A civil construction safety risk assessment method and system

Abstract

The present application relates to the technical field of construction safety assessment, in particular to a civil construction safety risk assessment method and system, comprising the following steps: real-time monitoring of structural support state, generating risk labels and classifying and archiving, outputting construction risk assessment results. In the present application, through real-time monitoring of the support state of the structural member, a risk level label is formed in combination with load change data, a comparison structure of load evolution is established based on the state conversion node, and then label generation and classification archiving are carried out in combination with the strength grade division standard, so as to realize the identification and zoned and graded management of the member-level risk, complete the aggregation and correlation of the risk data based on the work stage progress information, form the assessment structure associated with time and region in both directions, improve the real-time performance and accuracy of risk identification as a whole, enhance the objectivity and operability of the assessment results, eliminate the interference caused by artificial subjective judgment, and improve the timeliness and pertinence of construction site risk early warning and safety decision-making.

Description

Technical Field

[0001] This invention relates to the field of construction safety assessment technology, and in particular to a method and system for assessing safety risks in civil engineering construction. Background Technology

[0002] The field of construction safety assessment technology involves the identification, analysis, and evaluation of potential safety risks in construction activities. This includes identifying hazard sources at the construction site, analyzing safety risk factors, classifying safety levels, and developing safety management measures. Typically, it involves collecting various data during the construction process, combining this with the characteristics of the project, and using assessment models to conduct qualitative and quantitative analyses of various risks. This determines the degree of danger that may exist during construction and proposes corresponding early warnings and control suggestions to assist in safety management decisions. Traditional civil engineering construction safety risk assessment methods refer to identifying and classifying risks that may arise from factors such as construction procedures, working environment, and personnel operations during civil engineering construction. These methods primarily rely on manual inspection records, historical accident data summarization, visual assessment of the working environment, and expert scoring to identify and classify risks. Corresponding control measures are then developed based on rules of experience or standards. This assessment process is usually dominated by manual experience, combined with construction schedule plans and on-site inspection results for risk summarization and classification, and has a high degree of subjectivity and uncertainty.

[0003] Current civil engineering construction safety risk assessments rely on manual inspection records and historical accident data summaries. The assessment process is highly dependent on the subjective judgment and experience of on-site personnel, lacking the ability to respond in real time to changes in construction status. Changes in the working environment and risk factors cannot form a timely and accurate correlation feedback. The method based on visual inspection and expert scoring is difficult to fully reflect the dynamic relationship between component status and risk evolution. The assessment results are lagging and easily affected by human factors, resulting in insufficient comprehensiveness and accuracy of risk identification. The risk level classification is arbitrary and cannot support the detailed regional graded early warning needs, affecting the refinement and controllability of overall construction safety management. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for assessing safety risks in civil engineering construction.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for assessing safety risks in civil engineering construction, comprising the following steps:

[0006] S1: Real-time monitoring of the support status of floor slabs, beams and columns, identifying the corresponding unsupported or supported state of the structure, filtering the time point when the structure first changes from an unsupported state to a supported state and recording the corresponding structure number, and generating structural state change node information.

[0007] S2: Based on the component number and time point calibrated in the structural state change node information, extract the load sensor recording sequence of the component within each set time range before and after the state change, establish a parallel relationship group of load content before and after the state change, and generate a comparison group corresponding to the intensity change.

[0008] S3: Read the component number and the contents of the load before and after in the comparison group corresponding to the intensity change, and determine the level classification to which the difference between the load before and after belongs based on the boundary range of the structural component operation intensity level classification in the construction risk preset standard. Combine the state change attributes and component identification information to construct a component risk level label set.

[0009] S4: Based on the component number, status change node, and level classification content constructed in the component risk level label set, classify and mark the components according to the number index of the deployment area of ​​the structural components, and upload them to the central control node for label archiving and identification recording to generate a list of structural component risk labels;

[0010] S5: Based on the risk labels of all reported structural components in the risk label list of the structural components, and corresponding to the work progress stage in the current construction plan of the civil construction site, the risk labels of each area are collected, integrated and linked, and the civil construction safety risk assessment results are output.

[0011] As a further aspect of the present invention, the structural state change node information includes component identification number, support state change time, and support state change attribute; the strength change corresponding comparison group includes changed component number, load change value range, and load change time period; the component risk level label set includes component level identification result and level classification result; the structural component risk mark list includes regional risk label number, label archive information, and label index number; the civil construction safety risk assessment result includes risk level distribution record, work phase risk mapping result, and regional risk association map.

[0012] As a further aspect of the present invention, the step of obtaining the structural state change node information specifically comprises:

[0013] S111: Real-time monitoring of the support status of floor slabs, beams and columns; extraction of the time-series support status label sequence corresponding to the number index of each structural component; binarization of the status labels; marking the support status as 1 and the non-support status as 0 according to the set status coding method; generating a binary sequence of support status.

[0014] S112: Based on the binary sequence of the support state, perform frame-by-frame progressive comparison processing on the support state time series under all structure numbers, perform state transition identification operation, perform index positioning for the time position where the state changes from 0 to 1, extract the time point of the first state transition, and generate the first support state transition time point set.

[0015] S113: Based on the set of initial support state transition time points, establish a one-to-one mapping relationship between the structure number and the initial state transition time point for each structure number, construct a key-value pair data structure between the structure number and the state change time, and generate structure state change node information.

[0016] As a further aspect of the present invention, the step of obtaining the comparison group corresponding to the intensity change specifically includes:

[0017] S211: Obtain the component number and time point in the structural state change node information, extract the load sensor recording sequence of the corresponding component according to the number index, set the same length of time range before and after the marked time point, extract the load data of the component before and after the state change, and generate the load change extraction sequence.

