Fabricated construction site hoisting operation risk intelligent management and control method and system

Abstract

The invention relates to the technical field of risk management, in particular to a fabricated construction site hoisting operation risk intelligent management and control method and system. The method comprises the following steps: acquiring hoisting equipment operation parameters, hoisting point stress, operator positioning and environment information, analyzing equipment dynamic stability, and calculating operation parameter similarity and stability difference between hoisting areas to form a risk correlation coefficient; the area risk distance is corrected based on the risk correlation coefficient, the hoisting area is clustered to generate a risk distribution diagram, high-risk node identification and real-time early warning and linkage management and control are achieved in combination with personnel positioning and environment information, and hoisting operation risks are quantitatively managed in the whole process. Through multi-source data acquisition, hoisting area risk association analysis and dynamic stability adjustment, risk quantification, real-time early warning and linkage management and control of the whole hoisting process are realized, so that the operation safety and the intelligent management level are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of risk management, and particularly relates to a prefabricated construction site hoisting operation risk intelligent management and control method and system. BACKGROUND

[0002] Traditional hoisting operation risk management mainly relies on the experience judgment of on-site management personnel and operation procedures, and guarantees operation safety through manual inspection, hoisting scheme review, hoisting equipment operation specification and simple monitoring equipment. However, the existing technology has some subtle but safety-affecting defects, for example: instantaneous fluctuation of hoisting point stress, slight swing of hoisting equipment, dynamic influence of environmental disturbance on different hoisting areas, and slight deviation of operation personnel position and operation rhythm. These factors are difficult to be sensed and quantified in time and accurately under the traditional management mode.

[0003] At present, the common practice is to rely on the alarm system of the hoisting equipment or the visual monitoring of on-site personnel, and to judge the risk through regular inspection and construction scheme review. However, this method has limitations: on the one hand, a single alarm system or manual inspection is difficult to capture the slight changes of hoisting point stress or equipment operation parameters in the hoisting process; on the other hand, the risk correlation and dynamic stability between different hoisting areas cannot be scientifically quantified, which makes it difficult to identify potential high-risk nodes in advance, thereby existing safety hazards. SUMMARY

[0004] Therefore, it is necessary to provide a prefabricated construction site hoisting operation risk intelligent management and control method and system to solve at least one of the above technical problems.

[0005] To achieve the above-mentioned purpose, a prefabricated construction site hoisting operation risk intelligent management and control method comprises the following steps: Step S1: acquiring multi-source operation data in a hoisting operation cycle, including hoisting equipment operation parameters, component hoisting point stress data, operation personnel positioning information and environmental meteorological information; Step S2: confirming the dynamic stability of the hoisting equipment in the operation cycle according to the change degree of the hoisting equipment operation parameters at each time in the operation cycle and the environmental disturbance intensity of the adjacent time; Step S3: confirming the risk correlation coefficient of the corresponding hoisting area according to the similarity degree of the hoisting equipment operation parameter change trend of any two hoisting areas in the operation cycle and the difference of the corresponding dynamic stability; Step S4: adjusting the dynamic stability difference of the corresponding hoisting area in the operation cycle based on the risk correlation coefficient of any two hoisting areas, to obtain the corrected regional risk distance; Step S5: Based on the corrected regional risk distance, perform risk clustering and classification of the hoisting area to generate a hoisting risk distribution map; identify high-risk hoisting nodes based on the hoisting risk distribution map, and combine the operator's location information and environmental meteorological information for real-time early warning and coordinated control.

[0006] This specification provides an intelligent risk management and control system for prefabricated construction site hoisting operations, used to execute the aforementioned intelligent risk management and control method for prefabricated construction site hoisting operations. This intelligent risk management and control system for prefabricated construction site hoisting operations includes: The data acquisition module is used to acquire multi-source operational data during the hoisting operation cycle, including hoisting equipment operating parameters, component lifting point stress data, operator positioning information, and environmental meteorological information; The stability analysis module is used to determine the dynamic stability of the hoisting equipment during the work cycle based on the degree of change of the hoisting equipment's operating parameters at each time point and its adjacent time points, as well as the intensity of environmental disturbances at adjacent time points. The risk correlation module is used to determine the risk correlation coefficient of the corresponding hoisting area based on the similarity of the changing trends of the hoisting equipment operating parameters and the difference in the corresponding dynamic stability between any two hoisting areas during the operation cycle. The risk correction module is used to adjust the dynamic stability difference of the corresponding hoisting areas during the operation cycle based on the risk correlation coefficient between any two hoisting areas, so as to obtain the corrected area risk distance. The risk management module is used to cluster and classify the lifting area according to the corrected regional risk distance, and generate a lifting risk distribution map; based on the lifting risk distribution map, high-risk lifting nodes are identified, and real-time early warning and linkage management are carried out in combination with the location information of operators and environmental meteorological information.

[0007] The present invention has the following beneficial effects: I. By acquiring multi-source operational data, including hoisting equipment operating parameters, component lifting point stress data, operator positioning information, and environmental meteorological information, this method can comprehensively reflect the dynamic status of the entire hoisting operation process. By analyzing the changing trends of equipment operating parameters, dynamic stability, and lifting point stress deviation, it can accurately identify potential high-risk nodes during the hoisting process, achieving full-cycle, all-round risk perception and monitoring of hoisting operations, thereby effectively reducing safety accidents caused by equipment malfunctions or improper operation.

[0008] Second, by calculating the similarity of the changing trends of operating parameters, the differences in dynamic stability, and the deviation of the force on the lifting points between lifting areas, a risk correlation coefficient between areas is constructed. Based on the risk distance, correction and cluster analysis are performed to generate a lifting risk distribution map. This intelligent correlation analysis between areas can transform complex multi-source data into quantifiable risk indicators, upgrading the risk management of lifting operations from experience-based judgment to scientific quantification, and achieving accuracy and operability in risk early warning.

[0009] Third, by combining the lifting risk distribution map, personnel positioning information, and environmental meteorological data, this method can identify high-risk lifting nodes in real time and trigger early warnings. Simultaneously, it can implement coordinated control of lifting equipment and the work area. By dynamically adjusting regional stability, risk distance, and attitude parameters, it ensures that lifting operations maintain a safe state even under abnormal environmental or operational conditions, thereby significantly improving the safety, reliability, and intelligent management level of lifting operations. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the steps of an intelligent risk management method for hoisting operations at prefabricated construction sites; Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Figure 3 This is a risk distribution map of the hoisting operation risk intelligent management method for prefabricated construction site hoisting operations according to this application; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0013] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0014] To achieve the above objectives, please refer to Figures 1 to 3 A method for intelligent risk management and control of hoisting operations at prefabricated construction sites, the method comprising the following steps: Step S1: Obtain multi-source operation data during the hoisting operation cycle, including hoisting equipment operating parameters, component lifting point stress data, operator positioning information, and environmental weather information; In one embodiment, the operating parameters of the lifting equipment include information such as the crane's boom length, hook height, lifting weight, slewing angular velocity, boom extension / retraction speed, and power consumption. The lifting equipment can record these parameters in real time through built-in sensors and a control system, and transmit them to the operation data acquisition unit at a fixed sampling frequency.

