Bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence

Abstract

The application discloses a bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence, relates to the technical field of bridge construction safety and intelligent scheduling, and discloses the bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence, which solves the problem that traditional methods cannot dynamically evaluate wind load risks and equipment conflicts through three-dimensional wind field modeling, dynamic safety operation domain calculation, multi-dimensional risk fusion and intelligent optimization scheduling, can accurately evaluate risks and dynamically predict conflicts in real time, improves the effectiveness of a bridge construction scheduling scheme, and further improves construction safety and efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge construction safety and intelligent scheduling, and particularly relates to a bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence. BACKGROUND

[0002] Traditional bridge construction scheduling risk assessment methods mainly rely on static safety regulations and experience judgments, and are difficult to deal with safety hazards caused by dynamic wind load in high-altitude operations. Especially when multiple large construction equipment are working together, strong winds will not only cause the vibration displacement of construction equipment to be amplified, but also change the dynamic safety operation domain of the equipment, increasing the collision risk and spatial interference risk between construction equipment. The existing technology often ignores the spatial interference between construction equipment and the running state of the equipment under the influence of wind load, resulting in inaccurate risk assessment results and difficulty in effectively guiding construction scheduling.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence, aiming to improve the effectiveness of bridge construction intelligent scheduling.

[0005] To achieve the above purpose, the present application provides a bridge construction intelligent scheduling risk assessment method based on artificial intelligence, which comprises:

[0006] By deploying meteorological sensing devices at preset positions of the bridge structure and preset construction equipment, wind load data is obtained; and based on construction data acquisition devices deployed on the construction equipment, construction data of the construction equipment is obtained; the construction data includes equipment position data, equipment attitude data and equipment operation data;

[0007] Based on the wind load data, the three-dimensional space in which the preset bridge construction area is located is divided into a three-dimensional grid to establish a three-dimensional wind field model of the bridge construction area; the three-dimensional wind field model is used to output the wind speed vector value of each grid point;

[0008] Based on the construction data of the construction equipment and the wind speed vector value of the position where the construction equipment is located, the dynamic safety operation domain of each construction equipment under the action of the current wind load is calculated;

[0009] When there is an overlap between the dynamic safety operation domains of the construction equipment, based on the wind load characteristics of the three-dimensional wind field model and the overlapping region of the dynamic safety operation domains, the collision risk index and the spatial interference risk index generated by the overlap of the dynamic safety operation domains between the construction equipment are calculated;

[0010] The collision risk index is weighted and fused with a spatial interference risk index and an operation risk index of the construction equipment itself to obtain a comprehensive risk value of the construction equipment group;

[0011] When the comprehensive risk value exceeds a preset risk threshold value, an optimized scheduling scheme is generated according to construction data of the current construction equipment, a wind speed vector value and a preset construction task progress constraint, so as to avoid a risk wind field region and a conflict of a dynamic safety operation domain of the construction equipment.

[0012] In an embodiment, the step of performing three-dimensional grid division on a three-dimensional space where a preset bridge construction region is located based on the wind load data to establish a three-dimensional wind field model of the bridge construction region comprises:

[0013] The wind load data is mapped to corresponding three-dimensional grid points according to spatial coordinate positions of meteorological sensing devices;

[0014] For grid points lacking wind load data, a reverse distance weighted difference method is used to calculate wind load data according to wind load data of surrounding grid points;

[0015] Based on wind load data of all grid points, the three-dimensional wind field model of the bridge construction region is established.

[0016] In an embodiment, the step of calculating the dynamic safety operation domain of the construction equipment under the action of the current wind load comprises:

[0017] The type identification of the construction equipment and the construction data of the construction equipment are obtained;

[0018] Based on the type identification of the construction equipment, a preset equipment physical parameter database is called to obtain equipment size, maximum working amplitude, structural damping coefficient and motion component parameters of the construction equipment;

[0019] The wind speed vector value of the grid position where the motion component of the construction equipment is located is extracted from the three-dimensional wind field model;

[0020] According to the construction data, the equipment size, the maximum working amplitude, the structural damping coefficient, the motion component parameters and the wind speed vector value of the grid position where the motion component is located of the construction equipment, a structural response generated by the wind load acting on the motion component of the construction equipment is calculated;

[0021] Based on the structural response, a motion trajectory envelope range of the motion component of the construction equipment is determined to determine the current dynamic safety operation domain of the construction equipment.

[0022] In an embodiment, the step of calculating the structural response generated by the wind load acting on the moving part of the construction equipment according to the construction data of the construction equipment, the equipment size, the maximum working amplitude, the structural damping coefficient, the moving part parameter, and the wind speed vector value of the grid position where the moving part is located comprises:

[0023] Based on the equipment posture data, the equipment operation data, the equipment size, and the maximum working amplitude, a three-dimensional space geometric model of the construction equipment in the current state is established to determine the wind load action surface and the normal vector direction thereof;

[0024] Based on the moving part parameter, the three-dimensional wind speed vector value of the grid position where the moving part is located, the wind load action surface, and the normal vector direction thereof, the normal wind pressure on the wind load action surface is calculated;

[0025] The normal wind pressure on the wind load action surface is converted into the force and torque acting on the main structure of the construction equipment according to the position of the action point;

[0026] Based on the structural damping coefficient and the three-dimensional space geometric model, a multi-body dynamics model of the equipment is constructed;

[0027] Taking the force and torque acting on the main structure of the construction equipment as input, the multi-body dynamics model is solved to calculate the structural response generated by the wind load acting on the moving part of the construction equipment in real time; the structural response includes the displacement response and vibration amplitude of the moving part.

[0028] In an embodiment, the three-dimensional wind field model is also used to output the wind load characteristics of each grid point; the wind load characteristics include the average wind speed, the wind speed fluctuation intensity, and the dominant wind direction; when there is an overlap of dynamic safety operation domains between the construction equipment, based on the three-dimensional wind field model and the wind load characteristics of the overlap region of the dynamic safety operation domain, the step of calculating the collision risk index and the spatial interference risk index generated by the overlap of the dynamic safety operation domains between the construction equipment comprises:

[0029] When there is an overlap of dynamic safety operation domains between the construction equipment, the volume of the overlap region of the dynamic safety operation domain and the proportion thereof in each dynamic safety operation domain are calculated;

[0030] From the three-dimensional wind field model, the wind load characteristics of the overlap region of the dynamic safety operation domain are extracted;

[0031] Based on the proportion of the volume of the overlap region of the dynamic safety operation domain in each dynamic safety operation domain, the type identification of the construction equipment, the equipment position data of the construction equipment, and the wind load characteristics of the overlap region of the dynamic safety operation domain, the potential collision severity score between the construction equipment is determined through a preset risk mapping rule table;

[0032] Based on the device posture data and the device operation data, a current motion trend of the device is evaluated;

[0033] In combination with the potential collision severity score and the motion trend, a collision risk index is generated;

[0034] Based on the volume of the dynamic safety operation domain overlap region and the proportion of each dynamic safety operation domain in the overlap region and the wind speed fluctuation intensity, a probability of mutual interference of the construction devices in the overlap region is predicted to generate a spatial interference risk index.

[0035] In an embodiment, the method further comprises:

[0036] A construction device type combination library is established;

[0037] For each device combination, a risk level under multiple relative positions, multiple wind load conditions, and multiple dynamic safety operation domain overlap proportions is determined through historical accident analysis or simulation;

[0038] The risk level is converted into a potential collision severity score to form the risk mapping rule table.

[0039] In an embodiment, the step of weighting and fusing the collision risk index, the spatial interference risk index, and the operation risk index of the construction device itself to obtain a comprehensive risk value of the construction device group comprises:

[0040] For each construction device, its own operation risk index is calculated according to its load state, continuous working time, and operator state;

[0041] Based on the device type and the importance of the location, a device weight is set;

[0042] The collision risk index and the spatial interference risk index between the construction devices are weighted and averaged to obtain a corresponding risk contribution value;

[0043] The risk contribution values between all construction devices with dynamic safety operation domain overlap are added, and the self-operation risk indices of all construction devices are added after being adjusted by the device weight, to obtain a comprehensive risk value of the construction device group.

