BIM-based construction risk early warning method and system

Abstract

The application relates to the technical field of construction risk early warning, in particular to a building construction risk early warning method and system based on BIM, which comprises the following steps: constructing a correlation matrix between risk nodes based on time and space dimensions for risk factors of each node in a construction process, calculating risk propagation intensity values between nodes, judging the risk correlation relationship between nodes, classifying the risk coupling degree according to the propagation rate, and outputting the risk coupling matrix value. In the application, a high-risk area is extracted by combining spatial distribution data to realize dynamic partition management, the dynamic influence of environmental variables such as temperature, humidity and vibration is quantified, the complex relationship between environmental conditions and risk states is revealed, the accuracy and real-time performance of risk assessment are optimized, the node priority and resource allocation are dynamically adjusted by analyzing the risk transfer trend and the change of the propagation path, the influence of the newly-added risk signal is updated in real time, and the whole-process dynamic optimization of construction risk early warning is realized.

Description

Technical Field

[0001] This invention relates to the field of construction risk early warning technology, and in particular to a BIM-based construction risk early warning method and system. Background Technology

[0002] The field of construction risk early warning technology encompasses various safety risks that may occur during the construction process and their early warning management methods. The core of this technology is the use of information technology to identify, analyze, and manage risks at construction sites. Construction risk early warning technology mainly involves the collection of construction environment data, risk factor analysis models, risk level assessment standards, and the generation and dissemination of early warning plans. The overall technology field also includes research and application of dynamic risk monitoring technologies and emergency response strategies closely related to construction progress, construction quality, and construction safety.

[0003] The BIM-based construction risk early warning method and system refers to a method and system for identifying and issuing early warnings of construction risks through building information modeling technology. This patent covers risk data collection during the construction phase, risk factor modeling and assessment based on the BIM model, and correlation analysis between construction processes and dynamic changes in risks. Specifically, it integrates construction data, environmental data, and risk indicators into the BIM model, and uses the combination of construction information and early warning rules to complete risk identification and generate early warning signals. Simultaneously, it uses risk grading standards to quantitatively assess the degree of risk in each construction stage, ensuring the timely detection and handling of potential risks at the construction site.

[0004] Existing technologies for handling risk propagation patterns in construction processes typically employ static analysis, lacking dynamic assessment of the intensity and rate of risk propagation between nodes, making it difficult to accurately capture risk change trends in complex construction environments. In risk classification, most technologies fail to incorporate spatial distribution data of nodes, relying solely on a single indicator to assess risk levels, ignoring the regional characteristics of the construction environment and resulting in low applicability of risk classification. Regarding the introduction of environmental variables, existing methods fail to dynamically capture the real-time impact of environmental parameters such as temperature, humidity, and vibration on risks; most analyses rely on fixed parameter weight models, failing to reflect the complex dynamic relationship between environmental conditions and risk states. When responding to new risk signals, existing technologies lack rapid response mechanisms, failing to achieve real-time updates of risk priorities and status levels at construction nodes, easily leading to delays in risk management during construction. These problems increase the difficulty of handling potential risks in actual construction scenarios, reducing the safety and efficiency of construction sites. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a BIM-based method and system for early warning of construction risks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a BIM-based construction risk early warning method, comprising the following steps:

[0007] S1: For the risk factors of each node in the construction process, construct the correlation matrix between risk nodes based on time and space dimensions, calculate the risk propagation intensity value between nodes, determine the risk correlation between nodes, classify the degree of risk coupling according to the propagation rate, and output the risk coupling matrix value.

[0008] S2: Based on the risk coupling matrix value, set the risk propagation threshold, extract nodes with high intensity in the risk propagation path, combine spatial distribution data, calculate the risk level of the area around the node, divide the construction area according to the risk level and mark the boundary of the area risk level, and output the risk level partition data.

[0009] S3: Based on the risk level zoning data, extract environmental variables of the construction area, quantify the dynamic influence weights of temperature, humidity, and vibration variables on the risk level in the area, analyze the correlation between environmental variables and risk status, and generate dynamic influence values ​​of environmental variables.

[0010] S4: Analyze the changing trend of node risk status using the dynamic impact values ​​of the environmental variables, calculate the node risk transfer probability and assess the dynamic changes in risk intensity in the propagation path, adjust node priority and resource allocation ratio based on the change results, and generate dynamic change values ​​of risk status.

[0011] S5: Analyze the impact of newly added risk signals on the propagation of existing nodes based on the dynamic change value of the risk status, recalculate the risk priority and status level of the construction nodes, update the global risk status in combination with the propagation path, and output the risk priority and status update value of the construction nodes.

[0012] As a further embodiment of the present invention, the risk coupling matrix values ​​include the risk propagation intensity value between nodes, the risk correlation degree value between nodes, and the risk propagation rate level value; the risk level zoning data includes regional risk level identifiers, regional boundary information, and key node regional distribution; the dynamic influence values ​​of environmental variables include the influence weights of temperature, humidity, and vibration; the dynamic change values ​​of risk status include the node risk change trend value, the node risk transfer probability value, and the resource allocation adjustment ratio; and the construction node risk priority and status update values ​​include the node priority adjustment value, the node status level change value, and global risk update data.