[0015] The stress data at the lifting points of the component is obtained by installing strain sensors or force sensors at the key lifting points of the component. The sensors collect the force amplitude, force direction, and fluctuation at each lifting point, generating time series data for analyzing the stress fluctuation characteristics and potential overload risks during the lifting process.

[0016] Worker location information can be obtained by wearing safety helmets or vests equipped with high-precision positioning modules (such as UWB, RTK-GNSS, or indoor positioning systems). The location data includes the worker's real-time location, movement speed, and relative distance to the hoisted components and equipment, facilitating personnel risk protection analysis.

[0017] Environmental meteorological information includes wind speed, wind direction, temperature, relative humidity, and precipitation. Meteorological sensor stations are deployed at the hoisting site to collect meteorological data in real time, recording the impact of wind load changes, humidity changes, and other environmental factors on the hoisting operation.

[0018] Preferably, a time synchronization mechanism is used when collecting multi-source operation data, and the parameters of hoisting equipment, the force on the hoisting point, the personnel positioning and environmental meteorological data are marked with a unified timestamp to ensure the accuracy and time sequence consistency of subsequent data analysis.

[0019] The acquired data is transmitted to the operation data processing terminal via wired or wireless means for preliminary processing, cleaning, and formatting to generate a standardized multi-source operation data set, which serves as input data for the lifting operation risk management method. It is important to note that, to ensure data integrity and continuity, all types of sensors should be calibrated and tested before the lifting operation, and the data acquisition status should be monitored in real time during the operation to ensure that no multi-source data is lost or abnormally sampled.

[0020] Step S2: Determine the dynamic stability of the hoisting equipment during the work cycle based on the degree of change of the hoisting equipment's operating parameters at each time point and its adjacent time points, as well as the intensity of environmental disturbances at adjacent time points. In one embodiment, the operating parameters of the lifting equipment during the work cycle are acquired, including hook height, lifting speed, boom extension / retraction speed, slewing angular velocity, and load changes. These operating parameters are then arranged in chronological order to form time-series data.

[0021] For each moment in the time series, the degree of change of the operating parameters compared to the previous moment is calculated. This degree of change can be quantified by methods such as amplitude difference, rate of change of velocity, or acceleration change, reflecting the operational fluctuations of the hoisting equipment across consecutive moments. Simultaneously, data on the intensity of environmental disturbances is acquired, including wind speed, wind direction, temperature, humidity, and surrounding construction interference. Environmental disturbance intensity is used to describe the impact of external conditions on the operational stability of the hoisting equipment.

[0022] By comprehensively analyzing the changes in the operating parameters of the hoisting equipment with the intensity of environmental disturbances at adjacent moments, a dynamic stability index can be generated through weighted averaging or function mapping. The dynamic stability index can be expressed as a percentage or a normalized value from 0 to 1. A higher value indicates that the hoisting equipment is operating more stably at that moment, while a lower value indicates greater operational fluctuations or stronger susceptibility to external disturbances.

[0023] Preferably, to improve the accuracy of dynamic stability, the changes in operating parameters and environmental disturbances can be filtered to remove the influence of measurement noise and occasional interference.

[0024] By calculating the dynamic stability at each moment within the operation cycle, a dynamic stability curve for the hoisting equipment can be generated, which can be used to describe the smoothness of the equipment operation and potential risk points throughout the hoisting process.

[0025] It is important to note that the calculation of dynamic stability should take into account the different weights of various operating parameters. For example, changes in the force on the lifting points have a significant impact on equipment stability and can be given a higher weight in the calculation to reflect their importance in the dynamic stability assessment.

[0026] Step S3: Based on the similarity of the changing trends of the operating parameters of the hoisting equipment in any two hoisting areas during the work cycle and the difference in the corresponding dynamic stability, confirm the risk correlation coefficient of the corresponding hoisting areas; In one embodiment, for each lifting area, the operating parameters of the lifting equipment in that area are first acquired over a time series during the work cycle, including information such as hook height change, lifting speed, slewing angular velocity, boom extension / retraction speed, and load change. Simultaneously, dynamic stability indices for each lifting area are acquired. Dynamic stability can be calculated from parameters such as the vibration amplitude of the lifting equipment, the force fluctuation at the lifting point, and the swing amplitude of the lifting components, and is used to characterize the stability of the lifting process.

[0027] For any two hoisting areas, compare the changing trends of their hoisting equipment operating parameters and calculate the degree of similarity. For example, after normalizing the time series, use the correlation coefficient or Dynamic Time Warping (DTW) algorithm to measure the consistency of the operating trends between the two areas. A high degree of similarity indicates that the equipment operation in the two areas exhibits linkage characteristics.

[0028] Simultaneously, the dynamic stability differences of the corresponding hoisting areas are calculated. Significant stability differences indicate different stress states or operational risks in the two areas during hoisting, potentially causing anomalies in one area to affect the other.

[0029] Based on the similarity of the changing trends of the hoisting equipment's operating parameters and the differences in their dynamic stability, a risk correlation coefficient for the corresponding hoisting areas is obtained through weighted calculation or function mapping. This risk correlation coefficient can serve as a quantitative indicator to describe the potential risk transmission relationship between two hoisting areas during operation.

[0030] Preferably, different parameters can be assigned weights when calculating the risk correlation coefficient. For example, fluctuations in the force on the lifting point and changes in the lifting speed have a significant impact on risk transmission, and can be given higher weights in the calculation to improve the accuracy of the risk correlation coefficient.

[0031] It is important to note that the calculation of the risk correlation coefficient of the hoisting area should be based on preprocessed standardized data to ensure that the parameters of each hoisting area are compared on the same scale and to eliminate the influence of errors caused by sampling frequency or measurement noise.

[0032] Step S4: Based on the risk correlation coefficient between any two hoisting areas, adjust the dynamic stability difference of the corresponding hoisting areas within the operation cycle to obtain the corrected area risk distance; In one embodiment, the dynamic stability curves of each hoisting area obtained in step S2 during the operation cycle are first obtained, as well as the risk correlation coefficients between each hoisting area calculated in step S3.

[0033] For any two hoisting areas, the difference in their dynamic stability can be calculated. This difference can be quantified by the difference or distance index between the two dynamic stability curves over time, such as the root mean square difference, absolute value difference, or correlation difference, reflecting the magnitude of the difference in the operating states of the two areas. The dynamic stability difference is then weighted and adjusted with the corresponding risk correlation coefficient. Areas with higher risk correlation coefficients receive a larger weight for adjusting the dynamic stability difference, meaning that the difference in operating state between these areas has a more significant impact on the overall operational risk; areas with lower risk correlation coefficients have a smaller impact on the adjustment.

