Construction safety management and control system and method based on Internet of Things

Abstract

The invention discloses a construction safety management and control system and method based on the Internet of Things, and belongs to the technical field of engineering safety, and the method comprises the steps: collecting construction site personnel positioning, equipment state and project plan data, and binding a working region of a corresponding process and a handover triggering condition for a worker in combination with a project progress arrangement; based on historical normal operation data, a personal working mode base line including a moving path, a key staying point, staying duration and handover information is established; collecting a personnel movement track in real time, comparing the personnel movement track with a personal working mode baseline, and identifying and marking a working offset event; integrating cooperation process pairs and expected handover time in the project plan, analyzing personnel trajectory intersection, and calculating response delay time of process docking; and finally, the influenced equipment operation and process handover states are associated, safety violation behaviors are identified and early warning is carried out, and a comprehensive regulation and control scheme including personnel scheduling suggestions and process handover optimization is generated.

Description

Technical Field

[0001] This invention relates to the field of engineering safety technology, specifically to a construction safety management and control system and method based on the Internet of Things. Background Technology

[0002] During construction project execution, scheduling and control typically rely on pre-set project plans and core production systems. Key resources such as personnel, equipment, and materials are allocated to various work areas and processes according to the plan to ensure the orderly progress of core construction stages such as beam fabrication and erection. Under the existing management model, resource scheduling is mostly completed through written approvals or system entry, while the planning, scheduling, and resource status updates of the core production system depend on manual operation, thereby achieving the connection between various processes and the overall project schedule target.

[0003] However, the construction site environment is complex and ever-changing, frequently encountering unforeseen circumstances such as personnel cross-team support, temporary equipment relocation, and emergency material allocation. These ad-hoc scheduling decisions are often communicated verbally and are not recorded or updated in the core production system, leading to a disconnect between plans and actual execution. This not only results in opaque resource status and difficulty in predicting schedule deviations, but also forces subsequent scheduling plans to rely on manual experience, easily leading to resource waste or secondary conflicts. Furthermore, the lack of effective traceability after unexpected situations cause problems may ultimately result in project delays and failure to complete on time. Summary of the Invention

[0004] The purpose of this invention is to provide an Internet of Things-based construction safety management and control system and method to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a construction safety management and control method based on the Internet of Things, comprising: Step S100: Collect personnel location data, equipment status data, and project plan data at the construction site; in conjunction with the project schedule, bind the work area of ​​each worker to the corresponding process and the conditions for process handover; based on historical normal operation data, establish a personal work mode baseline for each worker, including the movement path, key stops, duration of stops, and time and location of process handover within the work area. Step S200: Collect the movement trajectory data of the staff in real time, compare it with the baseline of the individual work mode corresponding to the staff, dynamically identify behavioral events outside the work plan, and mark them as work deviation events; Step S300: Based on the project plan, integrate the work processes that have a cooperative relationship and their expected handover time; at the same time, analyze the trajectory intersection sequence of relevant personnel and calculate the personnel response delay time at the work process docking point; Step S400: Based on the work offset event and personnel response delay time, associate the operating status of the affected equipment with the handover status of the process, identify the safety violations and issue risk warnings, and generate a comprehensive control plan that includes personnel scheduling suggestions and process handover optimization.

[0006] Step S100 includes: Step S101: Collect personnel positioning data, equipment status data, and project plan data through an IoT sensor network deployed at the construction site, including GPS / UWB fusion positioning terminals, equipment status monitoring sensors, and a BIM management system interface. The personnel positioning data obtains the real-time 3D coordinates, timestamps, and personnel identifiers of the workers through the positioning terminals. The equipment status data includes operating status flags, real-time load rate, cumulative operating time, and fault type. The project plan data is synchronized from the BIM management system interface and includes process number, planned start time, planned end time, and a list of responsible personnel. After preprocessing the collected raw data, it is stored in the database to form a structured dataset. Step S102: Based on the process structure in the project schedule, determine the required work space range for each process. Combined with the safety isolation distance marked in the construction drawings, the operating radius of relevant mechanical equipment, and the protection range of hazardous sources, generate a polygonal work area in the digital map using a geofencing engine. At the same time, associate this work area with the worker identification of the process to form a mapping relationship between personnel, process, and work area. Based on the generated work area, construct a safety monitoring area. Extend the work area boundary outward by a preset safety buffer distance to form a safe work area Asafe. Two types of handover triggering conditions are defined: time-triggered conditions and event-triggered conditions. Time-triggered conditions are based on the planned time window [T_plan-Δt, T_plan+Δt], where T_plan is the planned handover time of the process and Δt is a preset time tolerance parameter. Event-triggered conditions are set so that the person in charge of the current process submits a completion confirmation instruction in the BIM management system, which automatically triggers the handover preparation of subsequent processes. Both types of triggering conditions are bound to specific process numbers, forming a complete process handover triggering rule library, and are associated with the corresponding staff.