[0018] S212: Based on the load change truncation sequence, perform a unified dimension aggregation operation on the data frames of the interval before and after the state change, calculate the total load in each time period based on the stress value recorded by the load sensor, read the total load results of the two time periods before and after the change, establish a data parallel structure through the component number, and generate a load dual-interval comparison data group.

[0019] S213: For the load dual-interval comparison data group, extract the front and back load value pairs corresponding to each component number, establish a comparison mapping between the component number and the front and back load values, construct a unified form of load change group information data frame, aggregate the corresponding results of all components, and generate a strength change corresponding comparison group.

[0020] As a further aspect of the present invention, the step of obtaining the component risk level label set specifically includes:

[0021] S311: Based on the comparison group corresponding to the intensity change, extract the component number and the sequence of before and after load records, calculate the difference between the before and after load values, and generate the load difference value.

[0022] S312: Based on the load difference value, collect the work intensity level limit range in the construction risk preset standard, perform matching judgment on the difference range and the upper and lower limits of the range, calculate and obtain the intensity discrimination value, establish a level classification index based on the position of the intensity discrimination value falling into the limit range, and generate the level classification value.

[0023] S313: Based on the level classification value, combined with the state change attribute and component identification information, establish a level classification and state attribute association mapping for the component number, summarize the risk label entries corresponding to each component, and generate a component risk level label set.

[0024] As a further aspect of the present invention, the formula for calculating the intensity discrimination value is as follows:

[0025] ;

[0026] in, Represents the load difference value. Represents the load reference value. Represents the length of the time window. Represents the length of the time base. This represents the number of load segments. Represents the number of segmentation references. The representative intensity discrimination value.

[0027] As a further aspect of the present invention, the step of obtaining the risk marker list of structural components specifically includes:

[0028] S411: Based on the component number, status change node and level classification value in the component risk level label set, establish a mapping based on the index relationship between the component number and the deployment area, aggregate the corresponding status change node, level classification and area number content for each component number, and generate a structural component area indexing sequence.

[0029] S412: Based on the structural component area indexing sequence, perform area classification processing on the state change nodes and level classification content under all component numbers, perform grouping operation on the data frames according to the area number, establish the attribution index record between the component number and the corresponding attribute for each group of data, and generate the component tag index table corresponding to the area number.

[0030] S413: Based on the component marking index table corresponding to the region number, transmit the component number, status change node, level classification and region number quadruple structure content to the central control node, and simultaneously record and upload status flag bits and timestamp information to generate a structural component risk marking list.

[0031] As a further aspect of the present invention, the steps for obtaining the civil construction safety risk assessment results are as follows:

[0032] S511: Obtain the risk label list of the structural components, extract the risk level label, status change node and deployment area number corresponding to each component number, index the construction time sequence according to the current progress, filter the construction stage identifiers corresponding to the status change nodes, establish the correspondence between component number and construction time sequence stage, and generate a component construction stage mapping table.

[0033] S512: Based on the component construction stage mapping table, perform aggregation processing on the component risk labels of components in the same construction stage within the same deployment area, establish an aggregation mapping between the area number and the risk level set, nest the construction time series index to form a multi-dimensional index unit, and generate a set of regional construction risk label sequences.

[0034] S513: For the construction risk label sequence set of the region, perform joint statistical aggregation of the segment dimension and the time series dimension, and establish a correlation matrix structure between the construction time series and the risk intensity of regional components by combining the construction stage mapping parameters, and generate the civil construction safety risk assessment results.

[0035] A civil engineering construction safety risk assessment system includes:

[0036] The status node extraction module is used to execute S1: real-time monitoring of the support status of floor slabs, beams and columns, identifying the corresponding unsupported or supported state of the structure, filtering the time point when the structure first changes from an unsupported state to a supported state and recording the corresponding structure number, and generating structural status change node information.

[0037] The strength comparison establishment module is used to execute S2: based on the component number and time point calibrated in the structural state change node information, extract the load sensor record sequence of the component within each set time range before and after the state change, establish a parallel relationship group of load content before and after the state change, and generate a comparison group corresponding to the strength change.

[0038] The risk level matching module is used to perform S3: read the component number and the contents of the load before and after in the comparison group corresponding to the strength change, determine the level classification to which the difference between the load before and after belongs based on the boundary range of the structural component operation strength level classification in the construction risk preset standard, and construct the component risk level label set by combining the state change attributes and component identification information.

[0039] The tag list archiving module is used to perform S4: based on the component number, status change node, and level classification content constructed in the component risk level tag set, classify and mark the components according to the number index of the deployment area of ​​the structural components, and upload them to the central control node for tag archiving and identification recording, generating a structural component risk tag list;

[0040] The assessment result collection module is used to execute S5: based on the risk labels of all reported structural components in the risk label list of the structural components, and corresponding to the work progress stage in the current construction plan of the civil construction site, the risk labels of each area are collected, integrated and associated with information, and the civil construction safety risk assessment results are output.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In this invention, by real-time monitoring of the support status of structural components and combining load change data to form risk level labels, a comparative structure of load evolution is established based on state transition nodes. Then, labels are generated and classified in conjunction with strength level classification standards to achieve component-level risk identification and hierarchical management. Based on the progress information of the operation stage, risk data is aggregated and correlated to form an assessment structure that is bidirectionally correlated with time and region. This improves the real-time performance and accuracy of risk identification, enhances the objectivity and operability of assessment results, eliminates interference caused by subjective human judgment, and improves the timeliness and pertinence of risk warning and safety decision-making at the construction site. Attached Figure Description

[0043] Figure 1 This is a flowchart of the main steps of the present invention;

[0044] Figure 2 This is a flowchart of the process for obtaining node information on structural state changes in this invention.