[0034] Preferably, a proportional mapping or functional mapping method can be used to combine the risk correlation coefficient and the difference in dynamic stability to obtain the corrected risk distance value. The corrected risk distance can reflect the potential risk correlation strength between any two hoisting areas within the operation cycle, while also taking into account the difference in dynamic stability.

[0035] The above calculations are performed on each pair of all hoisting areas to generate a complete and corrected regional risk distance matrix, which is used for subsequent risk analysis and work scheduling optimization.

[0036] It is important to note that when calculating the corrected risk distance, key moments within the work cycle should be considered, such as dynamic stability fluctuations during hoisting start-up, load changes, or sudden increases in environmental disturbances, to ensure that the risk distance reflects the risk characteristics of the most critical periods.

[0037] Step S5: Based on the corrected regional risk distance, perform risk clustering and classification of the hoisting area to generate a hoisting risk distribution map; identify high-risk hoisting nodes based on the hoisting risk distribution map, and combine the operator's location information and environmental meteorological information for real-time early warning and coordinated control.

[0038] In one embodiment, an appropriate clustering method, such as K-means clustering, hierarchical clustering, or density clustering, is selected based on the modified risk distance matrix to group the risk distances between each pair of lifting areas, classifying areas with high risk associations into the same risk level cluster. The clustering results are then spatially mapped, transferring the spatial location, time period, and risk level of the lifting areas at the work site to a two-dimensional or three-dimensional risk distribution map. Each area in the risk distribution map can use a different color or identifier to display its risk level: high-risk areas can be displayed in red, medium-risk areas in yellow, and low-risk areas in green.

[0039] Based on the risk distribution map, high-risk lifting nodes can be identified, including lifting points, lifting paths and adjacent lifting areas, and their corresponding risk levels, time periods and affected work units can be recorded.

[0040] After identifying high-risk nodes, the real-time location information of workers is correlated with environmental meteorological information and the hoisting risk distribution map. By analyzing the spatial overlap between personnel locations and high-risk nodes, as well as environmental disturbance factors such as wind speed, temperature, and rainfall, a real-time risk index is generated to determine whether workers are within a potentially hazardous area. When the real-time risk index exceeds a set threshold, early warning measures can be triggered, such as audible alarms, visual warnings, or mobile terminal notifications, to remind workers to pay attention to safety.

[0041] Simultaneously, the overall operational risk can be reduced by adjusting the hoisting sequence, suspending high-risk operations, or activating backup plans through coordinated management. It is important to note that risk clustering and real-time early warning should be continuously updated throughout the hoisting operation cycle to ensure that the risk distribution map and early warning information reflect dynamic changes during the operation, achieving precise and quantifiable risk control.

[0042] As an example of the present invention, reference is made to... Figure 2 As shown, step S3 in this example includes: Step S31: Calculate the sum of the absolute values ​​of the difference in the rate of change of the hoisting equipment operating parameters between any two hoisting areas during the corresponding time period within the operation cycle, and use it as the overall operating difference between the two hoisting areas within the cycle. Step S32: Arrange the operating parameters of the hoisting equipment in each hoisting area in chronological order during the operation cycle to obtain the equipment operating parameter sequence; Step S33: Based on the equipment operating parameter sequence and overall operating differences, confirm the operating parameter change curve of the hoisting equipment, and based on the similarity between the operating parameter change curve and the preset standard operating change curve and the difference in corresponding dynamic stability, confirm the risk correlation coefficient of the corresponding hoisting area.

[0043] In one embodiment, for any two lifting areas A and B, the operating parameters of the lifting equipment in both areas within the same work cycle are first determined. These operating parameters include at least key indicators such as hook lifting speed, boom amplitude variation angle, slewing angular velocity, lifting load, and hydraulic system pressure. To ensure data time alignment, it is preferable to perform time synchronization processing on the operating parameter data of the two areas, so that each sampling point corresponds to the same time node.

[0044] After synchronization is completed, the rate of change of the operating parameters of the hoisting equipment in each time interval is calculated, that is, the degree of change of the operating parameters between each moment and the adjacent moment is calculated. For example, when the hook lifting speed increases or decreases from one moment to the next, this change is taken as the rate of change of the parameters for that time period. Subsequently, the rates of change of two regions A and B at the same time node are compared to obtain their rate of change difference. To reflect the overall operational difference between the two regions throughout the entire cycle, the absolute value of the rate of change difference at each time node is taken and accumulated time by time to finally obtain the overall operational difference value between the two regions throughout the entire hoisting cycle. This overall operational difference value is used to measure the comprehensive deviation between the two regions in terms of work rhythm, operation mode, and equipment response; the larger the value, the more significant the difference.

[0045] To further characterize the dynamic changes of the equipment throughout the entire lifting cycle, the operating parameters of each lifting area were arranged chronologically to establish a complete sequence of equipment operating parameters. In this sequence, each data point represents the equipment status information at a specific time node, including the boom's pitch angle, slewing angle, lifting load, wind speed, wind direction, and the operator's control input signals. To avoid transient anomalies interfering with the overall analysis, moving averages or median filtering were used during sequence establishment to smooth and denoise the data. This sequence provides the temporal continuity characteristics of equipment operation in each lifting area, offering a stable input basis for subsequent risk analysis.

[0046] Based on the equipment operating parameter sequence obtained in step S32 and the overall operating differences obtained in step S31, an operating parameter change curve is generated for each hoisting area. In actual operation, time can be used as the horizontal axis, and the rate or magnitude of change of key operating parameters can be used as the vertical axis to plot the equipment's operating change process in chronological order. This curve can intuitively reflect the operational stability and fluctuations of the hoisting equipment at different stages, such as the continuity of speed when the boom rotates and the torque change trend when the hook is lifted.

[0047] Subsequently, the operating parameter variation curves of each hoisting area were compared with the pre-established standard operating variation curves. The standard operating variation curves are benchmark curves generated under normal and stable operating conditions based on historical stable data and safe operating procedures, and are used as a reference for judging the operating status of the equipment. The comparison process includes: (1) comparing the fluctuation amplitude of each curve with the consistency of the stable phase within the same time interval; (2) analyzing whether the changing trends are synchronized, that is, whether the actual curve shows the same trend when the standard curve enters the stable or rising phase; (3) judging the time difference of the curve inflection point to assess the operational response delay.