[0007] Step S103: Based on historical normal operation data, select data samples with no accident records in the most recent N working days to establish a multi-dimensional behavior baseline for each employee: First, construct the movement path baseline, organize the historical trajectory points in chronological order, and uniformly resample the trajectory data according to the real-time collection frequency to form a representative path sequence L_base={p1,p2,...,pm}, where Pm represents the m-th trajectory point in the path sequence, and each trajectory point contains coordinate information and a timestamp; Next, key stay points are extracted. By setting a spatial range threshold Rs, a time duration threshold Td, and a minimum occurrence threshold Nmin, when a person stays in a certain spatial range for a continuous time of Td and the spatial range appears at least Nmin in historical data, it is determined to be a key stay point, forming a stay point set S_stay={s1,s2,...,sk}, where sk represents the k-th key stay point; Then, the duration of stay was analyzed, historical stay time data for each key stop was collected, the average stay duration Tavg(s) and standard deviation σ(s) were calculated, and [Tavg-2σ, Tavg+2σ] was defined as the normal stay duration fluctuation range; Finally, a process handover baseline is constructed, historical compliant handover records are compiled, and the difference between each handover time and the planned handover time is calculated to form the handover time deviation ΔTh. The central distribution center Mh of all handover points is calculated to represent the main location of process handover activities. The specific process is as follows: collect the geographical coordinates of all historical compliant handover events, and calculate the arithmetic mean of these coordinate points as the distribution center: Mh=(1 / n)×(∑(xi),∑(yi)); where n is the total number of historical handover events, and (xi,yi) are the geographical coordinates of the i-th handover event. The path sequence, set of stops, duration of stay parameters, safe working area, handover time deviation, and center of handover point are integrated into a personal work mode baseline, and an index association is established with personnel identification ID.

[0008] Step S200 includes: Step S201: Continuously acquire the real-time movement trajectory sequence L_real={q1,q2,...,qn} of the staff, where qn represents the nth real-time trajectory point in the trajectory sequence, and each trajectory point contains planar coordinates and acquisition timestamp; firstly, align the real-time trajectory with the baseline path sequence L_base={p1,p2,...,pm} in time and space, and establish the correspondence between trajectory points through time series interpolation method; In the path trajectory dimension, the trajectory deviation D_path is calculated by calculating the mean minimum distance between real-time trajectory points and baseline path points: D_path = (1 / n) × ∑ i min j ||qi-pj||, where qi represents the i-th real-time trajectory point and pj represents the j-th baseline path point; At the work area level, based on the boundary of the baseline work area Asafe, the area violation index D_area = (1 / n) × ∑ i I(qi∉Asafe) indicates that the function I takes a value of 1 when the location point is outside the region, which is identified as abnormal out-of-bounds behavior. In the dimension of dwell behavior, for a dwell point s_real detected in real time, calculate the minimum spatial distance M_stay = min between it and the center coordinates of each dwell point in the baseline key dwell point set S_stay. j The distance is calculated as ||s_real-μ_coord(sj)||. This distance is then compared with a preset spatial tolerance threshold Rs: when M_stay≤Rs, the stay position is deemed compliant; when M_stay>Rs, it is deemed a position deviation event. Simultaneously, the deviation of the actual stay duration T_real from the baseline normal fluctuation range [T_avg-2σ,T_avg+2σ] is compared. When T_real falls within this range, the stay duration is deemed normal; when T_real exceeds the upper limit of the range, it is deemed a duration exceeding the limit event. In the process handover dimension, based on the baseline handover parameters, the time deviation Δt=|t_a-t_e| and the spatial deviation Δs=||l_a-Mh|| of the actual handover event are calculated respectively, where t_a and l_a are the actual handover time and location, t_e is the planned handover time, and Mh is the baseline handover center; Finally, a weighted fusion evaluation is performed on the abnormal features of each dimension. The overall behavioral deviation index P is calculated using the deviation degree: P = w1 × D_path + w2 × D_area + w3 × M_stay + w4 × Δt + w5 × Δs. Here, w1, w2, w3, w4, and w5 are the weight coefficients of the five dimensions corresponding to the path trajectory, work area, dwell behavior, process handover time, and process handover space, respectively, and w1 + w2 + w3 + w4 + w5 = 1. When the deviation index P exceeds the preset threshold P_th, the behavioral sequence is determined to be a work deviation event.

[0009] Step S300 includes: Step S301: Analyze the process docking relationship in the project plan, identify the set of process pairs C={(Pa,Pb)} with direct collaboration relationship, where Pa is the preceding process and Pb is the following process; for each process pair, extract its planned handover time window [Te-δ,Te+δ], where Te is the planned handover time point and δ is the time sequence tolerance parameter; Step S302: Based on the real-time trajectory data established in step S200, monitor the movement trajectory of the staff involved in each process. Call the center point Mh of the handover point in the individual work mode baseline, and pre-determine a circular expected handover area with a radius Rh centered on this point. When it is detected that staff from preceding and following processes simultaneously enter this expected handover area within the planned handover time window, and their dwell time within the area overlaps, with the overlap exceeding the minimum effective handover time threshold Tmin, it is determined as a valid trajectory intersection event. Simultaneously, record the actual timestamp Ta and spatial coordinates La of this intersection event, label it as a trajectory intersection event, and calculate the actual response delay time ΔTd = Ta - Te. When multiple intersection events are detected, calculate the average delay time ΔTd_avg and the maximum delay time ΔTd_max as evaluation indicators. Step S303: Count the number of events Ndelay in each process pair (Pa, Pb) that are delayed (ΔTd>0) within the statistical period; when Ndelay exceeds the set frequency threshold Fth, identify the process pair as a high-frequency delayed process pair; group all delayed events according to their time periods, calculate the number of delayed events Nt in each time period, and identify the high-incidence time periods of delayed events by comparing the statistics of each time period; combine the delay time statistics of each process pair, the list of high-frequency delayed process pairs, and the high-incidence time periods of delay to form a process docking efficiency evaluation report.