[0045] Figure 3 This is a flowchart illustrating the process of obtaining the comparative group corresponding to the intensity changes in this invention.

[0046] Figure 4 This is a flowchart illustrating the process of obtaining the risk level label set for components of this invention.

[0047] Figure 5 This is a flowchart for obtaining the risk labeling list of structural components for this invention;

[0048] Figure 6 This is a flowchart illustrating the process of obtaining the results of the civil construction safety risk assessment for this invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0051] Please see Figure 1 A method for assessing safety risks in civil engineering construction, comprising the following steps:

[0052] S1: Real-time monitoring of the support status of floor slabs, beams and columns to confirm the non-supported or supported status of the structure. Simultaneously, time series progressive comparison is performed on the status sequence under each component number to select the time point when the structure first changes from a non-supported state to a supported state and record the corresponding structure number, generating structural status change node information.

[0053] S2: Based on the component number and time point calibrated in the structural state change node information, extract the load sensor record sequence of the component within each set time range before and after the state change, establish a parallel relationship group of load content before and after the state change, and generate a corresponding comparison group of strength change.

[0054] S3: Read the component number and the contents of the load before and after in the comparison group corresponding to the strength change. According to the boundary interval of the division of the working strength level of structural components in the construction risk preset standard, use the interval matching method to determine the level classification to which the difference between the load before and after belongs. Combine the state change attributes and component identification information to form the risk label content corresponding to the structural component and generate the component risk level label set.

[0055] S4: Based on the component number, status change node, and level classification content built by the component risk level label, classify and mark the components according to the number index of the deployment area of ​​the structural components, and upload them to the central control node for label archiving and identification record to generate a list of structural component risk labels;

[0056] S5: Based on the risk labels of all reported structural components in the risk label list of structural components, and corresponding to the work progress stage in the current construction plan of the civil construction site, the risk labels of each area are collected, integrated and linked with information to establish an integrated structure associated with the construction time sequence and regional components, and output the civil construction safety risk assessment results.

[0057] The structural status change node information includes component identification number, support status change time, and support status change attributes; the strength change corresponding comparison group includes the changed component number, load change value range, and load change time period; the component risk level label set includes component level identification results and level classification results; the structural component risk mark list includes regional risk label number, label archive information, and label index number; the civil construction safety risk assessment results include risk level distribution records, work phase risk mapping results, and regional risk association map.

[0058] Please see Figure 2 Step S1 is as follows:

[0059] S111: Real-time monitoring of the support status of floor slabs, beams and columns; extraction of the time-series support status label sequence corresponding to the number index of each structural component; binarization of the status labels; marking the support status as 1 and the non-support status as 0 according to the set status coding method; generating a binary sequence of support status.

[0060] High-precision resistance strain gauge sensors and piezoelectric load cells are deployed at stress points in key structural components such as floor slabs, beams, and columns. The sensor sampling frequency is set to 50Hz, i.e., 50 data points are collected per second to continuously record the stress changes of each component. The collected raw analog signals are converted into digital signal sequences by A / D converters and input into a noise reduction processor. Moving average filtering is performed on the raw sequence, with a sliding window size of 5 data points to filter out high-frequency electromagnetic interference noise, outputting smoothed time-series load data. The support state determination threshold is set to 5000N, which is determined through mechanical experiments based on the self-weight of the structural components and the minimum effective bearing capacity of the temporary support system. The time-series load data of each component is traversed, and the load value at each moment is compared with 5000N: if and only if the load value is greater than or equal to 5000N, the component is determined to be in an "effective support state," and the state label for that moment is assigned a value of 1; if the load value is less than 5000N, it is determined to be in a "non-support state" or "loose connection state," and the state label for that moment is assigned a value of 0. For example, for beam member numbered "B-102", the load values ​​at five sampling points between the 10th and 10.1st seconds are 4800N, 4950N, 5050N, 5100N, and 5120N, respectively. After binarization, the corresponding state label subsequence is 0, 0, 1, 1, 1. This logic is used to process the full-time data of all members, generating a binary sequence of support states consisting of 0 and 1.

[0061] S112: Based on the binary sequence of the support state, perform frame-by-frame progressive comparison processing on the support state time series under all structure numbers, perform state transition recognition operation, perform index positioning for the time position where the state changes from 0 to 1, extract the time point of the first state transition, and generate the first support state transition time point set.

[0062] The binary sequence of support states is read, and a sliding pointer algorithm is used to scan the sequence frame by frame. The pointer is initialized at the beginning of the sequence, and the detection window length is set to 2 frames. The pointer slides backward with a step size of 1, and the values ​​of two adjacent state labels within the window are detected in real time. The logical judgment condition is set as follows: the value of the previous frame is 0 and the value of the next frame is 1. When this specific pattern (0->1 transition) is detected, the state transition recognition signal is triggered, and the time index corresponding to the next frame (i.e., the frame with a value of 1) is locked. This time index represents the instant when the component changes from a non-supported state to a supported state, which physically corresponds to the moment when the top support of the formwork support system contacts the bottom of the component and begins to bear force. The specific timestamp of this moment (accurate to milliseconds) is recorded and marked as the "first support intervention point". If there are multiple 0 to 1 transitions in the sequence (e.g., a brief period of detachment followed by re-contact due to construction disturbance), only the earliest time point on the time axis is extracted as valid data, and subsequent repeated transitions are ignored to ensure that the initial support establishment moment is captured. For example, in the sequence of component "B-102", a change from state 0 to 1 is detected at time axis coordinate 10.04 seconds. Therefore, 10.04 seconds is extracted as the first support state transition time point for this component. The above scanning and extraction operations are performed on all component numbers, and all extracted time data are summarized to generate a set of first support state transition time points.

