis is to divide the three-dimensional space and the time axis into multiple cells, and calculate the area ratio of the device trajectory penetrating the pipeline influence domain and the time overlap coefficient for each cell. The path conflict probability value is the possibility of the device trajectory penetrating the pipeline influence domain, obtained by weighting the area ratio and the time overlap coefficient. The time overlap coefficient refers to the time overlap ratio between the device trajectory and the activation period of the pipeline influence domain.

[0120] In the embodiments of the present application, a spatio-temporal grid division mechanism aligned with the coordinate system of the risk factor association model is established. The three-dimensional space coordinate axes are discretized into cubic units, and the time axis is divided into time windows according to the construction progress. Each spatio-temporal grid unit is traversed to extract the trajectory segments of the projected movement trend of the equipment, and the geometric intersection volume between the trajectory segments and the pipeline influence domain is calculated. The ratio of the intersection volume to the total volume of the pipeline influence domain is used as the spatial permeability, and the intersection duration between the trajectory segments and the activation period of the pipeline influence domain within the time window is calculated as the time coverage. Finally, preset spatial permeability weight factors and time coverage weight factors are obtained according to the pipeline type parameters, and a dynamic probability value reflecting the pipeline collision risk is generated.

[0121] 204. Construct a dynamic safety distance threshold function based on the stress threshold parameter of the load-bearing structure, and adjust the safety distance threshold in real time according to the equipment mass parameter and the movement acceleration.

[0122] In step 204, the stress threshold parameter of the load-bearing structure refers to the maximum stress value that the load-bearing structure can withstand at different construction stages. The dynamic safety distance threshold function is a function constructed based on the stress threshold parameter of the load-bearing structure. The equipment mass parameter refers to the mass and movement characteristics of the equipment, such as mass, acceleration, etc. The movement acceleration refers to the acceleration value generated by the equipment during movement.

[0123] In the embodiments of the present application, a dynamic safety distance threshold function is constructed according to the stress threshold parameter of the load-bearing structure. This function takes into account the mass and movement acceleration of the equipment and adjusts the safety distance between equipment or between equipment and fixed structures in real time. The mass and movement acceleration of the equipment are monitored in real time through sensors, and the safety distance threshold is dynamically adjusted to ensure the safe operation of the equipment during construction.

[0124] 205. Integrate the conflict probability value of the network equipment installation path and the safety distance threshold to construct a spatio-temporal association matrix, and generate a spatio-temporal risk topology network including warnings for network equipment installation path conflicts and safety distance alarms.

[0125] In step 205, the spatio-temporal association matrix integrates the conflict probability value of the network equipment installation path and the safety distance threshold and is used to generate a spatio-temporal risk topology network. The spatio-temporal risk topology network includes a network structure for warnings of network equipment installation path conflicts and safety distance alarms, and is used to identify and avoid potential risks during construction in advance.

[0126] In the embodiments of the present application, the conflict probability value of the network equipment installation path calculated in step 203 is combined with the safety distance threshold generated in step 204 to construct a spatio-temporal association matrix. This matrix includes the conflict probability value and the safety distance threshold of each spatio-temporal grid unit, forming a spatio-temporal risk topology network. This network can warn of equipment installation path conflicts and insufficient safety distances in real time, helping to optimize the construction plan and resource allocation.

[0127] The following is a specific example:

[0128] In a large commercial complex project, first collect all the pre-embedded pipeline parameters, including the positions and dimensions of water pipes, cables, etc., and generate a risk factor association model. The pre-embedded pipeline parameters include the specific three-dimensional coordinate positions and dimension information of all pipelines pre-embedded in the building, and these data are used to determine the minimum clear distance between pipelines and avoid physical collisions. At the same time, use an ultra-wideband positioning device array to monitor the movement trajectories of heavy machinery such as tower cranes and excavators in real time, and generate data on the distribution of hot zones of equipment space occupancy. Next, according to the real-time motion vector of the equipment, generate a projection of the equipment motion trend through interpolation prediction. The projection of the equipment motion trend is the prediction result of the future motion path of the equipment. Then, calculate the conflict probability value of the installation path of network equipment for each grid cell. Based on the stress threshold parameter of the load-bearing structure, construct a dynamic safety distance threshold function to adjust the safety distance threshold in real time. Finally, fuse the conflict probability value of the installation path of network equipment with the safety distance threshold. During the entire construction process, the system can identify and warn of potential conflicts in the installation path of equipment and insufficient safety distances in advance, adjust the construction plan in time, and ensure the smooth progress of the project.

[0129] In summary, steps 201 to 205 achieve comprehensive management and precise control of the complex situation at the construction site. By establishing a risk factor association model, calculating the projection of the equipment motion trend, conducting spatio-temporal grid discretization analysis, adjusting the safety distance threshold in real time, and constructing a spatio-temporal association matrix, a spatio-temporal risk topology network including early warnings of conflicts in the installation path of network equipment and safety distance alarms is formed. This method not only significantly improves the safety and efficiency of the construction site, but also greatly reduces potential safety accidents and construction conflicts through accurate risk prediction and real-time warning mechanisms, ensuring the smooth progress of the project and the safety of personnel.

[0130] To solve the problems of risk prediction and management of complex situations at the construction site, the solution establishes a spatio-temporal grid division mechanism aligned with the coordinate system of the risk factor association model, discretizes the three-dimensional space coordinate axes into cube units, and divides time windows according to the construction progress to obtain spatio-temporal grid cells. Traverse the spatio-temporal grid cells to extract the trajectory segments of the projection of the equipment motion trend, calculate the geometric intersection volume and time coverage, and obtain the spatial penetration weight factor and time coverage weight factor according to the pipeline type parameters to generate a dynamic probability value reflecting the pipeline collision risk, thereby improving the accuracy and reliability of risk assessment. In some embodiments, in step 203, the spatio-temporal grid discretization analysis of the spatio-temporal influence domain in the risk factor association model and the projection of the equipment motion trend, and calculating the conflict probability value of the installation path of network equipment for each grid cell includes:

[0131] 301. Establish a spatio-temporal grid division mechanism aligned with the coordinate system of the risk factor association model. Discretize the three-dimensional space coordinate axes into cubic cells, and divide the time axis into time windows according to the construction progress to obtain spatio-temporal grid cells.

[0132] In step 301, the spatio-temporal grid division mechanism is a method of dividing the three-dimensional space coordinate axes and the time axis into multiple cells. Cubic cells are a series of small cubes into which the three-dimensional space coordinate axes are discretized, and each cube represents a specific spatial region. These cubic cells constitute the basic analysis unit of the three-dimensional space. Spatio-temporal grid cells are the smallest analysis units generated by the above division mechanism and are used for subsequent calculations of spatial permeability and time coverage.