[0010] Step S400 includes: Step S401: Correlate various abnormal indicators in the work offset event with the equipment operating status. When the area violation index D_area is greater than zero and the associated equipment is in operation, it is determined to be a violation of entering a dangerous area; when the personnel response delay time ΔTd exceeds the maximum allowable delay threshold Δt_max, it is determined to be a violation of the process docking timeout; when the dwell behavior simultaneously involves positional offset and duration exceeding the limit and the associated equipment is in an abnormal state, it is determined to be a risk of violation operation; when the number of delay events Ndelay of high-frequency delayed process pairs exceeds the emergency threshold, it is determined to be a risk of systemic collaboration failure. Step S402: Based on the above violation determination results, establish a three-level risk classification system: classify a single work deviation event that does not cause docking delay as a Level 1 risk; classify multiple work deviation events occurring concurrently or a single process docking delay as a Level 2 risk; classify systemic collaboration failure or multiple key processes experiencing severe delays simultaneously as a Level 3 risk; generate corresponding graded early warnings according to the risk level; Step S403: When the work offset index P continuously exceeds the set threshold, personnel scheduling and control are triggered, generating a suggested plan that includes job adjustments and personnel regrouping; when the personnel response delay time ΔTd exceeds the allowable threshold, process handover optimization and control are triggered, generating an improved plan that includes handover time window adjustments and handover area optimization; based on the analysis of work offset time and personnel response delay time, a comprehensive control plan that includes personnel scheduling suggestions and process handover optimization is generated; finally, the generated risk warning information and comprehensive control plan are pushed to the relevant management personnel to complete the closed-loop management of safety risk control.

[0011] The IoT-based construction safety management and control system includes a baseline construction module, a behavior deviation identification module, a collaborative efficiency analysis module, and a comprehensive control module. The baseline construction module collects personnel location data, equipment status data, and project plan data at the construction site; combined with the project schedule, it binds the work area of ​​each worker's corresponding work process and the conditions for work process handover; based on historical normal operation data, it establishes a personal work mode baseline for each worker, including the worker's movement path, key stop points, stop duration, and the time and place of work process handover within the work area; The behavior deviation recognition module collects the movement trajectory data of the staff in real time, compares it with the baseline of the individual's work mode, and dynamically identifies behavioral events outside the work plan, which are marked as work deviation events. The collaborative efficiency analysis module is based on the project plan, integrates the collaborative work processes and their expected handover times; at the same time, it analyzes the trajectory convergence sequence of relevant personnel and calculates the personnel response delay time at the work process docking point; The integrated control module correlates the operating status of affected equipment with the handover status of processes based on work deviation events and personnel response delays, identifies safety violations and issues risk warnings, and generates an integrated control plan that includes personnel scheduling suggestions and process handover optimization.

[0012] Compared with the prior art, the beneficial effects achieved by the present invention are: This invention addresses the mismatch between traditional safety standards and individual operational differences by establishing personalized behavioral baselines for each worker, including movement paths, key stops, dwell time, and process handover characteristics. This makes safety monitoring more targeted and accurate. Secondly, through a multi-dimensional real-time comparison mechanism, it not only monitors traditional safety incidents such as location violations but also accurately identifies potential risks such as path deviations, abnormal work rhythms, and disordered handover sequences. This enables safety assessment based on behavioral patterns rather than just location points, greatly improving the early detection and comprehensiveness of risk identification. This invention overcomes the limitations of traditional methods that separate safety and efficiency management by correlating individual behavioral deviations with process collaboration delays. It can systematically diagnose process bottlenecks and collaboration failures caused by abnormal individual behavior. Based on the above analysis, it generates a comprehensive control scheme that integrates personnel scheduling and process optimization, realizing closed-loop management from risk warning to proactive control, effectively improving the overall safety level and collaborative operation efficiency of the construction site. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a construction safety management method based on the Internet of Things. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 This invention provides a technical solution: a construction safety management and control method based on the Internet of Things, comprising: Step S100: Collect personnel location data, equipment status data, and project plan data at the construction site; in conjunction with the project schedule, bind the work area of ​​each worker to the corresponding process and the conditions for process handover; based on historical normal operation data, establish a personal work mode baseline for each worker, including the movement path, key stops, duration of stops, and time and location of process handover within the work area. Step S200: Collect the movement trajectory data of the staff in real time, compare it with the baseline of the individual work mode corresponding to the staff, dynamically identify behavioral events outside the work plan, and mark them as work deviation events; Step S300: Based on the project plan, integrate the work processes that have a cooperative relationship and their expected handover time; at the same time, analyze the trajectory intersection sequence of relevant personnel and calculate the personnel response delay time at the work process docking point; Step S400: Based on the work offset event and personnel response delay time, associate the operating status of the affected equipment with the handover status of the process, identify the safety violations and issue risk warnings, and generate a comprehensive control plan that includes personnel scheduling suggestions and process handover optimization.

