Safety distance monitoring method and system for net rack and pipe truss construction

Abstract

The invention relates to the technical field of data processing, and discloses a safety distance monitoring method and system for net rack and pipe truss construction. The method comprises the following steps: acquiring key node safety distance data, construction state data and environment interference data, and integrating the data into a safety distance monitoring data set; performing anomaly screening and error compensation on the data set to generate a standardized data stream; analyzing the data flow, establishing a deformation feature table, and generating a deformation state report; positioning a dangerous area based on report matching early warning conditions, and outputting early warning information; determining an intervention node according to the early warning information, and generating an adjustment instruction; and in combination with the adjustment instruction and historical data, establishing an associated network to generate a management knowledge base. Intelligent monitoring of the safety distance in the construction process of the net rack and the pipe truss is achieved, an early warning mechanism is established through fusion analysis of multi-source data, a scientific disposal scheme is provided based on historical experience data, and therefore the accuracy and reliability of construction safety management are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and system for monitoring the safety distance during the construction of grid structures and pipe trusses. Background Art

[0002] In the existing construction of grid structures and pipe trusses, the monitoring of safety distance mainly relies on manual inspections and fixed sensors to collect data. Manual inspections obtain structural status information through on-site measurements, visual inspections, etc.; fixed sensors collect deformation data through devices such as displacement gauges and strain gauges installed at key nodes. After simple processing of these monitoring data, they are used to judge the safety status of the structure. When abnormalities are found, on-site managers decide on the disposal plan based on experience.

[0003] However, the existing monitoring methods have many deficiencies. First, the data collection method is relatively single, making it difficult to comprehensively reflect the real-time status of the grid structure; second, the data processing process lacks systematicness and cannot effectively integrate multi-source information; third, the early warning mechanism is too simple and often only discovers problems after they appear; finally, the formulation of the disposal plan relies too much on personal experience and lacks data support, making it difficult to ensure the reliability of the disposal effect. These problems seriously affect the safety management level of the construction of grid structures and pipe trusses. Summary of the Invention

[0004] This application provides a method and system for monitoring the safety distance during the construction of grid structures and pipe trusses, realizing the intelligent monitoring of the safety distance during the construction process of grid structures and pipe trusses, establishing an early warning mechanism through the fusion analysis of multi-source data, and providing a scientific disposal plan based on historical experience data, thereby improving the accuracy and reliability of construction safety management.

[0005] In a first aspect, the present application provides a method for monitoring the safety distance in the construction of a grid structure and a pipe truss. The method for monitoring the safety distance in the construction of a grid structure and a pipe truss includes: integrating the raw data collected by key node safety distance sensors, the construction status data collected by working condition identification sensors, and the environmental interference data collected by environmental parameter sensors into a raw data set for monitoring the safety distance of the grid structure through a distributed data acquisition terminal; screening out abnormal data and performing error compensation based on the raw data set for monitoring the safety distance of the grid structure to generate a standardized safety distance data stream; analyzing the grid structure deformation characteristics of the standardized safety distance data stream, establishing a deformation correlation feature table based on the node displacement data and the working condition feature data, and generating an evaluation report on the deformation status of the grid structure; matching the early warning trigger conditions based on the evaluation report on the deformation status of the grid structure, realizing the positioning of dangerous areas by combining the identification of abnormal deformation patterns, and outputting a safety early warning information package for the grid structure; determining key intervention nodes for the safety early warning information package of the grid structure according to the process influence evaluation matrix to generate a safety adjustment instruction for grid construction; and establishing a safety event feature correlation network based on the safety adjustment instruction for grid construction and historical safety event data to generate a safety management knowledge base for grid construction.

[0006] In a second aspect, the present application provides a system for monitoring the safety distance in the construction of a grid structure and a pipe truss. The system for monitoring the safety distance in the construction of a grid structure and a pipe truss includes: An acquisition module, configured to integrate the raw data collected by key node safety distance sensors, the construction status data collected by working condition identification sensors, and the environmental interference data collected by environmental parameter sensors into a raw data set for monitoring the safety distance of the grid structure through a distributed data acquisition terminal; A screening module, configured to screen out abnormal data and perform error compensation based on the raw data set for monitoring the safety distance of the grid structure to generate a standardized safety distance data stream; A generation module, configured to analyze the grid structure deformation characteristics of the standardized safety distance data stream, establish a deformation correlation feature table based on the node displacement data and the working condition feature data, and generate an evaluation report on the deformation status of the grid structure; A positioning module, configured to match the early warning trigger conditions based on the evaluation report on the deformation status of the grid structure, realize the positioning of dangerous areas by combining the identification of abnormal deformation patterns, and output a safety early warning information package for the grid structure; An adjustment module, configured to determine key intervention nodes for the safety early warning information package of the grid structure according to the process influence evaluation matrix to generate a safety adjustment instruction for grid construction; A management module, configured to establish a safety event feature correlation network based on the safety adjustment instruction for grid construction and historical safety event data to generate a safety management knowledge base for grid construction.