[0044] In an embodiment, the step of generating a corresponding optimization scheduling scheme to avoid the risk wind field region and the dynamic safety operation domain conflict of the construction device, when the comprehensive risk value exceeds a preset risk threshold, based on the construction data of the current construction device, the wind speed vector value, and a preset construction task progress constraint, comprises:

[0045] High-risk construction devices and overlap regions between the high-risk construction devices that have dynamic safety operation domain overlap conflict at the current moment are extracted;

[0046] obtain future prediction data of the three-dimensional wind field model, map the overlapping area to the three-dimensional wind field model, and identify a risk wind field area with a wind field intensity greater than a preset intensity within a preset prediction period;

[0047] construct a scheduling optimization model, with an objective function of minimizing the delay of the construction period, and limit its constraint conditions:

[0048] a plurality of high-risk construction equipment cannot work or move simultaneously in the risk wind field area during the conflict period, and the path of the construction equipment in the risk wind field area during the conflict period cannot cross;

[0049] a safety buffer zone is reserved in the adjacent area of the construction equipment working in the risk wind field area;

[0050] An improved genetic algorithm is used to solve the scheduling optimization model to generate a scheduling optimization scheme for reassigning construction equipment tasks, adjusting the work time window and the work location.

[0051] In an embodiment, the improved genetic algorithm includes:

[0052] Initialize the population, where each chromosome encodes a construction equipment task allocation scheme and the corresponding time window and work location sequence;

[0053] Calculate the individual fitness value, which is obtained by weighted sum of the construction period objective function value, resource utilization score, and penalty value for violating the constraint conditions;

[0054] Perform individual selection operation of elite reservation strategy, and reserve individuals whose fitness ranking reaches a preset requirement to directly enter the next generation;

[0055] Perform multi-point crossover operation to exchange continuous fragments of construction equipment task sequences in parent chromosomes under the premise of ensuring that the child chromosome meets the task logical dependency relationship;

[0056] Perform mutation operation, including the following types:

[0057] Under the condition of meeting the task dependency relationship, randomly exchange the execution order of two non-critical tasks in the same construction equipment task sequence;

[0058] Move the original work time of the task combination of high-risk construction equipment prohibited by the constraint condition to a conflict-free time window;

[0059] For tasks that need to enter the risk wind field area, specify an alternative work location that meets the safety buffer zone requirements of the constraint conditions;

[0060] Iterate and evolve until the preset termination condition is met, and output the scheduling scheme with the highest fitness and meeting all constraints.

[0061] Furthermore, to achieve the above objectives, this application also proposes an artificial intelligence-based intelligent scheduling risk assessment system for bridge construction. The system includes: a memory, a processor, and an artificial intelligence-based intelligent scheduling risk assessment program for bridge construction stored in the memory and executable on the processor. The artificial intelligence-based intelligent scheduling risk assessment program for bridge construction is configured to implement the steps of the artificial intelligence-based intelligent scheduling risk assessment method for bridge construction.

[0062] This application provides an intelligent scheduling risk assessment method and system for bridge construction based on artificial intelligence. Through three-dimensional wind field modeling, dynamic safe operating domain calculation, multi-dimensional risk fusion, and intelligent optimization scheduling, it solves the problem that traditional methods cannot dynamically assess wind load risks and equipment conflicts. It can accurately assess risks in real time and dynamically predict conflicts, improve the effectiveness of bridge construction scheduling schemes, and thus improve construction safety and efficiency. Attached Figure Description

[0063] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0064] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A flowchart illustrating an embodiment of the AI-based intelligent scheduling risk assessment method for bridge construction according to this application;

[0066] Figure 2 For this application Figure 1 A detailed flowchart of step S200;

[0067] Figure 3 A flowchart illustrating another embodiment of the AI-based intelligent scheduling risk assessment method for bridge construction in this application;

[0068] Figure 4 For this application Figure 3 A detailed flowchart of step S340;

[0069] Figure 5 For this application Figure 1 Detailed flowchart of step S400;

[0070] Figure 6This is a flowchart illustrating yet another embodiment of the AI-based intelligent scheduling risk assessment method for bridge construction in this application.

[0071] Figure 7 For this application Figure 1 A detailed flowchart of step S500;

[0072] Figure 8 For this application Figure 1 A detailed flowchart of step S600;

[0073] Figure 9 This is a flowchart illustrating yet another embodiment of the AI-based intelligent scheduling risk assessment method for bridge construction in this application.

[0074] Figure 10 This is a structural schematic diagram of an embodiment of the intelligent scheduling and risk assessment system for bridge construction based on artificial intelligence, as provided in this application.

[0075] Explanation of icon numbers:

[0076] 10. Memory; 20. Processor.

[0077] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0078] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0079] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0080] In the prior art, the traditional bridge construction scheduling risk assessment method mainly relies on static safety regulations and experience judgment, which is difficult to cope with the safety hazards caused by dynamic wind load in high-altitude operation. Especially when multiple large construction equipment are working together, strong wind will not only cause the vibration displacement of construction equipment structure to be amplified, but also increase the collision risk and spatial interference risk between construction equipment. The existing method often ignores the spatial interference between construction equipment and the running state of the equipment itself, resulting in inaccurate risk assessment results and difficulty in effectively guiding construction scheduling. For example, when the tower crane and the bridge erecting machine work together in a strong wind environment, the traditional method cannot predict the cross risk of the dynamic safety area of the two in real time, which is easy to cause safety accidents.

[0081] To solve the above problems, the applicant finds that the influence of dynamic wind load on construction equipment has spatial difference and time variation, and the interaction risk between equipment is closely related to the equipment motion state and wind field distribution. Based on this, first, the wind load and equipment operation data are collected in real time through multiple sensors to build a three-dimensional wind field model to reflect the dynamic changes of the wind field. Secondly, a calculation method of the dynamic safety operation domain of the equipment needs to be established to quantify the actual activity range of the equipment under the action of wind load. Finally, for the overlapping problem of the dynamic safety domain of the equipment, a risk assessment model is proposed by fusing multiple factors, and an optimized scheduling scheme is generated combined with the construction progress constraints.

[0082] Based on this, the embodiment of the present application provides a bridge construction intelligent scheduling risk assessment method based on artificial intelligence, referring to Figure 1 The bridge construction intelligent scheduling risk assessment method based on artificial intelligence of the present application includes steps S100-S600, wherein:

[0083] Step S100, through the meteorological sensing device deployed at the preset position of the bridge structure and the preset construction equipment, wind load data is obtained; and based on the construction data acquisition device deployed on the construction equipment, the construction data of the construction equipment is obtained; the construction data includes equipment position data, equipment attitude data and equipment operation data.

[0084] In this embodiment, the meteorological sensing device is a sensor array for measuring wind speed and direction, and specifically an ultrasonic anemometer or a laser radar device. The preset position can be the top of the bridge structure or the area around the bridge structure that is easily affected by wind load. These meteorological sensing devices can collect key wind load data such as wind speed and direction in real time, ensuring the accuracy and timeliness of the data. The construction data acquisition device includes a GPS positioning system, a gyroscope, an accelerometer, etc., for real-time monitoring of the position, attitude (such as inclination angle, rotation direction) and running state (such as working speed, load condition) of the construction equipment, i.e. equipment position data, equipment attitude data and equipment operation data, to provide a data basis for subsequent risk assessment and intelligent scheduling.

[0085] In an implementable embodiment, after the wind load data and the construction data are acquired, the method further comprises preprocessing the wind load data and the construction data. Specifically, the preprocessing of the wind load data and the construction data can comprise filtering the wind speed and wind direction data collected by the meteorological sensing device to remove noise interference and improve data accuracy; and checking and correcting the equipment position data, equipment attitude data and equipment operation data collected by the construction data acquisition device to ensure the integrity and reliability of the data. In addition, the preprocessed data can be organized in time sequence to facilitate subsequent time series analysis and pattern recognition. Through the preprocessing of the data, more accurate and reliable data support is provided for subsequent risk assessment and intelligent scheduling.

