A safety early warning method and system for concrete construction process

Abstract

The present invention relates to the field of construction supervision technology, specifically a method and system for early warning safety during concrete construction. First, the data of a support frame monitoring point and its adjacent monitoring points are obtained and a support frame deformation feature vector is constructed, wherein the support frame deformation feature vector includes distance feature, angle feature, deformation feature and vibration frequency feature; then the number of clusters is determined according to the BIM model of the building, and cluster analysis is performed on the support frame deformation feature vector, and then it is determined whether there is an abnormal cluster; then the support frame monitoring points within the abnormal cluster are obtained to obtain an abnormal support frame monitoring point set; the distance feature, angle feature, pouring feature component and vibration feature component of the abnormal support frame monitoring point are obtained to construct an abnormal evaluation index, and if the abnormal evaluation index is greater than a preset abnormal threshold, a safety warning is triggered. The method proposed by the present invention realizes real-time monitoring of the formwork support frame during concrete construction, thereby improving construction safety.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction supervision, and in particular to a concrete construction process safety early warning method and system. Background Art

[0002] Concrete pouring is a crucial part of construction projects, and the support frame system is an indispensable temporary structure in the construction process. It is mainly used to bear the weight of the formwork and newly poured concrete to ensure the stability and safety of the structure during the construction phase. However, in the actual construction process, support frame collapse accidents often occur due to problems such as support frame design, construction, environment or management, which seriously affects the construction progress and project quality.

[0003] The support frame is mainly composed of steel pipes, fasteners, horizontal rods, diagonal rods and top supports, forming a load-bearing frame for supporting the concrete formwork and the construction load above it. It is temporary, complex and high-risk. The collapse of the support frame is sudden and complex, involving multiple links and multiple influencing factors. There are technical problems such as difficulty in real-time monitoring, untimely data acquisition and analysis, insufficient prediction models and construction personnel behavior, which may lead to major casualties and property losses. Therefore, it is urgent to strengthen the safety analysis of the support frame during the concrete pouring process.

[0004] Therefore, a safety early warning method and system for concrete construction process are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a safety early warning method and system for concrete construction process, so as to improve the safety in the concrete pouring process and timely discover and prevent the collapse of the formwork support frame. Firstly, the support frame deformation feature vector is constructed by distance feature, angle feature, deformation feature and vibration frequency feature, and then the cluster cluster is determined according to the support frame structure in the BIM model. , and cluster analysis is performed on the deformation feature vector of the support frame to obtain the deformation cluster center; whether it is an abnormal cluster is determined according to the distance between the deformation cluster centers; finally, the distance characteristics, angle characteristics, casting characteristic components and vibration characteristic components of the abnormal support frame monitoring point are obtained and processed to obtain an abnormal evaluation index. If the abnormal evaluation index is greater than the preset abnormal threshold, a safety warning is triggered and an alarm is given to the dangerous position.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A safety early warning method for a concrete construction process, comprising:

[0008] Construct distance features based on the distance between the support frame monitoring point and its adjacent monitoring points; construct angle features based on the line and plane connecting the support frame monitoring point and its adjacent monitoring points; construct deformation features based on the deformation amount per unit time of the line connecting the support frame monitoring point and its adjacent monitoring points; construct vibration frequency features based on the vibration frequency data of the support frame monitoring point; construct a deformation feature vector of the support frame based on the distance features, angle features, deformation features and vibration frequency features;

[0009] Furthermore, the support frame deformation feature vector also includes:

[0010] Get real-time monitoring A support frame monitoring point to its The distance between adjacent monitoring points and preset distance And process to get the distance feature ,in, Indicates the number of adjacent monitoring points;

[0011] Taking the support frame monitoring point as the vertex, two different adjacent monitoring points are selected to obtain the edge angle to constitute the first angle feature; taking the support frame monitoring point and two different adjacent monitoring points as a plane, two different planes are selected and processed to obtain the plane angle to constitute the second angle feature; the first angle feature and the second angle feature are obtained and processed to obtain the angle feature.

[0012] Determine clusters based on the data of the support structure in the BIM model , and cluster analysis is performed on the deformation feature vector of the support frame to obtain the deformation cluster center; and whether it is an abnormal cluster is determined according to the distance between the deformation cluster centers;

[0013] Furthermore, clusters are determined based on the data of the support structure in the BIM model. The specific steps include:

[0014] Obtain node features in the BIM model to construct a node feature matrix, where each row in the node feature matrix represents a main node and each column represents the features of the main node; calculate the covariance matrix of the node feature matrix, and find the eigenvalues ​​of the covariance matrix to obtain an eigenvalue set; calculate the contribution rate of each eigenvalue, and select If the cumulative contribution rate reaches the preset threshold, the cluster is determined to be indivual.

[0015] Furthermore, the specific steps of judging whether it is an abnormal cluster according to the distance between the deformation cluster centers include:

[0016] Calculate the neighboring cluster distance between the deformation cluster center and its nearest deformation cluster center. The calculation formula is:

[0017] ;

[0018] in, Indicates Deformation cluster centers The neighboring cluster distance to its nearest deformation cluster center, Deformation cluster center and deformation cluster centers The Euclidean distance between

[0019] If the distance between neighboring clusters is greater than the mean of the neighboring cluster centers of the overall cluster center, it is determined to be an abnormal cluster.