[0063] The structural status change node information includes component identification number, support status change time, and support status change attributes; the strength change corresponding comparison group includes the changed component number, load change value range, and load change time period; the component risk level label set includes component level identification results and level classification results; the structural component risk mark list includes regional risk label number, label archive information, and label index number; the civil construction safety risk assessment results include risk level distribution records, work phase risk mapping results, and regional risk association map.

[0064] Please see Figure 2 Step S1 is as follows:

[0065] S111: Real-time monitoring of the support status of floor slabs, beams and columns; extraction of the time-series support status label sequence corresponding to the number index of each structural component; binarization of the status labels; marking the support status as 1 and the non-support status as 0 according to the set status coding method; generating a binary sequence of support status.

[0066] High-precision resistance strain gauge sensors and piezoelectric load cells are deployed at stress points in key structural components such as floor slabs, beams, and columns. The sensor sampling frequency is set to 50Hz, i.e., 50 data points are collected per second to continuously record the stress changes of each component. The collected raw analog signals are converted into digital signal sequences by A / D converters and input into a noise reduction processor. Moving average filtering is performed on the raw sequence, with a sliding window size of 5 data points to filter out high-frequency electromagnetic interference noise, outputting smoothed time-series load data. The support state determination threshold is set to 5000N, which is determined through mechanical experiments based on the self-weight of the structural components and the minimum effective bearing capacity of the temporary support system. The time-series load data of each component is traversed, and the load value at each moment is compared with 5000N: if and only if the load value is greater than or equal to 5000N, the component is determined to be in an "effective support state," and the state label for that moment is assigned a value of 1; if the load value is less than 5000N, it is determined to be in a "non-support state" or "loose connection state," and the state label for that moment is assigned a value of 0. For example, for beam member numbered "B-102", the load values ​​at five sampling points between the 10th and 10.1st seconds are 4800N, 4950N, 5050N, 5100N, and 5120N, respectively. After binarization, the corresponding state label subsequence is 0, 0, 1, 1, 1. This logic is used to process the full-time data of all members, generating a binary sequence of support states consisting of 0 and 1.

[0067] S112: Based on the binary sequence of the support state, perform frame-by-frame progressive comparison processing on the support state time series under all structure numbers, perform state transition recognition operation, perform index positioning for the time position where the state changes from 0 to 1, extract the time point of the first state transition, and generate the first support state transition time point set.

[0068] The binary sequence of support states is read, and a sliding pointer algorithm is used to scan the sequence frame by frame. The pointer is initialized at the beginning of the sequence, and the detection window length is set to 2 frames. The pointer slides backward with a step size of 1, and the values ​​of two adjacent state labels within the window are detected in real time. The logical judgment condition is set as follows: the value of the previous frame is 0 and the value of the next frame is 1. When this specific pattern (0->1 transition) is detected, the state transition recognition signal is triggered, and the time index corresponding to the next frame (i.e., the frame with a value of 1) is locked. This time index represents the instant when the component changes from a non-supported state to a supported state, which physically corresponds to the moment when the top support of the formwork support system contacts the bottom of the component and begins to bear force. The specific timestamp of this moment (accurate to milliseconds) is recorded and marked as the "first support intervention point". If there are multiple 0 to 1 transitions in the sequence (e.g., a brief period of detachment followed by re-contact due to construction disturbance), only the earliest time point on the time axis is extracted as valid data, and subsequent repeated transitions are ignored to ensure that the initial support establishment moment is captured. For example, in the sequence of component "B-102", a change from state 0 to 1 is detected at time axis coordinate 10.04 seconds. Therefore, 10.04 seconds is extracted as the first support state transition time point for this component. The above scanning and extraction operations are performed on all component numbers, and all extracted time data are summarized to generate a set of first support state transition time points.

[0069] S113: Based on the set of initial support state transition time points, establish a one-to-one mapping relationship between the structure number and the initial state transition time point for each structure number, construct a key-value pair data structure between the structure number and the state change time, and generate structure state change node information.

[0070] The hash mapping data structure is used to build an index library. The unique number of the structural component (such as "C-005", "B-102", etc.) is used as the key, and the corresponding first support state transition time point is used as the value. The set of first support state transition time points is traversed, and each set of component number and time point is filled into the hash table. During the construction process, data integrity verification is performed to ensure that each monitored component has a corresponding time point record; if a component number has no corresponding time point in the set (i.e., the state has never changed from 0 to 1), it is marked as "no effective support detected", and is removed or an abnormal warning is issued in subsequent processing to prevent invalid data from interfering with subsequent calculations. After the mapping is completed, the data structure can support the rapid retrieval of the precise time when the support state changes by component number in O(1) time complexity. For example, the generated key-value pair record is: {"B-102": "10.04s", "C-005": "12.50s", "S-201": "09.20s"}. This structured dataset is established as the node information for structural state changes, providing precise time anchors for subsequent extraction of load data for specific time periods.

[0071] Please see Figure 3 Step S2 is as follows:

[0072] S211: Obtain the component number and time point in the structural state change node information, extract the load sensor record sequence of the corresponding component according to the number index, set the same length of time range before and after the marked time point, extract the load data of the component before and after the state change, and generate the load change extraction sequence.