[0007] In the technical solution provided by this application, by integrating the original data collected by the key node safety distance sensors, the construction state data collected by the working condition identification sensors, and the environmental interference data collected by the environmental parameter sensors, an original data set for monitoring the safety distance of the grid structure is constructed, realizing the collaborative collection and fusion processing of multi-source data, overcoming the problem of incomplete information of a single data source. Through the setting of distributed data collection terminals, the real-time performance and reliability of data collection are improved; the abnormal data in the original data set for monitoring the safety distance of the grid structure is screened out and error compensation is performed to generate a standardized safety distance data stream, effectively eliminating the influence of environmental interference and random errors and improving the data quality; the standardized safety distance data stream is combined with the working condition characteristic data for deformation characteristic analysis, a deformation correlation characteristic table is established, and an evaluation report on the deformation state of the grid structure is generated, realizing the accurate evaluation of the structural deformation state; based on the evaluation report on the deformation state of the grid structure, an early warning trigger mechanism is established, and precise positioning of the dangerous area is achieved through the identification of abnormal deformation modes. The output grid safety early warning information package provides a reliable basis for construction management; by combining the process influence evaluation matrix, key intervention nodes are determined, and the generated grid construction safety adjustment instructions have strong pertinence and operability; by establishing a safety event characteristic association network and generating a knowledge base for grid construction safety management, the accumulation and inheritance of construction experience are realized, providing a reference basis for subsequent similar projects. The entire solution uses artificial intelligence algorithms to process and analyze multi-source data, giving full play to the advantages of deep learning in pattern recognition and feature extraction, realizing the full-process intelligence from data collection, processing, analysis to decision support, and significantly improving the scientificity and reliability of the safety monitoring of grid and pipe truss construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 FIG. [X] is a schematic diagram of an embodiment of the method for monitoring the safety distance of grid and pipe truss construction in an embodiment of this application; Figure 2 FIG. [X] is a schematic diagram of the process for integrating the original data set for monitoring the safety distance of the grid structure in an embodiment of this application; Figure 3 FIG. [X] is a schematic diagram of an embodiment of the system for the method for monitoring the safety distance of grid and pipe truss construction in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] The embodiments of the present application provide a method and system for monitoring the safety distance in the construction of grid frames and pipe trusses. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the method for monitoring the safety distance in the construction of grid frames and pipe trusses in the embodiments of the present application includes: Step S101: Integrate the raw data collected by the key node safety distance sensors, the construction status data collected by the working condition identification sensors, and the environmental interference data collected by the environmental parameter sensors into the original dataset for grid frame safety distance monitoring through a distributed data acquisition terminal; Step S102: Based on the original dataset for grid frame safety distance monitoring, screen out abnormal data and perform error compensation to generate a standardized safety distance data stream; Step S103: Analyze the deformation characteristics of the grid frame for the standardized safety distance data stream, establish a deformation correlation feature table based on the node displacement data and the working condition feature data, and generate an evaluation report on the deformation state of the grid frame structure; Step S104: Match the early warning trigger conditions based on the evaluation report on the deformation state of the grid frame structure, combine with the identification of abnormal deformation patterns to locate the dangerous area, and output the grid frame safety early warning information package; Step S105: Determine the key intervention nodes for the grid frame safety early warning information package according to the process influence evaluation matrix, and generate a safety adjustment instruction for grid frame construction; Step S106: Establish a safety event feature association network based on the safety adjustment instruction for grid frame construction and the historical safety event data, and generate a knowledge base for grid frame construction safety management.

[0012] It can be understood that the execution entity of the present application can be the system for the method of monitoring the safety distance in the construction of grid frames and pipe trusses, or it can also be a terminal or a server, and specific limitations are not made here. The embodiments of the present application will be described by taking the server as the execution entity as an example.

[0013] Specifically, data is collected through a distributed sensor network. The key node safety distance sensors collect real-time distance change data between nodes in three-dimensional space with a sampling frequency of 10 Hz. The data includes node numbers, timestamps, and relative displacement values in the X / Y / Z directions. The working condition identification sensors collect data such as the positions of construction machinery, the distribution of workers, and the stacking status of materials at the construction site, forming a construction status data packet. The environmental parameter sensors monitor environmental factors such as temperature, humidity, and wind speed, generating environmental interference data. The distributed data acquisition terminal performs time synchronization and spatial registration on these three types of data, integrating them to form the original dataset for monitoring the safety distance of the grid structure.

[0014] Data quality analysis is performed on the original dataset for monitoring the safety distance of the grid structure. A threshold range is set for the safety distance data, and data points outside the reasonable range are marked as abnormal. For the measurement errors caused by temperature changes, compensation and correction are performed based on the temperature-deformation relationship curve. For the random errors caused by environmental factors such as humidity and wind speed, they are eliminated through data smoothing. After screening out outliers and compensating for errors, a standardized safety distance data stream is formed. The standardized safety distance data stream is input into the grid deformation feature analysis module to extract the time-series change features of node displacements, including parameters such as displacement amplitude, deformation rate, and acceleration. Combining with the analysis of working condition feature data, the deformation characteristics in different construction stages are analyzed, such as the dynamic deformation during hoisting and the thermal deformation during welding. The corresponding relationship between node deformation and construction working conditions is established, forming a deformation correlation feature table. Based on the statistical analysis of deformation data, an evaluation report on the deformation status of the grid structure is generated.

[0015] The deformation data in the deformation status evaluation report is compared with the preset safety threshold, and an alarm is triggered when the threshold is exceeded. The abnormal deformation mode recognition module analyzes the spatial distribution characteristics of the deformation, positioning the core position and influence range of the deformation abnormal area. Information such as the alarm level, the range of the dangerous area, and the deformation development trend is packaged to form a grid safety warning information packet. The grid safety warning information packet is input into the construction process optimization module, and the influence degree of each process on the structural deformation is analyzed based on the process influence evaluation matrix. The process influence evaluation matrix includes the sensitivity coefficients of processes such as hoisting, welding, and reinforcement to node deformation. The key nodes that need to be intervened preferentially are determined, a construction adjustment plan is generated, and a grid construction safety adjustment instruction is formed.

[0016] The construction safety adjustment instruction is associated with the historical safety event data for analysis, extracting common features and laws. The historical event data includes information such as event types, influencing factors, disposal measures, and effect evaluations. Through data mining, an association network between safety events is established to identify high-incidence risk points and key influencing factors. A standardized construction safety management regulation is sorted out, and a knowledge base for grid construction safety management is established.

[0017] For example, in the construction of the steel structure grid of a large convention and exhibition center, the span of the roof grid is 120 meters and the height is 30 meters, which consists of 3,600 nodes and 12,000 members. During the construction process, the safety distance monitoring system monitors the key nodes in real time. When it is detected that during the hoisting process of the No. 3 main truss, the distance between the adjacent nodes N365 and N366 increases from the designed value of 1,500 mm to 1,580 mm within 10 minutes, exceeding the safety threshold. The system immediately analyzes the deformation data of the surrounding nodes and finds that the deformation area shows a radial expansion trend. Combining with the analysis of the working condition data, the main truss hoisting is in progress in this area, and the parking position of the 50-ton crawler crane is 12 meters away from the deformation center. The system automatically generates a warning message, suggesting to suspend the hoisting operation and gives a temporary support reinforcement plan for the truss. After the construction personnel take measures according to the system's suggestions, the node deformation is effectively controlled, and the distance between the adjacent nodes is stabilized at 1,520 mm. The key elements of the disposal experience in this warning process are extracted by the system and stored in the knowledge base for guiding the construction management of subsequent similar working conditions.