[0048] Meanwhile, by combining the dynamic stability index of each hoisting area during the operation cycle, dynamic stability can be determined by analyzing the smoothness of equipment attitude changes, the degree of fluctuation in the force on the hoisting points, and the strength of the response to environmental disturbances (such as sudden changes in wind speed, ground vibration, etc.). When the trend of the operating parameter change curve is similar to that of the standard curve, but the stability difference is large, it indicates that there is a risk of operational fluctuations or uneven load response in that area.

[0049] Therefore, by comprehensively analyzing the similarity of curves and the difference in dynamic stability, the risk correlation coefficient between any two hoisting areas is determined. The risk correlation coefficient is used to represent the degree of synchronization and the probability of risk transmission between different hoisting areas. For example, when two areas are similar in their operating trends and have small differences in stability, the risk correlation coefficient is low, indicating that the two areas have good operational coordination; conversely, when two areas have similar fluctuation trends but significant differences in stability, the risk correlation coefficient is high, representing the spread of risk from one area to another.

[0050] It is important to note that during the calculation of risk correlation coefficients, in order to prevent individual abnormal periods from affecting the overall assessment results, a time sliding window can be set to divide the hoisting cycle into multiple time periods for segmented statistical analysis, and the results can be weighted and summarized to improve the accuracy of the assessment and the feasibility of the project.

[0051] Preferably, the method for obtaining the similarity between the running variation curve and the preset standard running variation curve includes: Obtain the operating parameter variation curves of any two hoisting areas during the operation cycle, where the operating parameters include at least one of hoisting speed, sling tension and boom swing amplitude; The operating parameter change curves are compared with the preset standard operating change curves to determine the consistency of parameter changes at each moment, so as to characterize the similarity of the operating change curves of the two hoisting areas.

[0052] In one embodiment, the operating parameter variation curves of any two lifting areas are obtained throughout the entire operation cycle. The operating parameters preferably include at least one of lifting speed, sling tension, and boom swing amplitude, but can also be extended to other key indicators such as slewing angular velocity, hydraulic pressure variation, and wind speed response data, depending on the type of lifting equipment.

[0053] In practice, the lifting speed can be collected in real time by the speed sensor on the equipment, which can collect data on the lifting speed of the hook; the tension of the sling can be measured by the tension gauge on the sling; and the swing amplitude of the boom can be measured by the angle sensor or the attitude sensor. The data is collected at fixed sampling intervals to form a data sequence that changes continuously over time.

[0054] Subsequently, the collected operational parameter data were plotted as operational parameter variation curves in chronological order, with each hoisting area forming an independent set of operational variation curves. To ensure the comparability of the curves, the data could be normalized or subjected to time benchmark unification during the plotting process, ensuring that the time scale and parameter amplitude remained consistent across different areas.

[0055] The preset standard operating variation curve can be obtained through statistical analysis of a large amount of historical stable lifting operation data. Preferably, the standard curve should be generated under multiple safe and stable lifting operation conditions, and its shape can reflect the typical parameter variation patterns during the lifting, translation, and hook lowering stages of normal operation.

[0056] After the curve is established, the operation change curve of each hoisting area is compared and analyzed with the standard operation change curve. The comparison process can be divided into the following steps: (1) Time synchronization comparison: Align the start time of the two curves to ensure that they are compared in the same operation stage (such as hoisting, horizontal movement or hook lowering stage). If there is a deviation in the operation rhythm of different areas, the time sliding window alignment method can be used for correction. (2) Parameter change consistency judgment: For each time node, compare the direction and magnitude of the change of operating parameters. For example, when the standard curve shows an increase in hoisting speed in a certain period of time, if the target area curve also shows an increase in speed in the same period of time, it is judged as consistent; if the direction is opposite or the change magnitude is significantly different, it is judged as inconsistent. In the judgment process, a predetermined threshold can be set to distinguish between small fluctuations and actual changes. For example, when the parameter change magnitude is lower than the noise range, it is considered consistent. (3) Fluctuation stage comparison: In the entire hoisting cycle, identify the stable stage and fluctuation stage of the operation curve, and compare the duration and position of each stage. If two curves have similar start and end times and fluctuation patterns during the main fluctuation phase (e.g., when the boom turns or the wind speed is disturbed), they can be considered highly similar. (4) Stage continuity judgment: Analyze the transition continuity of the operating curve between different operation stages, such as whether the speed change trend from lifting to horizontal movement is consistent with the standard curve, in order to characterize the degree of operation continuity.

[0057] Based on the above comparison results, the similarity between the operational variation curve and the standard operational variation curve throughout the entire lifting cycle can be comprehensively evaluated. Preferably, a segmented weighting method can be adopted, assigning higher comparison weights to key operational stages (such as the initial lifting stage and the stable lifting stage), thereby improving the accuracy of the judgment.

[0058] After the comparison is completed, the system will output a set of results data to characterize the degree of similarity. This data can be reflected as the statistical proportion of the consistency of parameter changes over different time periods, or as a comprehensive similarity level. When the degree of similarity is high, it indicates that the equipment in the hoisting area is operating stably and conforms to standard operation; when the degree of similarity is low, there are risk factors such as abnormal operation, wind load interference, or equipment response delay.

[0059] It should be noted that, in order to eliminate the interference of occasional data, multiple sliding window analyses can be used in the similarity calculation process to smooth the comparison results of different time periods and ignore abnormal abrupt segments with extremely short durations, so as to improve the reliability of the results.

[0060] Preferably, based on the similarity between the operating parameter change curve and the preset standard operating change curve, and the difference in corresponding dynamic stability, the risk correlation coefficient of the corresponding hoisting area is determined, including: Based on the similarity between the operating parameter change curve and the preset standard operating change curve and the difference in the corresponding dynamic stability, the first risk correlation coefficient is determined. The second risk correlation coefficient is determined by the similarity between the operating parameter change curve and the preset standard operating change curve and the force deviation of the corresponding component lifting point. The average of the first and second risk correlation coefficients is taken as the risk correlation coefficient for the corresponding hoisting area.

[0061] In one embodiment, the degree of similarity is determined based on the morphological similarity between the operating parameter variation curves generated for each hoisting area and the standard operating variation curves. In practice, the degree of similarity can be determined by comparing the consistency of trends, the correspondence of peak positions, and the degree of agreement in fluctuation amplitudes between the two curves within the same time interval. For example, when the two curves maintain the same upward or downward trend for most periods, and the peak occurrence times are close and the fluctuation amplitude differences are small, the degree of similarity is high; if the curves fluctuate in opposite directions frequently or the peak differences are significant, the degree of similarity is low. This degree of similarity reflects the matching degree between the actual operating state of the hoisting equipment and the standard stable operating state.