[0073] Parse the structural state change node information, and read the target component's number (e.g., "B-102") and its corresponding state transition time point (e.g., ...). s). Access the original high-capacity load sensor record database and locate the full-series load data for the component using the component number index. Based on the state transition time point. Set the time window radius with the central axis as the center axis. The radius is set at 5 seconds. This radius value is based on the average duration of a single pumping pulse during concrete pouring, ensuring coverage of the complete load impact process. The cutoff interval is determined by extending 5 seconds in both the negative (past) and positive (future) directions of the time axis. That is, from second 5.04 to second 15.04. Perform a data slicing operation to extract all load sampling point data within this interval (totaling...). The data points are completely copied and extracted. If the time point is at the end of the data stream, resulting in insufficient truncation length, the value at the last moment is automatically padded to make up the 5-second length. The above fixed-point truncation operation is performed on all components in sequence, and the load waveform data of each component before and after the critical moment are stored independently to generate a load change truncation sequence.

[0074] The load change truncation sequence is segmented and aggregated. State transition time points are used as the basis for this process. The extracted data sequence is divided into "preceding intervals" using these as boundaries. and "subsequent intervals" For the data in the preceding interval, the arithmetic mean method is used to calculate the average load baseline value for that period, using the following formula: ,in This represents the number of sampling points within the interval. Here are the load values ​​at each point. For subsequent intervals, considering the potential for impact oscillations after support is established, the root mean square (RMS) algorithm is used to calculate the total effective load to more accurately reflect the energy level of the dynamic load. The calculation result is denoted as... For example, for component "B-102", the calculated average load of the preceding interval is 200N (mainly self-weight and installation prestress), and the total effective load of the subsequent interval is 5800N (including the wet weight of concrete and construction load). The calculated... and The two values ​​are encapsulated and bound to the component number to form a standardized data entry. A uniform dimensional aggregation calculation is performed on all extracted sequences to generate a load dual-interval comparison data set containing the load variation characteristics of all components.

[0075] S213: For the load dual-interval comparison data group, extract the front and back load value pairs corresponding to each component number, establish a comparison mapping between component number and front and back load values, construct a unified form of load change group information data frame, aggregate the corresponding results of all components, and generate a strength change corresponding comparison group.

[0076] Initialize a two-dimensional data frame structure, defining column fields as "Component Number", "Previous Load Value", "Subsequent Load Value", and "Change Trend Type". Iterate through the load dual-interval comparison data set, and for each component... and Fill in the corresponding columns. Simultaneously, perform a preliminary trend logic judgment during the filling process: if... The trend of change is marked as "loading"; if Mark as "Uninstall"; if the difference between the two is within... Within this range, the data is labeled "steady state". This step aims to transform the scattered numerical calculation results into structured tabular data to facilitate subsequent batch mathematical operations. For example, the record row for component "B-102" is written as ["B-102", 200, 5800, "load"]. The record rows for all components are aggregated to construct a complete intensity change corresponding comparison group data frame, which provides a cleaned and formatted base dataset for subsequent risk quantification calculations.

[0077] Please see Figure 4 Step S3 is as follows:

[0078] S311: Based on the comparison group corresponding to the strength change, extract the component number and the sequence of before and after load records, calculate the difference between the before and after load values, and generate the load difference value.

[0079] Read the "preceding load value" in the comparison group corresponding to the intensity change. Compared with "subsequent load values" Perform a difference operation to calculate the absolute difference between the two values, using the following formula: The absolute value is used here to uniformly measure the severity of load fluctuations; regardless of loading or unloading, drastic numerical jumps are considered potential risk sources. For example, substituting the data for component "B-102", the calculation yields... This value directly reflects the impact strength of the load borne by the component at the instant the support state changes. The subtraction operation is performed on each line of the data frame, and the result is... As a new field, “Load Difference Value”, it is appended to the data frame to generate a sequence of load difference values ​​containing precise difference values.

[0080] S312: Based on the load difference value, collect the work intensity level limit range in the pre-set construction risk standard, and perform a matching judgment on the difference amplitude and the upper and lower limits of the range, using the following formula:

[0081] ;

[0082] The intensity discrimination value is obtained through calculation. A grading index is established based on the position of the intensity discrimination value falling within the boundary interval, and grading values ​​are generated. Represents the load difference value. Represents the load reference value. Represents the length of the time window. Represents the length of the time base. This represents the number of load segments. Represents the number of segmentation references. Represents the intensity discrimination value;

[0083] The pre-defined construction risk assessment algorithm model is invoked, based on the load difference value. Quantify the current risk intensity. First, define the baseline parameters in the formula: set the load baseline value. The value is 10000N, determined based on the design bearing capacity of the uprights in the National Technical Specification for Safety of Construction Scaffolding with Couplers (JGJ130); the set time window length... The time base length is set to 10 seconds, which is the total duration captured in S211. A time interval of 60 seconds represents a standard construction operation cycle; the number of load segments is set. Five segments (corresponding to half the duration of the captured window); set the baseline number of segments. There are 10 segments. The above parameters and calculations... (Taking 5600N as an example) Substitute into the formula:

[0084] ;

[0085] The strength discrimination value was calculated. .

[0086] Subsequently, a matching judgment was made based on the risk level limit ranges shown in Table 1.

[0087] Table 1 Classification of Construction Intensity Risk Levels

[0088] ;

[0089] As shown in Table 1, the calculated result of 0.764 falls within... The risk level of this component is determined to be "3" (high risk) based on the given range. The risk level of each component is calculated... The values ​​are then used to find the corresponding grading values ​​in Table 1, generating a grading value sequence. Experimental data shows that introducing a time dimension ratio term ( After that, the algorithm improved the accuracy of short-term impact load identification compared to simply relying on load amplitude judgment, and effectively reduced the false alarm rate caused by instantaneous noise of the sensor.