[0133] In the embodiment of the present application, first, a spatio-temporal grid division mechanism aligned with the coordinate system of the risk factor association model is established. The three-dimensional space coordinate axes are discretized into cubic cells, and each cubic cell represents a spatial region. At the same time, the time axis is divided into several time windows according to the construction progress, and each time window represents a period of time. In this way, a plurality of spatio-temporal grid cells are formed, and each cell contains specific spatial position and time period information. These spatio-temporal grid cells provide a basis for subsequent trajectory segment analysis.

[0134] 302. Traverse the spatio-temporal grid cells, extract the trajectory segments of the projection of the equipment movement trend, calculate the geometric intersection volume of the trajectory segments, and use the ratio of the geometric intersection volume to the total volume of the pipeline influence domain as the spatial permeability.

[0135] In step 302, a trajectory segment is a part of the movement trajectory within a specific spatio-temporal grid cell extracted from the projection of the equipment movement trend. The geometric intersection volume is the overlapping volume between the trajectory segment and the pipeline influence domain. The pipeline influence domain refers to the safety distance range that needs to be maintained during construction to avoid physical collision of the pipeline by equipment or structures. The spatial permeability is the ratio of the geometric intersection volume to the total volume of the pipeline influence domain, indicating the degree to which the equipment trajectory penetrates the pipeline influence domain.

[0136] In the embodiment of the present application, first, each spatio-temporal grid cell is traversed. Then, the trajectory segments of the projection of the equipment movement trend are extracted, and the geometric intersection volume between the trajectory segment and the pipeline influence domain is calculated. The overlapping volume between the trajectory segment and the pipeline influence domain is determined through geometric algorithms (such as Boolean operations). Then, the ratio of the geometric intersection volume to the total volume of the pipeline influence domain is used as the spatial permeability. This step quantifies the degree of influence of the equipment trajectory on the pipeline within a specific spatio-temporal grid cell.

[0137] 303. Calculate the intersection duration of the trajectory segment with the active period of the pipeline influence domain within the time window, and use the ratio of the intersection duration to the preset time window length as the time coverage.

[0138] In step 303, the intersection duration is the overlapping time period of the trajectory segment within the time window and the activation period of the pipeline influence area. The activation period of the pipeline influence area refers to the time period during which the pipeline is in an active state during construction, such as the time period when laying or maintenance is in progress. The time coverage is the ratio of the intersection duration to the length of the preset time window, indicating the coverage degree of the equipment trajectory on the pipeline influence area in terms of time.

[0139] In the embodiment of the present application, first, calculate the intersection duration of the trajectory segment within the time window and the activation period of the pipeline influence area. Then, by comparing the time intervals of the trajectory segment and the pipeline influence area, find the overlapping time period between the two. Then, take the ratio of the intersection duration to the length of the preset time window as the time coverage. This step quantifies the influence degree of the equipment trajectory on the pipeline in the time dimension within a specific spatio-temporal grid cell and provides key data for further risk assessment.

[0140] 304. Obtain preset spatial penetration weight factors and time coverage weight factors according to the pipeline type parameter, perform a linear combination of the spatial permeability and the time coverage to generate a dynamic probability value reflecting the pipeline collision risk;

[0141] In step 304, the spatial penetration weight factor is the importance coefficient of the spatial permeability set according to the pipeline type parameter. The time coverage weight factor is the importance coefficient of the time coverage set according to the pipeline type parameter. The pipeline collision risk refers to the possibility and severity of the equipment trajectory overlapping with the pipeline influence area in terms of time and space, and is used to evaluate the potential collision risk during construction. The dynamic probability value is a probability value reflecting the pipeline collision risk generated by linearly combining the spatial permeability and the time coverage.

[0142] In the embodiment of the present application, first, obtain preset spatial penetration weight factors and time coverage weight factors according to the pipeline type parameter. Then, use the linear combination formula to perform weighted summation of the spatial permeability and the time coverage to generate a dynamic probability value reflecting the pipeline collision risk. For example, associate the spatial permeability, the spatial penetration weight factor, the time coverage, and the time coverage weight factor to generate the dynamic probability value. Then, comprehensively consider the risk factors in both the spatial and time dimensions to generate a quantified collision risk assessment value for subsequent risk management and warning.

[0143] 305. When the projection of the equipment movement trend involves a multi-pipeline intersection area, perform superposition calculation on the dynamic probability values of the pipelines within the same spatio-temporal grid cell to generate a network equipment installation path conflict probability value.

[0144] In step 305, the multi-pipeline intersection area is the overlapping area where multiple pipelines are within the same spatio-temporal grid cell. The superposition calculation is that when the projection of the device movement trend involves the multi-pipeline intersection area, the dynamic probability values of each pipeline within the same spatio-temporal grid cell are accumulated to generate the final network device installation path conflict probability value. The network device installation path conflict probability value is the total collision risk probability value after comprehensively considering the impacts of all pipelines, and is used to warn of potential conflicts in the device installation path.

[0145] In the embodiments of the present application, first, the multi-pipeline intersection area involved in the projection of the device movement trend is identified. Then, within these areas, the dynamic probability value of each pipeline is calculated separately. Next, the dynamic probability values of each pipeline within the same spatio-temporal grid cell are subjected to superposition calculation to generate the total network device installation path conflict probability value. This step ensures that all potential risk factors are fully considered in a complex environment, thereby providing a more accurate risk assessment result to help optimize the construction plan and reduce potential conflicts.

[0146] The following is a specific example:

[0147] In a large commercial complex project, assume that the installation work of the intelligent parking system in the underground parking lot is in progress. First, based on the construction site layout plan and the project schedule, a corresponding spatio-temporal grid system is established. Then, for each construction vehicle and the equipment it carries, its expected movement path is drawn, and the spatial penetration rate and time coverage with the existing underground water supply and drainage pipelines are calculated. Next, appropriate weights are assigned according to the characteristics of each type of pipeline, and the collision risk is comprehensively evaluated. Finally, for those grid cells with high dynamic probability values, preventive measures are taken, such as adjusting the construction sequence or changing the equipment transportation route, thus effectively avoiding possible collision accidents.

[0148] In summary, steps 301 to 305 achieve the comprehensive management and precise control of the complex situation at the construction site. By establishing a spatio-temporal grid division mechanism, calculating the geometric intersection volume and time coverage, a spatio-temporal risk topology network including network device installation path conflict warning and safety distance warning is formed. This method not only significantly improves the safety and efficiency of the construction site, but also greatly reduces potential safety accidents and construction conflicts through accurate risk prediction and real-time warning mechanisms, ensuring the smooth progress of the project and the safety of personnel.