[0013] As a further aspect of the present invention, for the risk factors at each node in the construction process, the specific steps are as follows: a correlation matrix between risk nodes is constructed based on time and space dimensions; the risk propagation intensity value between nodes is calculated; the risk correlation relationship between nodes is determined; the degree of risk coupling is graded according to the propagation rate; and the risk coupling matrix value is output.

[0014] S101: Based on the risk factors of nodes in the construction process, extract the time attributes and spatial location data of each node, calculate the time series difference value, analyze the spatial distance, cross-compare the difference value and distance parameters, analyze the risk correlation between nodes, calibrate the correlation parameters to construct the correlation matrix between nodes, and generate node correlation data.

[0015] S102: Based on the node association data, calculate the risk propagation intensity between nodes, filter node pairs with propagation intensity values ​​greater than a set threshold, calculate the strength of the relationship by the calibration value of the associated nodes and the difference in propagation intensity, classify and record the risk propagation characteristics between each node, and generate node risk propagation intensity data.

[0016] S103: Based on the node risk propagation intensity data and the propagation rate, calculate the cumulative risk value on the path, define the risk gradient according to the cumulative risk value, classify and mark the degree of coupling, record the propagation path information of the corresponding nodes at each level, and output the risk coupling matrix value.

[0017] As a further aspect of the present invention, based on the risk coupling matrix value, a risk propagation threshold is set, and nodes with higher intensity in the risk propagation path are extracted. Combined with spatial distribution data, the risk level of the area surrounding the node is calculated. Based on the risk level, the construction area is divided and the boundary of the regional risk level is marked. The specific steps for outputting the risk level zoning data are as follows:

[0018] S201: Based on the risk coupling matrix value, set a reference threshold for the propagation intensity, calculate the intensity value of each node in the matrix one by one, filter out node pairs with intensity values ​​greater than the threshold, analyze the correlation parameters of the risk propagation path in the node pairs, mark the filtering results and establish node group data, and generate high-intensity node group data.

[0019] S202: Based on the high-intensity node group data, extract the spatial location parameters and distribution relationships of the nodes, calculate the distance weights and distribution densities between the nodes one by one, analyze the influence of spatial distribution on risk values ​​in conjunction with the influence radius around the nodes, delineate the spatial distribution association information of nodes within the region, and generate node spatial distribution influence data.

[0020] S203: Based on the spatial distribution impact data of the nodes, combined with the relationship between the node risk propagation intensity and regional distribution, calculate the comprehensive risk value of the region, set the gradient level according to the risk value, classify and label the gradient level information and mark the regional boundary range, and generate risk level partition data.

[0021] As a further aspect of the present invention, the specific steps for extracting environmental variables of the construction area based on the risk level zoning data, analyzing the correlation between environmental variables and risk status, and generating dynamic influence values ​​of environmental variables by quantifying the dynamic influence weights of temperature, humidity, and vibration variables on the risk level within the area, are as follows:

[0022] S301: Based on the risk level zoning data, extract the spatial range and distribution information of nodes in the region, match temperature, humidity and vibration variables, classify the risk level and environmental variable values ​​of the region one by one, establish the initial association between environmental variables and zoning data, and generate initial distribution data of environmental variables.

[0023] S302: Based on the initial distribution data of the environmental variables, calculate the degree of influence of the change range of the environmental variables on the regional risk level, analyze the matching relationship between the variables and the risk level, extract the values ​​with larger change ranges among the variables and assign them dynamic weights, label the influence range and weight information of different variables, and generate dynamic weight data of environmental variables.

[0024] S303: Based on the dynamic weight data of the environmental variables, calculate the dynamic correlation between the environmental variables and the regional risk status, extract the parameter trends of the changes in variables and risk levels, calibrate the risk impact value of the dynamic changes in variables, and integrate the variable correlation results within the region to generate the dynamic impact value of environmental variables.

[0025] As a further aspect of the present invention, the specific steps for analyzing the changing trends of node risk status using the dynamic impact values ​​of the environmental variables, calculating the node risk transfer probability and assessing the dynamic changes in risk intensity in the propagation path, and adjusting node priority and resource allocation ratios based on the changes to generate dynamic risk status change values ​​are as follows:

[0026] S401: Based on the dynamic impact values ​​of the environmental variables, extract the time series risk status data of the nodes, calculate the change range of risk status in segments, analyze the risk rate in each time period, mark the change trend of the nodes in the time dimension, record the trend data, and generate node risk status trend data.

[0027] S402: Based on the node risk status trend data, calculate the risk status transition probability between nodes, extract the transition path data of adjacent nodes, perform cumulative calculation on the transition intensity, and conduct comparative analysis in combination with the risk intensity change parameters within the path to generate node risk transition probability data.

[0028] S403: Based on the node risk transfer probability data, calculate the node priority adjustment factor and resource allocation ratio parameter, proportionally match the node risk change parameter with the resource utilization data, calibrate the resource allocation adjustment value and the node priority dynamic change parameter, and generate the risk status dynamic change value.

[0029] As a further aspect of the present invention, the formula for calculating the risk state transition probability is as follows:

[0030]

[0031] in, Representative node and nodes The probability of transition between risky states. and Representing nodes respectively and nodes In the Risk status value for a given time period Indicates time period The weight value, This indicates the total number of time intervals between nodes. This represents the summation over all time periods. This represents the sum of squared weights.