[0018] In the embodiment of the present application, by integrating the original data collected by the key node safety distance sensors, the construction status data collected by the working condition identification sensors, and the environmental interference data collected by the environmental parameter sensors, an original data set for the grid safety distance monitoring is constructed, realizing the collaborative collection and fusion processing of multi-source data, overcoming the problem of incomplete information of a single data source. By setting up a distributed data collection terminal, the real-time performance and reliability of data collection are improved; the abnormal data in the original data set for the grid safety distance monitoring is screened out and error compensation is performed to generate a standardized safety distance data stream, effectively eliminating the influence of environmental interference and random errors and improving the data quality; the standardized safety distance data stream is combined with the working condition characteristic data for deformation characteristic analysis, establishing a deformation correlation characteristic table and generating an evaluation report on the deformation state of the grid structure, realizing the accurate evaluation of the structural deformation state; based on the evaluation report on the deformation state of the grid structure, a warning trigger mechanism is established, and the precise positioning of the dangerous area is realized through the identification of abnormal deformation modes. The output grid safety warning information package provides a reliable basis for construction management; by combining the process influence evaluation matrix to determine the key intervention nodes, the generated grid construction safety adjustment instructions are highly targeted and operable; by establishing a safety event characteristic correlation network and generating a knowledge base for grid construction safety management, the accumulation and inheritance of construction experience are realized, providing a reference basis for subsequent similar projects. The entire solution uses artificial intelligence algorithms to process and analyze multi-source data, giving full play to the advantages of deep learning in pattern recognition and feature extraction, realizing the full-process intelligence from data collection, processing, analysis to decision support, and significantly improving the scientificity and reliability of the safety monitoring of grids and pipe trusses.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Convert the three-dimensional coordinate data of the nodes collected by the key node safety distance sensor within a preset sampling period into a raw safety distance data group; (2) Integrate the construction machinery position data, operator distribution data, and material stacking state data collected by the working condition recognition sensor into a construction state feature data group; (3) Fuse the temperature field data, humidity field data, and vibration field data collected by the environmental parameter sensor into an environmental interference feature data group; (4) Perform timestamp matching on the raw safety distance data group, construction state feature data group, and environmental interference feature data group to generate a time-series correlation data table; (5) Perform spatial mapping on the time-series correlation data table according to the node numbers to establish a spatial relationship matrix for monitoring the safety distance of grid nodes; (6) Integrate the data in the spatial and temporal dimensions based on the spatial relationship matrix for monitoring the safety distance of grid nodes and the time-series correlation data table to generate a raw data set for monitoring the safety distance of the grid.

[0020] Specifically, as Figure 2 shown, it is a schematic flowchart for integrating the raw data set for monitoring the safety distance of the grid in the embodiment of the present application. Among them, in the data acquisition stage, it shows the process of converting the three-dimensional coordinate data of the nodes collected by the key node safety distance sensor into a raw safety distance data group, the process of integrating the construction machinery position data, operator distribution data, and material stacking state data collected by the working condition recognition sensor into a construction state feature data group, and the process of fusing the temperature field data, humidity field data, and vibration field data collected by the environmental parameter sensor into an environmental interference feature data group. The data processing stage shows the process of performing timestamp matching on the raw safety distance data group, construction state feature data group, and environmental interference feature data group to generate a time-series correlation data table, and the process of performing spatial mapping on the time-series correlation data table according to the node numbers to establish a spatial relationship matrix for monitoring the safety distance of grid nodes. The data integration stage shows the process of integrating the data in the spatial and temporal dimensions based on the spatial relationship matrix for monitoring the safety distance of grid nodes and the time-series correlation data table to generate a raw data set for monitoring the safety distance of the grid.

[0021] Perform conversion processing on the data collected by the key node safety distance sensor. The key node safety distance sensor collects the three-dimensional coordinate data of each node within a preset sampling period of 100 ms, including the node number, X / Y / Z coordinate values, and timestamp. Perform coordinate transformation on the collected three-dimensional coordinate data, calculate the actual distance between adjacent nodes, and organize the calculated distance data according to the node pairs to form a raw safety distance data group.

[0022] The working condition recognition sensors arranged at the construction site continuously track and record the real-time position information of construction machinery, obtain the two-dimensional coordinates of the machinery through GPS positioning, and combine with height sensors to obtain the working height of the equipment. The working condition recognition sensors also collect the spatial distribution of operators through infrared imaging, and record the number of personnel and their activity ranges in each working area. For the stacking state of materials, gravity sensors and displacement sensors are used to record the load distribution and occupied area of the storage yard. These data are organized according to time and space dimensions and integrated into a construction state feature data group. The environmental parameter sensor network consists of temperature sensors, humidity sensors and vibration sensors. The temperature sensors are arranged in a 5m×5m grid within the construction space to collect and form temperature field distribution data. The humidity sensors are arranged at the same points as the temperature sensors to obtain the spatial humidity distribution. The vibration sensors are arranged at the key nodes of the grid to record the structural vibration acceleration. These three types of environmental parameter data are processed by spatial interpolation to generate continuous environmental field distribution data, which are integrated into an environmental interference feature data group.

[0023] First, timestamp alignment is performed on the above three groups of data. The sampling period of the original safety distance data group is 100ms, the sampling period of the construction state feature data group is 1s, and the sampling period of the environmental interference feature data group is 10s. By means of sliding time window, the three groups of data are mapped to a unified time benchmark. The data within each time window are synthesized into one record by weighted average to generate a time series correlation data table.

[0024] For the spatial mapping process of the time series correlation data table, the following mathematical model is adopted: ;

[0025] Where: represents the spatial relationship matrix between node i and node j; represents the kth safety distance parameter; represents the pth working condition influence factor; represents the qth environmental interference factor; , , are the weight coefficients of the corresponding parameters respectively; , , are the numbers of safety distance parameters, working condition influence factors and environmental interference factors respectively.

[0026] Combine the spatial relationship matrix of the safety distance monitoring of the grid nodes with the time series correlation data table, and construct a four-dimensional data structure according to the spatial position relationship and time series change characteristics of the nodes. In the spatial dimension, it includes the three-dimensional coordinate information and mutual relationship of the nodes, and in the time dimension, it includes the change process of various monitoring data, forming a complete original data set for the safety distance monitoring of the grid.

[0027] For example, in the construction of a grid structure of a stadium, the grid is composed of 2,000 nodes, and 500 key monitoring points are set. During the hoisting process of the main truss, the safety distance sensors of the key node P156 record the distance data from the adjacent nodes P157 and P158. An 80-ton crawler crane at the construction site is performing hoisting operations, and the operators are distributed around the hoisting area. The environmental parameters show that the temperature in the hoisting area is 28 °C, the relative humidity is 75%, and the measured structural vibration acceleration is 0.15g.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Divide the original dataset of grid safety distance monitoring into safety distance monitoring data groups according to the safety distance relationship of adjacent nodes; (2) Based on the safety distance monitoring data groups, construct the change curve of node safety distance and generate the safety distance trend data table; (3) Detect mutation points according to the safety distance trend data table, mark the distance abnormal data points, and form the safety distance abnormal data record; (4) Conduct a correlation analysis between the safety distance abnormal data record and the environmental interference data, extract the abnormal characteristics caused by the interference, and generate the environmental interference impact assessment table; (5) Filter the original dataset of grid safety distance monitoring according to the environmental interference impact assessment table to obtain the effective dataset of grid safety distance; (6) Normalize the effective dataset of grid safety distance according to the standardization processing process to generate the standardized safety distance data stream.