[0149] To solve the problem of pipeline collision risk assessment in large commercial complex projects, this solution linearly combines the spatial permeability and the time coverage to generate a dynamic probability value reflecting the pipeline collision risk. By establishing a mapping relation table of pipeline type parameters, spatial penetration weight factors, and time coverage weight factors, a benchmark value is set for the engineering attribute characteristics and dynamically corrected. A two-dimensional weight vector is constructed to perform homogeneous coordinate transformation on the spatial permeability and the time coverage, generating a risk vector containing spatio-temporal characteristics. Combining the electromagnetic interference intensity parameter in the pipeline crossing area as an environmental correction factor, a final dynamic probability value is generated to improve the comprehensiveness and accuracy of the risk assessment. In some embodiments, the linear combination of the spatial permeability and the time coverage in step 304 to generate a dynamic probability value reflecting the pipeline collision risk includes:

[0150] 401. Establish a mapping relation table of pipeline type parameters, spatial penetration weight factors, and time coverage weight factors, set the benchmark values of the spatial permeability and the time coverage for the engineering attribute characteristics, and dynamically correct the benchmark values according to the current construction stage parameters;

[0151] In step 401, the pipeline type parameters are characteristic parameters describing different types of pipelines, such as material, diameter, use, etc. The spatial penetration weight factor is an importance coefficient of the spatial permeability set according to the pipeline type parameters. The time coverage weight factor is an importance coefficient of the time coverage set according to the pipeline type parameters. The mapping relation table is a corresponding relation table established between the pipeline type parameters and the spatial penetration weight factors and the time coverage weight factors. The benchmark value is the basic value of the spatial permeability and the time coverage set for the engineering attribute characteristics. The construction stage parameters are the specific stage information of the current construction, used to dynamically correct the benchmark value.

[0152] In the embodiments of the present application, first, a detailed pipeline type parameter table is established, and corresponding spatial penetration weight factors and time coverage weight factors are set according to different pipeline types (such as water pipes, cables, etc.). Then, according to the characteristics of large commercial complex projects, initial benchmark values of the spatial permeability and the time coverage are set. Then, according to the construction progress and the requirements of different stages, these benchmark values are dynamically corrected to ensure the accuracy and applicability of the data. Finally, through these corrected benchmark values, an accurate data basis is provided for the subsequent steps.

[0153] 402. According to the corrected benchmark values, construct a two-dimensional weight vector, perform homogeneous coordinate transformation on the spatial permeability and the time coverage, and generate a risk vector containing spatio-temporal characteristics;

[0154] In step 402, the two-dimensional weight vector includes a spatial penetration weight factor and a time coverage weight factor, which are used to represent spatio-temporal features. Homogeneous coordinate transformation is a mathematical transformation method that converts spatial permeability and time coverage into a risk vector containing spatio-temporal features. Spatio-temporal features are used to describe the distribution of underground pipelines in their surrounding environment and the risk status of these pipelines over time. The risk vector is a vector generated after homogeneous coordinate transformation and contains information about spatio-temporal features.

[0155] In the embodiment of the present application, based on the corrected reference value, a two-dimensional weight vector is constructed, which includes a spatial penetration weight factor and a time coverage weight factor. Then, using the homogeneous coordinate transformation method, the spatial permeability and time coverage are transformed to generate a risk vector containing spatio-temporal features. Specifically, through matrix operations, the original data is transformed into a vector in a new coordinate system, enabling the integration of information in the spatial and time dimensions. This step generates a risk vector containing spatio-temporal features, providing the basic data for further calculations.

[0156] 403. Calculate the risk coupling coefficient according to the pipeline type parameter and the construction machinery type parameter, and perform a tensor product operation on the risk vector and the risk coupling coefficient to obtain an initial probability tensor;

[0157] In step 403, the risk coupling coefficient is a coefficient calculated according to the pipeline type parameter and the construction machinery type parameter, and is used to measure the mutual influence between different factors. The tensor product operation is a method of operating on the risk vector and the risk coupling coefficient to generate an initial probability tensor. The initial probability tensor is a multi-dimensional array generated after the tensor product operation and is used to represent the preliminary risk assessment result.

[0158] In the embodiment of the present application, first, the risk coupling coefficient is calculated according to the specific pipeline type and construction machinery type parameters. Then, using the tensor product operation method, the previously generated risk vector is combined with the risk coupling coefficient to generate an initial probability tensor. Specifically, through tensor operations, the spatio-temporal features in the risk vector are combined with the risk coupling coefficient to generate a multi-dimensional array, that is, the initial probability tensor. This process realizes the fusion of multi-dimensional data through tensor operations, ensuring the comprehensiveness and accuracy of the risk assessment.

[0159] 404. Introduce the electromagnetic interference intensity parameter in the pipeline crossing area as an environmental correction factor, and perform a non-linear mapping on the initial probability tensor to generate a dynamic probability value reflecting the pipeline collision risk.

[0160] In step 404, the electromagnetic interference intensity parameter is the electromagnetic interference intensity present in the pipeline crossing area and serves as an environmental correction factor. The environmental correction factor refers to a parameter used to adjust the risk assessment model to reflect the impact of external environmental factors on the assessment results. Nonlinear mapping is a mathematical mapping method that combines the initial probability tensor with the environmental correction factor to generate the final dynamic probability value. The dynamic probability value is the final risk assessment value generated after nonlinear mapping and reflects the actual risk level of pipeline collision.

[0161] In the embodiments of the present application, first, the electromagnetic interference intensity parameter in the pipeline crossing area is introduced as an environmental correction factor. Then, using the nonlinear mapping method, the initial probability tensor is combined with the environmental correction factor to generate the final dynamic probability value. Specifically, the environmental correction factor and the initial probability tensor are combined through a nonlinear function to generate the final risk assessment value. This process realizes the further correction of data through the nonlinear function, ensuring the authenticity and reliability of the risk assessment results.

[0162] The following is a specific example:

[0163] In a large commercial complex project, for various underground pipelines (such as electricity, communication, etc.), first, a mapping relationship table of the parameters corresponding to each pipeline type and its relationship with the space penetration weight factor and the time coverage weight factor is established. Considering the frequent excavation work in the initial stage of the project, the space permeability and time coverage are adjusted accordingly. Subsequently, a two-dimensional weight vector is constructed using the adjusted parameters, and a risk vector is generated through homogeneous coordinate transformation. Next, the risk coupling coefficient is calculated according to the type of machinery used at the construction site, and a tensor product operation is performed with the risk vector to obtain the initial probability tensor. Finally, considering the strong electromagnetic interference present at the site, this is used as an environmental correction factor to perform nonlinear mapping on the initial probability tensor, obtaining a more accurate dynamic probability value of the pipeline collision risk.

[0164] In summary, steps 401 to 404 not only improve the accuracy of risk prediction by systematically quantifying and dynamically adjusting the key parameters in the pipeline collision risk assessment process, but also significantly reduce the safety hazards caused by pipeline collisions during construction, improving the construction efficiency and safety of the entire project. In addition, this method can flexibly adapt to different construction stages and environmental changes, providing a continuously optimized risk management strategy.