[0032] As a further aspect of the present invention, the specific steps for analyzing the impact of newly added risk signals on the propagation of existing nodes based on the dynamic change value of the risk status, recalculating the risk priority and status level of construction nodes, updating the global risk status in conjunction with the propagation path, and outputting the updated risk priority and status values ​​of construction nodes are as follows:

[0033] S501: Based on the dynamic change value of the risk status, extract the propagation path and influence range of the new risk signal, calculate the risk correlation strength value of the new signal to the existing nodes, analyze the influence of the signal on the change of the node risk status, record the change parameters of the node and generate analysis results, and generate the influence parameters of the new risk signal.

[0034] S502: Based on the newly added risk signal impact parameters, recalculate the risk priority values ​​of the affected nodes, analyze the difference between the priority adjustment value and the original state level, calibrate the new state level parameters of the nodes, dynamically update the priority and level of the node data, and generate node priority and state level data.

[0035] S503: Based on the node priority and status level data, calculate the global risk status change parameters in conjunction with the risk propagation path, update the status relationship and priority distribution between nodes, record the dynamic adjustment information of nodes in the global network, and generate the risk priority and status update value of construction nodes.

[0036] As a further aspect of the present invention, the formula for calculating the risk association strength value is specifically as follows:

[0037] ;

[0038] in, Represents newly added signal pairs to nodes and nodes The strength of the risk association between them and These represent the newly added signals within the time period. For nodes and nodes The intensity of the impact, Indicates time period The weight value, Indicates the total number of time periods. This represents the summation of all time periods. This represents the sum of squared weights.

[0039] A BIM-based construction risk early warning system includes:

[0040] The risk association module targets the risk factors at each node in the construction process, constructs an association matrix between risk nodes based on time and space dimensions, calculates the risk propagation intensity value between nodes, classifies the degree of risk coupling according to the propagation rate, and outputs the risk coupling matrix value.

[0041] Based on the risk coupling matrix value, the area division module sets the risk propagation threshold, extracts nodes with higher intensity in the risk propagation path, calculates the risk level of the area surrounding the node, divides the construction area according to the risk level, marks the boundary of the area risk level, and outputs the risk level partition data.

[0042] The environmental weighting module extracts environmental variables of the construction area based on the risk level zoning data, and generates dynamic impact values ​​of environmental variables by quantifying the dynamic impact weights of temperature, humidity and vibration variables on the risk level of the area.

[0043] The state analysis module uses the dynamic impact values ​​of the environmental variables to analyze the trend of node risk state changes, calculates the node risk transfer probability and assesses the dynamic changes of risk intensity in the propagation path, and generates dynamic change values ​​of risk state.

[0044] The priority update module analyzes the impact of newly added risk signals on the propagation of existing nodes based on the dynamic changes in the risk status, recalculates the risk priority and status level of the construction nodes, and outputs the risk priority and status update values ​​of the construction nodes.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In this invention, high-risk areas are extracted by combining spatial distribution data to achieve dynamic zoning management, quantify the dynamic impact of environmental variables such as temperature, humidity, and vibration, reveal the complex relationship between environmental conditions and risk status, optimize the accuracy and real-time performance of risk assessment, and dynamically adjust node priorities and resource allocation by analyzing risk transfer trends and propagation path changes, and update the impact of newly added risk signals in real time, thereby achieving dynamic optimization of the entire process of construction risk early warning. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the steps of the present invention;

[0049] Figure 2 This is a flowchart of steps S1 of the present invention;

[0050] Figure 3 This is a flowchart of steps S2 of the present invention;

[0051] Figure 4 This is a flowchart of steps S3 of the present invention;

[0052] Figure 5 This is a flowchart of step S4 of the present invention;

[0053] Figure 6 This is a flowchart of steps S5 of the present invention;

[0054] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0055] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0056] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0057] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0058] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0059] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0060] Please see Figure 1 A BIM-based construction risk early warning method includes the following steps:

[0061] S1: For the risk factors of each node in the construction process, construct the correlation matrix between risk nodes based on time and space dimensions, calculate the risk propagation intensity value between nodes, determine the risk correlation between nodes, classify the degree of risk coupling according to the propagation rate, and output the risk coupling matrix value.

[0062] S2: Based on the risk coupling matrix value, set the risk propagation threshold, extract nodes with high intensity in the risk propagation path, combine spatial distribution data, calculate the risk level of the area around the node, divide the construction area according to the risk level and mark the boundary of the area risk level, and output the risk level partition data.

[0063] S3: Based on risk level zoning data, extract environmental variables of the construction area, quantify the dynamic impact weight of variables such as temperature, humidity, and vibration on the risk level of the area, analyze the correlation between environmental variables and risk status, and generate dynamic impact values ​​of environmental variables.

[0064] S4: Analyze the changing trend of node risk status using the dynamic impact values ​​of environmental variables, calculate the probability of node risk transfer and assess the dynamic changes in risk intensity in the propagation path, adjust node priority and resource allocation ratio based on the changes, and generate dynamic change values ​​of risk status.

[0065] S5: Analyze the impact of newly added risk signals on the propagation of existing nodes based on the dynamic changes in risk status, recalculate the risk priority and status level of construction nodes, update the global risk status in combination with the propagation path, and output the risk priority and status update values ​​of construction nodes.