[0029] Specifically, the original dataset of the safety distance monitoring of the grid structure is divided according to node relationships. Based on the topological relationship of the grid structure, with a single node as the center, the adjacent node group directly connected to it is determined. The criterion for judging adjacent nodes is based on the physical connection relationship between nodes, that is, the nodes directly connected by rods are defined as adjacent nodes. The distance data between each node and its adjacent nodes are extracted and grouped to form a safety distance monitoring data group with nodes as the unit. After obtaining the safety distance monitoring data group, the data in each group are arranged according to the time series. Based on the time series analysis method, the curve fitting is performed on the distance data between each pair of adjacent nodes to obtain the trend curve of the distance between nodes changing with time. The trend curve reflects the change law of the relative displacement between nodes and contains characteristic information such as the distance change rate and acceleration. The trend curves and their characteristic parameters of all node pairs are sorted into a table form to generate a safety distance trend data table. The mutation point detection is carried out on the safety distance trend data table, and the time series data anomaly detection algorithm is used to identify the abnormal points in the distance change. The criteria for judging abnormal points include: the distance change rate exceeds the preset threshold, the acceleration mutation, and the significant deviation from the statistical characteristics of historical data, etc. The marked abnormal data points are recorded according to the time sequence and spatial position to form a safety distance abnormal data record.

[0030] The correlation analysis is carried out between the safety distance abnormal data record and the environmental interference data collected by the environmental parameter sensors. The environmental interference data includes parameters such as temperature, humidity, and vibration at the measuring point position. Through the correlation analysis method, the correlation relationship between the environmental parameters at the moment when the abnormal data points appear and the degree of abnormality is calculated. The analysis results are classified and sorted according to the environmental factor categories and the influence degrees to generate an environmental interference influence assessment table. According to the analysis results in the environmental interference influence assessment table, the data in the original dataset of the safety distance monitoring of the grid structure that are significantly affected by environmental factors are filtered. For different types of environmental interference, corresponding data correction methods are adopted: thermal compensation correction is carried out for the deformation caused by temperature, smoothing processing is adopted for the fluctuation caused by vibration, and drift correction is adopted for the influence of humidity, etc. The data after environmental interference filtering constitute the effective dataset of the safety distance of the grid structure.

[0031] The effective dataset of the safety distance of the grid structure is standardized to convert the data with different dimensions into a unified scale space. The standardization process includes two steps: data normalization and dimensionless processing. First, the zero-mean normalization processing is carried out on the data, and then the normalization mapping is carried out according to the preset data range, and finally the standardized safety distance data stream is generated.

[0032] For example, in the construction of a grid structure of a large stadium, the main grid is composed of 3,000 nodes, and 1,000 key nodes are selected for safety distance monitoring. For each node, its directly connected adjacent nodes are first identified. On average, each node has 6 adjacent nodes, forming a node safety distance monitoring data group. During the grid hoisting process, the change curve of the distance between the key node N245 and its adjacent node N246 in the second span of the main truss within 1 hour shows that the distance value gradually increases from the designed spacing of 1,200 mm to 1,245 mm. Through mutation point detection, it is found that an acceleration mutation occurs when the distance reaches 1,230 mm. Environmental data shows that the temperature in this area reaches 36 °C when the mutation occurs, and welding operations are being carried out, resulting in local thermal deformation. Through correlation analysis, it is determined that this is a temporary deformation caused by the welding thermal effect, and the node spacing returns to normal after cooling.

[0033] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Based on the grid topology structure, establish a node safety distance monitoring network for the standardized safety distance data stream, and output a spatial monitoring point group distribution matrix; (2) Construct a deformation gradient field along the main support direction of the grid according to the spatial monitoring point group distribution matrix to form a grid deformation measurement data set; (3) With the help of the grid deformation measurement data set, statistically analyze the peak value range of node deformation, and generate a working condition - deformation coupling data packet in combination with the working condition characteristic data; (4) Based on the working condition - deformation coupling data packet, extract the key deformation node links of the grid and construct a deformation propagation path map; (5) Analyze the node deformation evolution law through the deformation propagation path map and draw a deformation correlation characteristic table; (6) Integrate the deformation correlation characteristic table with the overall stability index of the grid and output a grid structure deformation state evaluation report.

[0034] Specifically, all monitoring nodes are encoded according to their spatial positions, and a connection relationship matrix between nodes is established, including the spatial coordinates of the nodes and information about adjacent nodes. Conduct a spatial distribution analysis of the surrounding monitoring points of each node, calculate the relative positions and connection relationships between nodes, and form a spatial monitoring point group distribution matrix.

[0035] When constructing the deformation gradient field along the main support direction of the grid, the following calculation method is adopted: ;

[0036] Where: represents the deformation gradient field in three - dimensional space; represents the horizontal displacement of the i - th node; represents the vertical displacement of the j - th node; , is the weight coefficient; , respectively represent the spatial coordinates in the horizontal and vertical directions; , are the numbers of horizontal and vertical monitoring points respectively.

[0037] Based on the calculated deformation gradient field data and combined with the measured displacement values of the nodes, a dataset for measuring the deformation of the grid structure is formed.

[0038] When statistically analyzing the peak deformation of nodes in the dataset for measuring the deformation of the grid structure, the following model is adopted: ;

[0039] where: represents the peak deformation at time t; represents the k-th working condition characteristic parameter; represents the l-th deformation parameter; , is the weight coefficient; represents the coupling operator; , are the numbers of working condition characteristics and deformation parameters respectively.

[0040] When extracting the key deformation node links of the grid structure, the following calculation method is adopted: ;

[0041] where: represents the strength of the deformation propagation path; represents the deformation response strength of the u-th node; represents the w-th propagation factor; , is the weight coefficient; , are the numbers of key nodes and propagation factors respectively. Temporal matching and correlation analysis are performed on the data in the peak deformation interval and the working condition characteristic data. The analysis process comprehensively considers the influence of working condition factors such as construction procedures, equipment layout, and load distribution on node deformation, and records the deformation response characteristics under different working conditions. The deformation data corresponding to each working condition is assigned a timestamp and a spatial identifier to facilitate tracking the deformation development process. In the construction of the deformation propagation path map, the focus is on the diffusion law of deformation in the grid structure. By analyzing the deformation transfer relationship between adjacent nodes, the main and secondary paths of deformation propagation are identified. Key parameters such as propagation speed and attenuation characteristics are marked for each propagation path, and a propagation path map in a network structure is drawn. This map clearly shows the propagation direction and influence range of deformation in the grid structure.