[0062] Secondly, the first risk correlation coefficient is calculated by combining the dynamic stability data of each hoisting area during the operation cycle. Dynamic stability describes the overall smoothness of the hoisting equipment during operation, comprehensively considering factors such as boom vibration amplitude, stress fluctuation at the hoisting point, and wind speed disturbance response. When determining the first risk correlation coefficient, the following logic is preferred: when the similarity is high and the dynamic stability difference is small, it indicates that the operating state of the area is stable and the risk correlation is low; when the similarity is high but the stability difference is significant, it indicates that although the equipment has a similar operating trend, the control balance is poor, and the first risk correlation coefficient increases; if the similarity is low and the stability difference is large, it indicates that the operating characteristics of the area are abnormal and the risk fluctuations are severe, and the first risk correlation coefficient takes a higher value. Through this multi-dimensional comparison, the risk deviation level of the hoisting area's operating state relative to the standard state can be accurately quantified.

[0063] Furthermore, a second risk correlation coefficient is calculated based on the stress characteristics of the component lifting points during the lifting process. Specifically, stress change data acquired by real-time monitoring force sensors at the lifting points is compared with a standard force distribution model to obtain the stress deviation. A small stress deviation indicates a reasonable load distribution and uniform stress on the equipment; a large stress deviation indicates uneven stress distribution at the lifting points, suggesting potential off-center loading or tilting hazards. The second risk correlation coefficient is then determined based on the similarity between the stress deviation and the operating parameter change curve and the standard curve. For example, a lower value is assigned to the second risk correlation coefficient when the similarity is high and the stress deviation is small; a higher value indicates that while the equipment's operating trend is normal, the stress state is abnormal, and a medium-high value is assigned to the second risk correlation coefficient; a lower value indicates that both operational deviation and stress imbalance exist, and the highest level of the second risk correlation coefficient is assigned. This method reflects the potential risk level of the lifting area in terms of stress safety.

[0064] Finally, the obtained first and second risk correlation coefficients are combined, and their average is taken as the comprehensive risk correlation coefficient for the corresponding hoisting area. This average value can take into account both the risk characteristics of equipment operation stability and the stress balance of the hoisting points, avoiding the bias caused by a single indicator and making the risk assessment results more comprehensive and objective. In some implementation scenarios, weighting coefficients can also be set for the first and second risk correlation coefficients to flexibly adjust according to the complexity of the hoisting conditions. For example, when the wind speed in the hoisting environment changes significantly, the weight of stability difference can be increased, while in heavy-load hoisting, the weight of stress deviation can be appropriately increased.

[0065] Preferably, the method for obtaining the force deviation of the corresponding component lifting point includes: Extract the force amplitude and force variation trend of the component's lifting point stress data; The fluctuation amplitude and rate of change during the hoisting process are determined based on the force amplitude and the force change trend. The temporal variation characteristics of the force at the suspension point are determined by the fluctuation amplitude and the rate of change. By utilizing the correlation between the operating parameter variation curves and the time-series variation characteristics, the stress fluctuation characteristics and time-series regularity of the lifting points can be determined; The force deviation of each lifting point is calculated based on the characteristics of force fluctuation and the temporal regularity.

[0066] In one embodiment, during the hoisting operation, force sensors installed at the connection points of the slings or hooks collect real-time force data at each hoisting point of the component. The force data includes the instantaneous force value and time information of the hoisting point throughout the entire hoisting cycle. To improve signal stability, the original force signal can be denoised and filtered to eliminate abnormal peaks caused by electromagnetic interference or sudden jitter. After data processing, the force change at each hoisting point over consecutive time intervals is calculated to obtain its force amplitude; simultaneously, the direction of force increase or decrease is identified through the time series trend of the force data, thereby obtaining the trend of force change at the hoisting point.

[0067] Specifically, by analyzing the peaks and troughs of the stress curve at the lifting point, the fluctuation range of stress changes at the lifting point during the lifting process is determined, and this is defined as the fluctuation amplitude. In the time dimension, the time interval between adjacent peaks or troughs is measured to determine the rate of stress change; this time interval reflects the rate of stress change at the lifting point. For example, when the lifting speed of the hoisting equipment increases or the component's posture is adjusted frequently, the stress curve at the lifting point will show more dense peaks, indicating a higher rate of change.

[0068] In a preferred embodiment, the system divides the stress curve of the lifting point into several time periods and statistically analyzes the fluctuation amplitude and rate of change within each time period. If the lifting point exhibits periodic fluctuations or a continuously intensifying trend in different time periods, it indicates that its stress has a clear temporal regularity. By comparing the fluctuation period, duration, and waveform of each lifting point, it is possible to further identify whether there are abnormal forces exceeding the normal lifting rhythm, such as instantaneous overloads caused by swaying of the lifting equipment or uncoordinated operation.

[0069] In this embodiment, the temporal variation characteristics of the lifting point force data are time-aligned with the change curves of the lifting equipment operating parameters to analyze the consistency of their responses. If the peak value of the lifting point force appears near critical nodes of boom speed, angle changes, or slewing operations, and the force fluctuation amplitude is synchronized with the equipment movement changes, then the force at that lifting point is considered to be strongly correlated with the equipment operation. When there is a significant lag or phase shift between the force fluctuation and the equipment operation changes, it indicates that the lifting point force is abnormal, caused by component center of gravity shift, uneven slings, or external disturbances. Based on this comparison, the force fluctuation characteristics (e.g., fluctuation synchronization, amplitude consistency) and temporal regularity (e.g., phase delay, response frequency) of the lifting point can be extracted.

[0070] To quantify the stress stability of different lifting points during the lifting process, the stress fluctuation characteristics of each lifting point were compared with a preset standard stress pattern. The standard stress pattern is a reference model established based on historical operation data under normal lifting conditions, representing the ideal stress distribution and temporal response of each lifting point under balanced lifting conditions. During the comparison, if the fluctuation amplitude of a certain lifting point is significantly higher than that of the standard pattern, or if its stress change is inconsistent with the response of the equipment movement, then the lifting point is determined to have a high degree of deviation. By statistically analyzing the duration and magnitude of the stress deviation at the lifting point, the stress deviation level of that lifting point can be obtained, which reflects the degree of abnormality in the stress during the lifting process.

[0071] Preferably, the temporal variation characteristics of the force on the suspension point are confirmed by the fluctuation amplitude and the rate of change, including: Based on the correspondence between the amplitude and rate of change of the force fluctuation, the temporal variation law of the force at the suspension point is obtained; Based on the temporal variation pattern of the force at the lifting point, the stage fluctuation characteristics and continuous variation characteristics of the force at the lifting point are extracted; Based on the characteristics of phased fluctuations and continuous changes, the temporal variation characteristics of the force on the lifting point are determined.

