A BIM-based Bridge Construction Progress Monitoring Method and System

Abstract

The present application provides a method and system for monitoring the construction progress of a bridge based on BIM, including: obtaining an observation matrix based on the BIM model data and sensor monitoring data of the target bridge; inputting the observation matrix into a preset Kalman filter to obtain a corresponding fusion matrix; using the overall progress deviation as the dependent variable, and using the stress ratio, environmental index, resource delay rate, and process progress deviation as independent variables respectively to conduct a Granger causality test to obtain the Granger causality test result; constructing a corresponding DAG, and obtaining the reasons for the overall progress deviation based on the DAG. This solution accurately fuses the BIM model data and sensor monitoring data, constructs a DAG between the overall progress deviation and various quantitative information, can accurately obtain the quantitative information causing the overall progress deviation, and provides an effective basis for subsequent decision-making.

Description

Technical Field

[0001] The present application relates to the field of bridge construction progress management, and particularly to a BIM-based bridge construction progress monitoring method and system. Background Technique

[0002] Bridge construction progress monitoring is a key link in bridge construction. In this process, monitoring personnel collect, analyze, and evaluate data to ensure the quality, safety, and reliability of bridge construction, promptly discover potential problems (such as abnormal structural deformation and unqualified construction quality), and take repair and reinforcement measures. Its significance lies in: ensuring the safety and stability of the bridge, preventing construction risks; providing a scientific basis, optimizing the construction plan, and improving efficiency; and supporting later maintenance, reducing the whole life cycle cost. In short, bridge construction progress monitoring is an important means to ensure the smooth, safe, and high-quality completion of bridge projects.

[0003] Currently, BIM (Building Information Modeling) technology is widely used in the construction industry and is also involved in the field of bridge construction progress monitoring. However, in the existing technology, the bridge BIM model data and actual construction information are often simply compared to obtain the progress deviation, resulting in the inability to determine the precise bridge construction progress deviation and the inability to give more detailed reasons for the progress deviation, and thus unable to provide an effective basis for subsequent decision-making. Summary of the Invention

[0004] The present invention provides a BIM-based bridge construction progress monitoring method and system to solve the problem in the existing technology that the bridge BIM model data and actual construction information are simply compared to obtain the progress deviation, resulting in the inability to determine the precise bridge construction progress deviation, the inability to give more detailed reasons for the progress deviation, and thus unable to provide an effective basis for subsequent decision-making.

[0005] On the one hand, an embodiment of the present application provides a BIM-based bridge construction progress monitoring method, including:

[0006] Based on the BIM model data and sensor monitoring data of the target bridge, an observation matrix is obtained; each row of the observation matrix corresponds to the BIM planned progress, measured progress, stress ratio, and environmental index at the corresponding time stamp.

[0007] The observation matrix is input into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, the process noise covariance matrix is determined based on the construction stage at the prediction moment, and the corresponding prediction matrix is predicted. In the update stage, the Kalman gain is adjusted based on the BIM constraint set, and the prediction matrix is updated to obtain the fusion matrix.

[0008] Based on the BIM planned schedule and the measured schedule in the fusion matrix, obtain the overall schedule deviation and the corresponding process schedule deviation. Based on the construction log, obtain the resource delay rate corresponding to each timestamp, and use the overall schedule deviation as the dependent variable, and the stress ratio, environmental index, resource delay rate, and process schedule deviation as independent variables respectively to conduct a Granger causality test to obtain the Granger causality test results between each independent variable and the overall schedule deviation;

[0009] Respectively use each independent variable and the dependent variable as nodes, and based on the Granger causality test results, construct the edges between each node and the overall schedule deviation to obtain the corresponding DAG, and based on the DAG, obtain the reasons for the overall schedule deviation.

[0010] In an alternative embodiment of the present application, the method further includes:

[0011] At the prediction moment, based on the BIM model data or sensor monitoring data, obtain the construction time of each process corresponding to the prediction moment, and determine the corresponding construction stage based on the construction time of each process and the total construction period; wherein, the construction stage includes: the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage;

[0012] Determine the process noise covariance matrix based on the construction stage at the prediction moment, which is achieved through the following formula:

[0013]

[0014] Wherein, Q k is the process noise covariance matrix, I b is the identity matrix of the BIM planned schedule, I s1 is the identity matrix of the measured schedule, I s2 is the identity matrix of the stress ratio, I e is the identity matrix of the environmental index, p is the construction stage indicator, indicates that the construction stage is the foundation construction stage, indicates that the construction stage is the superstructure construction stage, indicates that the construction stage is the bridge deck system construction stage.

[0015] In an alternative embodiment of the present application, the method further includes: Based on the BIM model data, obtain the time sequence of each process and the minimum time interval between adjacent processes as the BIM time sequence constraint, and obtain the stress interval of each process as the BIM mechanical constraint;

[0016] Construct a BIM constraint set based on BIM time sequence constraints and BIM mechanical constraints;

[0017] Adjust the Kalman gain based on the BIM constraint set, which is achieved by the following method:

[0018] Respectively use BIM time sequence constraints and BIM mechanical constraints to check the observed values in the observation matrix, and obtain the corresponding inspection results;

[0019] Adjust the Kalman gain based on the inspection results to obtain the corresponding adjusted Kalman gain.

[0020] In an alternative embodiment of the present application, respectively using BIM time sequence constraints and BIM mechanical constraints to check the observed values in the observation matrix, and obtaining the corresponding inspection results, including:

[0021] For the BIM time sequence constraint check, make the observed values of the BIM planned progress, stress ratio, and environmental index all conform to the BIM time sequence constraints. Based on the BIM model data, obtain the unfinished processes in the time window corresponding to the observed value of the actual progress, and conduct BIM time sequence constraint checks on each unfinished process to obtain the first inspection result;

[0022] For the BIM mechanical constraint check, make the observed values of the BIM planned progress, actual progress, and environmental index all conform to the BIM mechanical constraints, and conduct BIM mechanical constraint checks on the unfinished processes in the time window corresponding to the observed value of the stress ratio to obtain the second inspection result;

[0023] Obtain the inspection result based on the first inspection result and the second inspection result.