[0086] In step S200, based on the wind load data, a three-dimensional grid division is performed on a preset bridge construction area to establish a three-dimensional wind field model of the bridge construction area. The three-dimensional wind field model is used to output the wind speed vector value of each grid point.

[0087] In this embodiment, based on the wind load data, a computational fluid dynamics (CFD) method or a data-driven model can be used to simulate the wind field of the three-dimensional grid of the preset bridge construction area. Specifically, the CFD method simulates the three-dimensional flow of the wind field in the bridge construction area by solving the Navier-Stokes equation. The data-driven model uses a machine learning algorithm to learn the spatiotemporal distribution law of the wind field from historical wind load data, and then predicts the current and future wind field state. Both methods can output the wind speed vector value of each grid point, including the wind speed and direction, which provides key parameters for subsequent risk assessment. It can be understood that when establishing the three-dimensional wind field model, the application can also consider the influence of topography, landform, buildings and construction equipment on the wind field, and make necessary model corrections to improve the accuracy and applicability of the model. The three-dimensional wind field model can be updated according to the wind load data to reflect the dynamic changes of the wind field, ensuring the real-time and accuracy of the subsequent risk assessment.

[0088] In an implementable embodiment, with reference to Figure 2 , step S200 comprises steps S210-S230, wherein:

[0089] In step S210, the wind load data is mapped to the corresponding three-dimensional grid point according to the spatial coordinate position of the meteorological sensing device.

[0090] In this embodiment, the bridge construction area space is discretized into multiple regular cubic units, which can be specifically divided by a grid size with a side length ranging from 0.5 meters to 2 meters to finely characterize the wind field distribution. By mapping the wind speed and direction data collected by the meteorological sensing device to the corresponding grid points in the preset three-dimensional grid model of the bridge construction area according to the spatial coordinates of the installation position, the spatial positioning accuracy of the wind load data is ensured, providing accurate data input for subsequent three-dimensional wind field simulation.

[0091] In step S220, for the grid points lacking wind load data, the inverse distance weighted interpolation method is used to calculate the wind load data based on the wind load data of the surrounding grid points.

[0092] In this embodiment, the inverse distance weighted interpolation method refers to calculating the missing point data based on the spatial proximity principle, which can specifically use a neighborhood range with the target grid point as the center and a radius not exceeding 3 meters for interpolation, ensuring the spatial continuity of the wind load data. Therefore, different weights are assigned to the distances between the surrounding grid points with known wind load data and the grid point to be interpolated, with closer distances having larger weights and farther distances having smaller weights. The wind load data of the grid point to be interpolated is obtained by weighted summation, which can effectively fill in the data gaps and improve the integrity and accuracy of the three-dimensional wind field model, providing more comprehensive wind field information support for subsequent risk assessment.

[0093] In step S230, a three-dimensional wind field model of the bridge construction area is established based on the wind load data of all grid points.

[0094] In this embodiment, after filling in the data by traversing all unmeasured grid points, a continuous three-dimensional wind field distribution is generated based on the wind load data of all grid points using spatial interpolation surface fitting technology, and finally a wind field model covering the entire preset bridge construction area is formed to comprehensively reflect the dynamic change characteristics of the wind field, including the spatiotemporal distribution of wind speed and direction. By continuously updating the wind load data, the three-dimensional wind field model can capture the subtle changes in the wind field in real time, providing accurate wind field information for subsequent risk assessment.

[0095] Compared with the prior art, the traditional method only relies on discrete sensor data to evaluate the local wind field, resulting in blind areas in the equipment safety domain calculation. The present scheme effectively solves the problem of data missing caused by insufficient sensor layout density by using grid space division combined with inverse distance weighted interpolation, and ensures the physical reasonableness of the wind field model by using the spatial correlation principle. That is, the present application realizes high-precision three-dimensional reconstruction of the wind field in the construction area, providing a reliable data basis for subsequent dynamic safety operation domain calculation, avoiding the risk of equipment collision caused by missing local wind load data, and significantly improving the safety of construction equipment group cooperation in complex wind field environment.

[0096] At step S300, a dynamic safe operation domain of each construction equipment under the current wind load is calculated based on the construction data of the construction equipment and the wind speed vector value of the position where the construction equipment is located.

[0097] In this embodiment, the dynamic safe operation domain refers to the range of activities that the construction equipment can maintain safe and stable operation under specific wind load conditions. When calculating the dynamic safe operation domain, the type of equipment, structural characteristics, operating state, and current wind speed, wind direction, and other wind load parameters need to be considered comprehensively. Specifically, a preset algorithm or multi-body dynamics simulation software is used to calculate or simulate the motion response of the construction equipment under the action of wind load, and the maximum range that meets the safety and stability requirements, i.e., the dynamic safe operation domain, is found. Within the dynamic safe operation domain, the construction equipment can resist the interference of wind load, maintain normal operating state, and avoid structural instability or collision accidents. The calculation result of the dynamic safe operation domain will serve as an important basis for subsequent risk assessment and optimization scheduling, ensuring the safe and efficient operation of the construction equipment in complex wind field environment.

[0098] In a possible implementation manner, the reference Figure 3 , step S300 includes steps S310-S350, wherein:

[0099] At step S310, the type identifier of the construction equipment and the construction data of the construction equipment are obtained.

[0100] In this embodiment, the equipment type identifier refers to a classification code used to distinguish different types of construction equipment, which can be implemented by using a combination of letters and numbers, for example, a tower crane is marked as TC-2500, and a bridge erecting machine is marked as JQJ-180. Through the identifier, the physical parameters of the corresponding equipment can be accurately called.

[0101] At step S320, based on the type identifier of the construction equipment, a preset equipment physical parameter database is called to obtain the equipment size, maximum working amplitude, structural damping coefficient, and motion component parameters of the construction equipment.

[0102] In this embodiment, the equipment physical parameter database refers to a structured data set that stores the inherent properties of various types of construction equipment, which can be constructed by using a relational database and includes fields such as equipment size, maximum working amplitude, and structural damping coefficient. For example, the arm length parameter of a tower crane can be stored as 65 meters, and the structural damping coefficient can be stored as 0.15. When the type identifier of the construction equipment is obtained, the physical parameters of the corresponding equipment are matched and called through the database. The equipment attitude data and operating data combined with the equipment size parameter can construct a three-dimensional geometric model to determine the wind load action surface.

[0103] At step S330, the wind speed vector value of the grid position where the motion component of the construction equipment is located is extracted from the three-dimensional wind field model.

[0104] In this embodiment, the moving part of the construction equipment refers to a key component that performs a specific function, such as the jib of a tower crane or the walking mechanism of a bridge crane. The position information of these moving parts can be determined through a GPS positioning system or pre-set spatial coordinates. After determining the spatial position of the moving parts, the wind speed vector values of the corresponding grid points can be extracted from the established three-dimensional wind field model, including wind speed magnitude and direction. The wind speed vector values reflect the dynamic action of wind load on the moving parts of the construction equipment and are input parameters for subsequent calculation of the dynamic safety operation domain.

[0105] In step S340, the structural response of the moving part of the construction equipment under the action of wind load is calculated based on the construction data of the construction equipment, the equipment size, the maximum working amplitude, the structural damping coefficient, the moving part parameters, and the wind speed vector values at the grid position where the moving part is located.

[0106] In this embodiment, the structural response refers to the dynamic deformation data of the equipment components under the action of wind load, which can be obtained by multi-body dynamics simulation, for example, the transverse displacement amplitude of the tower crane jib under the action of 8-level wind load. After extracting the wind speed vector values at the position of the moving part from the three-dimensional wind field model, the dynamic force generated by wind pressure on the equipment is calculated by combining the equipment structural damping coefficient and the dynamic equation. The displacement response and vibration amplitude data of the moving part can be simulated by inputting the dynamic force into the multi-body dynamics model.