[0066] The risk coupling matrix values ​​include the risk propagation intensity value between nodes, the risk correlation value between nodes, and the risk propagation rate level value. The risk level zoning data includes the regional risk level identifier, regional boundary information, and the regional distribution of key nodes. The dynamic impact values ​​of environmental variables include the impact weight of temperature, the impact weight of humidity, and the impact weight of vibration. The dynamic change values ​​of risk status include the node risk change trend value, the node risk transfer probability value, and the resource allocation adjustment ratio. The risk priority and status update values ​​of construction nodes include the node priority adjustment value, the node status level change value, and the global risk update data.

[0067] Please see Figure 2 The specific steps of S1 are as follows:

[0068] S101: Based on the risk factors of nodes in the construction process, extract the time attributes and spatial location data of each node, calculate the time series difference value, analyze the spatial distance, cross-compare the difference value and distance parameters, analyze the risk correlation between nodes, calibrate the correlation parameters to construct the correlation matrix between nodes, and generate node correlation data.

[0069] The time attribute is quantified as a continuous variable in the form of timestamps. The spatial location is obtained with precise coordinate values ​​through a total station or a 3D positioning system. When calculating the time series difference value, the time attributes of adjacent nodes are interpolated to obtain the time interval. At the same time, when calculating the spatial distance, the spatial position difference between nodes is measured using the 3D Euclidean distance formula. The difference value and distance parameters are statistically summarized and standardized. The standardized difference value matrix and distance matrix are cross-compared to analyze the risk correlation between nodes, extract the correlation feature quantity between nodes, and use the correlation feature quantity as a weight parameter to calibrate the strength of the correlation, construct a risk correlation matrix, and generate node correlation relationship data.

[0070] S102: Based on node association data, calculate the risk propagation intensity between nodes, filter node pairs with propagation intensity values ​​greater than a set threshold, calculate the strength of the relationship by comparing the calibration values ​​of the associated nodes with the differences in propagation intensity, classify and record the risk propagation characteristics between each node, and generate node risk propagation intensity data.

[0071] The intensity of risk propagation between nodes is calculated according to the formula.

[0072] ;

[0073] Calculate the risk propagation strength between nodes.

[0074] In the formula, Represents a node and nodes The intensity of risk transmission between them Represents a node and The time propagation coefficient between them Represents a node and Spatial propagation coefficient between.

[0075] The time propagation coefficient is calculated by taking the reciprocal of the time difference between two nodes, for example, node 1. and The times are respectively sky, Day, time propagation coefficient The spatial propagation coefficient is calculated by taking the reciprocal of the spatial distance obtained from the difference in three-dimensional coordinates, for example, at nodes. The coordinates are ,node The coordinates are spatial distance Spatial propagation coefficient Risk transmission intensity This result indicates that the propagation intensity between nodes is low, and the direct risk propagation impact between nodes is weak.

[0076] S103: Based on the node risk propagation intensity data and the propagation rate, calculate the cumulative risk value on the path, define the risk gradient according to the cumulative risk value, classify and mark the degree of coupling, record the propagation path information of the corresponding nodes at each level, and output the risk coupling matrix value.

[0077] First, the propagation rate is calculated in segments. The risk propagation rate is multiplied by the propagation intensity between nodes as the incremental factor of the cumulative risk value. The cumulative risk value of each path node pair is accumulated sequentially. At the same time, the cumulative risk value on the path is divided into intervals. High-risk areas are marked in combination with key nodes on the path. The corresponding risk gradient is marked according to the interval size of the graded risk value. The graded marking is based on the calculated propagation path between nodes. The risk distribution characteristics on the path are analyzed. The cumulative risk value of the path is recorded and used as the input parameter of the risk coupling matrix. Finally, the risk coupling matrix value is output.

[0078] Please see Figure 3 The specific steps of S2 are as follows:

[0079] S201: Based on the risk coupling matrix value, a reference threshold for the propagation intensity is set. The intensity value of each node in the matrix is ​​calculated one by one. Node pairs with intensity values ​​greater than the threshold are selected. The correlation parameters of the risk propagation path in the node pairs are analyzed. The selection results are marked and node group data is established to generate high-intensity node group data.

[0080] The propagation intensity values ​​of all node pairs in the matrix are obtained. Each propagation intensity value is compared with a threshold, and node pairs with intensity values ​​greater than the threshold are selected. The information of the selected node pairs is recorded. Simultaneously, the risk-related parameters involved in the propagation paths of the node pairs are analyzed, including time differences, spatial distances, and propagation path sequences, and categorized statistically to determine the distribution of each parameter's characteristic values, identify typical propagation characteristics and patterns, and distinguish their role categories in the propagation network (e.g., key propagation nodes, secondary propagation nodes, etc.) by marking the selected high-intensity node pairs. Based on the selection results, high-intensity node group data is aggregated according to the correlation, path characteristics, or intensity level of the node pairs.

[0081] S202: Based on high-intensity node group data, extract the spatial location parameters and distribution relationships of nodes, calculate the distance weight and distribution density between nodes item by item, analyze the influence of spatial distribution on risk value in combination with the influence radius around the node, delineate the spatial distribution association information of nodes within the region, and generate node spatial distribution influence data.

[0082] Extract the spatial location parameters and distribution relationship of the nodes according to the formula.

[0083] ;

[0084] Calculate the distance weights and distribution densities between nodes.

[0085] In the formula, Represents the overall spatial distribution characteristic value. Represents a node and nodes Weights between Represents a node and nodes Spatial distance between them This represents the total number of nodes.