[0042] Analyze the spatio-temporal evolution law of node deformation for the data in the deformation propagation path map. Record the characteristics such as the starting position, propagation direction, and influence range where the deformation occurs, and correlate these characteristics with the spatial position relationship of the nodes. Through the comparative analysis of the deformation data within multiple time windows, summarize the regular characteristics of the deformation development, and draw up a deformation correlation feature table. During the generation of the evaluation report on the deformation state of the grid structure, it is necessary to integrate the deformation correlation feature table with the overall stability indicators of the grid. The overall stability indicators include parameters such as the limit value of node displacement, the allowable value of structural deformation, and the support reaction force. Form a detailed state evaluation conclusion by comprehensively evaluating the deformation development trend and the structural bearing capacity.

[0043] For example, in the construction of a grid structure of an exhibition hall, the grid span is 80 meters and it consists of a grid-like structure composed of 2,400 nodes. During the main truss hoisting construction stage, real-time monitoring found that the displacement value of node C156 in area C suddenly increased. Analyzing the deformation data of its surrounding nodes, it was found that the deformation area centered on C156 showed a radial expansion trend, and the deformation gradient field showed that the vertical deformation rate in this area was significantly higher than other areas. Combining the working condition data analysis showed that the roof panels were being installed during this period, and a material storage yard was arranged near the deformation center. Through the analysis of the deformation propagation path, it was found that the deformation started from node C156 and spread to both sides along the direction of the main truss, and the influence range covered about 20 surrounding nodes. According to the deformation correlation feature table, there was an obvious positive correlation between the deformation rate and the material stacking load. The final state evaluation report pointed out the cause of the deformation and put forward optimization suggestions for the material stacking layout.

[0044] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Subdivide the deformation states of the grid construction hoisting area, welding reinforcement area, and component installation area via the evaluation report on the deformation state of the grid structure, and generate area monitoring and early warning indicators; (2) Utilize the area monitoring and early warning indicators to analyze the node deformation characteristics caused by the movement of large construction equipment, and form equipment construction interference evaluation data; (3) Conduct a correlation analysis between the equipment construction interference evaluation data and the support stability of adjacent nodes, and establish a list of construction condition influence coefficients; (4) Extract the law of force change of grid nodes from the list of construction condition influence coefficients, and mark the construction areas with high stress concentration; (5) Draw a warning distribution map of dangerous operation areas for grid construction based on the deformation development trend of the construction areas with high stress concentration; (6) Set a graded early warning threshold according to the warning distribution map of dangerous operation areas for grid construction, and output a grid safety early warning information package.

[0045] Specifically, time series matching and correlation analysis are performed on the data of the deformation peak interval and the working condition characteristic data. The analysis process comprehensively considers the influence of working condition factors such as construction procedures, equipment layout, and load distribution on the node deformation, and records the deformation response characteristics under different working conditions. The deformation data corresponding to each working condition is assigned a timestamp and a spatial identifier. In the construction of the deformation propagation path map, the focus is on the diffusion law of deformation in the grid structure. By analyzing the deformation transfer relationship between adjacent nodes, the main and secondary paths of deformation propagation are identified. Key parameters such as propagation speed and attenuation characteristics are marked for each propagation path, and a propagation path map in a network structure is drawn. This map clearly shows the transfer direction and influence range of deformation in the grid structure.

[0046] Analyze the spatio-temporal evolution law of node deformation for the data in the deformation propagation path map. Record the characteristics such as the starting position, propagation direction, and influence range of the deformation, and correlate these characteristics with the spatial position relationship of the nodes. By comparing and analyzing the deformation data within multiple time windows, summarize the regular characteristics of the deformation development, and draw a deformation correlation characteristic table. In the process of generating the grid structure deformation state assessment report, it is necessary to integrate the deformation correlation characteristic table with the overall stability indicators of the grid. The overall stability indicators include parameters such as the limit value of node displacement, the allowable value of structural deformation, and the reaction force of the support. By comprehensively evaluating the deformation development trend and the structural bearing capacity, a detailed state assessment conclusion is formed.

[0047] For example, in the construction of a grid structure of an exhibition hall, the grid span is 80 meters and it consists of a grid structure composed of 2,400 nodes. During the main truss hoisting construction stage, real-time monitoring found that the displacement value of node C156 in area C suddenly increased. Analyzing the deformation data of its surrounding nodes, it was found that the deformation area centered on C156 showed a radial expansion trend, and the deformation gradient field showed that the vertical deformation rate in this area was significantly higher than other areas. Combining the analysis of the working condition data shows that the roof panel installation was being carried out during this period, and a material storage yard was arranged near the deformation center. Through the analysis of the deformation propagation path, it was found that the deformation started from node C156 and spread to both sides along the main truss direction, and the influence range covered about 20 surrounding nodes. According to the deformation correlation characteristic table, there is an obvious positive correlation between the deformation rate and the material stacking load. The final state assessment report pointed out the cause of the deformation and put forward optimization suggestions for the material stacking layout.

[0048] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Split the grid safety warning information packet into node deformation data packets, process interference data packets, and safety limit data packets, and generate a classification warning factor table; (2) Analyze the stress states of the grid support points, connection nodes, and main members according to the classification warning factor table to form a process influence assessment matrix; (3) Screen high-risk construction process combinations from the process impact assessment matrix, and sort out the grid construction intervention list; (4) Conduct conflict checks on the hoisting process, welding process, and reinforcement process according to the grid construction intervention list, and mark the priority of node intervention; (5) Divide the sections where the hazard sources are located into emergency disposal areas, key control areas, and regular monitoring areas according to the priority of node intervention, and output the grid key intervention node table; (6) Convert the grid key intervention node table into specific construction control measures, and generate grid construction safety adjustment instructions.