[0016] Step S100 includes the following steps: Step S101: Collect personnel positioning data, equipment status data, and project plan data through an IoT sensor network deployed at the construction site, including GPS / UWB fusion positioning terminals, equipment status monitoring sensors, and a BIM management system interface. The personnel positioning data obtains real-time 3D coordinates, timestamps, and personnel identifiers of workers through the positioning terminals. The equipment status data includes operating status indicators, real-time load rate, cumulative operating time, and fault type. The project plan data is synchronized from the BIM management system interface and includes process number, planned start time, planned end time, and a list of responsible personnel. The collected raw data is preprocessed and stored in a database to form a structured dataset. The personnel positioning data uses a Kalman filter algorithm to eliminate measurement noise and remove coordinate abrupt changes. The equipment status data is marked with abnormal operating conditions using physical rationality verification rules (e.g., load rate L_equip∈[0,1.2]). All valid data is stored in the database after time alignment. Step S102: Based on the process structure in the project schedule, determine the required work space range for each process. Combined with the safety isolation distance marked in the construction drawings, the operating radius of relevant mechanical equipment, and the protection range of hazardous sources, generate a polygonal work area in the digital map using a geofencing engine. At the same time, associate this work area with the worker identification of the process to form a mapping relationship between personnel, process, and work area. Based on the generated work area, construct a safety monitoring area. Extend the work area boundary outward by a preset safety buffer distance to form a safe work area Asafe. Two types of handover triggering conditions are defined: time-triggered conditions and event-triggered conditions. Time-triggered conditions are based on the planned time window [T_plan-Δt, T_plan+Δt], where T_plan is the planned handover time of the process and Δt is a preset time tolerance parameter. Event-triggered conditions are set so that the person in charge of the current process submits a completion confirmation instruction in the BIM management system, which automatically triggers the handover preparation of subsequent processes. Both types of triggering conditions are bound to specific process numbers, forming a complete process handover triggering rule library, and are associated with the corresponding staff.

[0017] Step S103: Based on historical normal operation data, select data samples with no accident records in the most recent N working days to establish a multi-dimensional behavior baseline for each employee: First, construct the movement path baseline, organize the historical trajectory points in chronological order, and uniformly resample the trajectory data according to the real-time collection frequency to form a representative path sequence L_base={p1,p2,...,pm}, where Pm represents the m-th trajectory point in the path sequence, and each trajectory point contains coordinate information and a timestamp; Next, key stay points are extracted. By setting a spatial range threshold Rs, a time duration threshold Td, and a minimum occurrence frequency threshold Nmin, a key stay point is determined when a person stays in a certain spatial range for a continuous time of Td and the spatial range appears at least Nmin in historical data. This forms a set of stay points S_stay={s1,s2,...,sk}, where sk represents the k-th key stay point. Then, the stay duration is analyzed. Historical stay time data for each key stay point is collected, and the average stay duration Tavg(s) and standard deviation σ(s) are calculated. The range [Tavg-2σ,Tavg+2σ] is defined as the normal stay duration fluctuation range. Finally, a process handover baseline is constructed, historical compliant handover records are compiled, and the difference between each handover time and the planned handover time is calculated to form the handover time deviation ΔTh. The central distribution center Mh of all handover points is calculated to represent the main location of process handover activities. The specific process is as follows: collect the geographical coordinates of all historical compliant handover events, and calculate the arithmetic mean of these coordinate points as the distribution center: Mh=(1 / n)×(∑(xi),∑(yi)); where n is the total number of historical handover events, and (xi,yi) are the geographical coordinates of the i-th handover event. The path sequence, set of stops, duration of stay parameters, safe working area, handover time deviation, and center of handover point are integrated into a personal work mode baseline, and an index association is established with personnel identification ID.

[0018] Step S200 includes: Step S201: Continuously acquire the real-time movement trajectory sequence L_real={q1,q2,...,qn} of the staff, where qn represents the nth real-time trajectory point in the trajectory sequence, and each trajectory point contains planar coordinates and acquisition timestamp; firstly, align the real-time trajectory with the baseline path sequence L_base={p1,p2,...,pm} in time and space, and establish the correspondence between trajectory points through time series interpolation method; In the path trajectory dimension, the trajectory deviation D_path is calculated by calculating the mean minimum distance between real-time trajectory points and baseline path points: D_path = (1 / n) × ∑ i min j ||qi-pj||, where qi represents the i-th real-time trajectory point and pj represents the j-th baseline path point; At the work area level, based on the boundary of the baseline work area Asafe, the area violation index D_area = (1 / n) × ∑ i I(qi∉Asafe) indicates that the function I takes a value of 1 when the location point is outside the region, which is identified as abnormal out-of-bounds behavior. In the dimension of dwell behavior, for a dwell point s_real detected in real time, calculate the minimum spatial distance M_stay = min between it and the center coordinates of each dwell point in the baseline key dwell point set S_stay. j The distance is calculated as ||s_real-μ_coord(sj)||. This distance is then compared with a preset spatial tolerance threshold Rs: when M_stay≤Rs, the stay position is deemed compliant; when M_stay>Rs, it is deemed a position deviation event. Simultaneously, the deviation of the actual stay duration T_real from the baseline normal fluctuation range [T_avg-2σ,T_avg+2σ] is compared. When T_real falls within this range, the stay duration is deemed normal; when T_real exceeds the upper limit of the range, it is deemed a duration exceeding the limit event. In the process handover dimension, based on the baseline handover parameters, the time deviation Δt=|t_a-t_e| and the spatial deviation Δs=||l_a-Mh|| of the actual handover event are calculated respectively, where t_a and l_a are the actual handover time and location, t_e is the planned handover time, and Mh is the baseline handover center; Finally, a weighted fusion evaluation is performed on the abnormal features of each dimension. The overall behavioral deviation index P is calculated using the deviation degree: P = w1 × D_path + w2 × D_area + w3 × M_stay + w4 × Δt + w5 × Δs. Here, w1, w2, w3, w4, and w5 are the weight coefficients of the five dimensions corresponding to the path trajectory, work area, dwell behavior, process handover time, and process handover space, respectively, and w1 + w2 + w3 + w4 + w5 = 1. When the deviation index P exceeds the preset threshold P_th, the behavioral sequence is determined to be a work deviation event.