[0024] In an alternative embodiment of the present application, adjust the Kalman gain based on the inspection result to obtain the corresponding adjusted Kalman gain, which is achieved by the following formula:

[0025]

[0026] Among them, represents the weight coefficient of the i th kind of data in the observation matrix; represents the weight coefficient matrix composed of the weight coefficients of various data in the observation matrix; represents that the observed value of the i th kind of data does not conform to the corresponding j th BIM constraint, m represents the number of corresponding BIM constraints; is an indicator function, and when the observed value of the i th kind of data does not conform to the BIM constraint, its value is 1, and when the observed value of the iThe observed value of a certain type of data is 0 when it conforms to the constraint; represents the Kalman gain before adjustment, represents the Kalman gain after adjustment, represents the Hadamard product, that is, the element-by-element multiplication of matrices.

[0027] In an alternative embodiment of the present application, taking each independent variable and dependent variable as nodes respectively, based on the Granger causality test results, edges are constructed between each node and the overall progress deviation to obtain the corresponding DAG, including:

[0028] For each independent variable node, add at least one corresponding attribute;

[0029] For any independent variable, if the independent variable is the Granger cause of the dependent variable, an edge is constructed between the independent variable and the dependent variable, and the direct contribution degree corresponding to the independent variable is used as the weight of the corresponding edge to obtain the DAG.

[0030] In an alternative embodiment of the present application, based on the DAG, the reasons for the overall progress deviation are obtained, including:

[0031] Traverse the DAG, and select the independent variables corresponding to the edges with weights within a preset interval as the basis for progress deviation;

[0032] Based on the BIM model data, obtain the process details in the time window corresponding to the progress deviation basis, and determine the reasons for the overall progress deviation.

[0033] In a second aspect, an embodiment of the present application provides a BIM-based bridge construction progress monitoring system, including:

[0034] An observation matrix acquisition module, configured to obtain an observation matrix based on the BIM model data and sensor monitoring data of the target bridge; where each row of the observation matrix corresponds to the BIM planned progress, measured progress, stress ratio, and environmental index at the corresponding time stamp;

[0035] A fusion matrix acquisition module, configured to input the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, determine the process noise covariance matrix based on the construction stage at the prediction moment, and predict to obtain the corresponding prediction matrix. In the update stage, adjust the Kalman gain based on the BIM constraint set, and update the prediction matrix to obtain the fusion matrix;

[0036] A causality test result acquisition module, configured to obtain the overall schedule deviation and the corresponding process schedule deviation based on the BIM planned schedule and the actual measured schedule in the fusion matrix, obtain the resource delay rate corresponding to each timestamp based on the construction log, and use the overall schedule deviation as the dependent variable, and use the stress ratio, the environmental index, the resource delay rate, and the process schedule deviation as independent variables respectively to perform a Granger causality test to obtain the Granger causality test results between each independent variable and the overall schedule deviation;

[0037] A schedule deviation cause acquisition module, configured to construct an edge between each node and the overall schedule deviation based on the Granger causality test results with each independent variable and the dependent variable as nodes, obtain the corresponding DAG, and obtain the cause of the overall schedule deviation based on the DAG.

[0038] In an optional embodiment of the present application, the system further includes a construction stage acquisition module, configured to:

[0039] At the prediction moment, based on the BIM model data or the sensor monitoring data, obtain the construction time of each process corresponding to the prediction moment, and determine the corresponding construction stage based on the construction time of each process and the total construction period; wherein, the construction stage includes: the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage;

[0040] Determine the process noise covariance matrix based on the construction stage where the prediction moment is located, which is implemented by the following formula:

[0041]

[0042] Wherein, Q k is the process noise covariance matrix, I b is the identity matrix of the BIM planned schedule, I s1 is the identity matrix of the actual measured schedule, I s2 is the identity matrix of the stress ratio, I e is the identity matrix of the environmental index, p is the construction stage indicator, indicates that the construction stage where it is located is the foundation construction stage, indicates that the construction stage where it is located is the superstructure construction stage, indicates that the construction stage where it is located is the bridge deck system construction stage.

[0043] In an optional embodiment of the present application, the system further includes a BIM constraint set acquisition module, configured to:

[0044] Based on the BIM model data, obtain the time sequence of each process and the minimum time interval between adjacent processes as the BIM time sequence constraint, and obtain the stress range of each process as the BIM mechanical constraint;

[0045] Construct a BIM constraint set based on the BIM time sequence constraint and the BIM mechanical constraint;

[0046] Adjust the Kalman gain based on the BIM constraint set, which is achieved in the following way:

[0047] Respectively use the BIM time sequence constraint and the BIM mechanical constraint to test the observed values in the observation matrix, and obtain the corresponding test results;

[0048] Adjust the Kalman gain based on the test results to obtain the corresponding adjusted Kalman gain.

[0049] In an alternative embodiment of the present application, respectively use the BIM time sequence constraint and the BIM mechanical constraint to test the observed values in the observation matrix, and obtain the corresponding test results, including:

[0050] For the BIM time sequence constraint test, make the observed values of the BIM planned progress, the stress ratio, and the environmental index all conform to the BIM time sequence constraint. Based on the BIM model data, obtain the unfinished processes in the time window corresponding to the observed value of the actual progress, and perform the BIM time sequence constraint test on each unfinished process to obtain the first test result;

[0051] For the BIM mechanical constraint test, make the observed values of the BIM planned progress, the actual progress, and the environmental index all conform to the BIM mechanical constraint, and perform the BIM mechanical constraint test on the unfinished processes in the time window corresponding to the observed value of the stress ratio to obtain the second test result;

[0052] Obtain the test result based on the first test result and the second test result.

[0053] In an alternative embodiment of the present application, adjust the Kalman gain based on the test results to obtain the corresponding adjusted Kalman gain, which is achieved through the following formula:

[0054]

[0055] Among them, represents the weight coefficient of the i th type of data in the observation matrix, represents the weight coefficient matrix composed of the weight coefficients of various types of data in the observation matrix; represents that the observed value of the i th type of data does not conform to the corresponding j th BIM constraint,m Indicates the quantity of corresponding BIM constraints; is an indicator function, and its value is 1 when the observed value of the i th type of data does not conform to the BIM constraint, and its value is 0 when the observed value of the i th type of data conforms to the constraint; represents the Kalman gain before adjustment, represents the adjusted Kalman gain, represents the Hadamard product, that is, element-wise multiplication of matrices.