[0049] Specifically, the data is divided into three categories according to its nature: the node deformation data packet records the displacement value, deformation rate, and acceleration of each monitoring point; the process interference data packet includes the position of construction equipment, the distribution of operators, and the stacking status of materials; the safety limit data packet stores the warning thresholds of various parameters. Classify and sort out these three types of data, establish a warning factor comparison table, and clarify the data sources and judgment criteria corresponding to each warning trigger condition. When analyzing the stress state of the grid structure according to the classified warning factor table, focus on three types of key components: the support points are located at the connection between the grid and the foundation, and bear the load transfer of the overall structure; the connection nodes are the intersections of the members and are responsible for transmitting internal forces; the main members are the main load-bearing components. Analyze the stress changes of these three types of components under different construction processes respectively, record mechanical parameters such as stress and deformation, and form a process impact assessment matrix. This matrix reflects the influence degree of various construction processes on the structural stress state.

[0050] Extract the process combinations with larger influence coefficients from the process impact assessment matrix to identify high-risk construction links. Focus on dynamic loads during hoisting, thermal stress during welding, and stress changes in the support system, etc. Organize the information such as these high-risk processes, their influence ranges, and durations into a grid construction intervention list. This list details the construction links that need to be focused on and the corresponding monitoring requirements. Conduct process conflict analysis according to the grid construction intervention list, and focus on checking the mutual influence between the hoisting process, welding process, and reinforcement process. The hoisting process involves the movement of large equipment and the installation of components, and it is necessary to ensure the working space; the welding process generates local high temperatures and affects the surrounding area; the reinforcement process needs to consider the coordination with the original structure. Conduct conflict checks on these processes in the time and space dimensions, and determine the priority order of node intervention according to the conflict degree and risk level.

[0051] Divide the safety levels of the construction areas based on the priority of node intervention: The emergency disposal area is the area where the deformation or stress has exceeded the safety limit and immediate intervention measures are required; the key control area is the area close to the warning value and requires enhanced monitoring and prevention; the regular monitoring area is the area in a normal state and daily monitoring is maintained. Determine the key intervention nodes according to this zoning method to form a key intervention node table for the grid structure. When converting the key intervention node table of the grid structure into specific construction control measures, formulate corresponding treatment plans for different areas and different risk levels. Include specific measures such as adjusting the construction sequence, optimizing the equipment layout, and strengthening local supports to form detailed safety adjustment instructions for the grid structure construction. These instructions clearly stipulate the execution steps, time nodes, and quality requirements of each measure.

[0052] For example, during the construction of the grid structure of a certain airport terminal building, during the installation process in the mid-span area of the main grid structure, abnormal conditions were found in node A12 and its surrounding areas through monitoring data. After analyzing the warning information, it was found that the deformation value of the nodes in this area reached 80% of the design value, and the deformation rate showed an accelerating trend. At the same time, the process data showed that the roof panel installation was underway in this area, and a 150-ton crawler crane was moving into position. After inputting this information into the process impact assessment system, it was identified that the superposition effect of the hoisting process and the roof construction led to local overloading. Through process conflict inspection, it was determined that the load distribution problem around node A12 needed to be prioritized. According to the intervention priority, this area was classified as an emergency disposal area, the roof panel installation work was suspended, the hoisting path was adjusted, and temporary supports were added at the key stressed nodes. These measures were issued to the construction team through the safety adjustment instructions for construction.

[0053] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Extract the key points of construction process control, requirements for intervention measures, and safety limit ranges from the safety adjustment instructions for grid structure construction to form a construction control experience data group; (2) Organize the grid structure hoisting failures, node instability situations, and support deformation records in the historical safety event data to establish a construction risk case library; (3) Perform scenario mapping between the construction control experience data group and the construction risk case library to generate a set of emergency disposal plans for grid structure construction; (4) With the help of the set of emergency disposal plans for grid structure construction, classify the node instability types, reasons for deformation exceeding the limit, and environmental interference factors in different construction stages to organize an experience knowledge entry table; (5) Based on the experience knowledge entry table, establish the correlation relationships among construction accident prevention measures, emergency intervention means, and disposal effect evaluation to form a safety event feature correlation network; (6) Summarize the management key points, technical key points, and operation key points in the safety event feature correlation network to generate a safety management knowledge base for grid structure construction.

[0054] Specifically, data extraction is carried out on the safety adjustment instructions for the grid structure construction. By parsing the adjustment instruction text, key points for construction process control are extracted, including monitoring parameters, warning thresholds, intervention timing, etc. for each node; requirements for intervention measures are extracted, including process adjustment plans, equipment layout requirements, personnel allocation, etc.; safety limit ranges are extracted, including parameters such as node displacement limits, deformation rate limits, stress limits, etc. The extracted data are sorted and classified according to process type, intervention type, and limit type to form a set of construction control experience data. The historical safety event data are sorted and classified, with a focus on three types of typical problems: data on grid structure hoisting failures include problems such as improper equipment layout, hoisting path conflicts, uneven load distribution, etc.; data on node instability conditions record problems such as loose node connections, welding deformation, support failure, etc.; support deformation records include temporary support deformation, permanent support settlement, foundation displacement, etc. The causes, development processes, and handling methods of each type of problem are recorded in detail to establish a structured construction risk case library.

[0055] Data association and scenario matching are carried out between the construction control experience data set and the construction risk case library. First, the data are preliminarily screened according to process type, and the experience data and risk cases under similar processes are paired. Then, scenario similarity analysis is carried out according to factors such as construction environment, structural characteristics, and construction conditions to find the historical cases closest to the current construction situation. Through this mapping relationship, applicable handling plans are extracted to form a set of emergency handling plans for grid structure construction. Based on the set of emergency handling plans for grid structure construction, various problems occurring during the construction process are systematically classified. For the node instability type, its performance characteristics at different construction stages are analyzed, such as dynamic instability during the hoisting stage, thermal deformation instability during the welding stage, fatigue instability during the use stage, etc. For the reasons for excessive deformation, typical reasons such as overloading, insufficient support, and poor node connection are summarized. Environmental interference factors include temperature effects, wind load impacts, foundation settlement, etc. These analysis results are sorted into an experience knowledge entry table.

[0056] According to the empirical knowledge entry table, a problem-measure-effect correlation network is established. For each type of construction accident, the corresponding preventive measures are sorted out, such as strengthening monitoring, optimizing processes, supplementing supports, etc.; corresponding emergency intervention measures, such as local reinforcement, load dispersion, emergency support, etc.; and corresponding treatment effect evaluation indicators, such as deformation recovery degree, structural stability improvement, etc. Through the analysis of these correlation relationships, a complete safety event feature correlation network is formed. Knowledge is extracted and summarized for the safety event feature correlation network. From the management level, management points such as construction organization, staffing, and quality control are extracted; from the technical level, technical points such as monitoring layout, early warning setting, and intervention plan are summarized; from the operational level, operational points such as process connection, equipment operation, and emergency disposal are summarized. These contents are systematically organized to form a knowledge base for grid construction safety management.