[0019] The initial weighting coefficients w1, w2, w3, w4, and w5 are based on the degree of impact of abnormal characteristics in each dimension on construction safety: Violations in the work area are directly related to the exposure of hazardous sources and the safety of equipment operation, and are the most direct factor triggering safety accidents; therefore, its initial weight w2 is set to the highest proportion (e.g., 0.35). Time deviation (Δt) and spatial deviation (Δs) of process handover directly affect the efficiency of process docking, easily leading to conflicts in cross-operations or process breaks, significantly impacting construction rhythm and safety coordination; therefore, the initial weights w4 and w5 are set to medium proportions (e.g., 0.2 and 0.15), respectively. Slightly higher than w5, because handover timeouts are more likely to cause delays in subsequent processes and safety hazards than spatial deviations; path deviation (D_path) reflects the compliance of the overall work behavior of personnel and is a quantitative indicator of basic behavioral abnormalities. Although it does not directly trigger high risks, it can predict behavioral trends. The initial weight w1 is set to a medium to low proportion (e.g., 0.2); abnormal stay behavior (M_stay) is mostly manifested as deviations in local work links, and its impact on overall safety is relatively limited. The initial weight w3 is set to the lowest proportion (e.g., 0.1); the initial weight values ​​of each dimension satisfy w1+w2+w3+w4+w5=1.

[0020] Step S300 includes: Step S301: Analyze the process docking relationship in the project plan, identify the set of process pairs C={(Pa,Pb)} with direct collaboration relationship, where Pa is the preceding process and Pb is the following process; for each process pair, extract its planned handover time window [Te-δ,Te+δ], where Te is the planned handover time point and δ is the time sequence tolerance parameter; Step S302: Based on the real-time trajectory data established in step S200, monitor the movement trajectory of the staff involved in each process. Call the center point Mh of the handover point in the individual work mode baseline, and pre-determine a circular expected handover area with a radius Rh centered on this point. When it is detected that staff from preceding and following processes simultaneously enter this expected handover area within the planned handover time window, and their dwell time within the area overlaps, with the overlap exceeding the minimum effective handover time threshold Tmin, it is determined as a valid trajectory intersection event. Simultaneously, record the actual timestamp Ta and spatial coordinates La of this intersection event, label it as a trajectory intersection event, and calculate the actual response delay time ΔTd = Ta - Te. When multiple intersection events are detected, calculate the average delay time ΔTd_avg and the maximum delay time ΔTd_max as evaluation indicators. Step S303: Count the number of events Ndelay in each process pair (Pa, Pb) that are delayed (ΔTd>0) within the statistical period; when Ndelay exceeds the set frequency threshold Fth, identify the process pair as a high-frequency delayed process pair; group all delayed events according to their time periods, calculate the number of delayed events Nt in each time period, and identify the high-incidence time periods of delayed events by comparing the statistics of each time period; combine the delay time statistics of each process pair, the list of high-frequency delayed process pairs, and the high-incidence time periods of delay to form a process docking efficiency evaluation report.

[0021] Step S400 includes: Step S401: Correlate various abnormal indicators in the work offset event with the equipment operating status. When the area violation index D_area is greater than zero and the associated equipment is in operation, it is determined to be a violation of entering a dangerous area; when the personnel response delay time ΔTd exceeds the maximum allowable delay threshold Δt_max, it is determined to be a violation of the process docking timeout; when the dwell behavior simultaneously involves positional offset and duration exceeding the limit and the associated equipment is in an abnormal state, it is determined to be a risk of violation operation; when the number of delay events Ndelay of high-frequency delayed process pairs exceeds the emergency threshold, it is determined to be a risk of systemic collaboration failure. Step S402: Based on the above violation determination results, establish a three-level risk classification system: classify a single work deviation event that does not cause docking delay as a Level 1 risk; classify multiple work deviation events occurring concurrently or a single process docking delay as a Level 2 risk; classify systemic collaboration failure or multiple key processes experiencing severe delays simultaneously as a Level 3 risk; generate corresponding graded early warnings according to the risk level; Step S403: When the work offset index P continuously exceeds the set threshold, personnel scheduling and control are triggered, generating a suggested plan that includes job adjustments and personnel regrouping; when the personnel response delay time ΔTd exceeds the allowable threshold, process handover optimization and control are triggered, generating an improved plan that includes handover time window adjustments and handover area optimization; based on the analysis of work offset time and personnel response delay time, a comprehensive control plan that includes personnel scheduling suggestions and process handover optimization is generated; finally, the generated risk warning information and comprehensive control plan are pushed to the relevant management personnel to complete the closed-loop management of safety risk control.