[0056] In an alternative embodiment of the present application, using respective independent variables and dependent variables as nodes, edges between each node and the overall schedule deviation are constructed based on the Granger causality test results to obtain the corresponding DAG, including:

[0057] For each independent variable node, add at least one corresponding attribute;

[0058] For any independent variable, if the independent variable is the Granger cause of the dependent variable, then construct an edge between the independent variable and the dependent variable, and use the direct contribution degree corresponding to the independent variable as the weight of the corresponding edge to obtain the DAG.

[0059] In an alternative embodiment of the present application, based on the DAG, the reasons causing the overall schedule deviation are obtained, including:

[0060] Traverse the DAG, and select the independent variables corresponding to the edges with weights within a preset interval as the basis for schedule deviation;

[0061] Based on the BIM model data, obtain the process details in the time window corresponding to the schedule deviation basis, and determine the reasons causing the overall schedule deviation.

[0062] In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements any one of the above-mentioned BIM-based bridge construction schedule monitoring methods.

[0063] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the above-mentioned BIM-based bridge construction schedule monitoring methods.

[0064] In a fifth aspect, the present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements any one of the above-mentioned BIM-based bridge construction schedule monitoring methods.

[0065] The solution provided by the embodiments of this application precisely fuses the BIM model data and sensor monitoring data during the bridge construction process. Specifically, it uses Kalman filtering to dynamically fuse the BIM planned progress, actual measured progress, stress ratio, and environmental index. Different process noise covariances are considered in the prediction stage for different construction stages, and the Kalman gain is adjusted based on the BIM constraint set in the update stage. Then, taking the overall progress deviation as the dependent variable, and the stress ratio, environmental index, resource delay rate, and process progress deviation as independent variables respectively, a Granger causality test is conducted to obtain the Granger causality test results between each independent variable and the overall progress deviation. Furthermore, based on the Granger causality test results, the edges between each node and the overall progress deviation are constructed to obtain the corresponding DAG. Finally, the reasons for the overall progress deviation are obtained based on the DAG. This solution precisely fuses the BIM model data and sensor monitoring data, can obtain accurate overall progress deviation, and constructs a DAG between the overall progress deviation and various quantitative information, enabling accurate acquisition of detailed quantitative information causing the overall progress deviation, thus providing an effective basis for subsequent construction decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] To more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0067] Figure 1 It is a flowchart of a method for monitoring the bridge construction progress based on BIM provided by the present invention;

[0068] Figure 2 It is a structural block diagram of a system for monitoring the bridge construction progress based on BIM provided by the present invention;

[0069] Figure 3 It is a structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] Figure 1 It is a flowchart of a method for monitoring the bridge construction progress based on BIM provided by the embodiments of this application. As Figure 1 shown, the method may include:

[0071] Step S101: Based on the BIM model data and sensor monitoring data of the target bridge, obtain an observation matrix; where each row of the observation matrix corresponds to the BIM planned progress, actual measured progress, stress ratio, and environmental index at the corresponding time stamp.

[0072] Among them, the target bridge is the bridge that is under construction and requires construction progress monitoring.

[0073] Among them, BIM (Building Information Modeling) model data is the core carrier of the building information model. It integrates the information of the entire life cycle of a building project in a digital way to form a three-dimensional visual parametric model. During the construction process, the timing information, structural information, and mechanical information of each process and each workpiece can be obtained through BIM model data.

[0074] Sensor data refers to the data collected by sensors installed on bridge components. These sensors include stress sensors that collect stress data of key bridge nodes, environmental sensors that collect construction environment data (which can include temperature sensors and wind speed sensors, etc.), and RFID (Radio Frequency Identification) tags on components that collect data from the time the component enters the factory to the completion of assembly.

[0075] Specifically, first obtain the original data of BIM model data and sensor data, and then set a fixed time window (that is, preset a time period). For example, this time window can be 15 minutes or one hour, and the size of the time window can be selected according to the computing power. Collect and align different types of original data according to the time window, and assign corresponding timestamps to each time window in chronological order, then the observation matrix for subsequent Kalman filtering can be obtained. It can be understood that each timestamp of this observation matrix corresponds to four types of observed values: BIM planned progress, measured progress, stress ratio, and environmental index.

[0076] Specifically, for any BIM planned progress, the planned progress at each moment in its corresponding time window can be obtained, and the average value of the planned progress at each moment is obtained by calculating. The planned progress at each moment is the ratio of the sum of the construction times of each process at that moment to the total construction period. For any measured progress, the measured progress at each moment in its corresponding time window can be obtained, and the average value of the measured progress at each moment is obtained by calculating. The measured progress at each moment is the ratio of the sum of the construction times recorded by the sensors to the total construction period. For any stress ratio, the stress ratio of the key nodes at each moment in its corresponding time window can be obtained, and the average value of the stress ratio of the key nodes at each moment is obtained by calculating. The stress ratio at each moment is the ratio of the stress at the key point to the design stress. For any environmental index, the temperature and wind speed at each moment in its corresponding time window can be obtained, and the temperature and wind speed are weighted and summed, and then the average value of the weighted sum at each moment is obtained by calculating.

[0077] Step S102: Input the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, determine the process noise covariance matrix based on the construction stage at the prediction moment, and predict the corresponding prediction matrix. In the update stage, adjust the Kalman gain based on the BIM constraint set and update the prediction matrix to obtain the fusion matrix.

[0078] Among them, according to the different construction progress during the entire construction period, the construction stage can be divided into different dimensions. In the embodiments of the present application, the construction stage is divided into: the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage. In the prediction stage of using Kalman filtering, the influence degrees of different factors on the accuracy of the predicted value are different. For example, in the foundation construction stage, the BIM model data has a higher influence degree on the predicted value. In the superstructure construction stage, the sensor monitoring data (i.e., the actual construction progress, stress ratio, and environmental index) has a higher influence degree on the predicted value. And in the bridge deck system construction stage, the environmental index has a higher influence degree on the predicted value. Then, in order to ensure that the process noise covariance matrix is more in line with the actual situation, in the embodiments of the present application, the weights of the influencing factors in different process noise covariance matrices are adjusted at different construction stages.