[0090] S313: Based on the grade classification value, combined with the state change attribute and component identification information, establish a grade classification and state attribute association mapping for the component number, summarize the risk label entries corresponding to each component, and generate a component risk grade label set;

[0091] Using the component number as the index key, the risk classification value calculated in S312 (e.g., "3") is associated and bound with the trend type attribute generated in S213 (e.g., "Loading"). A tag object containing rich semantics is constructed, in the format "{ID: B-102, Risk: 3, Type: Loading}". All components are traversed, and the scattered tag objects are aggregated into a list. During this process, data cleaning is performed, removing routine records with a risk level of 1 (low risk), and retaining only items of concern with a risk level of 2 and above to reduce data redundancy in subsequent transmission and processing. The final generated list contains the identity ID, risk level, and stress state attribute of all components requiring attention, constituting a component risk level tag set.

[0092] Please see Figure 5 Step S4 is as follows:

[0093] S411: Based on the component number, status change node and level classification value in the component risk level label set, establish a mapping based on the index relationship between the component number and the deployment area, aggregate the corresponding status change node, level classification and area number content for each component number, and generate a structural component area indexing sequence.

[0094] Load the Building Information Modeling (BIM) database or site zoning plan data, and establish a spatial index relationship between component numbers and construction deployment areas (such as "Area A", "Area B", and "Area C"). Read the component number (such as "B-102") from the component risk level label set, and query its area through the spatial index, assuming that "B-102" is located in "Area C". At the same time, retrieve the structural state change node information generated in S113 and obtain the state change time point of the component (10.04s). Aggregate the information of the four dimensions of component number, state change node, level classification value (3), and area number (Area C) to assemble a four-tuple structure: Performing this spatial attribute enhancement operation on each entry in the tag set generates a sequence of structural component area indexes, thus expanding the spatial dimension of risk information from "point" to "surface".

[0095] S412: Based on the structural component area indexing sequence, perform area classification processing on the state change nodes and level classification content under all component numbers, perform grouping operation on the data frame according to the area number, establish the attribution index record between the component number and the corresponding attribute for each group of data, and generate the component tag index table corresponding to the area number.

[0096] Group the structural component area indexing sequence based on the area number field. Create multiple empty lists named after the area numbers (e.g., List_Area_A, List_Area_B, List_Area_C). Traverse the indexing sequence and populate List_Area_C with all four-tuple structures belonging to "Area C". Within each area group, create a secondary index, using the component number as the key, pointing to its corresponding state change node and level classification content. For example, the Area C group contains index records: This step transforms the originally discrete component risk data into structured data clustered by physical space, generating a component tag index table corresponding to the region number, thus providing a data foundation for subsequent zoning control.

[0097] S413: Based on the component tag index table corresponding to the region number, transmit the component number, status change node, level classification and region number quadruple structure content to the central control node, and simultaneously record and upload status flag bits and timestamp information to generate a list of structural component risk tags.

[0098] Establish a wireless communication link with the central control node (using MQTT or HTTP protocol). Serialize the component tag index table corresponding to the area number into a JSON format data packet. The data packet structure includes: Header (upload status flag set to "Pending", timestamp set to the current system time, such as 2025-05-20-14:30:00) and Body (containing a list of grouped data for each area). Execute the data transmission command to transmit the data packet to the central server. Upon successful transmission, the server returns an ACK signal, at which point the upload status flag in the local record is updated to "Uploaded", and the completion time is recorded. If transmission fails, a retransmission mechanism is triggered. This step ensures that the risk data generated by the field edge computing can be reliably synchronized to the cloud or central control room in real time, generating a list of structural component risk tags.

[0099] Please see Figure 6 The S5 steps are as follows:

[0100] S511: Obtain the list of risk markers for structural components, extract the risk level label, status change node and deployment area number corresponding to each component number, index the construction time sequence according to the current progress, filter the construction stage identifiers corresponding to the status change nodes, establish the correspondence between component number and construction time sequence stage, and generate a component construction stage mapping table.

[0101] The central control node receives and parses the list of risk markers for structural components. It reads the timestamps of state change nodes (e.g., 10.04s, corresponding to an actual construction time of 10:00:10). It loads the project construction schedule (GanttChart data), which records the time span of each construction stage, such as "Concrete Pouring Stage: 09:00-12:00" and "Curing Stage: 12:00-48:00". The node compares the state change node times with the schedule, determining that 10:00:10 falls within the "Concrete Pouring Stage". It then binds the stage ID (PhaseID) to the component number "B-102". This time-process mapping is performed on all components in the list, clarifying the specific construction process background for each risk event and generating a component construction stage mapping table.

[0102] S512: Based on the component construction stage mapping table, perform aggregation processing on the component risk labels of the same construction stage in the same deployment area, establish an aggregation mapping between the area number and the risk level set, nest the construction time series index to form a multi-dimensional index unit, and generate a set of regional construction risk label sequences.

[0103] Based on the component construction stage mapping table and the area grouping information in S412, a multidimensional data cube (DataCube) is constructed. An aggregation model is established with "Area Number" as the X-axis, "Construction Stage" as the Y-axis, and "Risk Level" as the Z-axis. All data is traversed to statistically analyze the risk label distribution within the same area and construction stage. For example, it is found that in "Area C," during the "Concrete Pouring Stage," 5 components are labeled with risk level 3, and 2 components are labeled with risk level 2. These statistical data are packaged into a collection object, in the format... This process aggregates individual point risks into regional area risks, generating a set of regional construction risk label sequences.

[0104] S513: For the regional construction risk label sequence set, perform joint statistical aggregation of the segment dimension and the time series dimension, and establish the correlation matrix structure between the construction time series and the risk intensity of regional components by combining the construction stage mapping parameters, and generate the civil construction safety risk assessment results.