[0072] In one embodiment, multiple sets of high-precision force sensors can be deployed at key locations of the lifting point to collect force data in real time during the lifting operation. The force data sampling frequency can be set from 50Hz to 200Hz to ensure sensitivity to rapid load changes. During data acquisition, the system records the instantaneous force value and timestamp of each sampling point in real time, forming a time series of force at the lifting point. Subsequently, the collected raw data is preprocessed, including noise filtering, outlier removal, and signal smoothing. Next, the force change between adjacent sampling points is calculated, and the rate of change, i.e., the speed of force change per unit time, is obtained. By combining the correspondence between the rate of change and the amplitude of force fluctuation, the rising, falling, and stable stages of the force at the lifting point in different time periods can be identified. For example, when the rate of change of force is continuously positive and the amplitude of fluctuation gradually increases, it indicates that the lifting point is in the load rising stage; when the rate of change approaches zero and the amplitude of fluctuation decreases, it indicates that the force at the lifting point is stabilizing. By analyzing the continuous trend of each stage, the temporal variation law of the force at the lifting point can be established.

[0073] Secondly, based on the temporal variation pattern of the force at the lifting points, the staged fluctuation characteristics and continuous variation characteristics of the force at the lifting points are extracted. In this embodiment, the staged fluctuation characteristics are used to reflect abnormal changes in force within a short period of time during the lifting process, such as sudden acceleration, abrupt stops, or wind-induced eccentric loading. By analyzing the short-time window of the force time series, abrupt change segments or high-frequency fluctuation segments in the force curve are identified and used as staged fluctuation intervals. The continuous variation characteristics are used to characterize the gradual change trend of the load during the lifting process, such as the linear or slow nonlinear changes in force when the load is steadily rising or falling. By calculating the consistency of the rate of change and the fluctuation stability index of several consecutive sampling points, the intervals of continuous and stable changes can be distinguished.

[0074] Finally, based on the characteristics of phased fluctuations and continuous changes, the temporal variation characteristics of the stress on the lifting point are determined. In practice, the phased fluctuation characteristics and continuous change characteristics can be integrated for analysis to establish a temporal characteristic model of the stress on the lifting point. If phased fluctuations occur frequently and with large amplitudes during the lifting process, it indicates that the lifting point is in a highly dynamic and unstable state; if continuous change characteristics dominate, it indicates that the lifting process is stable and the stress changes are predictable. Through the above comprehensive judgment, a complete temporal variation characteristic of the stress on the lifting point can be formed, which can be used for subsequent lifting risk assessment, load stability analysis, and structural safety monitoring of the lifting point.

[0075] Preferably, step S4 includes the following steps: Step S41: Obtain real-time attitude change data and load swing amplitude data for any two hoisting areas during the operation cycle, and calculate the regional attitude change rate based on the real-time attitude change data and load swing amplitude data to generate attitude dynamic parameter data. Step S42: Calculate the stability change gradient between hoisting areas based on the attitude dynamic parameter data, and perform linear correction on the stability change gradient by combining the risk correlation coefficient to generate stability correction data; Step S43: Use stability correction data to correct the amplitude of dynamic stability differences between hoisting areas and generate dynamic stability correction data; Step S44: Adjust the rate of change of risk distance between hoisting areas based on the dynamic stability correction data to obtain the corrected regional risk distance.

[0076] In one embodiment, high-precision tilt sensors and accelerometers can be deployed in each hoisting area to collect real-time data on the boom attitude, rotation angle, and swing amplitude of the hoisting equipment. The sampling frequency can be set to 50 to 200 Hz to capture rapid attitude changes during the hoisting process. The collected data undergoes noise reduction and filtering by a data processing module to eliminate short-term random interference. Subsequently, the attitude change rate is calculated based on the attitude changes and swing amplitude at continuous intervals, forming a set of dynamic attitude parameter data for each hoisting area, which describes the dynamic response characteristics of the hoisting area during the operation cycle.

[0077] The attitude dynamic parameters of adjacent hoisting areas can be compared to calculate the differences in attitude changes and the rate of change of swing amplitude within the same operating time window, thereby obtaining the stability change gradient. Subsequently, the risk correlation coefficient of the hoisting areas calculated in step S3 is applied to the linear correction of the stability change gradient to enhance sensitivity to high-risk correlated areas. For example, when the risk correlation coefficient between two hoisting areas is high, their stability change gradient will be amplified after correction for subsequent risk distance correction. The correction results form stability correction data.

[0078] Stability correction data can be applied to the original dynamic stability differences between various lifting areas. By adjusting the stability indices of each area through amplitude correction methods, the stability differences in high-risk or rapidly changing attitude areas can be reasonably amplified or reduced. The stability indices after amplitude correction form dynamic stability correction data, which can be used to accurately reflect the relative dynamic stability state of the lifting area during the operation cycle.

[0079] The dynamic stability correction data can be applied to the regional risk distance calculation module. By adjusting the rate of change of risk distance between each hoisting area, the high-risk associated areas become more prominent in terms of risk distance, while the low-risk areas remain stable. The adjusted result forms the corrected regional risk distance, which is used in step S5 for hoisting area risk clustering classification and high-risk node identification, realizing real-time hoisting risk assessment based on dynamic attitude and load data.

[0080] Preferably, the method for obtaining the dynamic stability difference between hoisting areas includes: Calculate the gradient difference between any two hoisting areas based on the stability change gradient between hoisting areas; The average absolute difference is calculated by the gradient difference between any two hoisting areas, and the result is used as the difference in dynamic stability.

[0081] In one embodiment, dynamic attitude parameter data and stability correction data for each hoisting area are obtained through steps S41 and S42. Then, the stability change gradients of any two hoisting areas within the same operation time window are compared, and the difference in change is calculated, which is the gradient difference. The gradient difference reflects the differences in attitude change and stability response between the two areas during the hoisting operation.

[0082] The absolute value of the gradient difference for each time period within the entire operation cycle is taken, and the absolute gradient difference for each time period is averaged to obtain the dynamic stability difference value between any two hoisting areas. This dynamic stability difference can quantitatively reflect the overall difference in stability between the two hoisting areas within the operation cycle. The larger the value, the more significant the relative stability difference between the two areas; the smaller the value, the more similar the dynamic stability of the two areas.

[0083] It is important to note that when calculating the dynamic stability difference, the gradient data acquisition time window should be kept consistent, and short-term abnormal fluctuation data should be filtered to avoid occasional interference affecting the dynamic stability difference results. Simultaneously, this difference value can be used in step S44 to correct the risk distance between hoisting areas, providing a reliable basis for hoisting risk clustering and high-risk node identification.

[0084] Preferably, the method for confirming the hoisting area includes: Confirm the spatial coordinates of the component lifting points using a pre-set on-site hoisting BIM model; Based on the projection position of the spatial coordinates on the preset reference plane, determine the horizontal influence range of the component lifting point; The buffer zone corresponding to the lifting point is determined based on the horizontal influence range of the component lifting point, and the union of the buffer zones of all lifting points is calculated to obtain the lifting area.