[0165] To solve the problems of conflicts and risks in the collaborative design and construction processes of multiple specialties in construction projects, the solution integrates a multi-dimensional risk factor set including pipeline embedded parameter, load-bearing structure stress threshold, and equipment installation specification requirements, and forms a dynamic knowledge graph containing spatial constraint conditions and construction logic rules. Based on the pipeline embedded parameters, spatial collision rules are generated, the equipment installation specifications are transformed into a construction logic rule chain, and through the spatial coordinate system of the building information model, topological association coding is performed to define the spatial interference relationship between pipelines and load-bearing structures and the logical dependency relationship between construction processes and equipment trajectories, so as to achieve comprehensive integration and management of risk factors. In some embodiments, the step of integrating the multi-dimensional risk factor set including pipeline embedded parameter, load-bearing structure stress threshold, and equipment installation specification requirements in step 101 to form a dynamic knowledge graph containing spatial constraint conditions and construction logic rules includes:

[0166] 501. Based on the pipeline embedded parameters, generate spatial collision rules through the minimum clear distance constraint between pipelines, and the spatial collision rules include three-dimensional buffer zone parameters for the pipeline intersection area;

[0167] In step 501, the pipeline embedded parameters refer to the technical parameters such as the size, position, and material of various pipelines (such as water supply and drainage pipes, cable pipes, etc.) determined in the building design stage. The minimum clear distance constraint refers to the minimum distance that must be maintained between different pipelines to avoid mutual interference or damage. The three-dimensional buffer zone parameters define the safe range of the pipeline intersection area to ensure that there are no other pipelines or obstacles in this area.

[0168] In the embodiments of the present application, by analyzing different types of pipelines and their required safety distances, building information model technology is used for pipeline layout planning, and a collision detection algorithm is adopted to identify possible spatial conflicts. Combining the physical dimensions and functional requirements of the pipelines, the minimum clear distance between each pair of pipelines is determined, thereby generating spatial collision rules. The final result is a three-dimensional model that includes the safety distance requirements between all pipelines.

[0169] 502. Analyze the time-varying parameters of the load-bearing structure stress threshold, establish a stress propagation path model in combination with the topological relationship of the support points, and generate dynamic constraint conditions including the maximum allowable load according to the mechanical transfer characteristics between the support points;

[0170] In step 502, the time-varying parameters of the load-bearing structure stress threshold refer to the change situation of the maximum stress value borne by the building load-bearing structure under different usage conditions, and these parameters are adjusted over time. The topological relationship of the support points describes the connection method and interaction relationship between each support point in the load-bearing structure. The stress propagation path model simulates the process of stress transfer from one support point to another support point to help understand the distribution and transfer path of force.

[0171] In the embodiments of the present application, first, a detailed mechanical analysis of the building structure is carried out to obtain the initial design data and expected loads of each support point. Then, the finite element analysis method is used to calculate the stress distribution of each support point under different working conditions. Next, based on the stress propagation law, a mechanical transfer network between the support points is constructed to simulate the process of stress transfer from one support point to another. Through the iterative optimization algorithm, the maximum allowable load of each node is determined and converted into dynamic constraint conditions. Finally, a stress propagation path model including the maximum allowable load is formed and integrated into the dynamic knowledge graph.

[0172] 503. Convert the positioning tolerance, fastening torque, and heat dissipation spacing parameters in the equipment installation specification requirements into a construction logic rule chain, and the construction logic rule chain includes process execution priority parameters and mutual exclusion trigger conditions for parallel operations.

[0173] In step 503, the positioning tolerance refers to the allowable range of position deviation during equipment installation to ensure that the equipment can be correctly installed and operate normally. The fastening torque refers to the tightening force required for bolts or fasteners during installation to ensure the firmness and stability of the equipment. The heat dissipation spacing refers to the space required to be reserved around the equipment for effective heat dissipation to prevent overheating from affecting the performance or lifespan of the equipment.

[0174] In the embodiments of the present application, first, according to the equipment installation manual and technical standards provided by the manufacturer, key parameters such as positioning tolerance, fastening torque, and heat dissipation spacing are extracted. Then, these parameters are converted into specific construction logic rule chains to formulate detailed installation guidelines. Next, the workflow engine is used to convert these guidelines into an executable task sequence, setting process priorities and mutual exclusion conditions. Finally, by simulating the operation process of the construction site, the rationality and feasibility of the task sequence are verified, and these construction logic rule chains are integrated into the dynamic knowledge graph to ensure an orderly and efficient construction process.

[0175] 504. Based on the spatial coordinate system of the building information model, perform topological association encoding on the spatial collision rules, dynamic constraint conditions, and construction logic rule chains. By defining the spatial interference relationship between pipelines and load-bearing structures and the logical dependence relationship between construction processes and equipment trajectories, a dynamic knowledge graph is formed.

[0176] In step 504, the spatial coordinate system of the building information model refers to a three-dimensional coordinate system established based on building information model technology for accurately positioning the positions of building components and equipment. The topological association encoding encodes different building elements (such as pipelines, load-bearing structures) and the relationships between them for easy computer processing and analysis. The logical dependence relationship describes the sequence or parallel relationship between construction processes to ensure a reasonable and efficient construction process. The dynamic knowledge graph is a knowledge system based on the building information model and related data sources, forming a knowledge system including spatial constraint conditions and construction logic rules.

[0177] In the embodiments of the present application, first, based on the spatial coordinate system of the building information model, precise spatial positioning of all building elements is performed. Then, the topological association coding technology is adopted to code the spatial interference relationship between pipelines and load-bearing structures and the logical dependency relationship between construction processes and equipment trajectories. Then, by defining these relationships, a dynamic knowledge graph framework is constructed. Finally, the spatial collision rules, dynamic constraint conditions, and construction logic rule chains in the previous steps are integrated to form a complete dynamic knowledge graph to support the full life cycle management of the project.

[0178] The following is a specific example:

[0179] In a large commercial complex project, first, the design information of architecture, structure, and mechanical and electrical specialties is integrated through building information model software, and the collision detection technology is used to discover and solve potential conflicts in pipeline layout. Then, stress analysis is carried out on the main load-bearing members to optimize the support structure design. Subsequently, a detailed construction plan is prepared according to the equipment installation specifications to ensure the smooth progress of on-site operations. Finally, a dynamic knowledge graph is constructed based on the above results to support the full life cycle management of the project.

[0180] In summary, steps 501 to 504 achieve risk control and efficiency improvement in the whole process of building engineering from design to construction. Through precise spatial planning and reasonable construction arrangements, the number of engineering changes and reworks is reduced, and the overall quality and economic benefits of the project are improved. This solution not only enhances the operability and safety of the project but also provides strong support for subsequent maintenance and management.