[0020] Get the The data of the pouring points are processed to obtain the pouring characteristics; the pouring characteristics of the pouring points are mapped to the support frame monitoring points to obtain the first The pouring point is The pouring characteristic component at each support frame monitoring point;

[0021] Furthermore, the specific steps of obtaining the casting feature and the casting feature component include:

[0022] No. The calculation formula for the pouring characteristics of a pouring point is:

[0023] ;

[0024] in, Indicates The pouring characteristics of the pouring points, Indicates the pouring volume, Indicates the pouring area;

[0025] The calculation formula of the pouring characteristic component is:

[0026] ;

[0027] in, Indicates The pouring point is The pouring characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates The pouring point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates The pouring point to the The cosine value of the direction angle of the support frame monitoring point, Indicates The pour features for each pour point.

[0028] Get the The data of the vibration points are processed to obtain the vibration characteristics; the vibration characteristics of the vibration points are mapped to the support frame monitoring points to obtain the first The vibration point is The vibration characteristic component at each support frame monitoring point;

[0029] The specific steps of obtaining the vibration characteristics and the vibration characteristic components include:

[0030] No. The calculation formula for the vibration characteristics of a vibration point is:

[0031] ;

[0032] in, Indicates The vibration characteristics of each vibration point, represents the weight coefficient of vibration frequency, Indicates that the vibrating equipment is The number of vibrations in a period of time, represents the weight coefficient of the vibration amplitude, Indicates the maximum vibration speed of the vibrating equipment;

[0033] The calculation formula of the vibration characteristic component is:

[0034] ;

[0035] in, Indicates The vibration point is The vibration characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates Vibration point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates Vibration point to the The cosine value of the direction angle of the support frame monitoring point, Indicates The vibration characteristics of each vibration point.

[0036] An abnormal support frame monitoring point set is constructed according to the support frame monitoring points in the abnormal cluster; the distance characteristics, angle characteristics, the casting characteristic components and the vibration characteristic components of the abnormal support frame monitoring points are obtained and processed to obtain an abnormal evaluation index. If the abnormal evaluation index is greater than a preset abnormal threshold, a safety warning is triggered and an alarm is given to the dangerous position.

[0037] Further, The calculation formula of the abnormal evaluation index of an abnormal support frame monitoring point is:

[0038] ;

[0039] in, Indicates The abnormal evaluation index of the abnormal support frame monitoring point, represents the weight coefficient of the deformation feature, Indicates abnormal support frame monitoring point to its The real-time distance between adjacent monitoring points, Indicates abnormal support frame monitoring point to its The preset distance between adjacent monitoring points, Indicates The number of adjacent monitoring points of an abnormal support frame monitoring point, Represents the weight coefficient of the angle feature, Indicates The angle characteristics of the abnormal support frame monitoring point Quantity, Indicates The angle characteristics of the abnormal support frame monitoring point The preset value of each component, represents the length of the angular feature, represents the weight coefficient of the casting characteristic component, Indicates The pouring point is The pouring characteristic component at the abnormal support frame monitoring point, Indicates the number of pouring points, represents the weight coefficient of the vibration characteristic component, Indicates The vibration point is The vibration characteristic component at the abnormal support frame monitoring point, Indicates the number of vibration points.

[0040] The present invention also provides a concrete construction process safety early warning system, comprising:

[0041] The deformation feature extraction module is used to construct a distance feature according to the distance between the support frame monitoring point and its adjacent monitoring points, construct an angle feature according to the line and plane connecting the support frame monitoring point and its adjacent monitoring points, construct a deformation feature according to the deformation amount per unit time of the line connecting the support frame monitoring point and its adjacent monitoring points, and construct a vibration frequency feature according to the vibration frequency data of the support frame monitoring point; construct a deformation feature vector of the support frame according to the distance feature, angle feature, deformation feature and vibration frequency feature;

[0042] Furthermore, the support frame deformation feature vector also includes:

[0043] Get real-time monitoring A support frame monitoring point to its The distance between adjacent monitoring points and preset distance And process to get the distance feature ,in, Indicates the number of adjacent monitoring points;

[0044] Taking the support frame monitoring point as the vertex, two different adjacent monitoring points are selected to obtain the edge angle to constitute the first angle feature; taking the support frame monitoring point and two different adjacent monitoring points as a plane, two different planes are selected and processed to obtain the plane angle to constitute the second angle feature; the first angle feature and the second angle feature are obtained and processed to obtain the angle feature.

[0045] The abnormality judgment module includes a cluster determination unit, a cluster analysis unit and an abnormality judgment unit, wherein the cluster determination unit is used to determine the cluster according to the support frame structure data in the BIM model. , the cluster analysis unit is used to perform cluster analysis on the deformation feature vector of the support frame to obtain the deformation cluster center, and the abnormality judgment unit is used to judge whether it is an abnormal cluster according to the distance between the deformation cluster centers;

[0046] The pouring feature module includes a pouring feature calculation unit and a pouring feature component calculation unit, wherein the pouring feature calculation unit is used to obtain the first The data of the pouring points are processed to obtain the pouring characteristics, and the pouring characteristic component calculation unit is used to map the pouring characteristics of the pouring points to the support frame monitoring points to obtain the first The pouring point is The pouring characteristic component at each support frame monitoring point;

[0047] The vibration feature module includes a vibration feature calculation unit and a vibration feature component calculation unit, wherein the vibration feature calculation unit is used to obtain the first The data of the vibration points are processed and the vibration characteristics are obtained. The vibration characteristic component calculation unit is used to map the vibration characteristics of the vibration points to the support frame monitoring points to obtain the first The vibration point is The vibration characteristic component at each support frame monitoring point;

[0048] The safety warning module is used to obtain the support frame monitoring points within the abnormal cluster to obtain a set of abnormal support frame monitoring points; obtain the distance characteristics, angle characteristics, the casting characteristic components and the vibration characteristic components of the abnormal support frame monitoring points and process them to obtain an abnormal evaluation index. If the abnormal evaluation index is greater than a preset abnormal threshold, a safety warning is triggered and an alarm is given to the dangerous position.