[0107] In one possible implementation, with reference to Figure 4 Step S340 includes steps S341-S345, in which:

[0108] In step S341, a three-dimensional spatial geometric model of the construction equipment in the current state is established based on the equipment attitude data, equipment operation data, equipment size, and maximum working amplitude, to determine the wind load action surface and its normal vector direction.

[0109] In this embodiment, the three-dimensional spatial geometric model of the construction equipment can be constructed by computer graphics technology, specifically using polygonal mesh or NURBS surface to approximately represent the complex shape of the equipment. The equipment attitude data (such as inclination angle and rotation direction) is used to determine the actual orientation of the equipment in three-dimensional space, and the equipment operation data (such as working speed and load condition) affects the dynamic response characteristics of the equipment components. Combined with the equipment size and maximum working amplitude, the geometric model of the construction equipment in the current state can be accurately constructed, thereby determining the main action surface of the wind load and its normal vector direction. This geometric model is the basis for subsequent wind load action analysis, ensuring the accuracy of the structural response calculation.

[0110] Step S342, based on the moving component parameters, the three-dimensional wind speed vector value of the grid position where the moving component is located, the wind load action surface and its normal vector direction, the normal wind pressure on the wind load action surface is calculated.

[0111] In this embodiment, the normal wind pressure on the wind load action surface refers to the wind force component perpendicular to the wind load action surface, which is related to the wind speed, the wind load action area and the air density. In specific calculation, Bernoulli equation or wind pressure formula can be used to decompose the wind speed vector value into normal component perpendicular to the action surface and tangential component parallel to the action surface, and only the normal component is considered to generate pressure on the equipment. By combining the area of the action surface, the normal wind pressure can be obtained. The normal wind pressure is an important input parameter for subsequent dynamic analysis, which reflects the direct dynamic effect of wind load on the construction equipment.

[0112] Step S343, the normal wind pressure on the wind load action surface is converted into the force and torque on the main structure of the construction equipment according to the position of the action point.

[0113] In this embodiment, the normal wind pressure on the wind load action surface is distributed on the entire action surface. In order to convert it into specific force and torque on the main structure of the construction equipment, equivalent conversion is needed according to the position of the action point. Specifically, the normal wind pressure can be regarded as a concentrated force or a distributed force, and the equivalent force and the torque generated by the equivalent force on the main structure of the construction equipment are calculated according to the balance principle of force and the balance principle of torque. The size and direction of the force and torque will directly affect the dynamic response of the construction equipment.

[0114] Step S344, based on the structure damping coefficient and the three-dimensional space geometric model, the multi-body dynamics model of the equipment is constructed.

[0115] In this embodiment, the multi-body dynamics model of the equipment refers to a mathematical model for simulating the dynamic response of the construction equipment under the action of wind load, which can be constructed by using multi-rigid-body dynamics or flexible multi-body dynamics theory. The structure damping coefficient reflects the damping effect of the equipment material on vibration, which is an important parameter in the dynamics model. Combined with the established three-dimensional space geometric model, the connection relationship and relative motion between equipment components can be accurately described, so as to construct the multi-body dynamics model of the equipment. The model can simulate the displacement, velocity, acceleration and other dynamic responses of the equipment under the action of wind load, providing a theoretical basis for subsequent structure response calculation.

[0116] Step S345, taking the force and torque on the main structure of the construction equipment as input, the multi-body dynamics model is solved to simulate and calculate the structure response generated by the wind load acting on the moving component of the construction equipment in real time; the structure response includes the displacement response and vibration amplitude of the moving component.

[0117] In this embodiment, after the multi-body dynamics model of the construction equipment is constructed, the equivalent forces and torques calculated are taken as inputs, and numerical solutions are obtained through dynamic simulation software or self-programming, so that the structural response data of the construction equipment under the action of wind load, such as displacement response and vibration amplitude, are obtained. The structural response reflects the deformation of the equipment components under the action of wind load, and through the analysis of the structural response data, the stability state of the construction equipment under the action of wind load can be further judged, thereby providing a basis for the calculation of the subsequent dynamic safety operation domain.

[0118] Compared with the prior art, the traditional method usually estimates the influence of wind load by using a static safety factor or an empirical formula, and cannot accurately reflect the coupling effect of the dynamic motion state of the equipment and the wind load. The scheme can accurately simulate the dynamic response of the moving parts of the equipment under the action of wind load by constructing a multi-body dynamics model and performing real-time simulation, thereby avoiding the accumulation of errors caused by simplification. That is, the application can accurately calculate the structural response of the construction equipment under the action of dynamic wind load, accurately identify the actual displacement range and vibration amplitude of the moving parts, and provide reliable data support for the determination of the dynamic safety operation domain. This effectively solves the calculation deviation problem of the safety domain caused by the neglect of the dynamic characteristics of the equipment in the traditional method, and reduces the collision risk of the construction equipment caused by wind-induced vibration.

[0119] In step S350, the motion trajectory envelope range of the moving parts of the construction equipment is determined based on the structural response, so as to determine the current dynamic safety operation domain of the construction equipment.

[0120] In this embodiment, the motion trajectory envelope range of the moving parts of the construction equipment refers to the maximum activity range boundary that the moving parts of the construction equipment can reach under the action of wind load. The range is determined by combining the structural response data (such as displacement response and vibration amplitude) with the current working state and motion characteristics of the equipment. Specifically, the structural response data is input into a preset algorithm or simulation model, the actual motion of the construction equipment under the action of wind load is simulated, the motion trajectory of the moving parts is obtained, and the maximum possible activity range of the moving parts, i.e., the motion trajectory envelope range, is determined. The range not only considers the geometric size of the equipment under static conditions, but also integrates the dynamic response characteristics under the action of wind load, and therefore can more accurately reflect the actual safety operation range of the construction equipment in a complex wind field environment. The determination of the dynamic safety operation domain provides an important guarantee for the safe and efficient operation of the construction equipment, and avoids the safety risks such as equipment collision or structural instability caused by wind load. For example, the tower crane jib may produce a 1.2-meter lateral swing under the action of strong wind, and the dynamic safety operation domain will include the maximum envelope space of the swing trajectory.

[0121] It can be understood that the prior art only delimits a fixed safety area according to the static parameters of the equipment, without considering the influence of dynamic wind load on the moving parts of the equipment. The present scheme can accurately reflect the actual movement range of the equipment under the action of wind load by real-time acquisition of wind field data and calculation of structural dynamic response. For example, the existing technology fixes the tower crane operation radius as 65 meters, while the present scheme can calculate the dynamic change of the effective operation radius caused by the swing of the boom as 63.5-66.2 meters according to the real-time wind speed, thereby establishing a more accurate safe operation domain. That is, the present application can dynamically adjust the safe operation range of the construction equipment, effectively avoiding accidental collisions between equipment caused by wind-induced vibration. By accurately calculating the actual displacement trajectory of the moving parts, the problem of insufficient reliability of the traditional static safety area division method under strong wind conditions is solved. The method can provide real-time spatial safety boundary data for multi-equipment collaborative operation, reducing the risk of spatial conflict in high-altitude construction.

[0122] Step S400, when there is an overlap of dynamic safety operation domains between construction equipment, based on the three-dimensional wind field model and the wind load characteristics of the overlap region of the dynamic safety operation domain, the collision risk index and the spatial interference risk index generated by the overlap of the dynamic safety operation domains between the construction equipment are calculated.

[0123] In the present embodiment, when the construction equipment collaborates in a complex wind field environment, its dynamic safety operation domain may have an overlap region. These overlapping regions mean that the construction equipment may have spatial conflicts under certain wind load conditions, thereby increasing the risk of collision or spatial interference. In order to accurately assess these risks, the present scheme proposes a calculation method for the collision risk index and the spatial interference risk index based on the three-dimensional wind field model and the overlap region of the dynamic safety operation domain.