[0181] To solve the accuracy control and safety problems in the equipment installation process of building engineering, this solution converts the positioning tolerance, fastening torque, and heat dissipation spacing parameters in the equipment installation specification requirements into a construction logic rule chain. By establishing a mapping relationship table between equipment types and installation parameters, equipment placement verification rules, installation strength rules, and thermal field mutual exclusion trigger rules are generated and sequenced according to the construction process stages to form a complete construction logic rule chain. This process ensures that all links in the equipment installation process can follow strict standards and sequences, avoiding potential operation errors and safety hazards. In some embodiments, the conversion of the positioning tolerance, fastening torque, and heat dissipation spacing parameters in the equipment installation specification requirements into a construction logic rule chain in step 503 includes:

[0182] 601. Establish a mapping relationship table between the equipment types and installation parameters in the equipment installation specification requirements, and generate equipment placement verification rules according to the horizontal deviation threshold and vertical settlement tolerance in the positioning tolerance parameters. The equipment placement verification rules include the linkage constraint conditions of the allowable deviation range and the spacing between adjacent equipment;

[0183] In step 601, the mapping relationship table between equipment type and installation parameters contains detailed information of different equipment types and their corresponding installation parameters, such as positioning tolerance, tightening torque, etc. The horizontal deviation threshold is the maximum distance that the equipment is allowed to deviate from the design position in the horizontal direction. The vertical settlement tolerance is the maximum distance that the equipment is allowed to settle in the vertical direction. The equipment in-place verification rules are rules generated based on the above deviation thresholds, which are used to ensure that the equipment is installed correctly and take into account the spacing constraints between adjacent equipment.

[0184] In the embodiment of the present application, firstly, the design data and installation manuals of all the equipment involved are collected, and a mapping relationship table between the equipment type and the installation parameters is established. Then, according to the horizontal deviation threshold and the vertical settlement tolerance in the positioning tolerance parameters, the equipment in-place verification rules are generated using geometric modeling technology. Then, by analyzing the layout of adjacent equipment, the minimum safe spacing between adjacent equipment is calculated and determined to form linkage constraints. The final result is a set of verification rules that include all equipment installation accuracy requirements.

[0185] 602. Analyze the functional relationship between the tightening torque and the equipment quality parameters, establish a torque gradient adjustment model, and generate installation strength rules including the torque graded loading strategy and the tightening sequence dependency;

[0186] In step 602, the functional relationship between the tightening torque and the equipment mass parameter describes how the tightening torque is adjusted as the equipment mass changes. The torque gradient adjustment model simulates the change law of the tightening torque required for equipment of different masses to ensure that the equipment is firm and stable. The torque graded loading strategy applies different tightening torques in stages according to the equipment mass and installation requirements; the tightening sequence dependency specifies the order in which the components are tightened during the equipment installation process to avoid stress concentration or structural deformation.

[0187] In the embodiment of the present application, the relationship between the tightening torque and the equipment quality parameters provided by the equipment manufacturer is first analyzed, and a torque gradient adjustment model is established using a regression analysis method. Next, a torque graded loading strategy is generated based on the model to ensure that different quality equipment can obtain appropriate tightening torques. Then, a detailed tightening sequence dependency is formulated to ensure that each tightening point is operated in the correct order. Finally, these rules are integrated to form an installation strength rule that includes a torque graded loading strategy and a tightening sequence dependency.

[0188] 603. Based on the thermal radiation attenuation coefficient in the heat dissipation spacing parameter and the device power parameter, the dynamic safe heat dissipation distance is calculated, and the thermal field mutual exclusion triggering rules when the device cluster is deployed are generated;

[0189] In step 603, the heat radiation attenuation coefficient describes the attenuation rate of heat when it propagates in the air and affects the heat dissipation distance between devices. The device power parameter refers to the amount of heat generated when the device is running. The dynamic safe heat dissipation distance is the safe heat dissipation distance calculated based on factors such as device power and ambient temperature, ensuring that overheating does not occur during the deployment of the device cluster. The heat field mutual exclusion trigger rule defines the minimum heat dissipation distance that should be maintained between devices, preventing device overheating failure caused by heat accumulation.

[0190] In the embodiment of the present application, first, the power parameters and heat radiation attenuation coefficients of the devices are collected, and the dynamic safe heat dissipation distances of each device are calculated using the heat conduction model. Then, in combination with the actual layout plan of the device cluster, the heat radiation effects between each device are evaluated to generate the heat field mutual exclusion trigger rule. Then, by simulating the heat dissipation effects under different device layout plans, the heat field mutual exclusion trigger rule is verified and optimized. The final result is a complete plan including the heat field mutual exclusion trigger rule during the deployment of the device cluster.

[0191] 604. Arrange the device in-place verification rule, installation strength rule, and heat field mutual exclusion trigger rule in chronological order according to the construction process stages to obtain a construction logic rule chain.

[0192] In step 604, the construction process stages refer to different stages in the device installation process, such as foundation preparation, device hoisting, fastener installation, etc. Chronological arrangement sorts various construction logic rules in chronological order to ensure the orderly progress of each task. The construction logic rule chain integrates all device in-place verification rules, installation strength rules, and heat field mutual exclusion trigger rules to form a complete construction process guidance plan.

[0193] In the embodiment of the present application, first, sort out each process stage in the device installation process and clarify the tasks and goals of each stage. Then, arrange the device in-place verification rule, installation strength rule, and heat field mutual exclusion trigger rule in chronological order according to the process stages to ensure that each task is completed on time and there are no conflicts. Then, use project management software to simulate and optimize the entire construction process to ensure that all rules can be effectively executed. Finally, integrate all the content to obtain a complete construction logic rule chain to support the efficient implementation of the project.

[0194] The following is a specific example:

[0195] In a large commercial complex project, first, integrate the design information through building information modeling software and establish a mapping relationship table between equipment types and installation parameters. Then, use geometric modeling technology to generate equipment installation verification rules and determine linkage constraint conditions by analyzing the layout of adjacent equipment. Next, analyze the relationship between fastening torque and equipment mass parameters to generate a torque grading loading strategy and fastening sequence dependency. At the same time, collect equipment power parameters and heat radiation attenuation coefficients, calculate the dynamic safe heat dissipation distance, and generate a mutual exclusion trigger rule for the thermal field. Finally, arrange all the rules in chronological order according to the construction process stages to form a complete construction logic rule chain, ensuring the efficiency and safety of the equipment installation process.

[0196] In summary, steps 601 to 604 achieve high-precision control and safety guarantee during the equipment installation process. Through precise space planning and reasonable construction arrangements, installation errors and the risk of equipment overheating are reduced, improving the overall quality and economic benefits of the project. This solution not only enhances the operability and safety of the project but also provides strong support for subsequent maintenance and management. The application of the dynamic knowledge graph makes the construction process more intelligent and automated, greatly improving the efficiency and accuracy of project management.