[0079] Among them, the BIM constraint set in the embodiments of the present application includes the time sequence constraint or constraint set of workpieces, and also includes the stress range of each process, that is, the BIM mechanical constraint set. The BIM constraint set is to split the BIM model data to obtain the work process set of the construction, obtain the time sequence relationship of each process, and the time sequence relationship between adjacent processes to obtain the BIM time sequence constraint. Further, each process is split into different components, and the stress range of the key nodes of each component is obtained to obtain the BIM mechanical constraint. Then, in the update stage of Kalman filtering, through the construction of the BIM constraint set, higher weights are given to the observed values that meet the constraints, making the update process more in line with the engineering logic under the BIM data model and providing more accurate output results for subsequent causal analysis.

[0080] Specifically, the Kalman filter algorithm is mainly divided into two steps: prediction and update. In the embodiments of the present application, the preset Kalman filter is used to fuse the four types of data: the BIM planned progress, the actual measured progress, the stress ratio, and the environmental index. In the update stage, estimate the state at the current moment (k moment, that is, the kth time stamp) based on the posterior estimate value at the previous moment (k - 1 moment, that is, the (k - 1)th time stamp) to obtain the prior estimate value at the k moment. In this process, first obtain the current construction stage and determine the process noise covariance matrix required for estimation in the prediction stage based on this construction stage. Q k. In the update phase, the observations at the current moment are used to correct the estimates in the prediction phase, obtaining the posterior estimates at the current moment, and updating the posterior estimate covariance of the state variables as the input for the next iteration. During this process, it is necessary to adjust the Kalman gain calculation process through the BIM constraint set, that is, by passing the test of the BIM constraint set, increasing the weights of the observations that meet the constraints, so as to obtain the adjusted Kalman gain.

[0081] Step S103: Based on the BIM planned progress and the actual measured progress in the fusion matrix, obtain the overall progress deviation and the corresponding process progress deviation. Based on the construction log, obtain the resource delay rate corresponding to each time stamp, and use the overall progress deviation as the dependent variable, and the stress ratio, environmental index, resource delay rate, and process progress deviation as independent variables respectively to conduct Granger causality tests to obtain the Granger causality test results between each independent variable and the overall progress deviation.

[0082] Among them, the Granger causality test is a statistical hypothesis testing method based on time series, used to determine whether the past values of one variable (or time series) have a significant contribution to predicting the future values of another variable. Its core idea is: if the past information of variable X can significantly improve the prediction accuracy of variable Y, then X is considered the "Granger cause" of Y. In the embodiments of the present application, the overall progress deviation is used as the dependent variable, and the stress ratio, environmental index, resource delay rate, and process progress deviation are used as independent variables, so as to analyze whether each independent variable is the "Granger cause" of the overall progress deviation respectively.

[0083] Among them, obtaining the overall progress deviation and the corresponding process progress deviation based on the BIM planned progress and the actual measured progress in the fusion matrix includes: taking the ratio of the difference between the BIM planned progress and the actual measured progress corresponding to the same time stamp to the total construction period as the overall progress deviation.

[0084] Among them, obtaining the corresponding process progress deviation based on the BIM planned progress and the actual measured progress in the fusion matrix includes: splitting the BIM model data to obtain the data of each process. For the time window corresponding to any time stamp, obtain the BIM planned progress and the actual measured progress of all started processes in this time window, and take the ratio of the difference between the BIM planned progress and the actual measured progress of each started process to the designed construction period of this started process as the overall progress deviation. In the embodiments of the present application, the designed construction period can be obtained through the design document.

[0085] Among them, obtaining the resource delay rate corresponding to each time stamp based on the construction log includes: for any time stamp, obtaining the construction log of the time window corresponding to this time stamp, and extracting the resource delay rate through a preset natural language processing algorithm. For example, mechanical failures result in a 15% time loss, etc.

[0086] Specifically, taking the stress ratio, environmental index, resource delay rate, and process schedule deviation as independent variables respectively, Granger causality tests are conducted to determine whether there is a causal relationship between each independent variable and the overall schedule deviation. Specifically, the F values between the overall schedule deviation and each independent variable are calculated respectively, and based on the F values, it is determined whether each independent variable is the Granger cause of the overall schedule deviation.

[0087] Specifically, the causal relationship between variables is judged through the following steps: Model construction, establishing a regression model of the lag terms of the dependent variable Y (overall schedule deviation) with respect to the independent variable X (such as stress ratio) (which can be determined by selecting through AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion)) and other control variables. Hypothesis testing, testing whether the lag terms of the independent variable X have a significant incremental contribution to predicting Y. Result output: If the null hypothesis is rejected (i.e., X is the Granger cause of Y), then the standardized regression coefficient is further calculated to quantify the direct contribution degree of X to Y.

[0088] Step S104, taking each independent variable and the dependent variable as nodes respectively, based on the Granger causality test results, construct the edges between each node and the overall schedule deviation to obtain the corresponding DAG, and based on the DAG, obtain the reasons for the overall schedule deviation.

[0089] Specifically, based on the Granger causality test results between each independent variable and the dependent variable, construct the corresponding DAG, and then determine the reasons for the overall schedule deviation according to the DAG.

[0090] The solution provided by the embodiment of the present application, through the precise fusion of BIM model data and sensor monitoring data in the bridge construction process, that is, using Kalman filtering to dynamically fuse the BIM planned progress, measured progress, stress ratio, and environmental index, considering different process noise covariances in the prediction stage, and adjusting the Kalman gain based on the BIM constraint set in the update stage; and taking the overall schedule deviation as the dependent variable, taking the stress ratio, the environmental index, resource delay rate, and process schedule deviation as independent variables respectively, conducting Granger causality tests to obtain the Granger causality test results between each independent variable and the overall schedule deviation, and then based on the Granger causality test results, construct the edges between each node and the overall schedule deviation to obtain the corresponding DAG; finally, based on the DAG, obtain the reasons for the overall schedule deviation. This solution precisely fuses the BIM model data and sensor monitoring data, can obtain accurate overall schedule deviation, and constructs a DAG between the overall schedule deviation and each quantitative information, can accurately obtain the detailed quantitative information causing the overall schedule deviation, so as to provide an effective basis for subsequent construction decisions.