[0022] This invention uses a high-rise building main structure construction project as an application scenario. The project is in the stage of overlapping operations of rebar binding and formwork installation on the 12th floor, involving two core process groups: rebar binding group and formwork installation group, with a total of 12 workers, 2 tower cranes and 3 rebar cutting machines. It is necessary to control personnel behavior and process coordination to avoid safety risks of overlapping operations; an Internet of Things system is deployed on the construction site: GPS / UWB fusion positioning terminal, equipment status sensors, and data interface is connected with the BIM management system; The deployed GPS / UWB terminals collect the 3D coordinates of the workers every 5 seconds (the coordinates of the steel reinforcement team leader Zhang San are (32.5, 18.3, 45.2)), timestamps, and personnel IDs; the equipment sensors upload the tower crane's operating status (running / standby) and load rate (tower crane 1 load rate 0.8) in real time; the project plan is synchronized from the BIM system: the steel reinforcement binding process (No. G12-01) is scheduled from 8:00 to 11:30, and the person in charge is Zhang San; the formwork installation process (No. M12-01) is scheduled from 11:00 to 15:00, and the person in charge is Li Si. The two processes are a collaborative process pair; Kalman filtering is used to eliminate coordinate abrupt changes caused by occlusion in Zhang San's trajectory; the steel reinforcement cutter 2 (load rate 1.3) is marked as an abnormal working condition according to the load rate ∈ [0, 1.2] rule; all data are stored in the database after being aligned by timestamps. Based on the construction drawings, a polygonal work area is defined for process G12-01 in the BIM digital map. Centered on the east work surface of the 12th floor, with a radius of 15 meters, a safe work area Asafe is formed by extending outwards by a 2-meter safety buffer distance. This area is associated with the IDs of 6 steelworkers, including Zhang San. The handover triggering rules are as follows: the planned handover time between G12-01 and M12-01 is Te=11:00, and the time triggering window is [10:55, 11:05] (δ=5 minutes). The event triggering condition is that after Zhang San submits "Confirmation of Completion of Steel Binding" in the BIM system, a handover reminder is automatically pushed to Li Si. A baseline was established by selecting data from Zhang San's last 7 accident-free workdays: The mobile path baseline L_base is formed by resampling at a frequency of 5 seconds, resulting in a fixed path sequence of "rebar stacking area → working face → tool storage point", with a total of 1200 trajectory points; Critical stay point S_stay: With Rs=2m, Td=3 minutes, and Nmin=5 times set, two critical stay points are identified: the rebar stacking area (s1) and the center of the working face (s2). Duration of stay: s1 has a Tavg of 5 minutes and σ of 1 minute, which is within the normal range of [3,7] minutes; s2 has a Tavg of 45 minutes and σ of 5 minutes, which is within the normal range of [35,55] minutes. Handover baseline: ΔTh = 2 minutes for 10 historical compliant handovers, handover center Mh = (35.1, 20.2, 45.0) (handover area next to the work site). From 10:30 to 10:35, Zhang San's trajectory sequence L_real (6 trajectory points in total) was collected in real time. After spatiotemporal alignment with L_base, the following metrics were calculated: Path deviation D_path: The average distance from each real-time point to the nearest point in L_base is 0.8 meters; Area violation index D_area: All 6 trajectory points are within Asafe, D_area=0; Dwelling behavior: At 10:30, a dwelling point s_real (coordinates 33.0, 19.0, 45.0) was triggered, M_stay=1.2m≤Rs=2m (location compliant), and the dwelling duration T_real=4 minutes (within the normal range of s2). (Duration compliant); handover dimension: handover not yet triggered, Δt and Δs are counted as 0; weights are set w1=0.2, w2=0.4, w3=0.2, w4=0.1, w5=0.1, offset index P=0.16 is calculated, which is lower than the threshold P_th=0.5, so no offset is determined; from 10:40 to 10:45, Zhang San's trajectory point was detected to exceed Asafe (coordinates 38.0, 22.0, 45.0, entering tower crane 1 working area) 3 times in a row. D_area=0.5, D_path=3.2 meters, P=0.84>0.5, which is marked as a "area boundary crossing + path deviation" type work offset event; Analyze the project plan and identify the collaborative process pair C={(G12-01,M12-01)}, with a planned handover window of [10:55,11:05]. Zhang San submits completion confirmation at 10:58 and enters the expected handover area centered on Mh with Rh=3 meters at 10:59. Li Si enters the area at 11:03. The two overlap and stay for 4 minutes ≥ Tmin=2 minutes, which is determined to be a valid handover event. Ta=11:03, La=(35.0,20.3,45.0), ΔTd=11:03-11:00=3 minutes. Delay statistics and evaluation report: On the same day, the delay event Ndelay=1 for this process pair (not exceeding Fth=3). The delay period is concentrated between 11:00-11:10. An evaluation report is generated: The process pair (G12-01,M12-01) has a single delay of 3 minutes. The delay is related to Zhang San's boundary deviation event at 10:40. After correlating the data, the following judgments were made: Zhang San's area boundary deviation (D_area=0.5) overlapped with the operating status of Tower Crane 1 (operating at 10:40), and was judged as "violation of entering a dangerous area"; ΔTd=3 minutes>Δt_max=2 minutes, and was judged as "violation of exceeding the time limit for work process handover"; the two types of violations occurred concurrently and were classified as Level 2 risks, and a warning was pushed to the safety officer: "Steelworker Zhang San entered the Tower Crane 1 work area at 10:40 (the tower crane is operating), the handover of G12-01 and M12-01 work processes is delayed by 3 minutes, and immediate action is required"; Comprehensive control plan: Personnel scheduling: Adjust Zhang San's Asafe boundary and set the Tower Crane 1 work area as a "no entry" geofence; Work process optimization: Adjust the handover time window of this work process to [11:05, 11:15] (avoiding Zhang San's material transfer peak), update Mh to the midpoint of the two work process work areas, and simplify the handover process to "BIM system electronic signature + on-site verbal briefing".

[0023] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A construction safety management and control method based on the Internet of Things, characterized in that: The method includes: Step S100: Collect personnel location data, equipment status data, and project plan data at the construction site; in conjunction with the project schedule, bind the work area of ​​each worker to the corresponding process and the conditions for process handover; based on historical normal operation data, establish a personal work mode baseline for each worker, including the movement path, key stops, duration of stops, and time and location of process handover within the work area. Step S200: Collect the movement trajectory data of the staff in real time, compare it with the baseline of the individual work mode corresponding to the staff, dynamically identify behavioral events outside the work plan, and mark them as work deviation events; Step S300: Based on the project plan, integrate the work processes that have a cooperative relationship and their expected handover time; at the same time, analyze the trajectory intersection sequence of relevant personnel and calculate the personnel response delay time at the work process docking point; Step S400: Based on the work offset event and personnel response delay time, associate the operating status of the affected equipment with the handover status of the process, identify the safety violations and issue risk warnings, and generate a comprehensive control plan that includes personnel scheduling suggestions and process handover optimization.