[0049] Further, The calculation formula of the abnormal evaluation index of an abnormal support frame monitoring point is:

[0050] ;

[0051] in, Indicates The abnormal evaluation index of the abnormal support frame monitoring point, represents the weight coefficient of the deformation feature, Indicates abnormal support frame monitoring point to its The real-time distance between adjacent monitoring points, Indicates abnormal support frame monitoring point to its The preset distance between adjacent monitoring points, Indicates The number of adjacent monitoring points of an abnormal support frame monitoring point, Represents the weight coefficient of the angle feature, Indicates The angle characteristics of the abnormal support frame monitoring point Quantity, Indicates The angle characteristics of the abnormal support frame monitoring point The preset value of each component, represents the length of the angular feature, represents the weight coefficient of the casting characteristic component, Indicates The pouring point is The pouring characteristic component at the abnormal support frame monitoring point, Indicates the number of pouring points, represents the weight coefficient of the vibration characteristic component, Indicates The vibration point is The vibration characteristic component at the abnormal support frame monitoring point, Indicates the number of vibration points.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. By obtaining the distance changes between the support frame monitoring point and its adjacent monitoring points, the stability of the support structure can be reflected in real time; the first angle feature formed by the monitoring point connection line and the second angle feature formed by the angles of different planes can be used to fully describe the distortion, tilt and deformation of the support frame in space; by integrating multi-dimensional features to construct the support frame deformation feature vector, the support frame state can be more comprehensively described, providing data support for subsequent high-precision anomaly detection and risk assessment.

[0054] 2. According to the characteristics of the main nodes in the BIM model, the feature matrix is ​​established and the eigenvalues ​​are calculated to determine the number of clusters, which enhances the interpretability of the model; through the abnormal cluster judgment based on the proximity distance, fast and accurate abnormal cluster identification can be achieved, providing technical support for the deformation state monitoring of the support frame and subsequent construction safety warning.

[0055] 3. The abnormal assessment index integrates deformation characteristics, angle characteristics, casting characteristic components and vibration characteristic components, and comprehensively evaluates the status of the support frame from multiple dimensions. It can accurately locate potential dangerous monitoring points and provide clear early warning locations for the construction team. It not only improves the real-time and accuracy of construction site safety management, but also provides scientific support for construction optimization, while significantly reducing the probability of construction safety accidents and management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of a concrete construction process safety early warning method provided by an embodiment of the present invention;

[0057] Figure 2 A flow chart for constructing a support frame deformation feature vector for the present invention;

[0058] Figure 3 A structural schematic diagram of a concrete construction process safety early warning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1

[0061] During the concrete pouring process, a construction unit introduced a concrete construction process safety early warning method provided by the present invention to monitor whether there is a risk of collapse of the formwork support frame. The method flow is as follows: Figure 1 As shown, the specific implementation is as follows:

[0062] First, the monitoring points of the support frame are selected. The reasonable arrangement of the monitoring points of the support frame is the basis for safety monitoring during the construction process. The selection of monitoring points needs to comprehensively consider the structural characteristics of the support frame, the key stress positions, the operation frequency of the construction area, and the possible risk points. By obtaining the BIM model of the building to be poured and analyzing the node distribution and structural characteristics of the support frame, the monitoring points of the support frame can be arranged at the main load-bearing nodes or connection nodes, weak areas, high vibration areas, and areas with large support spans. Table 1 shows the locations of some monitoring points of the support frame.

[0063] Table 1. Location of monitoring points of some support frames

[0064]

[0065] After all the monitoring points of the support frame are determined, the support frame deformation feature vector is constructed according to the distance feature, angle feature, deformation feature and vibration frequency feature. The process is as follows: Figure 2 As shown in the figure; firstly, a distance feature is constructed according to the distance between the support frame monitoring point and its adjacent monitoring points, and then an angle feature is constructed according to the line connecting the support frame monitoring point and its adjacent monitoring points and the plane connecting the support frame monitoring point and its adjacent monitoring points. A deformation feature is constructed according to the deformation amount per unit time of the line connecting the support frame monitoring point and its adjacent monitoring points. A vibration frequency feature is constructed according to the vibration frequency data of the support frame monitoring point. After constructing the support frame deformation feature vectors for all the support frame monitoring points, a set of support frame deformation feature vectors is obtained.