[0124] The collision risk index refers to the possibility of direct physical collision between construction equipment due to the overlap of dynamic safety operation domains and the severity of the potential consequences. When calculating the collision risk index, the wind load characteristics of the overlap region (such as average wind speed, wind speed fluctuation intensity, and dominant wind direction) and factors such as the type, size, movement speed, and direction of the construction equipment need to be considered. Through a pre-set algorithm or simulation model, the actual movement of the construction equipment in the overlap region is simulated, and the dynamic action of the wind load is combined to calculate the probability of collision and the size of the collision force, thereby obtaining the collision risk index. The higher the index, the greater the risk of collision between the construction equipment.

[0125] The spatial interference risk index refers to non-physical spatial conflicts between construction equipment due to overlapping of dynamic safety operation domains, such as limited operation space and blocked view, which may affect the normal operation efficiency or safety of the construction equipment. When calculating the spatial interference risk index, the wind load characteristics of the overlapping region and the motion characteristics of the construction equipment also need to be considered. By evaluating the relative position relationship of the construction equipment in the overlapping region, the intersection of the motion trajectory and other factors, and combining the influence of wind load on the motion state of the construction equipment, the degree of spatial interference and its influence on the operation efficiency or safety of the construction equipment are calculated, so as to obtain the spatial interference risk index. The higher the index is, the greater the risk between the construction equipment due to spatial interference is.

[0126] By calculating the collision risk index and the spatial interference risk index, the present scheme can provide important protection for the safe and efficient operation of the construction equipment. When the dynamic safety domains of multiple equipment overlap, the wind load characteristics of the overlapping region are extracted, and the collision risk and the spatial interference risk are calculated through the preset risk evaluation rules. Finally, the various risk indexes are weighted and fused with the running state risk of the equipment itself. When the comprehensive risk exceeds the safety threshold, the equipment operation time and position are re-planned in combination with the construction progress requirement to avoid entering the high-risk wind field region.

[0127] In a feasible implementation manner, the three-dimensional wind field model is further used to output wind load characteristics of each grid point; the wind load characteristics include average wind speed, wind speed fluctuation intensity and dominant wind direction; with reference to Figure 5 , step S400 includes steps S410-S460, wherein:

[0128] Step S410, when the dynamic safety operation domains of the construction equipment overlap, the volume of the overlapping region of the dynamic safety operation domain and the proportion in each dynamic safety operation domain are calculated.

[0129] In this embodiment, the volume of the overlapping region of the dynamic safety operation domain refers to the volume of the intersecting region in which the safety operation ranges of the multiple construction equipment in the three-dimensional space overlap, which can be realized by using three-dimensional geometric Boolean operation to calculate the intersection volume, for quantifying the degree of spatial conflict between the equipment. At the same time, the proportion of the overlapping volume in the dynamic safety operation domain of each construction equipment also needs to be calculated to reflect the influence of the overlapping degree on the safety domain of each construction equipment. The higher the proportion is, the greater the influence of the overlapping on the safe and efficient operation of the construction equipment is.

[0130] Step S420, the wind load characteristics of the overlapping region of the dynamic safety operation domain are extracted from the three-dimensional wind field model.

[0131] In this embodiment, the wind load characteristics refer to the key factors affecting the dynamic response of the construction equipment in the overlapping area, specifically including parameters such as average wind speed, wind speed fluctuation intensity, and dominant wind direction, so as to comprehensively describe the space-time distribution characteristics of the wind load in the overlapping area, which has important value for subsequent risk assessment. In order to accurately extract these characteristics, the corresponding grid points in the three-dimensional wind field model are selected according to the spatial position and range of the overlapping area, the corresponding wind speed vector values are extracted, and statistical analysis is performed to obtain parameters such as average wind speed and wind speed fluctuation intensity. At the same time, by analyzing the directionality of the wind speed vector, the dominant wind direction can be determined. These wind load characteristic parameters will be important inputs for subsequent risk assessment, providing data support for accurately calculating the collision risk and spatial interference risk.

[0132] In step S430, based on the proportion of the volume of the dynamic safety operation domain overlapping area in each dynamic safety operation domain, the construction equipment type identifier, the equipment position data of the construction equipment, and the wind load characteristics of the dynamic safety operation domain overlapping area, the potential collision severity score between the construction equipment is determined through a preset risk mapping rule table.

[0133] In this embodiment, the potential collision severity score refers to a quantitative index for evaluating the severity of a collision event that may occur between construction equipment due to the overlapping of dynamic safety operation domains. This score takes into account multiple factors such as the volume proportion of the overlapping area, the type of construction equipment, the equipment position data, and the wind load characteristics of the overlapping area. In order to determine this score, the present scheme presets a risk mapping rule table, which establishes a mapping relationship between different factors and collision severity based on historical data, expert experience, and theoretical analysis. In actual application, only by inputting parameters such as the volume proportion of the overlapping area, the equipment type identifier, the equipment position data, and the wind load characteristics into the risk mapping rule table, the corresponding potential collision severity score can be quickly obtained. The higher the score, the more severe the potential risk of collision between construction equipment and the possible consequences.

[0134] In a possible implementation manner, referring to Figure 6 , the method further includes steps S710-S730, wherein:

[0135] In step S710, a construction equipment type combination library is established.

[0136] In this embodiment, the construction equipment type combination library refers to a database that classifies construction equipment according to equipment type and collaborative work scenarios. It can be established by combining equipment type codes and work scenario labels, such as tower crane and concrete pump truck combination, bridge erecting machine and beam transport vehicle combination, etc., and is used to store the risk association relationship of different equipment combinations in a specific work environment.

[0137] Step S720, for each device combination, determine the risk level under multiple relative positions, multiple wind load conditions, and multiple dynamic safety operation domain overlap ratios through historical accident analysis or simulation.

[0138] In this embodiment, the risk level refers to the severity classification of safety risks such as collision or spatial interference of construction equipment under different relative positions, wind load conditions, and dynamic safety operation domain overlap ratios. In order to accurately determine the risk level, historical accident data and simulation results can be used to analyze different device combinations under various possible operating conditions. By evaluating the relative position relationship between devices, the influence of wind load on the dynamic response of construction equipment, and the size of the overlap area, combined with expert experience and theoretical analysis, the safety risk is divided into different levels, such as low risk, medium risk, and high risk.

[0139] Step S730, convert the risk level into a potential collision severity score to form the risk mapping rule table.

[0140] In this embodiment, the risk mapping rule table refers to a corresponding table that converts the risk level of different device combinations under different operating conditions into a potential collision severity score. According to the risk level determined in step S720, combined with factors such as device type, relative position, wind load characteristics, and overlap ratio, the table presets corresponding scoring standards and conversion rules. In actual application, only the combination type, relative position, wind load condition, and overlap ratio of the construction equipment are needed to find the corresponding potential collision severity score in the risk mapping rule table, which can quickly evaluate the collision risk between construction equipment. The establishment of this rule table provides a convenient and effective tool for risk assessment under the condition of dynamic safety operation domain overlap, improving the accuracy and efficiency of risk assessment. By considering multiple factors, this scheme can more comprehensively evaluate the potential collision risk between construction equipment, providing strong support for the safe operation of construction equipment.

[0141] Compared with the prior art, the prior art only considers the static safety range of a single device, does not establish risk correlation rules between device combinations, and cannot predict the dynamic interaction risk in the multi-device collaborative operation. The scheme combines historical experience and simulation data by constructing a device type combination library and a risk mapping rule table, forming a risk assessment basis covering various working condition parameters, so that the system can identify the potential collision mode of a specific device combination in a complex wind field environment, significantly improving the comprehensiveness and accuracy of risk assessment. That is, the application effectively solves the evaluation blind area problem caused by the lack of device combination risk data in the traditional method, and realizes dynamic risk prediction in the multi-device collaborative operation scene. The rapid matching mechanism based on the rule table greatly shortens the risk assessment time, provides reliable basis for real-time scheduling decision, and at the same time, quantifies the influence weight of different working condition parameters on the danger level, supporting dynamic optimization and adjustment of the construction scheduling scheme.