[0086] Node weight Calculations are performed using standardized data on the strength of risk associations, such as nodes. and The association strength is 0.8, and the standardized weights are... Spatial distance between nodes Calculated using the three-dimensional Euclidean distance formula, for example, nodes. The coordinates are ,node The coordinates are ,distance .

[0087] Overall spatial distribution characteristic value The calculation is based on the total number of nodes. For example, select several pairs of node weights and distance Substitute into the formula to calculate:

[0088] ;

[0089] The calculation results are used to analyze the degree of influence of the spatial distribution of nodes on the risk value.

[0090] S203: Based on the impact data of node spatial distribution, combined with the relationship between node risk propagation intensity and regional distribution, calculate the comprehensive risk value of the region, set gradient levels according to the risk value, classify and label the gradient level information and mark the regional boundary range, and generate risk level zoning data.

[0091] Based on the risk propagation intensity data and the node density information in the regional distribution, the risk value of each node is superimposed according to its influence range. The influence range of the node is set by a radius value. The risk values ​​within the coverage area of ​​the node are accumulated to obtain the comprehensive risk value of each sub-region. At the same time, the accumulated risk values ​​are divided according to the set gradient level. In the classification labeling, the number of covered nodes and the risk value changes of the regional boundary are combined to delineate the regional boundary range and label the classification results, generating risk level zoning data.

[0092] Please see Figure 4 The specific steps of S3 are as follows:

[0093] S301: Based on risk level zoning data, extract the spatial range and distribution information of nodes in the region, match environmental variable data such as temperature, humidity, and vibration, classify the risk level and environmental variable values ​​of the region one by one, establish the initial association between environmental variables and zoning data, and generate initial distribution data of environmental variables.

[0094] The spatial range of nodes is determined by the minimum and maximum coordinate values ​​of nodes within the region. Distribution information includes the number density and positional relationship of nodes. It is matched with environmental variable data such as temperature, humidity, and vibration. Environmental variable data are acquired through real-time sensors or monitoring equipment. When classifying the risk level of a region and the environmental variable values, the average environmental variable value corresponding to each risk level is statistically analyzed, and the correlation between environmental variables and regional risks is analyzed to establish an initial correlation between risk level and environmental variables. Finally, initial distribution data of environmental variables is generated.

[0095] S302: Based on the initial distribution data of environmental variables, calculate the impact of the change range of environmental variables on the regional risk level, analyze the matching relationship between variables and risk levels, extract the values ​​with large change ranges among the variables and assign them dynamic weights, label the influence range and weight information of different variables, and generate dynamic weight data of environmental variables.

[0096] To calculate the impact of changes in environmental variables on regional risk levels, follow the formula...

[0097] ;

[0098] Calculate the matching relationship between variables and risk levels.

[0099] In the formula, Indicates the first The degree of influence of each environmental variable on the risk level Indicates the first The magnitude of change of each variable Indicates the magnitude of change in regional risk level. Indicates the first Dynamic weights of each variable.

[0100] Variation of environmental variables Calculated using maximum and minimum values, for example, the maximum value of a temperature variable is... Degree, minimum value Degree, then The magnitude of change in regional risk levels The risk level is determined by the range of risk level values; for example, the highest risk level is... The lowest risk level is ,but Dynamic weights Calculated using variable influence factors, for example, the influence factor for temperature variable is... Substitute each term into the formula:

[0101] ;

[0102] This result indicates that the degree of influence of temperature variables on regional risk levels is... It can also be used for further variable weighting and range division.

[0103] S303: Based on dynamic weight data of environmental variables, calculate the dynamic correlation between environmental variables and regional risk status, extract the parameter trends of variable and risk level changes, label the risk impact value of dynamic variable changes, and integrate the variable correlation results within the region to generate dynamic impact values ​​of environmental variables.

[0104] By combining dynamic weights with variable correlation trends, the risk impact value of dynamic variable changes is analyzed. The dynamic changes are calculated by time series analysis to determine the rate of change of variables in different time periods, and the rate of change is fitted with the time period change value of risk level. At the same time, the variable correlation results within the region are integrated, and a comprehensive dynamic impact value of environmental variables is generated by summarizing the risk impact range of different variables.

[0105] Please see Figure 5 The specific steps of S4 are as follows:

[0106] S401: Based on the dynamic impact values ​​of environmental variables, extract the time series risk status data of nodes, calculate the change magnitude of risk status in segments, analyze the risk rate in each time period, label the change trend of nodes in the time dimension, record the trend data, and generate node risk status trend data.

[0107] Time series risk status is obtained through dynamic monitoring and recording of nodes. The risk status data is segmented and processed. The change range of risk status is calculated based on the time node interval of each segment. The change range is calculated by the difference between the maximum and minimum risk values. At the same time, the change trend of risk status within each segment is analyzed, the change pattern of nodes in the time dimension is marked, the change trend data is recorded, and node risk status trend data is generated.

[0108] S402: Based on node risk status trend data, calculate the risk status transition probability between nodes, extract the transition path data of adjacent nodes, perform cumulative calculation on the transition intensity, and conduct comparative analysis with the risk intensity change parameters within the path to generate node risk transition probability data.

[0109] The specific formula for calculating the probability of risk state transition is as follows:

[0110]

[0111] in, Representative node and nodes The probability of transition between risk states. and Representing nodes respectively and nodes In the Risk status value for a given time period Indicates time period The weight value, This indicates the total number of time intervals between nodes. This represents the summation over all time periods. This represents the sum of squared weights.