[0197] To solve the dynamic adjustment problem after the composite risk assessment in construction projects, the solution reversely adjusts the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array according to the composite risk level assessment result. Generate the stress threshold parameter by establishing a dynamic mapping relationship between the collision probability threshold and the stress threshold of the load-bearing structure, and adjust the sampling frequency according to the time distribution characteristics of the construction process conflict index, increasing the sampling frequency during the peak period to improve accuracy and reducing the sampling frequency during the intermittent period to save resources. At the same time, set the coupling constraint conditions for the stress threshold parameter and the sampling frequency to ensure the safety and efficiency of the construction process. In some embodiments, the reverse adjustment of the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array according to the composite risk level assessment result described in step 105 includes:

[0198] 701. Based on the equipment collision probability value in the composite risk level assessment result, establish a dynamic mapping relationship between the collision probability threshold and the stress threshold of the load-bearing structure to generate the stress threshold parameter;

[0199] In step 701, the composite risk level assessment result is a risk assessment result obtained by comprehensively considering various factors such as the equipment collision probability and the construction process conflict index. The equipment collision probability value refers to the likelihood of collision between different equipment during the construction process. The collision probability threshold sets a critical value, and when the actual collision probability exceeds this value, corresponding adjustment measures are triggered. The stress threshold of the load-bearing structure refers to the maximum stress value that the building's load-bearing structure can withstand under different working conditions. The stress threshold parameter is a parameter generated based on the collision probability threshold for controlling the safety of the load-bearing structure.

[0200] In the embodiment of the present application, first, historical data of the equipment collision probability and design parameters of the current project are collected. Then, the collision probability threshold is determined through statistical analysis methods, and a dynamic mapping relationship between it and the stress threshold of the load-bearing structure is established. Next, finite element analysis technology is used to calculate the structural safety under different stress conditions, and stress threshold parameters are generated. The final result is a solution that can reflect the equipment collision risk in real time and adjust the stress threshold.

[0201] 702. Analyze the time distribution characteristics of the construction process conflict index, divide the construction period into a peak period and an intermittent period in combination with construction stage parameters, increase the sampling frequency of the ultra-wideband positioning device array according to the predicted deviation amplitude of the mobile device trajectory during the peak period, and reduce the sampling frequency proportionally according to the change rate of the hot zone distribution of the mobile device space occupancy during the intermittent period;

[0202] In step 702, the time distribution characteristics of the construction process conflict index describe the frequency and time distribution of conflicts occurring in each stage during the construction process. The peak period and the intermittent period of the construction period divide the construction process into a high-load working period (peak period) and a low-load working period (intermittent period). The predicted deviation amplitude of the mobile device trajectory refers to the range of position prediction error caused by the movement of the device during the peak period.

[0203] In the embodiment of the present application, first, analyze the time distribution characteristics of the construction process conflict index, and divide the construction period into a peak period and an intermittent period in combination with construction stage parameters. Then, increase the sampling frequency of the ultra-wideband positioning device array according to the predicted deviation amplitude of the mobile device trajectory during the peak period to improve the position tracking accuracy. Next, reduce the sampling frequency proportionally according to the change rate of the hot zone distribution of the mobile device space occupancy during the intermittent period to save resources. The final result is to dynamically adjust the signal sampling frequency of the ultra-wideband positioning device according to the construction progress to ensure efficient and economical device tracking.

[0204] 703. Establish a coupling constraint condition between the stress threshold parameter and the sampling frequency, so that when the stress threshold parameter is triggered to decrease and the sampling frequency is triggered to increase simultaneously, the adjustment of the stress threshold parameter does not exceed the structural safety boundary;

[0205] In step 703, the coupling constraint condition between the stress threshold parameter and the sampling frequency ensures that when adjusting the stress threshold and the sampling frequency simultaneously, the structural safety boundary will not be exceeded. The structural safety boundary refers to the maximum stress and the minimum safety factor allowed for the load-bearing structure of the building. The coupling constraint condition defines the relationship between the downward adjustment of the stress threshold and the upward adjustment of the sampling frequency, preventing over-adjustment from affecting the overall safety.

[0206] In the embodiments of the present application, first, a coupling constraint condition between the stress threshold parameter and the sampling frequency is established to ensure that the adjustment of both does not exceed the structural safety boundary. Then, an optimization algorithm is used to simulate the impact of different adjustment strategies on structural safety and device positioning accuracy to find the optimal balance point. Next, detailed adjustment rules are formulated so that when the downward adjustment of the stress threshold parameter and the upward adjustment of the sampling frequency are triggered simultaneously, the adjustment of the stress threshold parameter does not exceed the structural safety boundary. The final result is a coupling constraint mechanism that can ensure both structural safety and improve device positioning accuracy.

[0207] 704. Inject the adjusted stress threshold parameter back into the load-bearing structure node attributes of the engineering management data model, and at the same time, send the coupling constraint condition to the signal sampling frequency configuration of the ultra-wideband positioning device array.

[0208] In step 704, the adjusted stress threshold parameter refers to the new stress threshold generated according to the composite risk level assessment result. The load-bearing structure node attributes of the engineering management data model include the design parameters and stress thresholds of each load-bearing node. Sending the coupling constraint condition to the signal sampling frequency configuration of the ultra-wideband positioning device array applies the adjusted coupling constraint condition to the actual operation of the ultra-wideband positioning device.

[0209] In the embodiments of the present application, first, the adjusted stress threshold parameter is injected back into the load-bearing structure node attributes of the engineering management data model to update the safety indicators of each node. Then, the coupling constraint condition is sent to the signal sampling frequency configuration of the ultra-wideband positioning device array to ensure that the sampling frequency can be dynamically adjusted during actual construction. Next, the entire process is monitored using the building information model system to verify the adjustment effect and make necessary fine-tuning. The final result is a comprehensively integrated engineering management solution that can remain efficient and safe in a dynamic environment.

[0210] The following is a specific example:

[0211] In a large commercial complex project, first, based on the equipment collision probability value in the composite risk level assessment results, a dynamic mapping relationship between the collision probability threshold and the stress threshold of the load-bearing structure is established to generate stress threshold parameters. Then, the time distribution characteristics of the construction process conflict index are analyzed. During the peak period, the sampling frequency of the ultra-wideband positioning device array is increased according to the predicted deviation amplitude of the mobile equipment trajectory, and during the intermittent period, the sampling frequency is reduced proportionally according to the change rate of the spatial occupancy hot zone distribution of the mobile equipment. Then, coupling constraint conditions between the stress threshold parameters and the sampling frequency are established to ensure that the adjustment of the stress threshold parameters does not exceed the structural safety boundary. Finally, the adjusted stress threshold parameters are injected back into the load-bearing structure node attributes of the engineering management data model to ensure the efficiency and safety of the construction process.