[0091] In an alternative embodiment of the present application, the method may further include:

[0092] At the prediction moment, based on the BIM model data or sensor monitoring data, obtain the construction time of each process corresponding to the prediction moment, and determine the corresponding construction stage based on the construction time of each process and the total construction period; wherein, the construction stage includes: the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage;

[0093] Determine the process noise covariance matrix based on the construction stage where the prediction moment is located, which is achieved through the following formula:

[0094]

[0095] Wherein, Q k is the process noise covariance matrix, I b is the identity matrix of the BIM planned progress, I s1 is the identity matrix of the measured progress, I s2 is the identity matrix of the stress ratio, I e is the identity matrix of the environmental index, p is the construction stage indicator, indicates that the construction stage is the foundation construction stage, indicates that the construction stage is the superstructure construction stage, indicates that the construction stage is the bridge deck system construction stage.

[0096] Specifically, the division of the construction stage can have multiple granularities. For example, on the basis of being divided into the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage, the foundation construction stage can further be divided into multiple construction stages according to the progress. Correspondingly, according to the different granularities of the construction stage division, the weights of different types of data in the corresponding process noise covariance matrix equation are also different, so as to realize the dynamic adjustment of the process noise covariance matrix in the prediction stage. This method can make the result obtained by the Kalman filter more accurate.

[0097] In an alternative embodiment of the present application, the method may further include: based on the BIM model data, obtain the time sequence of each process and the minimum time interval between adjacent processes as the BIM time sequence constraint, and obtain the stress interval of each process as the BIM mechanical constraint;

[0098] Construct a BIM constraint set based on the BIM time sequence constraint and the BIM mechanical constraint;

[0099] Adjust the Kalman gain based on the BIM constraint set, which is achieved in the following manner:

[0100] Respectively use the BIM time sequence constraint and the BIM mechanics constraint to check the observed values in the observation matrix, and obtain the corresponding check results;

[0101] Adjust the Kalman gain based on the check results to obtain the corresponding adjusted Kalman gain.

[0102] Among them, the time sequence of processes refers to the sequence of process starts. For example, process A must start after process B is completed, or process A must start after process B starts, etc. The minimum time interval between adjacent processes refers to the interval time between processes. For example, process A must start 24 hours after process B is completed, or process A must start 2 hours after process B starts, etc.

[0103] Furthermore, respectively use the BIM time sequence constraint and the BIM mechanics constraint to check the observed values in the observation matrix, and obtain the corresponding check results, including:

[0104] For the BIM time sequence constraint check, make the observed values of the BIM planned progress, the stress ratio, and the environmental index all conform to the BIM time sequence constraint. Based on the BIM model data, obtain the unfinished processes in the time window corresponding to the observed value of the actual progress, and conduct the BIM time sequence constraint check on each unfinished process to obtain the first check result;

[0105] For the BIM mechanics constraint check, make the observed values of the BIM planned progress, the actual progress, and the environmental index all conform to the BIM mechanics constraint, and conduct the BIM mechanics constraint check on the unfinished processes in the time window corresponding to the observed value of the stress ratio to obtain the second check result;

[0106] Obtain the check result based on the first check result and the second check result.

[0107] Specifically, adjust the Kalman gain based on the check results to obtain the corresponding adjusted Kalman gain, which is achieved through the following formula:

[0108]

[0109] Among them, represents the weight coefficient of the i th type of data in the observation matrix, represents the weight coefficient matrix composed of the weight coefficients of various types of data in the observation matrix; represents that the observed value of the i th type of data does not conform to the corresponding j th BIM constraint,m Indicates the quantity of corresponding BIM constraints; is an indicator function, and its value is 1 when the observed value of the i th type of data does not conform to the BIM constraints, and its value is 0 when the observed value of the i th type of data conforms to the constraints; represents the Kalman gain before adjustment, represents the adjusted Kalman gain, represents the Hadamard product, that is, element-by-element multiplication of matrices.

[0110] In an alternative embodiment of the present application, taking respective independent variables and dependent variables as nodes, edges between each node and the overall schedule deviation are constructed based on the Granger causality test results to obtain a corresponding DAG, including:

[0111] For each independent variable node, at least one corresponding attribute is added;

[0112] For any independent variable, if the independent variable is the Granger cause of the dependent variable, an edge is constructed between the independent variable and the dependent variable, and the direct contribution degree corresponding to the independent variable is used as the weight of the corresponding edge to obtain a DAG.

[0113] Further, based on the DAG, the reasons causing the overall schedule deviation are obtained, including:

[0114] Traverse the DAG, and select the independent variables corresponding to the edges whose weights are within a preset interval as the basis for schedule deviation;

[0115] Based on the BIM model data, obtain the process details in the time window corresponding to the basis for schedule deviation, and determine the reasons causing the overall schedule deviation.

[0116] Among them, DAG (Directed Acyclic Graph) is a graph structure composed of vertices and directed edges. Its characteristic is that the directions of all edges are arranged in a specific order, and there is no closed-loop path. It has two characteristics: directivity, the edges in the graph have directivity, representing a one-way relationship from the starting point to the ending point, usually used to describe dependencies or causal relationships. Acyclicity, there is no closed-loop path in the graph, that is, starting from any vertex and advancing along the directed edges, it is impossible to return to the starting point. This characteristic enables the DAG to avoid circular dependency problems and ensures the clarity and executability of the dependency relationship.

[0117] In the embodiment of the present application, first, nodes are defined in the following manner: A dependent variable node is constructed, namely the overall progress deviation (such as the total project duration deviation rate). Independent variable nodes are constructed: potential influencing factors (such as stress ratio, resource delay rate, environmental index, etc.). Then, the edge construction rules are determined in the following manner: Through Granger causality test screening, only the edges between the independent variables that pass the significance test (such as p≤0.05) and the overall progress deviation are retained. And the direction of the edges is set in the following manner: The edges point from the independent variable nodes to the overall progress deviation node, indicating that "the independent variable affects the dependent variable".