[0112] Represents a node and nodes The probability of transition between risk states.

[0113] Risk Status Value and By analyzing the nodes and nodes In time period Risk status data monitoring and acquisition within the time frame. Taking time period and 2 as an example, node... and The risk status values ​​are as follows:

[0114] Time period 1: ,

[0115] Time period 2: , .

[0116] Time period weight Time period: The importance of each risk factor is determined by the volatility of the risk data within a given time period; the greater the volatility, the higher the weight. The weight values ​​are calculated using normalization.

[0117] Weight of time period 1:

[0118] Weight of time period 2: .

[0119] Total number of time periods Determined from time series data, the current .

[0120] Calculation of risk state transition probability:

[0121] Calculate the numerator:

[0122] ;

[0123] Calculate the denominator:

[0124] ;

[0125] Transfer intensity:

[0126] ;

[0127] This result indicates the node and nodes The risk state transition probability between nodes is 0.282. This value is used to analyze the risk transmission relationship between nodes and serves as the basis for probability calculation in subsequent steps.

[0128] S403: Based on node risk transfer probability data, calculate the adjustment factor of node priority and the proportional parameter of resource allocation, proportionally match the node risk change parameter with the resource utilization data, calibrate the resource allocation adjustment value and the dynamic change parameter of node priority, and generate the dynamic change value of risk status.

[0129] Node priority is determined by a weighted combination of transfer probability and risk change value. Resource allocation ratio parameter is calculated by the ratio of resource demand and priority of each node. At the same time, the risk change parameter and resource utilization data of the nodes are analyzed for proportional allocation. Combined with time series monitoring and changes in risk intensity of transfer path, resource allocation adjustment value and dynamic change parameter of node priority are calibrated, and finally, dynamic change value of risk status is generated.

[0130] Please see Figure 6 The specific steps of S5 are as follows:

[0131] S501: Based on the dynamic change value of risk status, extract the propagation path and impact range of the new risk signal, calculate the risk correlation strength value of the new signal to the existing nodes, analyze the impact of the signal on the change of node risk status, record the change parameters of the node and generate analysis results, and generate the impact parameters of the new risk signal.

[0132] The specific formula for calculating the risk association strength value is as follows:

[0133] ;

[0134] in, Represents newly added signal pairs to nodes and nodes The strength of the risk association between them and These represent the newly added signals within the time period. For nodes and nodes The intensity of the impact, Indicates time period The weight value, Indicates the total number of time periods. This represents the summation of all time periods. This represents the sum of squared weights.

[0135] node and nodes Strength of risk association between It is calculated by the difference in the intensity of the impact of newly added signals within a time period and the weight value.

[0136] Intensity of New Signal Impact and Based on time-series monitoring data, record newly added signal pairs at nodes. and In time period The intensity of the influence within. For example:

[0137] Time period 1: ,

[0138] Time period 2: ,

[0139] Time period 3: , .

[0140] Time period weight The weights are calculated based on the fluctuation values ​​affected by the new signals; the larger the fluctuation value, the higher the weight. The weights are obtained through normalization.

[0141] Time period 1:

[0142] Time period 2:

[0143] Time period 3: .

[0144] Total number of time periods .

[0145] Calculation process:

[0146] Calculate the numerator:

[0147] ;

[0148] ;

[0149] Calculate the denominator:

[0150] ;

[0151] Calculate the risk association strength:

[0152] ;

[0153] This result indicates the node and nodes The risk correlation strength between nodes is 0.27. This value is used to analyze the impact of new signals on the risk status between nodes and to provide a basis for calculating the impact parameters of new risk signals in the future.

[0154] S502: Based on the newly added risk signal impact parameters, recalculate the risk priority values ​​of the affected nodes, analyze the difference between the priority adjustment value and the original state level, calibrate the new state level parameters of the nodes, dynamically update the priority and level of the node data, and generate node priority and state level data.

[0155] Recalculate the risk priority values ​​of the affected nodes according to the formula.

[0156] ;

[0157] Calculate the priority value of the node.

[0158] In the formula, Represents a node The priority value, Represents a node Risk weights, Represents a node The risk level value, This represents the total number of nodes.

[0159] Node risk weight Standardize the calculation by adding new signal influence parameters, such as nodes. The influence parameter is 0.6, and the standardized weights are... Risk level value of the node Extracted from dynamically changing risk status values, such as nodes. The risk level is 4. Substituting these values ​​into the formula, we get... For example:

[0160] ;

[0161] This result indicates the node The priority value is 57%, which is used to dynamically adjust the node's state level and priority distribution.

[0162] S503: Based on node priority and status level data, combined with risk propagation path, calculate global risk status change parameters, update the status relationship and priority distribution between nodes, record the dynamic adjustment information of nodes in the global network, and generate construction node risk priority and status update values.

[0163] The risk propagation path is calculated through the dynamic relationship matrix between nodes. The status relationship update between nodes is analyzed by correlation analysis based on the changes in node priority. The priority distribution is recalculated by normalizing the priority value of each node. The dynamic adjustment information of nodes in the global network is recorded, and the distribution of node priority is dynamically adjusted in combination with the risk status changes in the time dimension. Finally, the risk priority and status update value of construction nodes are generated.