[0142] Step S440, based on the device posture data and the device operation data, the current motion trend of the device is evaluated.

[0143] In this embodiment, by analyzing the device posture data and the device operation data, the motion trend of the device in the future period of time can be predicted, such as the moving direction, the speed change, etc., which can be calculated through the displacement change rate and the angle change rate in the device operation data, and is used to judge whether the device is accelerating towards the overlapping area.

[0144] Step S450, combining the potential collision severity score and the motion trend, a collision risk index is generated.

[0145] In this embodiment, the collision risk index not only considers the potential collision severity caused by the overlap of the dynamic safety operation domain between the construction devices, but also integrates the current motion trend of the device, to comprehensively reflect the size of the collision risk. Specifically, the potential collision severity score and the device motion trend are combined, and the collision risk index is calculated through a preset algorithm or model. The index comprehensively considers the size of the collision possibility, the severity of the collision consequence, and the trend of the device accelerating towards the overlapping area, so it can more accurately evaluate the collision risk between the construction devices. When the collision risk index exceeds a preset safety threshold, the system will issue a warning signal to prompt the operator to take appropriate risk prevention measures, such as adjusting the device operation position, reducing the operation speed, etc., to avoid the occurrence of collision events. By real-time monitoring the overlap of the dynamic safety operation domain between the devices, combining the comprehensive evaluation of the potential collision severity score and the motion trend, the scheme can provide strong guarantee for the safe and efficient operation of the construction devices.

[0146] Step S460, based on the volume of the dynamic safety operation domain overlap region and the proportion in each dynamic safety operation domain and the wind speed fluctuation intensity, the probability of mutual interference of construction equipment in the overlap region is predicted to generate a spatial interference risk index.

[0147] In this embodiment, the spatial interference risk index not only reflects the risk size of the construction equipment in the dynamic safety operation domain overlap region due to non-physical space conflict, but also considers the influence of wind speed fluctuation intensity on the dynamic response of the construction equipment. Specifically, by analyzing the volume proportion of the overlap region, the relative position relationship and the motion characteristics between the equipment, combining the space-time distribution characteristics of the wind speed fluctuation intensity, a preset algorithm or model is used to calculate the probability of mutual interference of the construction equipment in the overlap region. The higher the probability value is, the greater the risk between the construction equipment due to spatial interference is. The calculation of the spatial interference risk index comprehensively considers the influence of various factors on the operation efficiency or safety of the construction equipment, and provides a more comprehensive risk assessment result for the construction scheduling personnel. When the spatial interference risk index exceeds the preset safety threshold, the system will suggest adjusting the equipment operation plan, such as optimizing the equipment layout, adjusting the operation sequence, etc., to reduce the risk of spatial conflict and ensure the safe and efficient operation of the construction equipment. Through real-time monitoring and dynamic evaluation of the spatial interference risk between the construction equipment, this scheme provides a powerful guarantee for the bridge construction safety in complex wind field environment.

[0148] Specifically, when it is detected that the safety operation domains of two tower cranes overlap within the swing range of the jib, first, the volume proportion of the overlap region to each safety domain is calculated. For example, if the overlap proportion of tower crane A is 15% and that of tower crane B is 20%, it indicates that the degree of intersection of the two in this region is high. Then the average wind speed of this region is extracted from the three-dimensional wind field model as 8 m / s, the wind speed fluctuation intensity is 0.3, and the dominant wind direction is southeast. Based on the historical collision data of tower crane and bridge erecting machine in the equipment type combination library, the current distance between the equipment and the wind direction angle are matched, and the potential collision severity score is obtained as 7 levels through the risk mapping rule table. Further analysis of the equipment posture data finds that the tower crane A hook is moving towards the overlap region, and the collision risk index is calculated to be increased to 0.78 combined with the motion trend parameters. At the same time, according to the wind speed fluctuation intensity, it is predicted that the equipment trajectory deviation probability within the next 30 seconds reaches 42%, and thus the spatial interference risk index is generated as 0.65.

[0149] The scheme can accurately identify the risk of unexpected entry of equipment into a dangerous area caused by wind speed fluctuation by introducing three-dimensional wind load characteristics and multi-dimensional analysis of equipment motion state, that is, the application can dynamically quantify the influence of wind load on the spatial position of construction equipment, and accurately assess the direct collision risk and indirect interference risk caused by the overlap of safety domains between equipment. For example, under gust conditions, the overlap trend of the safety domains of the tower crane and the concrete pump truck can be predicted 15 minutes in advance, and the operation height difference between the two is adjusted based on real-time wind field data, reducing the collision risk to an acceptable range. At the same time, by distinguishing the risk levels of different combinations of equipment types, differentiated avoidance strategies can be developed for different combinations of tower cranes and transport vehicles, avoiding the loss of construction efficiency caused by the use of a uniform conservative safety threshold.

[0150] Step S500, the collision risk index and the spatial interference risk index, and the operation risk index of the construction equipment itself are weighted and fused to obtain a comprehensive risk value of the construction equipment group.

[0151] In the embodiment, the comprehensive risk value is a quantitative index considering multiple risk factors to comprehensively reflect the overall safety risk level of the construction equipment group in a complex wind field environment. The scheme uses a weighted fusion method to effectively integrate the collision risk index, the spatial interference risk index, and the operation risk index of the construction equipment itself to obtain the comprehensive risk value. The higher the comprehensive risk value, the greater the safety risk faced by the construction equipment group, and more stringent risk prevention and control measures need to be taken to ensure construction safety.

[0152] In a feasible implementation manner, referring to Figure 7 , step S500 includes steps S510-S540, wherein:

[0153] Step S510, for each construction equipment, the operation risk index of the construction equipment itself is calculated according to the load state, continuous working time, and operator state of the construction equipment;

[0154] Step S520, the equipment weight is set based on the type of the equipment and the importance of the position of the equipment;

[0155] Step S530, the collision risk index and the spatial interference risk index between the construction equipment are weighted and averaged to obtain a corresponding risk contribution value;

[0156] Step S540, the risk contribution values between all construction equipment with dynamic safety operation domain overlap are added, and the operation risk index of all construction equipment adjusted by the equipment weight is added to obtain a comprehensive risk value of the construction equipment group.

[0157] In this embodiment, the operation risk index refers to a probability index reflecting the abnormal state of the construction equipment itself, which can be realized by using a scoring model based on the equipment load rate, continuous working time threshold and operator fatigue state. For example, when the load rate exceeds 80% of the rated value or the continuous working time exceeds 8 hours, the operation risk index will be dynamically increased. The equipment weight refers to the influence degree parameter of different equipment types and operation positions on the overall construction safety, which can be determined by expert evaluation method or historical accident data statistics. For example, the weight coefficient of the tower crane in the cantilever operation area can be higher than that of the ground transportation equipment. The weighted average refers to the linear superposition calculation method of different risk indexes according to the preset proportion, which can use the analytic hierarchy process to determine the weight distribution of collision risk and spatial interference risk. For example, the collision risk accounts for 60% and the spatial interference risk accounts for 40%.

[0158] Specifically, the method first collects the load data, operation time and operator physiological indicators of the construction equipment in real time through the sensor, and calculates the operation risk index of a single device using the preset scoring rules. Further, according to the criticality of the equipment type in the construction process and the danger level of its position, for example, the weight of the aerial work equipment is higher than that of the ground equipment, a higher adjustment coefficient is allocated to the core equipment. Then, the collision risk and spatial interference risk between devices are fused into risk contribution values according to the preset weight, for example, the collision risk index is multiplied by 0.6 and the spatial interference index is multiplied by 0.4. Finally, the risk contribution values of all devices are added, and the operation risk index of each device after weight adjustment is summed up to form a comprehensive risk value reflecting the overall construction safety situation.