[0118] After the DAG construction is completed, traverse the DAG and screen for key edges, that is, find the main reasons for the progress deviation. Starting from the "overall progress deviation" node, trace back all the edges pointing to this node in reverse. A weight interval can be set first (that is, the direct contribution interval, for example, the weight is greater than 0.3), and then traverse the entire DAG, select the edges whose weights belong to the weight interval, and obtain the independent variables that have a significant impact on the overall progress deviation. For example, two significant edges, the stress ratio (weight = 0.48) and the resource delay rate (weight = -0.35), are screened out, indicating that these two variables have significant positive and negative impacts on the overall progress deviation.

[0119] Furthermore, the specific processes affecting the overall progress deviation can be further located by combining the BIM model data, and the time offset of the influence of the independent variable can be determined according to the lag order used in the Granger test (such as lag 1 period). For example: If the lag order is 1 month, then analyze the influence of the independent variable in the current month on the overall progress deviation in the next month. At the same time, extract the process parameters associated with the key independent variables from the BIM model (such as the time, quality, and resource allocation records of the "concrete pouring process" corresponding to the stress ratio, which can also be added as attributes of the corresponding nodes to the nodes). Calculate the parameter deviation threshold of the key process. For example, the construction progress lags when the stress ratio exceeds the design value by 15%; a resource delay rate exceeding 20% causes a chain delay. In this way, the specific reasons for the overall progress deviation are determined, providing a data-driven improvement basis for progress management.

[0120] Figure 2 The structural block diagram of a BIM-based bridge construction progress monitoring system provided by the embodiment of the present application is as Figure 2 shown. The system may include: an observation matrix acquisition module 201, a fusion matrix acquisition module 202, a causality test result acquisition module 203, and a progress deviation cause acquisition module 204, where:

[0121] The observation matrix acquisition module 201 is used to obtain an observation matrix based on the BIM model data and sensor monitoring data of the target bridge; each row of the observation matrix corresponds to the BIM planned progress, measured progress, stress ratio, and environmental index at the corresponding timestamp.

[0122] The fusion matrix acquisition module 202 is used to input the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, the process noise covariance matrix is determined based on the construction stage at the prediction moment, and the corresponding prediction matrix is predicted. In the update stage, the Kalman gain is adjusted based on the BIM constraint set, and the prediction matrix is updated to obtain the fusion matrix.

[0123] The causal test result acquisition module 203 is used to obtain the overall progress deviation and the corresponding process progress deviation based on the BIM planned progress and measured progress in the fusion matrix. Based on the construction log, the resource delay rate corresponding to each timestamp is obtained, and the Granger causal test is performed with the overall progress deviation as the dependent variable and the stress ratio, environmental index, resource delay rate, and process progress deviation as independent variables respectively to obtain the Granger causal test results between each independent variable and the overall progress deviation.

[0124] The progress deviation cause acquisition module 204 is used to construct edges between each node and the overall progress deviation based on the Granger causal test results with each independent variable and the dependent variable as nodes respectively to obtain the corresponding DAG, and obtain the cause of the overall progress deviation based on the DAG.

[0125] The solution provided in the embodiment of the present application accurately fuses the BIM model data and sensor monitoring data during the bridge construction process, that is, uses Kalman filtering to dynamically fuse the BIM planned progress, measured progress, stress ratio, and environmental index, considers different process noise covariances in different construction stages in the prediction stage, and adjusts the Kalman gain based on the BIM constraint set in the update stage; and performs the Granger causal test with the overall progress deviation as the dependent variable and the stress ratio, environmental index, resource delay rate, and process progress deviation as independent variables respectively to obtain the Granger causal test results between each independent variable and the overall progress deviation, and then constructs edges between each node and the overall progress deviation based on the Granger causal test results to obtain the corresponding DAG; finally, obtains the cause of the overall progress deviation based on the DAG. This solution accurately fuses the BIM model data and sensor monitoring data, can obtain the accurate overall progress deviation, and constructs a DAG between the overall progress deviation and each quantitative information, and can accurately obtain the detailed quantitative information causing the overall progress deviation, so as to provide an effective basis for subsequent construction decisions.

[0126] In an alternative embodiment of the present application, the system further includes a construction stage acquisition module for:

[0127] At the prediction time, based on the BIM model data or sensor monitoring data, obtain the construction time of each process corresponding to the prediction time, and determine the corresponding construction stage based on the construction time of each process and the total construction period; wherein, the construction stage includes: the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage;

[0128] Determine the process noise covariance matrix based on the construction stage where the prediction time is located, which is achieved through the following formula:

[0129]

[0130] Wherein, Q k is the process noise covariance matrix, I b is the identity matrix of the BIM planned progress, I s1 is the identity matrix of the measured progress, I s2 is the identity matrix of the stress ratio, I e is the identity matrix of the environmental index, p is the construction stage indicator, indicates that the construction stage is the foundation construction stage, indicates that the construction stage is the superstructure construction stage, indicates that the construction stage is the bridge deck system construction stage.

[0131] In an alternative embodiment of the present application, the system further includes a BIM constraint set acquisition module for:

[0132] Based on the BIM model data, obtain the time sequence of each process and the minimum time interval between adjacent processes as the BIM time sequence constraint, and obtain the stress interval of each process as the BIM mechanical constraint;

[0133] Construct a BIM constraint set based on the BIM time sequence constraint and the BIM mechanical constraint;

[0134] Adjust the Kalman gain based on the BIM constraint set, which is achieved through the following method:

[0135] Respectively use the BIM time sequence constraint and the BIM mechanical constraint to test the observed values in the observation matrix to obtain the corresponding test results;

[0136] Adjust the Kalman gain based on the test results to obtain the corresponding adjusted Kalman gain.

[0137] In an alternative embodiment of the present application, the observed values in the observation matrix are respectively tested using BIM time sequence constraints and BIM mechanical constraints to obtain corresponding test results, including:

[0138] For the BIM time sequence constraint test, let the observed values of the BIM planned progress, stress ratio, and environmental index all conform to the BIM time sequence constraints. Based on the BIM model data, the unfinished processes in the time window corresponding to the observed value of the actual progress are obtained, and the BIM time sequence constraint test is performed on each unfinished process to obtain the first test result;

[0139] For the BIM mechanical constraint test, let the observed values of the BIM planned progress, actual progress, and environmental index all conform to the BIM mechanical constraints, and the BIM mechanical constraint test is performed on the unfinished processes in the time window corresponding to the observed value of the stress ratio to obtain the second test result;

[0140] The test result is obtained based on the first test result and the second test result.