[0057] For example, during the construction of a large stadium steel structure grid, abnormal deformation of the node group in area B was monitored during the hoisting of the main truss. By querying the historical case library, it was found that similar working conditions had caused excessive local deformation due to improper layout of the hoisting equipment. Compared with the current construction conditions, it was found that there was a force contradiction between the layout position of the large lifting equipment and the support system. With reference to historical disposal experience, the position of the hoisting equipment was immediately adjusted, and a temporary support structure was added. During the disposal process, it was also found that the welding sequence deviated from the predetermined plan, and the process optimization and adjustment were carried out in a timely manner.

[0058] The above describes the safety distance monitoring method for the construction of the grid and the pipe truss in the embodiment of the present application. The following describes the safety distance monitoring method system for the construction of the grid and the pipe truss in the embodiment of the present application. Figure 3 In the embodiment of the present application, an embodiment of a method system for monitoring the safe distance between the grid and the pipe truss construction includes: The acquisition module 201 is used to integrate the original data collected by the key node safety distance sensor, the construction status data collected by the working condition identification sensor, and the environmental interference data collected by the environmental parameter sensor into a grid safety distance monitoring original data set through a distributed data acquisition terminal; A screening module 202 is used to screen out abnormal data and perform error compensation based on the grid safety distance monitoring original data set to generate a standardized safety distance data stream; A generating module 203 is used to analyze the deformation characteristics of the grid structure on the standardized safety distance data stream, and to establish a deformation correlation characteristic table according to the node displacement data and the working condition characteristic data, and to generate a grid structure deformation state assessment report; A positioning module 204 is used to match the warning trigger conditions based on the grid structure deformation state assessment report, realize the positioning of the dangerous area in combination with the abnormal deformation pattern recognition, and output the grid safety warning information package; An adjustment module 205, configured to determine key intervention nodes based on a process impact assessment matrix for the grid frame safety early warning information packet, and generate a grid frame construction safety adjustment instruction; A management module 206, configured to establish a safety event feature association network according to the grid frame construction safety adjustment instruction and historical safety event data, and generate a grid frame construction safety management knowledge base.

[0059] Through the collaborative cooperation of the above-mentioned various components, by integrating the original data collected by the key node safety distance sensor, the construction state data collected by the working condition identification sensor, and the environmental interference data collected by the environmental parameter sensor, a raw data set for grid frame safety distance monitoring is constructed, realizing the collaborative collection and fusion processing of multi-source data, overcoming the problem of incomplete information of a single data source. Through the setting of the distributed data collection terminal, the real-time performance and reliability of data collection are improved; abnormal data screening and error compensation are performed on the raw data set for grid frame safety distance monitoring to generate a standardized safety distance data stream, effectively eliminating the influence of environmental interference and random errors, and improving the data quality; the standardized safety distance data stream is combined with the working condition characteristic data for deformation characteristic analysis, establishing a deformation association characteristic table and generating a grid frame structure deformation state evaluation report, realizing the accurate evaluation of the structure deformation state; based on the grid frame structure deformation state evaluation report, an early warning trigger mechanism is established, and the precise positioning of the dangerous area is realized through abnormal deformation mode recognition. The output grid frame safety early warning information packet provides a reliable basis for construction management; by combining the process impact assessment matrix to determine key intervention nodes, the generated grid frame construction safety adjustment instruction has strong pertinence and operability; by establishing a safety event feature association network and generating a grid frame construction safety management knowledge base, the accumulation and inheritance of construction experience are realized, providing a reference basis for subsequent similar projects. The entire solution uses artificial intelligence algorithms to process and analyze multi-source data, giving full play to the advantages of deep learning in pattern recognition and feature extraction, realizing the full-process intelligence from data collection, processing, analysis to decision support, and significantly improving the scientific nature and reliability of grid frame and pipe truss construction safety monitoring.

[0060] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0061] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A safety distance monitoring method for the construction of a grid frame and a pipe truss, characterized in that, The safety distance monitoring method for grid frame and pipe truss construction includes: Integrating the original data collected by key node safety distance sensors, the construction state data collected by working condition identification sensors, and the environmental interference data collected by environmental parameter sensors into the original data set for grid frame safety distance monitoring through a distributed data acquisition terminal; Based on the original data set for grid frame safety distance monitoring, screening out abnormal data and performing error compensation to generate a standardized safety distance data stream; Analyzing the grid frame deformation characteristics of the standardized safety distance data stream, establishing a deformation correlation feature table according to the node displacement data and working condition feature data, and generating an evaluation report on the deformation state of the grid frame structure; Matching the early warning trigger conditions based on the evaluation report on the deformation state of the grid frame structure, combining with the identification of abnormal deformation patterns to locate dangerous areas, and outputting a grid frame safety early warning information package; Determining key intervention nodes for the grid frame safety early warning information package according to the process impact evaluation matrix, and generating a safety adjustment instruction for grid frame construction; Based on the safety adjustment instruction for grid frame construction and historical safety event data, establishing a safety event feature correlation network and generating a knowledge base for grid frame construction safety management.

2. The safety distance monitoring method for grid frame and pipe truss construction according to claim 1, characterized in that The integration of the original data collected by key node safety distance sensors, the construction state data collected by working condition identification sensors, and the environmental interference data collected by environmental parameter sensors into the original data set for grid frame safety distance monitoring through a distributed data acquisition terminal includes: Converting the node three-dimensional coordinate data collected by key node safety distance sensors within a preset sampling period into a raw data group of safety distance; Integrating the construction machinery position data, operator distribution data, and material stacking state data collected by working condition identification sensors into a construction state feature data group; Fusing the temperature field data, humidity field data, and vibration field data collected by environmental parameter sensors into an environmental interference feature data group; Performing timestamp matching on the raw data group of safety distance, the construction state feature data group, and the environmental interference feature data group to generate a time series correlation data table; Performing spatial mapping on the time series correlation data table according to the node numbers to establish a spatial relationship matrix for grid frame node safety distance monitoring; According to the spatial relationship matrix for grid frame node safety distance monitoring and the time series correlation data table, integrating the data in the time and space dimensions to generate the original data set for grid frame safety distance monitoring.