[0085] In one embodiment, information on prefabricated components at the construction site can be imported into the BIM model, and the lifting point location of each component can be marked. The spatial coordinates corresponding to each lifting point include three-dimensional spatial location data, including X, Y, and Z coordinates. These spatial coordinates can be used to determine the actual location of the lifting point on the construction site, providing a basis for subsequent area division.

[0086] The three-dimensional spatial coordinates of each lifting point are projected onto the horizontal reference plane of the construction site. Based on the operating radius of the lifting equipment, the size of the lifting components, and the safety spacing, the horizontal radius of influence or rectangular range of the lifting point is determined. This horizontal range of influence is used to describe the potential working area of ​​the lifting point on the ground plane.

[0087] A buffer zone is set up for each lifting point to cover any offset or swaying that may occur during the lifting process. The size of the buffer zone can be adjusted according to the weight of the component, the lifting height, and the operational safety factor. Subsequently, the buffer zones of all lifting points are spatially unioned to obtain the overall coverage area of ​​the entire lifting operation. This overall area is the lifting area, used for subsequent risk analysis, dynamic monitoring, and work plan development.

[0088] It is important to note that during implementation, the buffer zone can be visually marked to allow construction personnel to intuitively understand the extent of each hoisting area. Furthermore, this hoisting area can be combined with personnel location information and construction environment data for high-risk node identification and early warning control.

[0089] Most importantly, identifying high-risk lifting nodes based on the lifting risk distribution map, and combining this with real-time early warning and coordinated control based on operator location information and environmental meteorological information, also includes: High-risk lifting nodes are identified based on the lifting risk distribution map, and a list of high-risk nodes is generated. By matching the list of high-risk nodes with the location information of workers, the correspondence between high-risk node personnel is generated; Based on the analysis of the correspondence between personnel at high-risk nodes using environmental meteorological information, a suitability assessment of the node's work environment is generated. Real-time hoisting early warning information is generated by utilizing the personnel correspondence at high-risk nodes and the suitability assessment of the node operation environment; By issuing control commands based on real-time hoisting early warning information, coordinated control of high-risk hoisting nodes can be achieved.

[0090] In one embodiment, each hoisting area in the hoisting risk distribution map is scanned according to its risk value to identify nodes with a risk level higher than a preset threshold. For each high-risk node, its spatial coordinates, the number of the component to which it belongs, the stress condition of the node, and its risk level are recorded. All nodes that meet the conditions are summarized to form a list of high-risk nodes for subsequent matching and control.

[0091] Real-time data collection of on-site personnel location information, including personnel ID, current location coordinates, and movement speed. Matching personnel's current location with the spatial coordinates of high-risk nodes determines whether personnel are within or about to enter the horizontal and vertical influence range of high-risk nodes. A node-person correspondence table is generated for matched personnel, including personnel ID, node ID, distance between personnel and node, and potential exposure time.

[0092] Collect environmental meteorological information at the construction site, including data on wind speed, wind direction, precipitation, temperature, humidity, and visibility. Analyze the environmental data for each high-risk node to determine whether the current environment may exacerbate the hoisting risk. For example, strong winds may cause the hoisted components to swing more. Based on the spatial characteristics of the node, the location of the workers, and environmental conditions, generate a node operation environment suitability assessment, including risk level correction values ​​and safe operation recommendations.

[0093] By integrating the personnel correspondence at high-risk nodes with environmental suitability assessments, real-time early warning information is generated. This information includes: a list of personnel about to enter the high-risk node area, the affected node number, the risk level, the type of potential hazard, and safety tips. The early warning information can be displayed in real time through the construction site monitoring platform or mobile terminal, making it easier for managers and workers to keep abreast of the safety situation.

[0094] Based on real-time early warning information, specific control instructions are issued, including: prompting personnel to evacuate high-risk areas; adjusting the hoisting sequence and delaying hoisting operations at high-risk points; pausing or slowing down the hoisting speed to ensure operational safety. Linked control is implemented for hoisting equipment, personnel, and the site environment, such as through on-site warning signs, audible prompts, and automatic limiting functions for hoisting machinery, to achieve dynamic safety management of high-risk points. All control actions are logged, including triggering conditions, execution time, personnel involved, and point information, for subsequent safety analysis and optimization.

[0095] Of particular importance, matching the list of high-risk nodes with the location information of workers also includes: Based on the list of high-risk nodes, high-risk nodes in the current work area are selected, and a set of high-risk nodes in the area is generated. By acquiring the real-time location information of the workers, the workers are spatially matched with high-risk nodes in the area to generate a preliminary node-person correspondence. Based on the preliminary node personnel correspondence, the distance and density of workers around each high-risk node are calculated, and node personnel risk correlation indicators are generated. By using risk correlation indicators for node personnel, the corresponding relationships of node personnel are corrected and confirmed, and the final corresponding relationships of high-risk node personnel are generated.

[0096] In one embodiment, spatial boundary data of the current construction area is obtained based on the hoisting risk distribution map and the high-risk node list, including the horizontal plane range and the vertical working height range. The coordinates of nodes in the high-risk node list are filtered against the spatial boundary of the current work area to extract nodes located within that area. The filtered nodes are then compiled into a "high-risk node set within the area," recording the node's number, location coordinates, risk level, and associated component information.

[0097] Real-time collection of worker location information, including worker ID, current location coordinates, and direction of movement. For each node in the set of high-risk nodes within the area, calculate its horizontal and vertical distances to the workers. Determine whether workers are within the influence range of high-risk nodes, including the horizontal buffer zone of the lifting point and the working height range. Establish a preliminary correspondence between workers within the influence range and nodes, forming a preliminary node-person correspondence table, including worker ID, node ID, spatial distance, and potential exposure time.

[0098] Statistical analysis was performed on the preliminary node personnel correspondence table to calculate the number, density, and average distance of workers around each high-risk node. Based on worker density and distance, the risk level of each high-risk node was quantified, generating node personnel risk correlation indicators. These indicators are used to assess the potential threat level of that node to personnel under current operational conditions, providing a basis for subsequent management and control.

[0099] The risk correlation indicators of personnel at each node are analyzed in conjunction with the preliminary personnel correspondence at each node, and personnel with mismatches or those in adjacent affected areas are corrected. The preliminary correspondence is dynamically updated due to personnel movement or changes in the node environment to confirm the actual exposure status of workers. The final high-risk node personnel correspondence table is output, including the risk level of each node, the list of exposed personnel, and the corresponding spatial distance and risk indicators, providing data support for real-time early warning and coordinated management of hoisting operations.

[0100] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, 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 the equivalents of the application are intended to be included within the invention.