2. The construction safety management and control method based on the Internet of Things according to claim 1, characterized in that: Step S100 includes: Step S101: Collect personnel positioning data, equipment status data, and project plan data through an IoT sensor network deployed at the construction site, including GPS / UWB fusion positioning terminals, equipment status monitoring sensors, and a BIM management system interface. The personnel positioning data is obtained through the positioning terminal, which acquires the real-time 3D coordinates, timestamps, and personnel identifiers of the workers. The equipment status data includes operating status flags, real-time load rate, cumulative operating time, and fault type. The project plan data is synchronized from the BIM management system interface and includes process number, planned start time, planned end time, and a list of responsible personnel. After preprocessing the collected raw data, it is stored in the database to form a structured dataset. Step S102: Based on the process structure in the project schedule, determine the work space required for each process. Combine the safety isolation distance marked in the construction drawings, the operating radius of relevant mechanical equipment, and the protection range of hazardous sources, generate a polygonal work area in the digital map using a geofencing engine. At the same time, associate the work area with the worker identification of the process to form a mapping relationship between personnel, process, and work area. Two types of handover triggering conditions are defined: time-triggered conditions and event-triggered conditions. Time-triggered conditions are based on the planned time window [T_plan-Δt, T_plan+Δt], where T_plan is the planned handover time of the process and Δt is a preset time tolerance parameter. Event-triggered conditions are set so that the person in charge of the current process submits a completion confirmation instruction in the BIM management system, which automatically triggers the handover preparation of subsequent processes. Both types of triggering conditions are bound to specific process numbers, forming a complete process handover triggering rule library, and are associated with the corresponding staff.

3. The construction safety management and control method based on the Internet of Things according to claim 2, characterized in that: Step S100 further includes: Step S103: Based on historical normal operation data, select data samples with no accident records in the most recent N working days to establish a multi-dimensional behavior baseline for each employee: First, construct the movement path baseline, organize the historical trajectory points in chronological order, and uniformly resample the trajectory data according to the real-time collection frequency to form a representative path sequence L_base={p1,p2,...,pm}, where Pm represents the m-th trajectory point in the path sequence, and each trajectory point contains coordinate information and a timestamp; Next, key stay points are extracted. By setting a spatial range threshold Rs, a time duration threshold Td, and a minimum occurrence threshold Nmin, when a person stays in a certain spatial range for a continuous time of Td and the spatial range appears at least Nmin in historical data, it is determined to be a key stay point, forming a stay point set S_stay={s1,s2,...,sk}, where sk represents the k-th key stay point; Then, the duration of stay was analyzed, historical stay time data for each key stop was collected, the average stay duration Tavg(s) and standard deviation σ(s) were calculated, and [Tavg-2σ, Tavg+2σ] was defined as the normal stay duration fluctuation range; Finally, a work process handover baseline is constructed, historical compliant handover records are compiled, and the difference between each handover time and the planned handover time is statistically analyzed to form a handover time deviation ΔTh. The central distribution center Mh of all handover points is calculated to represent the main location of work process handover activities. The specific process is as follows: collect the geographical coordinates of all historical compliant handover events, and calculate the arithmetic mean of these coordinate points as the distribution center: Mh=(1 / n)×(∑(xi),∑(yi)); where n is the total number of historical handover events, and (xi,yi) is the geographical coordinate of the i-th handover event. The path sequence, the set of dwell points, the dwell time parameter, the safe working area, the handover time deviation, and the center of the handover point are integrated into the personal work mode baseline, and an index association with the personnel identification ID is established.

4. The construction safety management and control method based on the Internet of Things according to claim 3, characterized in that: Step S200 includes: Step S201: Continuously acquire the real-time movement trajectory sequence L_real={q1,q2,...,qn} of the staff, where qn represents the nth real-time trajectory point in the trajectory sequence, and each trajectory point contains planar coordinates and acquisition timestamp; First, align the real-time trajectory with the baseline path sequence L_base={p1,p2,...,pm} in time and space, where pm represents the mth baseline path point; Step S202: In the path trajectory dimension, calculate the trajectory deviation D_path=(1 / n)×∑ by calculating the mean minimum distance between the real-time trajectory points and the baseline path points. i min j ||qi-pj||, where qi represents the i-th real-time trajectory point and pj represents the j-th baseline path point; At the work area level, based on the boundary of the baseline work area Asafe, the area violation index D_area = (1 / n) × ∑ i I(qi∉Asafe) indicates that the function I takes a value of 1 when the location point is outside the region, which is identified as abnormal out-of-bounds behavior. In the dimension of dwell behavior, for a dwell point s_real detected in real time, calculate the minimum spatial distance M_stay = min between it and the center coordinates of each dwell point in the baseline key dwell point set S_stay. j The distance is calculated as ||s_real-μ_coord(sj)||. This distance is then compared with a preset spatial tolerance threshold Rs: when M_stay≤Rs, the stay position is deemed compliant; when M_stay>Rs, it is deemed a position deviation event. Simultaneously, the deviation of the actual stay duration T_real from the baseline normal fluctuation range [T_avg-2σ,T_avg+2σ] is compared. When T_real falls within this range, the stay duration is deemed normal; when T_real exceeds the upper limit of the range, it is deemed a duration exceeding the limit event. In the process handover dimension, based on the baseline handover parameters, the time deviation Δt=|t_a-t_e| and the spatial deviation Δs=||l_a-Mh|| of the actual handover event are calculated respectively, where t_a and l_a are the actual handover time and location, t_e is the planned handover time, and Mh is the baseline handover center.