[0212] In summary, steps 701 to 704 achieve the dynamic adjustment after the composite risk assessment in construction engineering, improving the accuracy of the engineering management data model and the efficiency of the ultra-wideband positioning device array. Through precise risk assessment and real-time adjustment, potential risks during the construction process are reduced, and the overall quality and economic benefits of the project are enhanced. This solution not only enhances the operability and safety of the project but also provides strong support for subsequent maintenance and management. The application of the dynamic knowledge graph makes the construction process more intelligent and automated, greatly improving the efficiency and accuracy of engineering management.

[0213] Figure 2 The present application provides a schematic structural diagram of a risk adaptive assessment system for an information system engineering supervision project, as Figure 2 shown. The system includes:

[0214] A construction module 21 that constructs an engineering management data model integrating a building information model and a geographic information system, integrates a multi-dimensional risk element set including pipeline embedding parameters, stress thresholds of load-bearing structures, and equipment installation specification requirements, and forms a dynamic knowledge graph containing spatial constraint conditions and construction logic rules;

[0215] A capture module 22 that deploys an ultra-wideband positioning device array to capture the three-dimensional coordinate trajectory of construction machinery in real time, eliminates positioning deviation through multi-path interference suppression technology, and generates spatial occupancy hot zone distribution data of mobile equipment;

[0216] A coupling module 23 that establishes a job simulation environment driven by digital twins, dynamically couples the risk elements in the dynamic knowledge graph with the spatial occupancy hot zone distribution data, and generates a spatio-temporal risk topology network containing warnings about conflicts in the installation paths of network equipment and safety distance alarms;

[0217] The forming module 24 dynamically adjusts the weight allocation strategy of the construction machinery trajectory risk assessment parameters based on the node density distribution characteristics of the spatio-temporal risk topology network, and forms a composite risk level assessment result including the equipment collision probability and the construction process conflict index;

[0218] The adjustment module 25 reversely adjusts the stress threshold parameter in the project management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array according to the composite risk level assessment result.

[0219] Figure 2 The described risk adaptive assessment system for information system engineering supervision projects can execute Figure 1 The risk adaptive assessment method for information system engineering supervision projects described in the illustrated embodiments, and its implementation principle and technical effects will not be elaborated further. For the risk adaptive assessment system for information system engineering supervision projects in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0220] In a possible design, Figure 2 The risk adaptive assessment system for information system engineering supervision projects in the illustrated embodiments can be implemented as a computing device, such as Figure 3 shown, and this computing device can include a storage component 31 and a processing component 32;

[0221] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are for the processing component 32 to call and execute.

[0222] The processing component 32 is used for the Figure 1 risk adaptive assessment method for information system engineering supervision projects described in the above

[0223] Among them, the processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above method.

[0224] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0225] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0226] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module may be an output device, an input device, etc.

[0227] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0228] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0229] The embodiments of the present application also provide a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 shown in the embodiments of an information system engineering supervision project risk adaptive assessment method.

[0230] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0231] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0232] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An adaptive risk assessment method for information system engineering supervision projects, characterized in that Including: Construct an engineering management data model that integrates building information modeling and geographic information systems, integrate a multi-dimensional risk element set of pipeline embedded parameters, load-bearing structure stress thresholds, and equipment installation specification requirements, and form a dynamic knowledge graph containing spatial constraint conditions and construction logic rules; Deploy an ultra-wideband positioning device array to capture the three-dimensional coordinate trajectories of construction machinery in real time, eliminate positioning errors through multi-path interference suppression technology, and generate mobile device spatial occupancy hot zone distribution data; Establish a digital twin-driven operation simulation environment, dynamically couple the risk elements in the dynamic knowledge graph with the mobile device spatial occupancy hot zone distribution data, and generate a spatio-temporal risk topology network containing network equipment installation path conflict warnings and safety distance warnings; Based on the node density distribution characteristics of the spatio-temporal risk topology network, dynamically adjust the weight allocation strategy of the construction machinery trajectory risk assessment parameters, and form a composite risk level assessment result containing equipment collision probability and construction process conflict index; According to the composite risk level assessment result, reversely adjust the stress threshold parameters in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array; The dynamically coupling the risk elements in the dynamic knowledge graph with the mobile device spatial occupancy hot zone distribution data to generate a spatio-temporal risk topology network containing network equipment installation path conflict warnings and safety distance warnings includes: Based on the spatial coordinate set of the pipeline embedded parameters in the dynamic knowledge graph and the safety distance threshold of the equipment installation specification requirements, establish a risk element association model; According to the real-time motion vector in the mobile device spatial occupancy hot zone distribution data, calculate the three-dimensional trajectory envelope of each mobile device in a preset time window, and generate a device motion trend projection through interpolation prediction; Perform spatio-temporal grid discretization analysis on the spatio-temporal influence domain in the risk element association model and the device motion trend projection, calculate the network equipment installation path conflict probability value for each grid unit, and the network equipment installation path conflict probability value is weighted by the area ratio of the device trajectory penetrating the pipeline influence domain and the time overlap coefficient; Construct a dynamic safety distance threshold function based on the load-bearing structure stress threshold parameter, and adjust the safety distance threshold in real time according to the equipment mass parameter and motion acceleration; Fuse the network equipment installation path conflict probability value and the safety distance threshold to construct a spatio-temporal association matrix, and generate a spatio-temporal risk topology network containing network equipment installation path conflict warnings and safety distance warnings.

2. The method according to claim 1, characterized in that, The performing spatio-temporal grid discretization analysis on the spatio-temporal influence domain in the risk element association model and the device motion trend projection, and calculating the network equipment installation path conflict probability value for each grid unit includes: Establish a spatio-temporal grid division mechanism aligned with the coordinate system of the risk element association model, discretize the three-dimensional space coordinate axes into cubic units, and divide the time axis into time windows according to the construction progress to obtain spatio-temporal grid units; Traverse the spatio-temporal grid cells, extract the trajectory segments of the device motion trend projection, calculate the geometric intersection volume of the trajectory segments, and use the ratio of the geometric intersection volume to the total volume of the pipeline influence domain as the spatial permeability; Calculate the intersection duration of the trajectory segments with the activation period of the pipeline influence domain within the time window, and use the ratio of the intersection duration to the preset time window length as the time coverage; Obtain the preset spatial permeability weight factor and time coverage weight factor according to the pipeline type parameters, and perform a linear combination of the spatial permeability and the time coverage to generate a dynamic probability value reflecting the pipeline collision risk; When the device motion trend projection involves a multi-pipeline intersection area, superimpose and calculate the dynamic probability values of the pipelines within the same spatio-temporal grid cell to generate a network device installation path conflict probability value.