[0164] Please see Figure 7 A BIM-based construction risk early warning system includes:

[0165] The risk association module targets the risk factors at each node in the construction process, constructs an association matrix between risk nodes based on time and space dimensions, calculates the risk propagation intensity value between nodes, classifies the degree of risk coupling according to the propagation rate, and outputs the risk coupling matrix value.

[0166] The region division module sets the risk propagation threshold based on the risk coupling matrix value, extracts nodes with high intensity in the risk propagation path, calculates the risk level of the area surrounding the node, divides the construction area according to the risk level, marks the boundary of the regional risk level, and outputs risk level partition data.

[0167] The environmental weighting module extracts environmental variables from the construction area based on risk level zoning data. It generates dynamic impact values ​​of environmental variables by quantifying the dynamic impact weights of variables such as temperature, humidity, and vibration on the risk level within the area.

[0168] The status analysis module uses the dynamic impact values ​​of environmental variables to analyze the trend of node risk status changes, calculates the node risk transfer probability and assesses the dynamic changes of risk intensity in the propagation path, and generates dynamic change values ​​of risk status.

[0169] The priority update module analyzes the impact of newly added risk signals on the propagation of existing nodes based on the dynamic changes in risk status, recalculates the risk priority and status level of construction nodes, and outputs the updated risk priority and status values ​​of construction nodes.

[0170] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A BIM-based method for early warning of construction risks, characterized in that, Includes the following steps: S1: For the risk factors of each node in the construction process, construct the correlation matrix between risk nodes based on time and space dimensions, calculate the risk propagation intensity value between nodes, determine the risk correlation between nodes, classify the degree of risk coupling according to the propagation rate, and output the risk coupling matrix value. S2: Based on the risk coupling matrix value, set the risk propagation threshold, extract nodes with high intensity in the risk propagation path, combine spatial distribution data, calculate the risk level of the area around the node, divide the construction area according to the risk level and mark the boundary of the area risk level, and output the risk level partition data. S3: Based on the risk level zoning data, extract environmental variables of the construction area, quantify the dynamic influence weights of temperature, humidity, and vibration variables on the risk level in the area, analyze the correlation between environmental variables and risk status, and generate dynamic influence values ​​of environmental variables. S4: Analyze the changing trend of node risk status using the dynamic impact values ​​of the environmental variables, calculate the node risk transfer probability and assess the dynamic changes in risk intensity in the propagation path, adjust node priority and resource allocation ratio based on the change results, and generate dynamic change values ​​of risk status. S5: Analyze the impact of newly added risk signals on the propagation of existing nodes based on the dynamic change value of the risk status, recalculate the risk priority and status level of construction nodes, update the global risk status in combination with the propagation path, and output the risk priority and status update value of construction nodes. The specific steps for S4 are as follows: S401: Based on the dynamic impact values ​​of the environmental variables, extract the time series risk status data of the nodes, calculate the change range of risk status in segments, analyze the risk rate in each time period, mark the change trend of the nodes in the time dimension, record the trend data, and generate node risk status trend data. S402: Based on the node risk status trend data, calculate the risk status transition probability between nodes, extract the transition path data of adjacent nodes, perform cumulative calculation on the transition intensity, and conduct comparative analysis in combination with the risk intensity change parameters within the path to generate node risk transition probability data. S403: Based on the node risk transfer probability data, calculate the node priority adjustment factor and resource allocation ratio parameter, proportionally match the node risk change parameter with the resource utilization data, calibrate the resource allocation adjustment value and the node priority dynamic change parameter, and generate the risk status dynamic change value. The specific formula for calculating the risk state transition probability is as follows: ； in, Representative node and nodes The probability of transition between risky states. and Representing nodes respectively and nodes In the Risk status value for a given time period Indicates time period The weight value, This indicates the total number of time intervals between nodes. This represents the summation over all time periods. This represents the sum of the squares of the weights; The specific steps of S5 are as follows: S501: Based on the dynamic change value of the risk status, extract the propagation path and influence range of the new risk signal, calculate the risk correlation strength value of the new signal to the existing nodes, analyze the influence of the signal on the change of the node risk status, record the change parameters of the node and generate analysis results, and generate the influence parameters of the new risk signal. S502: Based on the newly added risk signal impact parameters, recalculate the risk priority values ​​of the affected nodes, analyze the difference between the priority adjustment value and the original state level, calibrate the new state level parameters of the nodes, dynamically update the priority and level of the node data, and generate node priority and state level data. S503: Based on the node priority and status level data, calculate the global risk status change parameters in combination with the risk propagation path, update the status relationship and priority distribution between nodes, record the dynamic adjustment information of nodes in the global network, and generate the risk priority and status update value of construction nodes. The specific formula for calculating the risk association strength value is as follows: ； in, Represents a newly added signal pair node and nodes The strength of the risk association between them and These represent the newly added signals within the time period. For nodes and nodes The intensity of the impact, Indicates time period The weight value, Indicates the total number of time periods. This represents the summation of all time periods. This represents the sum of squared weights.

2. The BIM-based construction risk early warning method according to claim 1, characterized in that, The risk coupling matrix values ​​include the risk propagation intensity value between nodes, the risk correlation degree value between nodes, and the risk propagation rate level value. The risk level zoning data includes regional risk level identifiers, regional boundary information, and key node regional distribution. The dynamic impact values ​​of environmental variables include the influence weights of temperature, humidity, and vibration. The dynamic change values ​​of risk status include the node risk change trend value, the node risk transfer probability value, and the resource allocation adjustment ratio. The risk priority and status update values ​​of construction nodes include the node priority adjustment value, the node status level change value, and global risk update data.