[0066] Further, obtain the real-time monitoring A support frame monitoring point to its The distance between adjacent monitoring points and preset distance And process to get the distance feature ,in, Indicates the number of adjacent monitoring points;

[0067] Furthermore, taking the support frame monitoring point as the vertex, two different adjacent monitoring points are selected to obtain the side angle, which constitutes the first angle feature; taking the support frame monitoring point and the two different adjacent monitoring points as a plane, two different planes are selected and processed to obtain the plane angle, which constitutes the second angle feature;

[0068] Further, for example, if the support frame monitoring point Z1 has three adjacent monitoring points Z2, Z3 and Z4, then Z1 is taken as the vertex, Z2 and Z3 are taken as the edges to obtain the side angle ∠1, Z1 is taken as the vertex, Z2 and Z4 are taken as the edges to obtain the side angle ∠2, Z1 is taken as the vertex, Z3 and Z4 are taken as the edges to obtain the side angle ∠3, and ∠1, ∠2 and ∠3 are obtained to constitute the first angle feature;

[0069] Furthermore, the monitoring points Z1, Z2, Z3 and Z4 form a triangular pyramid with Z1 as the vertex. In the three-dimensional space, any two planes among the three planes where the vertex Z1 is located are selected, and there are three plane angles, which constitute the second angle feature;

[0070] Furthermore, the first angle feature and the second angle feature are acquired and processed to obtain an angle feature.

[0071] Furthermore, by installing sensor equipment at the monitoring points of the support frame, the vibration frequency data of each monitoring point is collected in real time, including various data such as vibration frequency, vibration amplitude and vibration direction, forming the vibration frequency characteristics.

[0072] By integrating distance features, angle features, deformation features and vibration frequency features to construct the support frame deformation feature vector of the support frame monitoring point, the spatial form and mechanical state of the support frame can be described more comprehensively. Based on the data of the support frame monitoring point and adjacent monitoring points, a high real-time data model can be constructed to quickly capture abnormal changes in the deformation and vibration frequency of the support frame.

[0073] Determine clusters based on the data of the support structure in the BIM model , and cluster analysis is performed on the deformation feature vector of the support frame to obtain the deformation cluster center; and whether it is an abnormal cluster is determined according to the distance between the deformation cluster centers;

[0074] Furthermore, clusters are determined based on the support structure data in the BIM model. The specific steps include:

[0075] Obtain node features in the BIM model to construct a node feature matrix, where each row represents a main node and each column represents the features of the main node; obtain the node feature matrix and calculate the covariance matrix of the node feature matrix, calculate the eigenvalue of the covariance matrix, and obtain the eigenvalue set; calculate the contribution rate of each eigenvalue, and select eigenvalues, so that the cumulative contribution rate reaches the preset threshold of 90%, the cluster is determined to be indivual.

[0076] By selecting characteristic values ​​whose cumulative contribution rate reaches the preset threshold, the most representative features of the support frame structure are retained, the interference of irrelevant or redundant information on the clustering results is avoided, and the complexity and diversity of the support frame structure in different construction scenarios are adapted. The number of clusters determined by scientific calculations is avoided to avoid the result deviation caused by too many or too few clusters, providing a reasonable basis for the subsequent support frame status assessment.

[0077] Furthermore, after the clustering clusters are determined, the K-means algorithm is used to perform cluster analysis on all the support frame deformation feature vectors in the support frame deformation feature vector set to obtain deformation cluster centers.

[0078] Furthermore, the specific steps of judging whether it is an abnormal cluster according to the distance between the deformation cluster centers include:

[0079] Calculate the neighboring cluster distance between the deformation cluster center and its nearest deformation cluster center. The calculation formula is:

[0080] ;

[0081] in, Indicates Deformation cluster centers The neighboring cluster distance to its nearest deformation cluster center, Deformation cluster center and deformation cluster centers The Euclidean distance between

[0082] If the distance between neighboring clusters is greater than the mean of the neighboring cluster centers of the overall cluster center, it is determined to be an abnormal cluster.

[0083] Using Euclidean distance to evaluate the spatial distribution between cluster centers can intuitively determine the structural correlation between clusters and effectively capture clusters with abnormal distribution. By calculating the distance between the cluster center and the nearest cluster center and comparing it with the mean distance, it is possible to quickly discover deviated cluster centers and locate potential abnormal areas, which helps to discover potential hidden dangers in advance. Normal clusters do not require further processing, which improves safety monitoring efficiency and shortens the time for troubleshooting safety issues.

[0084] Get the The data of the pouring points are processed to obtain the pouring characteristics; the pouring characteristics of the pouring points are mapped to the monitoring points of the support frame to obtain the The pouring point is The pouring characteristic component at each support frame monitoring point;

[0085] Furthermore, the pouring point is a location where concrete is poured, and the specific steps of obtaining the pouring characteristics and the pouring characteristic components include:

[0086] No. The calculation formula for the pouring characteristics of a pouring point is:

[0087] ;

[0088] in, Indicates The pouring characteristics of the pouring points, Indicates the pouring volume, Indicates the pouring area;

[0089] The calculation formula for the pouring characteristic component is:

[0090] ;

[0091] in, Indicates The pouring point is The pouring characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates The pouring point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates The pouring point to the The cosine value of the direction angle of the support frame monitoring point, Indicates pour features for each pour point.

[0092] Furthermore, the direction angle refers to the The pouring point to the The direction angle of the vector formed by the monitoring points of the support frame in the three-dimensional space of the BIM model.

[0093] Table 2. Casting characteristics of some casting points

[0094]

[0095] As shown in Table 2, the pouring characteristics of some pouring points are as follows. By combining the pouring volume and the pouring area, the calculation of the pouring characteristics can fully reflect the specific pouring characteristics of each pouring point, providing an accurate data basis for subsequent analysis. By introducing the pouring characteristic components, the influence of a single pouring point on each support frame monitoring point is refined.