5. The construction safety management and control method based on the Internet of Things according to claim 4, characterized in that: Step S200 includes: Step S203: Finally, a weighted fusion evaluation is performed on the abnormal features of each dimension. The overall behavioral deviation index P = w1 × D_path + w2 × D_area + w3 × M_stay + w4 × Δt + w5 × Δs is calculated using the deviation degree. Here, w1, w2, w3, w4, and w5 are the weight coefficients of the five dimensions corresponding to the path trajectory, work area, dwell behavior, process handover time, and process handover space, respectively, and w1 + w2 + w3 + w4 + w5 = 1. When the deviation index P exceeds the preset threshold P_th, the behavioral sequence is determined to be a work deviation event.

6. The construction safety management and control method based on the Internet of Things according to claim 1, characterized in that: Step S300 includes: Step S301: Analyze the process docking relationship in the project plan, identify the set of process pairs C={(Pa,Pb)} with direct collaboration relationship, where Pa is the preceding process and Pb is the following process; for each process pair, extract its planned handover time window [Te-δ,Te+δ], where Te is the planned handover time point and δ is the time sequence tolerance parameter; Step S302: Based on the real-time trajectory data established in step S200, monitor the movement trajectory of the staff involved in each process. Call the center point Mh of the handover point in the individual work mode baseline, and pre-determine a circular expected handover area with a radius Rh centered on this point. When it is detected that staff from preceding and following processes simultaneously enter this expected handover area within the planned handover time window, and their dwell time within the area overlaps, with the overlap exceeding the minimum effective handover time threshold Tmin, it is determined as a valid trajectory intersection event. Simultaneously, record the actual timestamp Ta and spatial coordinates La of this intersection event, label it as a trajectory intersection event, and calculate the actual response delay time ΔTd = Ta - Te. When multiple intersection events are detected, calculate the average delay time ΔTd_avg and the maximum delay time ΔTd_max as evaluation indicators. Step S303: Count the number of delay events Ndelay for each process pair (Pa, Pb) within the statistical period; when Ndelay exceeds the set frequency threshold Fth, identify the process pair as a high-frequency delay process pair; group all delay events according to their time periods, calculate the number of delay events Nt in each time period, and identify the high-incidence time periods of delay event clusters by comparing the statistics of each time period; combine the delay time statistics of each process pair, the list of high-frequency delay process pairs, and the high-incidence time periods of delay to form a process docking efficiency evaluation report.

7. The construction safety management and control method based on the Internet of Things according to claim 1, characterized in that: Step S400 includes: Step S401: Correlate various abnormal indicators in the work offset event with the equipment operating status. When the area violation index D_area is greater than zero and the associated equipment is in operation, it is determined to be a violation of entering a dangerous area; when the personnel response delay time ΔTd exceeds the maximum allowable delay threshold Δt_max, it is determined to be a violation of the process docking timeout; when the dwell behavior simultaneously involves positional offset and duration exceeding the limit and the associated equipment is in an abnormal state, it is determined to be a risk of violation operation; when the number of delay events Ndelay of high-frequency delayed process pairs exceeds the emergency threshold, it is determined to be a risk of systemic collaboration failure. Step S402: Based on the above violation determination results, establish a three-level risk classification system: classify a single work deviation event that does not cause docking delay as a Level 1 risk; classify multiple work deviation events occurring concurrently or a single process docking delay as a Level 2 risk; classify systemic collaboration failure or multiple key processes experiencing severe delays simultaneously as a Level 3 risk; generate corresponding graded early warnings according to the risk level; Step S403: When the work offset index P continuously exceeds the set threshold, personnel scheduling and control are triggered, generating a suggested plan that includes job adjustments and personnel regrouping; when the personnel response delay time ΔTd exceeds the allowable threshold, process handover optimization and control are triggered, generating an improved plan that includes handover time window adjustments and handover area optimization; based on the analysis of work offset time and personnel response delay time, a comprehensive control plan that includes personnel scheduling suggestions and process handover optimization is generated; finally, the generated risk warning information and comprehensive control plan are pushed to the relevant management personnel to complete the closed-loop management of safety risk control.

8. A construction safety management and control system based on the Internet of Things, characterized in that: The system includes a baseline construction module, a behavior deviation identification module, a collaborative efficiency analysis module, and a comprehensive control module; The baseline construction module collects personnel location data, equipment status data, and project plan data at the construction site; combined with the project schedule, it binds the work area of ​​each worker's corresponding work process and the conditions for work process handover; based on historical normal operation data, it establishes a personal work mode baseline for each worker, including the worker's movement path, key stop points, stop duration, and the time and place of work process handover within the work area; The behavior deviation recognition module collects the movement trajectory data of the staff in real time, compares it with the baseline of the individual's work mode, and dynamically identifies behavioral events outside the work plan, which are marked as work deviation events. The collaborative efficiency analysis module is based on the project plan, integrates the cooperative process pairs and their expected handover time; at the same time, it analyzes the trajectory convergence time of relevant personnel and calculates the personnel response delay time at the process docking point. The integrated control module associates the operating status of affected equipment with the handover status of processes based on work deviation events and personnel response delays, identifies safety violations and issues risk warnings, and generates an integrated control plan that includes personnel scheduling suggestions and process handover optimization.

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