[0105] Perform a final weighted scoring and matrix mapping on the regional construction risk label sequence set. Define weight coefficients for different risk levels: Level 2 with a weight of 0.5, Level 3 with a weight of 1.5, and Level 4 with a weight of 5.0. Calculate the cumulative risk index for each region at the current construction stage. .

[0106] This represents the cumulative risk index for each area during the current construction phase. This value is the final quantitative basis for assessing the overall safety status of the area (e.g., classified as safe, warning, or dangerous), and is derived by weighted summation of all sub-risks.

[0107] : Represents different risk level classification indices. Corresponds to the specific level classification values ​​generated in the preceding steps (in this embodiment, specifically referring to risk levels that require attention, such as Level 2, Level 3, and Level 4);

[0108] : Represents a specific area and a specific construction phase that is marked as the first The statistical value of the number of components at each risk level. For example, during the concrete pouring stage in Zone C, if the statistics show that there are 5 components marked as risk level 3, then the corresponding risk level is... ;

[0109] : Represents the first The preset weighting coefficients correspond to different risk levels. These coefficients are used to distinguish the degree of impact of different risk levels on the overall structural safety (in this example, the weights are set as follows: Level 2 has a weight of 0.5, Level 3 has a weight of 1.5, and Level 4 has a weight of 5.0).

[0110] : Represents the summation operator. It indicates that the weighted scores (i.e., the quantity multiplied by the weight) calculated for all different risk levels in the region are summed to obtain the total risk intensity.

[0111] Taking area C as an example, let's calculate using the aforementioned statistical data: .

[0112] Set security assessment thresholds: For safety, As a warning, It is dangerous.

[0113] Based on calculation result 8.5, Zone C is currently classified as "Warning". A correlation matrix is ​​established between the construction time series (each stage) and the zone number, with each cell filled with the corresponding risk index or status color (e.g., yellow represents a warning). This matrix visually displays the evolution of risk intensity in each zone over time. The final civil construction safety risk assessment results will be presented as a visual chart on the monitoring screen, guiding management personnel to focus on inspecting high-risk areas (such as Zone C). Experimental verification shows that this weighted matrix assessment method can identify the tendency for support frame instability caused by concentrated local loads in advance, effectively improving the proactive safety defense capabilities of the construction site.

[0114] A civil engineering construction safety risk assessment system includes:

[0115] The status node extraction module is used to execute S1: real-time monitoring of the support status of floor slabs, beams and columns, identifying the corresponding unsupported or supported state of the structure, filtering the time point when the structure first changes from an unsupported state to a supported state and recording the corresponding structure number, and generating structural status change node information.

[0116] The strength comparison establishment module is used to execute S2: based on the component number and time point calibrated in the structural state change node information, extract the load sensor record sequence of the component within each set time range before and after the state change, establish a parallel relationship group of load content before and after the state change, and generate a corresponding comparison group of strength changes.

[0117] The risk level matching module is used to execute S3: read the component number and the contents of the load before and after in the comparison group corresponding to the strength change, determine the level classification to which the difference between the load before and after is determined according to the boundary range of the structural component operation strength level classification in the construction risk preset standard, and construct the component risk level label set by combining the state change attributes and component identification information.

[0118] The tag list archiving module is used to execute S4: based on the component number, status change node, and level classification content built in the component risk level tag set, classify and mark the structural components according to the number index of the deployment area, and upload them to the central control node for tag archiving and identification record, generating a structural component risk tag list;

[0119] The assessment result collection module is used to execute S5: based on the risk labels of all reported structural components in the structural component risk label list, corresponding to the work progress stage in the current construction plan of the civil construction site, the risk labels of each area are collected, integrated and linked, and the civil construction safety risk assessment results are output.

[0120] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for assessing safety risks in civil engineering construction, characterized in that, Includes the following steps: S1: Real-time monitoring of the support status of floor slabs, beams and columns, identifying the corresponding unsupported or supported state of the structure, filtering the time point when the structure first changes from an unsupported state to a supported state and recording the corresponding structure number, and generating structural state change node information. S2: Based on the component number and time point calibrated in the structural state change node information, extract the load sensor recording sequence of the component within each set time range before and after the state change, establish a parallel relationship group of load content before and after the state change, and generate a comparison group corresponding to the intensity change. S3: Read the component number and the contents of the load before and after in the comparison group corresponding to the intensity change, and determine the level classification to which the difference between the load before and after belongs based on the boundary range of the structural component operation intensity level classification in the construction risk preset standard. Combine the state change attributes and component identification information to construct a component risk level label set. S4: Based on the component number, status change node, and level classification content constructed in the component risk level label set, classify and mark the components according to the number index of the deployment area of ​​the structural components, and upload them to the central control node for label archiving and identification recording to generate a list of structural component risk labels; S5: Based on the risk labels of all reported structural components in the risk label list of the structural components, and corresponding to the work progress stage in the current construction plan of the civil construction site, collect, integrate and associate the risk labels of each area, and output the civil construction safety risk assessment results. The structural state change node information includes component identification number, support state change time, and support state change attribute; the strength change corresponding comparison group includes changed component number, load change value range, and load change time period. The component risk level label set includes component level identification results and level classification results; The risk labeling list for structural components includes regional risk label numbers, label archiving information, and label index numbers; the civil construction safety risk assessment results include risk level distribution records, risk mapping results for work phases, and regional risk correlation maps. The specific steps for obtaining the component risk level label set are as follows: S311: Based on the comparison group corresponding to the intensity change, extract the component number and the sequence of before and after load records, calculate the difference between the before and after load values, and generate the load difference value. S312: Based on the load difference value, collect the work intensity level limit range in the construction risk preset standard, perform matching judgment on the difference range and the upper and lower limits of the range, calculate and obtain the intensity discrimination value, establish a level classification index based on the position of the intensity discrimination value falling into the limit range, and generate the level classification value. S313: Based on the grade classification value, combined with the state change attribute and component identification information, establish a grade classification and state attribute association mapping for the component number, summarize the risk label entries corresponding to each component, and generate a component risk grade label set; The formula for calculating the intensity discrimination value is: ； in, Represents the load difference value. Represents the load reference value. Represents the length of the time window. Represents the length of the time base. This represents the number of load segments. Represents the number of segmentation references. The representative intensity discrimination value.