3. The BIM-based construction risk early warning method according to claim 1, characterized in that, The specific steps for constructing a correlation matrix between risk nodes based on time and space dimensions, targeting the risk factors at each node in the construction process, calculating the risk propagation intensity value between nodes, determining the risk correlation between nodes, classifying the degree of risk coupling according to the propagation rate, and outputting the risk coupling matrix value are as follows: S101: Based on the risk factors of nodes in the construction process, extract the time attributes and spatial location data of each node, calculate the time series difference value, analyze the spatial distance, cross-compare the difference value and distance parameters, analyze the risk correlation between nodes, calibrate the correlation parameters to construct the correlation matrix between nodes, and generate node correlation data. S102: Based on the node association data, calculate the risk propagation intensity between nodes, filter node pairs with propagation intensity values ​​greater than a set threshold, calculate the strength of the relationship by the calibration value of the associated nodes and the difference in propagation intensity, classify and record the risk propagation characteristics between each node, and generate node risk propagation intensity data. S103: Based on the node risk propagation intensity data and the propagation rate, calculate the cumulative risk value on the path, define the risk gradient according to the cumulative risk value, classify and mark the degree of coupling, record the propagation path information of the corresponding nodes at each level, and output the risk coupling matrix value.

4. The BIM-based construction risk early warning method according to claim 1, characterized in that, Based on the risk coupling matrix value, a risk propagation threshold is set, and nodes with higher intensity in the risk propagation path are extracted. Combined with spatial distribution data, the risk level of the area surrounding the node is calculated. Based on the risk level, the construction area is divided and the boundary of the regional risk level is marked. The specific steps for outputting the risk level partition data are as follows: S201: Based on the risk coupling matrix value, set a reference threshold for the propagation intensity, calculate the intensity value of each node in the matrix one by one, filter out node pairs with intensity values ​​greater than the threshold, analyze the correlation parameters of the risk propagation path in the node pairs, mark the filtering results and establish node group data, and generate high-intensity node group data. S202: Based on the high-intensity node group data, extract the spatial location parameters and distribution relationships of the nodes, calculate the distance weights and distribution densities between the nodes one by one, analyze the influence of spatial distribution on risk values ​​in conjunction with the influence radius around the nodes, delineate the spatial distribution association information of nodes within the region, and generate node spatial distribution influence data. S203: Based on the spatial distribution impact data of the nodes, combined with the relationship between the node risk propagation intensity and regional distribution, calculate the comprehensive risk value of the region, set the gradient level according to the risk value, classify and label the gradient level information and mark the regional boundary range, and generate risk level partition data.

5. The BIM-based construction risk early warning method according to claim 1, characterized in that, Based on the risk level zoning data, environmental variables of the construction area are extracted. By quantifying the dynamic influence weights of temperature, humidity, and vibration variables on the risk level within the area, the correlation between environmental variables and risk status is analyzed, and the specific steps for generating dynamic influence values ​​of environmental variables are as follows: S301: Based on the risk level zoning data, extract the spatial range and distribution information of nodes in the region, match temperature, humidity and vibration variables, classify the risk level and environmental variable values ​​of the region one by one, establish the initial association between environmental variables and zoning data, and generate initial distribution data of environmental variables. S302: Based on the initial distribution data of the environmental variables, calculate the degree of influence of the change range of the environmental variables on the regional risk level, analyze the matching relationship between the variables and the risk level, extract the values ​​with larger change ranges among the variables and assign them dynamic weights, label the influence range and weight information of different variables, and generate dynamic weight data of environmental variables. S303: Based on the dynamic weight data of the environmental variables, calculate the dynamic correlation between the environmental variables and the regional risk status, extract the parameter trends of the changes in variables and risk levels, calibrate the risk impact value of the dynamic changes in variables, and integrate the variable correlation results within the region to generate the dynamic impact value of environmental variables.

6. A BIM-based construction risk early warning system, characterized in that, The BIM-based construction risk early warning method according to any one of claims 1-5, wherein the system comprises: The risk association module targets the risk factors at each node in the construction process, constructs an association matrix between risk nodes based on time and space dimensions, calculates the risk propagation intensity value between nodes, classifies the degree of risk coupling according to the propagation rate, and outputs the risk coupling matrix value. Based on the risk coupling matrix value, the area division module sets the risk propagation threshold, extracts nodes with higher intensity in the risk propagation path, calculates the risk level of the area surrounding the node, divides the construction area according to the risk level, marks the boundary of the area risk level, and outputs the risk level partition data. The environmental weighting module extracts environmental variables of the construction area based on the risk level zoning data, and generates dynamic impact values ​​of environmental variables by quantifying the dynamic impact weights of temperature, humidity and vibration variables on the risk level of the area. The state analysis module uses the dynamic impact values ​​of the environmental variables to analyze the trend of node risk state changes, calculates the node risk transfer probability and assesses the dynamic changes of risk intensity in the propagation path, and generates dynamic change values ​​of risk state. The priority update module analyzes the impact of newly added risk signals on the propagation of existing nodes based on the dynamic changes in the risk status, recalculates the risk priority and status level of the construction nodes, and outputs the risk priority and status update values ​​of the construction nodes.

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