[0159] This scheme can more accurately reflect the comprehensive risk level in complex construction scenarios by dynamically calculating the operation risk of the equipment itself and weighting the fusion according to the importance difference of the equipment, solving the evaluation deviation problem caused by ignoring the operation state and risk weight difference of the equipment in the traditional method. For example, when multiple aerial equipment is in a high load state at the same time, the system can identify the safety hazards caused by the superposition of equipment overload and spatial interference by increasing the weight coefficient of this type of equipment. Further, by distinguishing the influence degree of collision risk and spatial interference risk, the defect that a single risk index may cover up the overall risk peak is avoided, thereby providing more reliable risk warning basis for construction scheduling.

[0160] Step S600, when the comprehensive risk value exceeds the preset risk threshold, an optimized scheduling scheme is generated according to the construction data of the current construction equipment, the wind speed vector value and the preset construction task progress constraint, to avoid the conflict between the risk wind field area and the dynamic safety operation domain of the construction equipment.

[0161] In this embodiment, the optimized scheduling scheme refers to a series of strategies to adjust the operation state, order or position of the construction equipment in response to the safety risks existing in the current construction scene. When the comprehensive risk value exceeds the preset safety threshold, the system will automatically trigger the optimization scheduling mechanism to quickly generate one or more feasible optimization scheduling schemes based on the real-time monitoring of construction data, wind speed vector values and the preset construction task progress constraints. These schemes aim to avoid risky wind field areas and reduce the overlap of dynamic safety operation domains between construction equipment, thereby reducing the risk of collision and spatial interference, ensuring the safe and efficient progress of the construction project.

[0162] In generating the optimized scheduling scheme, the system first considers the current operation state of the construction equipment, including the device position, operation direction, speed, etc., as well as the influence of wind speed vector values on the dynamic response of construction equipment. By comprehensively analyzing these factors, the system can predict the possible motion trajectory of the construction equipment in the future period of time and the potential risk area. Subsequently, the system will filter and optimize the feasible scheduling schemes according to the preset construction task progress constraints, such as the operation nodes on the critical path, the operation sequence dependency relationship of the construction equipment, etc. In the filtering process, the system will give priority to those schemes that have less impact on the construction progress and can effectively reduce the risk. In the optimization stage, the system will further adjust the operation parameters of the equipment, such as operation height, speed, direction, etc., to minimize the overlap of risk areas while ensuring the smooth completion of the construction task.

[0163] The generated optimized scheduling scheme will include specific device scheduling instructions, adjusted operation sequence and expected risk reduction effect, etc. The operator can quickly adjust the operation state of the construction equipment according to the system's suggestion, effectively avoiding potential safety risks. At the same time, these schemes can also serve as a reference for subsequent construction planning, helping managers to develop more reasonable construction equipment group collaborative operation plans in complex wind field environments, ensuring the smooth progress of the construction project. Through real-time monitoring, intelligent early warning and optimization scheduling, this scheme provides comprehensive safety protection for bridge construction.

[0164] In a feasible implementation manner, with reference to Figure 8 , step S600 includes steps S610-S640, wherein:

[0165] Step S610, extract high-risk construction equipment and overlapping areas between high-risk construction equipment that exist dynamic safety operation domain overlap conflict at the current time;

[0166] Step S620, obtain future prediction data of the three-dimensional wind field model, map the overlapping area to the three-dimensional wind field model, and identify the risk wind field area with wind field intensity greater than the preset intensity in the preset prediction period;

[0167] Step S630, a scheduling optimization model is constructed, whose objective function is to minimize the delay of the construction period, and whose constraint conditions are defined as follows:

[0168] Multiple high-risk construction equipment cannot operate or move in the risk wind field area at the same time during the conflict period, and the path intersection occurs;

[0169] When the construction equipment operates in the risk wind field area, a safety buffer zone is reserved in the adjacent area;

[0170] Step S640, the improved genetic algorithm is used to solve the scheduling optimization model to generate a scheduling optimization scheme of reassigning the construction equipment tasks, adjusting the operation time window and the operation position.

[0171] In the embodiment, the high-risk construction equipment with dynamic safety operation domain overlap conflict refers to the combination of equipment that has collision or interference risk determined by the dynamic safety operation domain overlap area volume ratio and wind load characteristics, which can be realized by using a preset collision severity score threshold for screening to focus on key conflict objects. The future prediction data refers to a three-dimensional wind field time series generated based on a meteorological model or historical data, which can be realized by using a numerical weather prediction model combined with a short-term extrapolation algorithm to predict the spatio-temporal evolution of the risk area. The objective function of the scheduling optimization model is defined as minimizing the delay of the construction period, and the constraint conditions include the equipment operation space-time exclusion rule and the safety buffer zone requirement, which can be realized by using a mixed integer programming model to balance safety and efficiency. The improved genetic algorithm embeds a task logic dependency checking mechanism in the crossover and mutation operations, which can be realized by setting a task dependency relationship matrix in the chromosome coding to ensure the feasibility of the scheduling scheme.

[0172] Specifically, when the system detects that the comprehensive risk value exceeds the threshold, it first locates the high-risk equipment combination with dynamic safety operation domain overlap and its conflict area. By accessing the meteorological prediction system, the wind field evolution data within the next few hours is obtained, the current conflict area is mapped into the predicted wind field model, and the high-risk area with continuously increasing wind speed is identified. Then, an optimization model containing the construction period objective function, space-time exclusion constraint and safety buffer zone requirement is constructed. For example, for the path intersection conflict of two tower cranes in the beam erection operation area, the model will prohibit both from entering the predicted high wind speed area at the same time period, and at least five meters of safety distance is forced to be reserved. The improved genetic algorithm iteratively generates a scheduling scheme that meets all safety conditions and has the minimum delay of the construction period by task sequence coding, fitness function calculation and constraint-aware mutation operation, such as postponing the operation time window of one of the tower cranes by two hours and adjusting its hoisting path to the low wind speed area.

[0173] Compared with the prior art, the traditional method only performs static avoidance according to the current wind field state, and cannot predict the influence of wind field changes on equipment conflicts, while the scheme can avoid dynamic risk areas in advance by fusing future wind field prediction data. The existing scheduling optimization mostly adopts manual experience adjustment, and it is difficult to process the space-time constraints of multiple devices, and the scheme realizes automatic intelligent scheduling by improving the genetic algorithm, optimizes the construction period control while ensuring construction safety.

[0174] Through the above technical scheme, the application effectively solves the problems of difficult prediction and avoidance of construction equipment conflict risks in a dynamic wind field environment, realizes dynamic adjustment of the space-time relationship of construction tasks through three-dimensional wind field model mapping and intelligent optimization algorithm, reduces the probability of equipment collision, and reduces the delay of construction period caused by risk avoidance.

[0175] In a feasible implementation manner, with reference to Figure 9 , the improved genetic algorithm includes steps S810-S860, wherein:

[0176] Step S810, initialize the population, wherein each chromosome code represents a construction equipment task allocation scheme and the corresponding time window and work location sequence;

[0177] Step S820, calculate the individual fitness value, which is obtained by weighted summation of the construction period target function value, resource utilization rate score, and penalty value for violating the constraint conditions;

[0178] Step S830, perform individual selection operation of the elite reservation strategy, and reserve individuals whose fitness ranks reach the preset requirements to directly enter the next generation;

[0179] Step S840, perform multi-point crossover operation, exchange the continuous fragments of the construction equipment task sequence in the parent chromosome under the premise of ensuring that the child chromosome meets the task logical dependency relationship;

[0180] Step S850, perform mutation operation, including the following types:

[0181] Under the condition of meeting the task dependency relationship, randomly exchange the execution order of two non-critical tasks in the same construction equipment task sequence;

[0182] Move the tasks of the high-risk construction equipment combination prohibited by the constraint condition to a conflict-free time window;

[0183] For the tasks that need to enter the risk wind field area, specify an alternative work location that meets the safety buffer requirement of the constraint condition;

[0184] Step S860, iterate and evolve until the preset termination condition is met, and output the scheduling scheme with the highest fitness and meeting all constraints.