[0141] In an alternative embodiment of the present application, the Kalman gain is adjusted based on the test result to obtain the corresponding adjusted Kalman gain, which is achieved through the following formula:

[0142]

[0143] Among them, represents the weight coefficient of the i th type of data in the observation matrix, represents the weight coefficient matrix composed of the weight coefficients of various types of data in the observation matrix; represents that the observed value of the i th type of data does not conform to the corresponding j th BIM constraint, m represents the quantity of the corresponding BIM constraint; is an indicator function, and its value is 1 when the observed value of the i th type of data does not conform to the BIM constraint, and its value is 0 when the observed value of the i th type of data conforms to the constraint; represents the Kalman gain before adjustment, represents the adjusted Kalman gain, represents the Hadamard product, that is, element-by-element multiplication of matrices.

[0144] In an alternative embodiment of the present application, taking each independent variable and dependent variable as nodes, edges between each node and the overall progress deviation are constructed based on the Granger causality test results to obtain the corresponding DAG, including:

[0145] Add the corresponding at least one attribute to each independent variable node;

[0146] For any independent variable, if the independent variable is the Granger cause of the dependent variable, then construct an edge between the independent variable and the dependent variable, and use the direct contribution degree corresponding to the independent variable as the weight of the corresponding edge to obtain a DAG.

[0147] In an alternative embodiment of the present application, obtaining the reasons for the overall progress deviation based on the DAG includes:

[0148] Traverse the DAG, and select the independent variables corresponding to the edges with weights within the preset interval as the basis for progress deviation;

[0149] Based on the BIM model data, obtain the process details in the time window corresponding to the progress deviation basis, and determine the reasons for the overall progress deviation.

[0150] Figure 3 Illustrates a schematic diagram of the physical structure of an electronic device, such as Figure 3As shown in the figure, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communications interface 320, and the memory 330 complete communication with each other through the communication bus 340. The processor 310 may call the logical instructions in the memory 330 to execute the BIM-based bridge construction progress monitoring method, and the method includes: based on the BIM model data and sensor monitoring data of the target bridge, obtaining an observation matrix; wherein each row of the observation matrix corresponds to the BIM planned progress, the measured progress, the stress ratio, and the environmental index at the corresponding timestamp; inputting the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, based on the construction stage where the prediction moment is located, determining the process noise covariance matrix, and predicting to obtain the corresponding prediction matrix. In the update stage, adjusting the Kalman gain based on the BIM constraint set, and updating the prediction matrix to obtain a fusion matrix; based on the BIM planned progress and the measured progress in the fusion matrix, obtaining the overall progress deviation and the corresponding process progress deviation, based on the construction log, obtaining the resource delay rate corresponding to each of the timestamps, and using the overall progress deviation as the dependent variable, and using the stress ratio, the environmental index, the resource delay rate, and the process progress deviation as independent variables respectively, performing a Granger causality test to obtain the Granger causality test results between each independent variable and the overall progress deviation; respectively using each independent variable and the dependent variable as nodes, based on the Granger causality test results, constructing edges between each node and the overall progress deviation to obtain the corresponding DAG, and obtaining the reasons for causing the overall progress deviation based on the DAG.

[0151] In addition, when the logical instructions in the above-mentioned memory 330 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0152] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the BIM-based bridge construction progress monitoring method provided by the above-mentioned various methods. The method includes: obtaining an observation matrix based on the BIM model data and sensor monitoring data of a target bridge; wherein each row of the observation matrix corresponds to the BIM planned progress, measured progress, stress ratio, and environmental index at a corresponding timestamp; inputting the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, determine the process noise covariance matrix based on the construction stage at the prediction moment, and predict the corresponding prediction matrix. In the update stage, adjust the Kalman gain based on the BIM constraint set, and update the prediction matrix to obtain a fusion matrix; obtain the overall progress deviation and the corresponding process progress deviation based on the BIM planned progress and measured progress in the fusion matrix, obtain the resource delay rate corresponding to each timestamp based on the construction log, and perform a Granger causality test with the overall progress deviation as the dependent variable and the stress ratio, the environmental index, the resource delay rate, and the process progress deviation as independent variables respectively, to obtain the Granger causality test results between each independent variable and the overall progress deviation; respectively use each independent variable and the dependent variable as nodes, and based on the Granger causality test results, construct edges between each node and the overall progress deviation to obtain a corresponding DAG, and obtain the reasons causing the overall progress deviation based on the DAG.

[0153] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the BIM-based bridge construction progress monitoring method provided by the above-mentioned various methods. The method includes: based on the BIM model data and sensor monitoring data of the target bridge, obtaining an observation matrix; wherein each row of the observation matrix corresponds to the BIM planned progress, the measured progress, the stress ratio, and the environmental index at the corresponding timestamp; inputting the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, based on the construction stage at the prediction moment, determining the process noise covariance matrix, and predicting to obtain the corresponding prediction matrix. In the update stage, adjusting the Kalman gain based on the BIM constraint set, and updating the prediction matrix to obtain a fusion matrix; based on the BIM planned progress and the measured progress in the fusion matrix, obtaining the overall progress deviation and the corresponding process progress deviation, based on the construction log, obtaining the resource delay rate corresponding to each of the timestamps, and using the overall progress deviation as the dependent variable, and using the stress ratio, the environmental index, the resource delay rate, and the process progress deviation as independent variables respectively, conducting a Granger causality test, and obtaining the Granger causality test results between each independent variable and the overall progress deviation; respectively using each independent variable and the dependent variable as nodes, based on the Granger causality test results, constructing edges between each node and the overall progress deviation, obtaining the corresponding DAG, and based on the DAG, obtaining the reasons for causing the overall progress deviation.

[0154] 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 efforts.

[0155] 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, can also be implemented by hardware. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The 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 for causing 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.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention 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 on 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 various embodiments of the present invention.