2. The method for assessing safety risks in civil construction according to claim 1, characterized in that, The specific steps for obtaining the structural state change node information are as follows: S111: Real-time monitoring of the support status of floor slabs, beams and columns; extraction of the time-series support status label sequence corresponding to the number index of each structural component; binarization of the status labels; marking the support status as 1 and the non-support status as 0 according to the set status coding method; generating a binary sequence of support status. S112: Based on the binary sequence of the support state, perform frame-by-frame progressive comparison processing on the support state time series under all structure numbers, perform state transition identification operation, perform index positioning for the time position where the state changes from 0 to 1, extract the time point of the first state transition, and generate the first support state transition time point set. S113: Based on the set of initial support state transition time points, establish a one-to-one mapping relationship between the structure number and the initial state transition time point for each structure number, construct a key-value pair data structure between the structure number and the state change time, and generate structure state change node information.

3. The method for assessing safety risks in civil construction according to claim 1, characterized in that, The specific steps for obtaining the comparison group corresponding to the intensity change are as follows: S211: Obtain the component number and time point in the structural state change node information, extract the load sensor recording sequence of the corresponding component according to the number index, set the same length of time range before and after the marked time point, extract the load data of the component before and after the state change, and generate the load change extraction sequence. S212: Based on the load change truncation sequence, perform a unified dimension aggregation operation on the data frames of the interval before and after the state change, calculate the total load in each time period based on the stress value recorded by the load sensor, read the total load results of the two time periods before and after the change, establish a data parallel structure through the component number, and generate a load dual-interval comparison data group. S213: For the load dual-interval comparison data group, extract the front and back load value pairs corresponding to each component number, establish a comparison mapping between the component number and the front and back load values, construct a unified form of load change group information data frame, aggregate the corresponding results of all components, and generate a strength change corresponding comparison group.

4. The method for assessing safety risks in civil construction according to claim 1, characterized in that, The specific steps for obtaining the risk marker list of structural components are as follows: S411: Based on the component number, status change node and level classification value in the component risk level label set, establish a mapping based on the index relationship between the component number and the deployment area, aggregate the corresponding status change node, level classification and area number content for each component number, and generate a structural component area indexing sequence. S412: Based on the structural component area indexing sequence, perform area classification processing on the state change nodes and level classification content under all component numbers, perform grouping operation on the data frames according to the area number, establish the attribution index record between the component number and the corresponding attribute for each group of data, and generate the component tag index table corresponding to the area number. S413: Based on the component marking index table corresponding to the region number, transmit the component number, status change node, level classification and region number quadruple structure content to the central control node, and simultaneously record and upload status flag bits and timestamp information to generate a structural component risk marking list.

5. The method for assessing safety risks in civil construction according to claim 1, characterized in that, The specific steps for obtaining the civil construction safety risk assessment results are as follows: S511: Obtain the risk label list of the structural components, extract the risk level label, status change node and deployment area number corresponding to each component number, index the construction time sequence according to the current progress, filter the construction stage identifiers corresponding to the status change nodes, establish the correspondence between component number and construction time sequence stage, and generate a component construction stage mapping table. S512: Based on the component construction stage mapping table, perform aggregation processing on the component risk labels of components in the same construction stage within the same deployment area, establish an aggregation mapping between the area number and the risk level set, nest the construction time series index to form a multi-dimensional index unit, and generate a set of regional construction risk label sequences. S513: For the construction risk label sequence set of the region, perform joint statistical aggregation of the segment dimension and the time series dimension, and establish a correlation matrix structure between the construction time series and the risk intensity of regional components by combining the construction stage mapping parameters, and generate the civil construction safety risk assessment results.

6. A civil engineering construction safety risk assessment system, characterized in that, The system is used to implement the civil construction safety risk assessment method according to any one of claims 1-5, including: The status node extraction module is used to execute S1: real-time monitoring of the support status of floor slabs, beams and columns, identifying the corresponding unsupported or supported state of the structure, filtering the time point when the structure first changes from an unsupported state to a supported state and recording the corresponding structure number, and generating structural status change node information. The strength comparison establishment module is used to execute S2: based on the component number and time point calibrated in the structural state change node information, extract the load sensor record sequence of the component within each set time range before and after the state change, establish a parallel relationship group of load content before and after the state change, and generate a comparison group corresponding to the strength change. The risk level matching module is used to perform S3: read the component number and the contents of the load before and after in the comparison group corresponding to the strength change, determine the level classification to which the difference between the load before and after belongs based on the boundary range of the structural component operation strength level classification in the construction risk preset standard, and construct the component risk level label set by combining the state change attributes and component identification information. The tag list archiving module is used to perform S4: based on the component number, status change node, and level classification content constructed in the component risk level tag set, classify and mark the components according to the number index of the deployment area of ​​the structural components, and upload them to the central control node for tag archiving and identification recording, generating a structural component risk tag list; The assessment result collection module is used to execute S5: based on the risk labels of all reported structural components in the risk label list of the structural components, and corresponding to the work progress stage in the current construction plan of the civil construction site, the risk labels of each area are collected, integrated and associated with information, and the civil construction safety risk assessment results are output.

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