3. The method according to claim 2, characterized in that, The linear combination of the spatial permeability and the time coverage to generate a dynamic probability value reflecting the pipeline collision risk includes: Establish a mapping relation table of pipeline type parameters, spatial permeability weight factor and time coverage weight factor, set the reference values of the spatial permeability and the time coverage according to the engineering attribute characteristics, and dynamically correct the reference values according to the current construction stage parameters; According to the corrected reference values, construct a two-dimensional weight vector, perform homogeneous coordinate transformation on the spatial permeability and the time coverage, and generate a risk vector containing spatio-temporal characteristics; Calculate the risk coupling coefficient according to the pipeline type parameters and the construction machinery type parameters, and perform a tensor product operation on the risk vector and the risk coupling coefficient to obtain an initial probability tensor; Introduce the electromagnetic interference intensity parameter of the pipeline intersection area as an environmental correction factor, and perform a non-linear mapping on the initial probability tensor to generate a dynamic probability value reflecting the pipeline collision risk.

4. The method according to claim 1, wherein The integration of the multi-dimensional risk factor set of pipeline pre-burial parameters, load-bearing structure stress threshold and equipment installation specification requirements to form a dynamic knowledge graph including spatial constraint conditions and construction logic rules includes: Based on the pipeline pre-burial parameters, generate spatial collision rules through the minimum net distance constraint between pipelines, and the spatial collision rules include three-dimensional buffer zone parameters of the pipeline intersection area; Analyze the time-varying parameters of the load-bearing structure stress threshold, establish a stress propagation path model in combination with the topological relationship of the support points, and generate dynamic constraint conditions including the maximum allowable load according to the mechanical transfer characteristics between the support points; Convert the positioning tolerance, fastening torque and heat dissipation spacing parameters in the equipment installation specification requirements into a construction logic rule chain, and the construction logic rule chain includes process execution priority parameters and parallel operation mutual exclusion trigger conditions; Based on the spatial coordinate system of the building information model, perform topological association coding on the spatial collision rules, dynamic constraint conditions and construction logic rule chain, and form a dynamic knowledge graph by defining the spatial interference relationship between the pipeline and the load-bearing structure and the logical dependence relationship between the construction process and the equipment trajectory.

5. The method according to claim 4, characterized in that, The conversion of the positioning tolerance, fastening torque and heat dissipation spacing parameters in the equipment installation specification requirements into a construction logic rule chain includes: Establish a mapping relationship table between equipment types and installation parameters in equipment installation specification requirements, and generate equipment in-place verification rules based on the horizontal deviation threshold and vertical settlement tolerance in the positioning tolerance parameters. The equipment in-place verification rules include linkage constraints on the allowable deviation range and the spacing between adjacent equipment. Analyze the functional relationship between the tightening torque and the equipment quality parameters, establish a torque gradient adjustment model, and generate installation strength rules that include the torque graded loading strategy and the tightening sequence dependency; Based on the thermal radiation attenuation coefficient in the heat dissipation spacing parameters and the equipment power parameters, the dynamic safe heat dissipation distance is calculated to generate the thermal field mutual exclusion triggering rules when the equipment cluster is deployed; The equipment placement verification rules, installation strength rules and thermal field mutually exclusive triggering rules are arranged in time sequence according to the construction process stages to obtain a construction logic rule chain.

6. The method according to claim 1, characterized in that The reversely adjusting the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array according to the composite risk level assessment result includes: Based on the equipment collision probability value in the composite risk level assessment result, a dynamic mapping relationship between a collision probability threshold and a load-bearing structure stress threshold is established to generate a stress threshold parameter; Analyze the time distribution characteristics of the construction process conflict index, and divide the construction period into a peak period and an intermittent period in combination with the construction stage parameters. During the peak period, increase the sampling frequency of the ultra-wideband positioning device array according to the deviation amplitude of the mobile device trajectory prediction, and during the intermittent period, reduce the sampling frequency in proportion to the change rate of the hot zone distribution of the mobile device space occupation; Establishing a coupling constraint condition between the stress threshold parameter and the sampling frequency, so that when the stress threshold parameter is lowered and the sampling frequency is increased at the same time, the stress threshold parameter is adjusted so as not to exceed the structural safety boundary; The adjusted stress threshold parameters are reversely injected into the load-bearing structure node attributes of the engineering management data model, and the signal sampling frequency configuration sent to the ultra-wideband positioning device array under the coupling constraint condition is configured.

7. An information system engineering supervision project risk adaptive assessment system, characterized in that, include: Construction module, build an engineering management data model that integrates building information model and geographic information system, integrates the multi-dimensional risk factor set of pipeline pre-buried parameters, load-bearing structure stress threshold and equipment installation specification requirements, and forms a dynamic knowledge map containing spatial constraints and construction logic rules; The capture module deploys an array of ultra-wideband positioning devices to capture the three-dimensional coordinate trajectory of construction machinery in real time, eliminates positioning deviations through multipath interference suppression technology, and generates mobile equipment space occupancy hot zone distribution data; A coupling module is used to establish a digital twin-driven operation simulation environment, dynamically couple the risk factors in the dynamic knowledge graph with the spatial occupancy hot zone distribution data, and generate a spatiotemporal risk topology network including network equipment installation path conflict warnings and safety spacing warnings; A formation module is provided to dynamically adjust the weight allocation strategy of the construction machinery trajectory risk assessment parameters based on the node density distribution characteristics of the spatiotemporal risk topological network to form a composite risk level assessment result including the equipment collision probability and the construction process conflict index; An adjustment module that reversely adjusts the stress threshold parameter in the engineering management data model and the signal sampling frequency configuration of the ultra-wideband positioning device array according to the composite risk level assessment result; The dynamic coupling of the risk factors in the dynamic knowledge graph with the mobile device space occupancy hot zone distribution data to generate a spatio-temporal risk topology network including network device installation path conflict warnings and safety distance warnings, includes: Based on the spatial coordinate set of the pipeline embedding parameters in the dynamic knowledge graph and the safety distance threshold required by the device installation specification, establish a risk factor association model; According to the real-time motion vector in the mobile device space occupancy hot zone distribution data, calculate the three-dimensional trajectory envelope of each mobile device in a preset time window, and generate a device motion trend projection through interpolation prediction; Perform spatio-temporal grid discretization analysis on the spatio-temporal influence domain in the risk factor association model and the device motion trend projection, and calculate the network device installation path conflict probability value for each grid cell. The network device installation path conflict probability value is obtained by weighting the area ratio of the device trajectory penetrating the pipeline influence domain and the time overlap coefficient; Construct a dynamic safety distance threshold function based on the stress threshold parameter of the load-bearing structure, and adjust the safety distance threshold in real time according to the device mass parameter and motion acceleration; Fuse the network device installation path conflict probability value and the safety distance threshold to construct a spatio-temporal association matrix, and generate a spatio-temporal risk topology network including network device installation path conflict warnings and safety distance warnings.

8. A computing device, characterized in that, Includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the information system engineering supervision project risk adaptive assessment method according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that, Stores a computer program, and when the computer program is executed by the computer, it implements the information system engineering supervision project risk adaptive assessment method according to any one of claims 1 to 6.

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