[0096] Get the The data of the vibration points are processed to obtain the vibration characteristics; the vibration characteristics of the vibration points are mapped to the support frame monitoring points to obtain the The vibration point is The vibration characteristic component at each support frame monitoring point;

[0097] Furthermore, the vibration point is a location where the concrete is vibrated using a vibration device, and the specific steps of obtaining the vibration characteristics and the vibration characteristic components include:

[0098] No. The calculation formula for the vibration characteristics of each vibration point is:

[0099] ;

[0100] in, Indicates The vibration characteristics of each vibration point, represents the weight coefficient of vibration frequency, Indicates that the vibrating equipment is The number of vibrations in a period of time, represents the weight coefficient of the vibration amplitude, Indicates the maximum vibration speed of the vibrating equipment;

[0101] The calculation formula of the vibration characteristic component is:

[0102] ;

[0103] in, Indicates The vibration point is The vibration characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates Vibration point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates Vibration point to the The cosine value of the direction angle of the support frame monitoring point, Indicates The vibration characteristics of each vibration point.

[0104] Combining the vibration frequency and vibration amplitude and balancing the influence of different factors through weight coefficients can reflect the vibration characteristics of each vibration point and provide a basis for subsequent safety analysis. The vibration characteristic components are calculated based on the weighting of distance and angle, and the influence of the vibration point on the support frame monitoring point is decomposed to each monitoring point, thereby improving the accuracy of the monitoring results.

[0105] An abnormal support frame monitoring point set is constructed according to the support frame monitoring points in the abnormal cluster; the distance characteristics, angle characteristics, casting characteristic components and vibration characteristic components of the abnormal support frame monitoring points are obtained and processed to obtain an abnormal evaluation index. If the abnormal evaluation index is greater than the preset abnormal threshold of 0.80, a safety warning is triggered and an alarm is given for the dangerous position.

[0106] Further, The calculation formula of the abnormal evaluation index of an abnormal support frame monitoring point is:

[0107] ;

[0108] in, Indicates The abnormal evaluation index of the abnormal support frame monitoring point, represents the weight coefficient of the deformation feature, Indicates abnormal support frame monitoring point to its The real-time distance between adjacent monitoring points, Indicates abnormal support frame monitoring point to its The preset distance between adjacent monitoring points, Indicates The number of adjacent monitoring points of an abnormal support frame monitoring point, Represents the weight coefficient of the angle feature, Indicates The angle characteristics of the abnormal support frame monitoring point Quantity, Indicates The angle characteristics of the abnormal support frame monitoring point The preset value of each component, represents the length of the angular feature, represents the weight coefficient of the casting characteristic component, Indicates The pouring point is The pouring characteristic component at the abnormal support frame monitoring point, Indicates the number of pouring points, represents the weight coefficient of the vibration characteristic component, Indicates The vibration point is The vibration characteristic component at the abnormal support frame monitoring point, Indicates the number of vibration points.

[0109] Furthermore, the preset value of the angle feature of the support frame monitoring point is obtained based on the angle of each support frame monitoring point measured in the initial BIM model.

[0110] The abnormal assessment index integrates deformation characteristics, angle characteristics, casting characteristic components and vibration characteristic components, which helps to comprehensively evaluate the abnormal conditions of the support frame from multiple dimensions and avoid judgment bias caused by a single feature. It not only improves the real-time and accuracy of construction site safety management, but also provides scientific support for construction optimization and predictive maintenance, significantly reducing the probability of construction safety accidents and management costs.

[0111] Table 3 shows the abnormal evaluation indicators of some abnormal support frame monitoring points, which realizes the real-time monitoring of abnormal support frame monitoring points and improves the efficiency of safety warning.

[0112] Table 3. Abnormal evaluation indicators of some abnormal support frame monitoring points

[0113]

[0114] A safety early warning method for a concrete construction process provided by the present invention first constructs a deformation feature vector of the support frame through distance features, angle features, deformation features and vibration frequency features, thereby realizing comprehensive monitoring of the state of the support frame; then cluster analysis is performed on the deformation feature vector of the support frame to obtain the deformation clustering center, and whether it is an abnormal cluster is determined according to the distance between the deformation clustering centers, thereby realizing rapid positioning of the abnormal area; finally, the distance features, angle features, casting feature components and vibration feature components of the abnormal support frame monitoring point are obtained and an abnormal evaluation index is constructed. If the abnormal evaluation index is greater than a preset abnormal threshold, a safety early warning is triggered, thereby realizing real-time monitoring of the support frame and improving construction safety.

[0115] Embodiment 2

[0116] The present invention also provides a concrete construction process safety early warning system, the structure of which is as follows: Figure 3 As shown, the specific implementation is as follows:

[0117] The deformation feature extraction module is used to construct a distance feature according to the distance between the support frame monitoring point and its adjacent monitoring points, construct an angle feature according to the line and plane connecting the support frame monitoring point and its adjacent monitoring points, construct a deformation feature according to the deformation amount per unit time of the line connecting the support frame monitoring point and its adjacent monitoring points, and construct a vibration frequency feature according to the vibration frequency data of the support frame monitoring point; construct a deformation feature vector of the support frame according to the distance feature, angle feature, deformation feature and vibration frequency feature;

[0118] Furthermore, the support frame deformation feature vector also includes:

[0119] Get real-time monitoring A support frame monitoring point to its The distance between adjacent monitoring points and preset distance And process to get the distance feature ,in, Indicates the number of adjacent monitoring points;

[0120] Taking the support frame monitoring point as the vertex, two different adjacent monitoring points are selected to obtain the edge angle to constitute the first angle feature; taking the support frame monitoring point and two different adjacent monitoring points as a plane, two different planes are selected and processed to obtain the plane angle to constitute the second angle feature; the first angle feature and the second angle feature are obtained and processed to obtain the angle feature.