3. The safety distance monitoring method for grid frame and pipe truss construction according to claim 1, characterized in that, Based on the original data set for grid frame safety distance monitoring, screening out abnormal data and performing error compensation to generate a standardized safety distance data stream, including: Dividing the original data set for grid frame safety distance monitoring into safety distance monitoring data groups according to the safety distance relationship between adjacent nodes; Based on the safety distance monitoring data groups, constructing a curve of the change in node safety distance and generating a safety distance trend data table; Performing mutation point detection based on the safety distance trend data table, marking the distance abnormal data points, and forming a safety distance abnormal data record; Performing a correlation analysis on the safety distance abnormal data record and the environmental interference data, extracting the abnormal characteristics caused by interference, and generating an environmental interference impact evaluation table; Filter the original data set of the grid frame safety distance monitoring according to the environmental interference impact assessment form to obtain an effective data set of the grid frame safety distance; Normalize the effective data set of the grid frame safety distance according to the standardization processing flow to generate a standardized safety distance data stream.

4. The safety distance monitoring method for grid frame and pipe truss construction according to claim 1, characterized in that Analyze the deformation characteristics of the grid frame for the standardized safety distance data stream, and establish a deformation correlation feature table based on the node displacement data and the working condition characteristic data, and generate an evaluation report on the deformation state of the grid frame structure, including: Establish a node safety distance monitoring network for the standardized safety distance data stream based on the grid frame topology structure, and output a spatial monitoring point group distribution matrix; Construct a deformation gradient field along the main support direction of the grid frame according to the spatial monitoring point group distribution matrix to form a grid frame deformation measurement data set; Statistically analyze the peak deformation interval of the nodes by means of the grid frame deformation measurement data set, and generate a working condition-deformation coupling data packet in combination with the working condition characteristic data; Based on the working condition-deformation coupling data packet, extract the key deformation node links of the grid frame and construct a deformation propagation path map; Analyze the node deformation evolution law through the deformation propagation path map and draw a deformation correlation feature table; Fuse the deformation correlation feature table with the overall stability index of the grid frame and output an evaluation report on the deformation state of the grid frame structure.

5. The safety distance monitoring method for grid frame and pipe truss construction according to claim 1, characterized in that Match the early warning trigger conditions based on the evaluation report on the deformation state of the grid frame structure, and combine the abnormal deformation mode recognition to realize the positioning of the dangerous area, and output a grid frame safety early warning information packet, including: Subdivide the deformation states of the grid frame construction hoisting area, welding reinforcement area, and component installation area through the evaluation report on the deformation state of the grid frame structure to generate sub-area monitoring early warning indicators; Analyze the node deformation characteristics caused by the movement of large construction equipment by using the sub-area monitoring early warning indicators to form equipment construction interference evaluation data; Conduct a correlation analysis between the equipment construction interference evaluation data and the support stability of adjacent nodes to establish a list of construction working condition influence coefficients; Extract the law of the force change of the grid frame nodes from the list of construction working condition influence coefficients and mark the construction areas with high stress concentration; Draw a warning distribution map of the dangerous operation area of the grid frame construction according to the deformation development trend of the high stress concentration construction area; Set a grading early warning threshold according to the warning distribution map of the dangerous operation area of the grid frame construction and output a grid frame safety early warning information packet.

6. The safety distance monitoring method for grid frame and pipe truss construction according to claim 1, characterized in that, Determine the key intervention nodes for the grid frame safety early warning information packet according to the process influence evaluation matrix and generate a grid frame construction safety adjustment instruction, including: Split the grid frame safety early warning information packet into node deformation data packets, process interference data packets, and safety limit data packets to generate a classification early warning factor table; Analyze the stress states of the grid frame support points, connection nodes, and main members according to the classification early warning factor table to form a process influence evaluation matrix; Screen out high-risk construction process combinations from the process influence evaluation matrix and sort out a grid frame construction intervention list; Conduct conflict checks on the hoisting process, welding process, and reinforcement process according to the grid frame construction intervention list and mark the node intervention priorities; Divide the sections where the hazard sources are located into emergency disposal areas, key control areas, and regular monitoring areas according to the node intervention priorities, and output a grid frame key intervention node table; Convert the key intervention node table of the grid structure into specific construction control measures to generate a safety adjustment instruction for grid structure construction.

7. The safety distance monitoring method for grid frame and pipe truss construction according to claim 1, characterized in that, Based on the safety adjustment instruction for grid structure construction and historical safety event data, establish a correlation network of safety event characteristics and generate a safety management knowledge base for grid structure construction, including: Extract the key points of construction process control, requirements for intervention measures, and safety limit ranges from the safety adjustment instruction for grid structure construction to form a construction control experience data set; Sort out the grid structure hoisting failures, node instability situations, and support deformation records in the historical safety event data to establish a construction risk case base; Perform scenario mapping on the construction control experience data set and the construction risk case base to generate a set of emergency response plans for grid structure construction; With the help of the set of emergency response plans for grid structure construction, classify the node instability types, reasons for deformation exceeding the limit, and environmental interference factors in different construction stages, and sort out an experience knowledge item table; Based on the experience knowledge item table, establish the correlation relationship among construction accident prevention measures, emergency intervention means, and disposal effect evaluation to form a correlation network of safety event characteristics; Summarize the management key points, technical key points, and operation key points in the correlation network of safety event characteristics to generate a safety management knowledge base for grid structure construction.

8. A safety distance monitoring method and system for grid frame and pipe truss construction, which is used to implement the safety distance monitoring method for grid frame and pipe truss construction described in any one of claims 1-7, characterized in that, The safety distance monitoring method system for grid structure and pipe truss construction includes: An acquisition module, which is used to integrate the original data collected by the key node safety distance sensor, the construction status data collected by the working condition identification sensor, and the environmental interference data collected by the environmental parameter sensor into an original data set for grid structure safety distance monitoring through a distributed data acquisition terminal; A screening module, which is used to screen out abnormal data and perform error compensation based on the original data set for grid structure safety distance monitoring to generate a standardized safety distance data stream; A generation module, which is used to analyze the grid structure deformation characteristics of the standardized safety distance data stream, establish a deformation correlation feature table based on the node displacement data and working condition characteristic data, and generate an evaluation report on the deformation state of the grid structure; A positioning module, which is used to match the early warning trigger conditions based on the evaluation report on the deformation state of the grid structure, and realize the positioning of the dangerous area by combining the abnormal deformation mode recognition, and output a safety early warning information package for the grid structure; An adjustment module, which is used to determine the key intervention nodes according to the process influence evaluation matrix for the safety early warning information package of the grid structure and generate a safety adjustment instruction for grid structure construction; A management module, which is used to establish a correlation network of safety event characteristics based on the safety adjustment instruction for grid structure construction and historical safety event data, and generate a safety management knowledge base for grid structure construction.

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