Claims

1. A BIM-based bridge construction progress monitoring method, characterized in that Including: Obtain an observation matrix based on the BIM model data and sensor monitoring data of the target bridge; Where each row of the observation matrix corresponds to the BIM planned progress, measured progress, stress ratio, and environmental index at the corresponding timestamp; Input the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, determine the process noise covariance matrix based on the construction stage at the prediction moment, and predict the corresponding prediction matrix. In the update stage, adjust the Kalman gain based on the BIM constraint set, and update the prediction matrix to obtain a fusion matrix; Based on the BIM planned progress and measured progress in the fusion matrix, obtain the overall progress deviation and the corresponding process progress deviation. Based on the construction log, obtain the resource delay rate corresponding to each timestamp, and use the overall progress deviation as the dependent variable, and the stress ratio, the environmental index, the resource delay rate, and the process progress deviation as independent variables respectively, and conduct a Granger causality test to obtain the Granger causality test results between each independent variable and the overall progress deviation; Respectively use each independent variable and the dependent variable as nodes, and based on the Granger causality test results, construct edges between each node and the overall progress deviation to obtain the corresponding DAG, and based on the DAG, obtain the reasons for the overall progress deviation.

2. The method according to claim 1, characterized in that The method further includes: At the prediction moment, based on the BIM model data or the sensor monitoring data, obtain the construction time of each process corresponding to the prediction moment, and determine the corresponding construction stage based on the construction time of each process and the total construction period; wherein, the construction stage includes: the foundation construction stage, the superstructure construction stage, and the bridge deck system construction stage; The determination of the process noise covariance matrix based on the construction stage at the prediction moment is achieved through the following formula: Among them, Q k is the process noise covariance matrix, I b is the identity matrix of the BIM planned schedule, I s1 is the identity matrix of the measured schedule, I s2 is the identity matrix of the stress ratio, I e is the identity matrix of the environmental index, p is the construction stage indicator, indicates that the construction stage is the foundation construction stage, indicates that the construction stage is the superstructure construction stage, indicates that the construction stage is the bridge deck system construction stage.

3. The method according to claim 1, wherein The method further includes: Based on the BIM model data, obtain the time sequence of each process and the minimum time interval between adjacent processes as the BIM time sequence constraint, and obtain the stress interval of each process as the BIM mechanical constraint; Construct the BIM constraint set based on the BIM time sequence constraint and the BIM mechanical constraint; The adjustment of the Kalman gain based on the BIM constraint set is achieved through the following method: Respectively use the BIM time sequence constraint and the BIM mechanical constraint to test the observed values in the observation matrix to obtain the corresponding test results; Adjust the Kalman gain based on the test results to obtain the corresponding adjusted Kalman gain.

4. The method according to claim 3, characterized in that, The step of respectively using the BIM time sequence constraint and the BIM mechanical constraint to test the observed values in the observation matrix to obtain the corresponding test results includes: For the BIM time - series constraint test, make the observed values of the BIM planned progress, the observed values of the stress ratio, and the observed values of the environmental index all conform to the BIM time - series constraints. Based on the BIM model data, obtain the unfinished processes in the time window corresponding to the observed value of the actual progress, and conduct the BIM time - series constraint test on each unfinished process to obtain the first test result; For the BIM mechanical constraint test, make the observed values of the BIM planned progress, the observed values of the actual progress, and the observed values of the environmental index all conform to the BIM mechanical constraints. Conduct the BIM mechanical constraint test on the unfinished processes in the time window corresponding to the observed value of the stress ratio to obtain the second test result; Obtain the test result based on the first test result and the second test result.

5. The method according to claim 3, wherein Adjust the Kalman gain based on the test result to obtain the corresponding adjusted Kalman gain, which is achieved through the following formula: Among them, represents the weight coefficient of the i th type of data in the observation matrix, represents the weight coefficient matrix composed of the weight coefficients of various types of data in the observation matrix; represents that the observed value of the i th type of data does not conform to the corresponding j th BIM constraint, m represents the quantity of the corresponding BIM constraint; is an indicator function, and its value is 1 when the observed value of the i th type of data does not conform to the BIM constraint, and its value is 0 when the observed value of the i th type of data conforms to the constraint; represents the Kalman gain before adjustment, represents the Kalman gain after adjustment, represents the Hadamard product, that is, element-by-element multiplication of matrices.

6. The method according to claim 1, wherein Respectively taking each independent variable and the dependent variable as nodes, based on the Granger causality test result, construct the edges between each node and the overall progress deviation to obtain the corresponding DAG, including: For each independent variable node, add the corresponding at least one attribute; For any independent variable, if the independent variable is the Granger cause of the dependent variable, then construct an edge between the independent variable and the dependent variable, and take the direct contribution degree corresponding to the independent variable as the weight of the corresponding edge to obtain the DAG.

7. The method according to claim 1, wherein Based on the DAG, obtain the reasons for the overall progress deviation, including: Traverse the DAG, and select the independent variables corresponding to the edges with weights within the preset interval as the basis for the progress deviation; Based on the BIM model data, obtain the process details in the time window corresponding to the progress deviation basis, and determine the reasons for the overall progress deviation.

8. A BIM-based bridge construction progress monitoring system, characterized in that Include: An observation matrix acquisition module, which is used to obtain an observation matrix based on the BIM model data of the target bridge and the sensor monitoring data; each row of the observation matrix corresponds to the BIM planned progress, actual progress, stress ratio, and environmental index at the corresponding time stamp; A fusion matrix acquisition module, which is used to input the observation matrix into a preset Kalman filter. In the prediction stage, for each row of the observation matrix, determine the process noise covariance matrix based on the construction stage at the prediction moment, and predict to obtain the corresponding prediction matrix. In the update stage, adjust the Kalman gain based on the BIM constraint set and update the prediction matrix to obtain the fusion matrix; A causality test result acquisition module, which is used to obtain the overall progress deviation and the corresponding process progress deviation based on the BIM planned progress and actual progress in the fusion matrix, obtain the resource delay rate corresponding to each time stamp based on the construction log, and conduct a Granger causality test with the overall progress deviation as the dependent variable and the stress ratio, environmental index, resource delay rate, and process progress deviation as independent variables respectively to obtain the Granger causality test results between each independent variable and the overall progress deviation; The progress deviation cause acquisition module is used to construct edges between each node and the overall progress deviation based on the Granger causality test results, with each independent variable and the dependent variable as nodes respectively, to obtain the corresponding DAG, and to acquire the causes resulting in the overall progress deviation based on the DAG.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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