[0121] The abnormality judgment module includes a cluster determination unit, a cluster analysis unit and an abnormality judgment unit, wherein the cluster determination unit is used to determine the cluster according to the support frame structure data in the BIM model. The cluster analysis unit is used to perform cluster analysis on the data in the support frame deformation feature vector set to obtain the deformation cluster center, and the abnormality judgment unit is used to judge whether it is an abnormal cluster according to the distance between the deformation cluster centers;

[0122] Furthermore, clusters are determined based on the support structure data in the BIM model. The specific steps include:

[0123] Obtain node features in the BIM model to construct a node feature matrix, where each row represents a main node and each column represents the features of the main node; obtain the node feature matrix and calculate the covariance matrix of the node feature matrix, calculate the eigenvalue of the covariance matrix, and obtain the eigenvalue set; calculate the contribution rate of each eigenvalue, and then sort it from large to small, and select eigenvalues, so that the cumulative contribution rate reaches the preset threshold, the cluster is determined to be indivual.

[0124] Furthermore, the calculation formula for the contribution rate of the eigenvalue is:

[0125] ;

[0126] in, Indicates Eigenvalues The contribution rate of Represents the number of eigenvalues.

[0127] Furthermore, after determining the clusters, the K-means algorithm is used to perform cluster analysis on the support frame deformation feature vector set to obtain deformation cluster centers.

[0128] Furthermore, the specific steps of judging whether it is an abnormal cluster according to the distance between the deformation cluster centers include:

[0129] Calculate the neighboring cluster distance between the deformation cluster center and its nearest deformation cluster center. The calculation formula is:

[0130] ;

[0131] in, Indicates Deformation cluster centers The neighboring cluster distance to its nearest deformation cluster center, Deformation cluster center and deformation cluster centers The Euclidean distance between

[0132] If the distance between neighboring clusters is greater than the mean of the neighboring cluster centers of the overall cluster center, it is determined to be an abnormal cluster.

[0133] Furthermore, the neighboring cluster distances of all deformation cluster centers are calculated and averaged to obtain the mean of the neighboring cluster centers of the overall cluster center.

[0134] The pouring feature module includes a pouring feature calculation unit and a pouring feature component calculation unit, wherein the pouring feature calculation unit is used to obtain the first The data of the pouring points are processed to obtain the pouring characteristics. The pouring characteristic component calculation unit is used to map the pouring characteristics of the pouring points to the support frame monitoring points to obtain the first The pouring point is The pouring characteristic component at each support frame monitoring point;

[0135] The vibration feature module includes a vibration feature calculation unit and a vibration feature component calculation unit, wherein the vibration feature calculation unit is used to obtain the first The data of the vibration points are processed and the vibration characteristics are obtained. The vibration characteristic component calculation unit is used to map the vibration characteristics of the vibration points to the support frame monitoring points to obtain the first The vibration point is The vibration characteristic component at each support frame monitoring point;

[0136] Furthermore, the specific steps of obtaining the vibration characteristics and the vibration characteristic components include:

[0137] No. The calculation formula for the vibration characteristics of each vibration point is:

[0138] ;

[0139] in, Indicates The vibration characteristics of each vibration point, represents the weight coefficient of vibration frequency, Indicates that the vibrating equipment is The number of vibrations in a period of time, represents the weight coefficient of the vibration amplitude, Indicates the maximum vibration speed of the vibrating equipment;

[0140] The calculation formula of the vibration characteristic component is:

[0141] ;

[0142] in, Indicates The vibration point is The vibration characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates Vibration point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates Vibration point to the The cosine value of the direction angle of the support frame monitoring point, Indicates Table 4 shows the vibration characteristics of some vibration points.

[0143] Table 4. Vibration characteristics of some vibration points

[0144]

[0145] The safety warning module is used to obtain the support frame monitoring points within the abnormal cluster to obtain a set of abnormal support frame monitoring points; obtain the distance characteristics, angle characteristics, the casting characteristic components and the vibration characteristic components of the abnormal support frame monitoring points and process them to obtain an abnormal evaluation index. If the abnormal evaluation index is greater than a preset abnormal threshold, a safety warning is triggered and an alarm is given to the dangerous position.

[0146] Further, The calculation formula of the abnormal evaluation index of an abnormal support frame monitoring point is:

[0147] ;

[0148] in, Indicates The abnormal evaluation index of the abnormal support frame monitoring point, represents the weight coefficient of the deformation feature, Indicates abnormal support frame monitoring point to its The real-time distance between adjacent monitoring points, Indicates abnormal support frame monitoring point to its The preset distance between adjacent monitoring points, Indicates The number of adjacent monitoring points of an abnormal support frame monitoring point, Represents the weight coefficient of the angle feature, Indicates The angle characteristics of the abnormal support frame monitoring point Quantity, Indicates The angle characteristics of the abnormal support frame monitoring point The preset value of each component, represents the length of the angular feature, represents the weight coefficient of the casting characteristic component, Indicates The pouring point is The pouring characteristic component at the abnormal support frame monitoring point, Indicates the number of pouring points, represents the weight coefficient of the vibration characteristic component, Indicates The vibration point is The vibration characteristic component at the abnormal support frame monitoring point, Indicates the number of vibration points. Table 5 shows the abnormal evaluation indicators of some abnormal support frame monitoring points. It can be seen from the table that none of them triggered safety warnings.