[0101] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An intelligent management and control method for site hoisting operation risks of fabricated structures, characterized in that, The method comprises the following steps: Step S1: acquiring multi-source operation data in a hoisting operation cycle, including hoisting equipment operation parameters, component hoisting point force data, operation personnel positioning information and environmental meteorological information; Step S2: confirming the dynamic stability of the hoisting equipment in the operation cycle according to the change degree of the hoisting equipment operation parameters at each time and the adjacent time and the environmental disturbance intensity of the adjacent time; Step S3: confirming the risk correlation coefficient of the corresponding hoisting area according to the similarity of the hoisting equipment operation parameter change trend of any two hoisting areas in the operation cycle and the difference of the corresponding dynamic stability; Step S4: adjusting the dynamic stability difference of the corresponding hoisting area in the operation cycle based on the risk correlation coefficient of any two hoisting areas, and obtaining the corrected regional risk distance; Step S5: performing risk clustering classification on the hoisting area according to the corrected regional risk distance, and generating a hoisting risk distribution map; Based on the hoisting risk distribution map, high-risk hoisting nodes are identified, and operation personnel positioning information and environmental meteorological information are combined for real-time early warning and linkage control.

2. The method of claim 1, wherein the method further comprises: Step S3 comprises the following steps: Step S31: calculating the absolute value cumulative sum of the hoisting equipment operation parameter change rate difference value of any two hoisting areas in the corresponding period in the operation cycle as the overall operation difference of the corresponding two hoisting areas in the cycle; Step S32: arranging the hoisting equipment operation parameters of each hoisting area in the operation cycle in time sequence to obtain an equipment operation parameter sequence; Step S33: confirming the operation parameter change curve of the hoisting equipment based on the equipment operation parameter sequence and the overall operation difference, and confirming the risk correlation coefficient of the corresponding hoisting area according to the difference between the similarity of the operation parameter change curve and the preset standard operation change curve and the corresponding dynamic stability.

3. The method of claim 2, wherein the method further comprises: The method for obtaining the similarity between the operation change curve and the preset standard operation change curve comprises: Obtaining the operation parameter change curve of any two hoisting areas in the operation cycle, wherein the operation parameters include at least one of the lifting speed, the sling tension and the boom swing amplitude; Comparing the operation parameter change curve with the preset standard operation change curve to determine the parameter change consistency at each time to represent the similarity of the operation change curves of the two hoisting areas.

4. The method of claim 2, wherein the method further comprises: Confirming the risk correlation coefficient of the corresponding hoisting area according to the difference between the similarity of the operation parameter change curve and the preset standard operation change curve and the corresponding dynamic stability comprises: Confirming a first risk correlation coefficient according to the difference between the similarity of the operation parameter change curve and the preset standard operation change curve and the corresponding dynamic stability; Confirming a second risk correlation coefficient through the similarity between the operation parameter change curve and the preset standard operation change curve and the force deviation degree of the corresponding component hoisting point; Taking the average of the first risk correlation coefficient and the second risk correlation coefficient as the risk correlation coefficient of the corresponding hoisting area.

5. The method of claim 4, wherein the method further comprises: The method for obtaining the force deviation degree of the corresponding component hoisting point comprises: Extracting the force amplitude and force change trend of the component hoisting point force data; Confirm the fluctuation amplitude and change rate in the hoisting process according to the force amplitude and force change trend; Confirm the timing change characteristics of the hoisting point force through the fluctuation amplitude and change rate; Determine the force fluctuation characteristics and timing regularity of the hoisting point by using the correlation degree of the operation parameter change curve and the timing change characteristics; Calculate the force deviation degree of each hoisting point based on the force fluctuation characteristics and timing regularity.

6. The method of claim 5, wherein the method further comprises: Confirm the timing change characteristics of the hoisting point force through the fluctuation amplitude and change rate, including: According to the corresponding relationship between the force fluctuation amplitude and change rate, obtain the timing change regularity of the hoisting point force; According to the timing change regularity of the hoisting point force, extract the stage fluctuation characteristics and continuous change characteristics of the hoisting point force; Determine the timing change characteristics of the hoisting point force based on the stage fluctuation characteristics and continuous change characteristics.

7. The method of claim 1, wherein the method further comprises: Step S4 includes the following steps: Step S41: Obtain the real-time attitude change data and load swing amplitude data of any two hoisting areas in the operation cycle, and calculate the area attitude change rate according to the real-time attitude change data and load swing amplitude data to generate attitude dynamic parameter data; Step S42: Calculate the stability change gradient between the hoisting areas according to the attitude dynamic parameter data, and linearly correct the stability change gradient in combination with the risk correlation coefficient to generate stability correction data; Step S43: Use the stability correction data to correct the amplitude of the dynamic stability difference between the hoisting areas to generate dynamic stability correction data; Step S44: Adjust the risk distance change rate between the hoisting areas according to the dynamic stability correction data to obtain the corrected area risk distance.

8. The method of claim 7, wherein the method further comprises: The method for obtaining the dynamic stability difference between the hoisting areas includes: Calculate the gradient difference between any two hoisting areas based on the stability change gradient between the hoisting areas; Perform average absolute difference calculation through the gradient difference between any two hoisting areas, and take the calculation result as the dynamic stability difference. 9.The method according to claim 1, wherein, The confirmation method of the hoisting area includes: Confirm the spatial coordinates of the component hoisting point through the pre-set construction site hoisting BIM model; Confirm the horizontal influence range of the component hoisting point according to the projection position of the spatial coordinates on the pre-set reference plane; Determine the buffer area corresponding to the hoisting point based on the horizontal influence range of the component hoisting point, and calculate the union set of all hoisting point buffer areas to obtain the hoisting area.

10. An assembled site hoisting operation risk intelligent management and control system, characterized in that, The prefabricated construction site hoisting operation risk intelligent management and control system for executing the prefabricated construction site hoisting operation risk intelligent management and control method as claimed in claim 1 includes: A data acquisition module for acquiring multi-source operation data in a hoisting operation cycle, including hoisting equipment operation parameters, component hoisting point force data, operation personnel positioning information and environmental meteorological information; A stability analysis module for confirming the dynamic stability of the hoisting equipment in the operation cycle according to the change degree of the hoisting equipment operation parameters at each time and its adjacent time and the environmental disturbance intensity of the adjacent time; A risk correlation module for confirming the risk correlation coefficient of the corresponding hoisting area according to the similarity degree of the hoisting equipment operation parameter change trend and the difference of the corresponding dynamic stability between any two hoisting areas in the operation cycle. A risk correction module is configured to adjust the dynamic stability difference of the corresponding hoisting area in a work cycle based on the risk correlation coefficient of any two hoisting areas, and obtain a corrected regional risk distance. A risk management module is configured to perform risk clustering and classification on the hoisting area according to the corrected regional risk distance, generate a hoisting risk distribution map, identify a high-risk hoisting node based on the hoisting risk distribution map, and combine the work personnel positioning information and the environmental meteorological information to perform real-time early warning and linkage management.

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