[0149] Table 5. Abnormal evaluation indicators of some abnormal support frame monitoring points

[0150]

[0151] Through the concrete construction process safety early warning system provided by the present invention, real-time monitoring of the formwork support frame during the concrete pouring process can be achieved, data processing time is shortened, the safety early warning speed and efficiency are improved, and the support frame collapse problem can be prevented in time.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A safety early warning method for a concrete construction process, characterized in that: include: Construct distance features based on the distance between the support frame monitoring point and its adjacent monitoring points; construct angle features based on the line and plane connecting the support frame monitoring point and its adjacent monitoring points; construct deformation features based on the deformation amount per unit time of the line connecting the support frame monitoring point and its adjacent monitoring points; construct vibration frequency features based on the vibration frequency data of the support frame monitoring point; construct a deformation feature vector of the support frame based on the distance features, angle features, deformation features and vibration frequency features; Determine clusters based on the data of the support structure in the BIM model , and cluster analysis is performed on the deformation feature vector of the support frame to obtain the deformation cluster center, and whether it is an abnormal cluster is determined according to the distance between the deformation cluster centers; Get the The data of each pouring point is processed to obtain the pouring characteristics. The formula is: ； in, Indicates The pouring characteristics of each pouring point, Indicates the pouring volume, Indicates the pouring area; Map the pouring characteristics to the support frame monitoring points to obtain the The pouring point is The pouring characteristic component at the monitoring point of the support frame is as follows: ； in, Indicates The pouring point is The pouring characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates The pouring point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates The pouring point to the The cosine value of the direction angle of the support frame monitoring point, Indicates The pouring characteristics of each pouring point; Get the The data of the vibration points are processed to obtain the vibration characteristics, the formula is: ； in, Indicates The vibration characteristics of each vibration point, represents the weight coefficient of vibration frequency, Indicates that the vibrating equipment is The number of vibrations in a period of time, represents the weight coefficient of the vibration amplitude, Indicates the maximum vibration speed of the vibrating equipment; Map the vibration characteristics to the support frame monitoring points to obtain the The vibration point is The vibration characteristic component at the monitoring point of the support frame is expressed as follows: ； in, Indicates The vibration point is The vibration characteristic component at the monitoring point of the support frame, represents the weight coefficient of distance, Indicates Vibration point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates Vibration point to the The cosine value of the direction angle of the support frame monitoring point, Indicates Vibration characteristics of each vibration point; According to the support frame monitoring points in the abnormal cluster, a set of abnormal support frame monitoring points is constructed; the distance characteristics, angle characteristics, pouring characteristic components and vibration characteristic components of the abnormal support frame monitoring points are obtained and processed to obtain the abnormal evaluation index, and the formula is: ； in, Indicates The abnormal evaluation index of the abnormal support frame monitoring point, represents the weight coefficient of the deformation feature, Indicates abnormal support frame monitoring point to its The real-time distance between adjacent monitoring points, Indicates abnormal support frame monitoring point to its The preset distance between adjacent monitoring points, Indicates The number of adjacent monitoring points of an abnormal support frame monitoring point, Represents the weight coefficient of the angle feature, Indicates The angle characteristics of the abnormal support frame monitoring point Quantity, Indicates The angle characteristics of the abnormal support frame monitoring point The preset value of each component, represents the length of the angular feature, represents the weight coefficient of the pouring characteristic component, Indicates The pouring point is The pouring characteristic component at the abnormal support frame monitoring point, Indicates the number of pouring points, represents the weight coefficient of the vibration characteristic component, Indicates The vibration point is The vibration characteristic component at the abnormal support frame monitoring point, Indicates the number of vibration points; If the abnormal evaluation index is greater than the preset abnormal threshold, a safety warning is triggered and the dangerous location is warned.

2. A concrete construction process safety early warning method according to claim 1, characterized in that: The support frame deformation feature vector also includes: Get real-time monitoring A support frame monitoring point to its The distance between adjacent monitoring points and preset distance And process to get the distance feature ,in, Indicates the number of adjacent monitoring points; Taking the support frame monitoring point as the vertex, two different adjacent monitoring points are selected to obtain the edge angle to constitute the first angle feature; taking the support frame monitoring point and two different adjacent monitoring points as a plane, two different planes are selected and processed to obtain the plane angle to constitute the second angle feature; the first angle feature and the second angle feature are obtained and processed to obtain the angle feature.

3. A concrete construction process safety early warning method according to claim 1, characterized in that: Determine clusters based on the data of the support structure in the BIM model The specific steps include: Obtain node features in the BIM model to construct a node feature matrix, where each row in the node feature matrix represents a main node and each column represents the features of the main node; calculate the covariance matrix of the node feature matrix, and find the eigenvalues ​​of the covariance matrix to obtain an eigenvalue set; calculate the contribution rate of each eigenvalue, and select If the cumulative contribution rate reaches the preset threshold, the cluster is determined to be indivual.

4. A concrete construction process safety early warning method according to claim 1, characterized in that: The specific steps of judging whether it is an abnormal cluster based on the distance between the deformation cluster centers include: Calculate the neighboring cluster distance between the deformation cluster center and its nearest deformation cluster center. The calculation formula is: ； in, Indicates Deformation cluster centers The neighboring cluster distance to its nearest deformation cluster center, Deformation cluster center and deformation cluster centers The Euclidean distance between If the neighboring cluster distance is greater than the mean of the neighboring cluster centers of the overall cluster center, it is determined to be an abnormal cluster.

5. A concrete construction process safety early warning system, characterized in that: include: The deformation feature extraction module is used to construct a distance feature according to the distance between the support frame monitoring point and its adjacent monitoring points, construct an angle feature according to the line and plane connecting the support frame monitoring point and its adjacent monitoring points, construct a deformation feature according to the deformation amount per unit time of the line connecting the support frame monitoring point and its adjacent monitoring points, and construct a vibration frequency feature according to the vibration frequency data of the support frame monitoring point; construct a deformation feature vector of the support frame according to the distance feature, angle feature, deformation feature and vibration frequency feature; The abnormality judgment module includes a cluster determination unit, a cluster analysis unit and an abnormality judgment unit, wherein the cluster determination unit is used to determine the cluster according to the support frame structure data in the BIM model. The cluster analysis unit is used to perform cluster analysis on the deformation feature vector of the support frame to obtain the deformation cluster center, and the abnormality judgment unit is used to judge whether it is an abnormal cluster according to the distance between the deformation cluster centers; The pouring feature module includes a pouring feature calculation unit and a pouring feature component calculation unit, wherein the pouring feature calculation unit is used to obtain the first The data of each pouring point is processed to obtain the pouring characteristics. The formula is: ； in, Indicates The pouring characteristics of each pouring point, Indicates the pouring volume, Indicates the pouring area; The pouring characteristic component calculation unit is used to map the pouring characteristics of the pouring point to the support frame monitoring point to obtain the first The pouring point is The pouring characteristic component at the monitoring point of the support frame is as follows: ； in, Indicates The pouring point is The pouring characteristic component at each support frame monitoring point, represents the weight coefficient of distance, Indicates The pouring point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates The pouring point to the The cosine value of the direction angle of the support frame monitoring point, Indicates The pouring characteristics of each pouring point; The vibration feature module includes a vibration feature calculation unit and a vibration feature component calculation unit, wherein the vibration feature calculation unit is used to obtain the first The data of the vibration points are processed to obtain the vibration characteristics, the formula is: ； in, Indicates The vibration characteristics of each vibration point, represents the weight coefficient of vibration frequency, Indicates that the vibrating equipment is The number of vibrations in a period of time, represents the weight coefficient of the vibration amplitude, Indicates the maximum vibration speed of the vibrating equipment; The vibration characteristic component calculation unit is used to map the vibration characteristics of the vibration point to the support frame monitoring point to obtain the first The vibration point is The vibration characteristic component at the monitoring point of the support frame is expressed as follows: ； in, Indicates The vibration point is The vibration characteristic component at the monitoring point of the support frame, represents the weight coefficient of distance, Indicates Vibration point to the The distance between the monitoring points of the support frame, represents the distance decay exponent, represents the weight coefficient of the angle, Indicates Vibration point to the The cosine value of the direction angle of the support frame monitoring point, Indicates Vibration characteristics of each vibration point; The safety warning module is used to obtain the support frame monitoring points within the abnormal cluster and obtain the abnormal support frame monitoring point set; obtain the distance characteristics, angle characteristics, pouring characteristic components and vibration characteristic components of the abnormal support frame monitoring points and process them to obtain the abnormal evaluation index, the formula is: ； in, Indicates The abnormal evaluation index of the abnormal support frame monitoring point, represents the weight coefficient of the deformation feature, Indicates abnormal support frame monitoring point to its The real-time distance between adjacent monitoring points, Indicates abnormal support frame monitoring point to its The preset distance between adjacent monitoring points, Indicates The number of adjacent monitoring points of an abnormal support frame monitoring point, Represents the weight coefficient of the angle feature, Indicates The angle characteristics of the abnormal support frame monitoring point Quantity, Indicates The angle characteristics of the abnormal support frame monitoring point The preset value of each component, represents the length of the angular feature, represents the weight coefficient of the pouring characteristic component, Indicates The pouring point is The pouring characteristic component at the abnormal support frame monitoring point, Indicates the number of pouring points, represents the weight coefficient of the vibration characteristic component, Indicates The vibration point is The vibration characteristic component at the abnormal support frame monitoring point, Indicates the number of vibration points; If the abnormal evaluation index is greater than the preset abnormal threshold, a safety warning is triggered and the dangerous location is warned.

6. A concrete construction process safety early warning system according to claim 5, characterized in that: The support frame deformation feature vector also includes: Get real-time monitoring A support frame monitoring point to its The distance between adjacent monitoring points and preset distance And process to get the distance feature ,in, Indicates the number of adjacent monitoring points; Taking the support frame monitoring point as the vertex, two different adjacent monitoring points are selected to obtain the edge angle to constitute the first angle feature; taking the support frame monitoring point and two different adjacent monitoring points as a plane, two different planes are selected and processed to obtain the plane angle to constitute the second angle feature; the first angle feature and the second angle feature are obtained and processed to obtain the angle feature.

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