[0185] In this embodiment, initializing the population means generating multiple possible scheduling schemes through encoding, which can specifically use binary or real number encoding to represent the task sequence, time window, and location coordinates of different construction equipment. The role is to provide an initial solution space for the optimization algorithm. The fitness value refers to a comprehensive index for evaluating the pros and cons of the scheduling scheme, which can be achieved by weighted calculation of the degree of delay, resource utilization efficiency, and constraint violation. The role is to convert the multi-objective optimization problem into a single-objective optimization. The elite preservation strategy means preserving individuals with higher fitness during genetic iteration, which can specifically set the top 10% individuals in each generation to directly enter the next generation. The role is to prevent the loss of excellent genes and accelerate convergence. The multi-point crossover operation means selecting multiple continuous task fragments in the parent chromosome for exchange, which needs to verify whether the task logical dependency of the offspring is complete. The role is to explore better solutions through recombination.

[0186] Specifically, the improved genetic algorithm first encodes the scheduling scheme into a chromosome, with each gene corresponding to the task allocation, time window, and location coordinates of the construction equipment. During initialization, a number of chromosomes are randomly generated to form an initial population. Then, the fitness value of each individual is calculated, considering the project duration, resource utilization, and constraint violation. During iteration, individuals with high fitness are preferentially preserved, and the multi-point crossover operation is used to exchange continuous fragments in the parent task sequence, ensuring that the task logical relationship is not damaged. In the mutation stage, some individuals are randomly adjusted, such as exchanging non-critical task order to avoid conflicts or moving high-risk tasks to conflict-free periods. For tasks that must enter the risk area, their location coordinates are automatically adjusted to meet the safety buffer requirements. After multiple generations of evolution, the scheduling scheme that meets all constraints and has the highest fitness is output.

[0187] In some specific embodiments, the chromosome encoding can use a three-dimensional vector structure to represent task allocation, time window, and location coordinates. When calculating fitness, the weight of delay can be set to 0.6, the weight of resource utilization to 0.3, and the weight of constraint penalty to 0.1. The crossover operation can use two-point crossover, randomly selecting two crossover points in the task sequence for fragment exchange. The mutation operation can set a probability of 10% for randomly adjusting individuals, with a time window mutation range of no more than two hours.

[0188] Compared with the prior art, the traditional scheduling optimization is usually processed by a heuristic algorithm for a single target, and it is difficult to meet the dynamic adjustment demand under multiple constraint conditions. The scheme improves the genetic algorithm, verifies the task dependency relationship in the crossover operation, and embeds the constraint condition processing mechanism in the mutation operation, so that the generated scheduling scheme meets the construction logic and avoids the risk area. Compared with the standard genetic algorithm, the improved method increases the proportion of feasible solutions from 32% to 89%, and accelerates the convergence speed by 40%. Through the above technical scheme, the application effectively solves the complex constraint problem of multi-device collaborative scheduling in the dynamic wind field environment. By encoding the three-dimensional relationship of task allocation, time window and spatial position in the chromosome, and combining the directional mutation operation to automatically avoid the conflict period and risk area, the device collision risk can be significantly reduced while ensuring the construction progress. The improved genetic algorithm improves the convergence efficiency and solution quality of the optimization process through the elite reservation and constraint driven mutation strategy, and provides reliable scheduling decision support for complex construction scenes.

[0189] The application provides a bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence, which solves the problem that the traditional method cannot dynamically evaluate the wind load risk and device conflict by means of three-dimensional wind field modeling, dynamic safety operation domain calculation, multi-dimensional risk fusion and intelligent optimization scheduling, can accurately evaluate the risk and dynamically predict the conflict in real time, improves the effectiveness of the bridge construction scheduling scheme, and further improves the construction safety and efficiency.

[0190] The application also provides a bridge construction intelligent scheduling risk assessment system based on artificial intelligence, which refers to Figure 10 The system comprises a memory 10, a processor 20, and a bridge construction intelligent scheduling risk assessment program based on artificial intelligence stored on the memory 10 and capable of running on the processor 20, wherein the bridge construction intelligent scheduling risk assessment program based on artificial intelligence is configured to implement the steps of the bridge construction intelligent scheduling risk assessment method based on artificial intelligence.

[0191] The bridge construction intelligent scheduling risk assessment system based on artificial intelligence provided by the application adopts the bridge construction intelligent scheduling risk assessment method based on artificial intelligence in the above embodiments, and can improve the effectiveness of the bridge construction intelligent scheduling. Compared with the prior art, the bridge construction intelligent scheduling risk assessment system based on artificial intelligence provided by the application has the same beneficial effects as the bridge construction intelligent scheduling risk assessment method based on artificial intelligence provided by the above embodiments, and other technical features in the bridge construction intelligent scheduling risk assessment system based on artificial intelligence are the same as the features disclosed in the above method, which will not be repeated here.

[0192] The above merely describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or the like, which is made based on the technical concept of the present application and the content of the specification and drawings, is included in the patent protection scope of the present application.

Claims

1. A risk assessment method for intelligent scheduling of bridge construction based on artificial intelligence, characterized in that, The method includes: Wind load data is acquired by meteorological sensors deployed at predetermined locations on the bridge structure and on predetermined construction equipment; and construction data of the construction equipment is acquired based on construction data acquisition devices deployed on the construction equipment; the construction data includes equipment location data, equipment attitude data, and equipment operation data. Based on the wind load data, the three-dimensional space where the pre-defined bridge construction area is located is divided into three-dimensional grids to establish a three-dimensional wind field model of the bridge construction area; the three-dimensional wind field model is used to output the wind speed vector value of each grid point. Based on the construction data of the construction equipment and the wind speed vector value of the location of the construction equipment, the dynamic safe operating domain of each construction equipment under the current wind load is calculated. When there is an overlap of dynamic safe operating domains between construction equipment, the collision risk index and spatial disturbance risk index caused by the overlap of dynamic safe operating domains are calculated based on the three-dimensional wind field model and the wind load characteristics of the overlapping area of ​​dynamic safe operating domains. The collision risk index, spatial disturbance risk index, and the operational risk index of the construction equipment itself are weighted and fused to obtain the comprehensive risk value of the construction equipment group. When the overall risk value exceeds the preset risk threshold, a corresponding optimized scheduling scheme is generated based on the current construction data of the construction equipment, the wind speed vector value, and the preset construction task progress constraints, so as to avoid conflicts between the risky wind field area and the dynamic safe operation domain of the construction equipment. The step of dividing the three-dimensional space of the pre-defined bridge construction area into a three-dimensional mesh based on the wind load data to establish a three-dimensional wind field model of the bridge construction area includes: The wind load data is mapped to the corresponding three-dimensional grid points according to the spatial coordinates of the meteorological sensor. For grid points lacking wind load data, the inverse distance weighted difference method is used to calculate the wind load data based on the wind load data of the surrounding grid points. Based on the wind load data of all grid points, a three-dimensional wind field model of the bridge construction area is established. The steps for calculating the dynamic safe operating range of the construction equipment under the current wind load include: Obtain the type identifier of the construction equipment and the construction data of the construction equipment; Based on the construction equipment type identifier, a preset equipment physical parameter database is called to obtain the equipment size, maximum working radius, structural damping coefficient and moving part parameters of the construction equipment; Extract the wind speed vector value of the grid position where the moving parts of the construction equipment are located from the three-dimensional wind field model; Based on the construction data, equipment size, maximum working radius, structural damping coefficient, moving part parameters, and wind speed vector value at the grid location of the moving part, calculate the structural response generated by wind load acting on the moving part of the construction equipment. Based on the structural response, the motion trajectory envelope of the moving parts of the construction equipment is determined to determine the current dynamic safe operating domain of the construction equipment.

2. The intelligent scheduling risk assessment method for bridge construction based on artificial intelligence as described in claim 1, characterized in that, The step of calculating the structural response generated by wind load acting on the moving parts of the construction equipment based on the construction data, equipment size, maximum working radius, structural damping coefficient, moving part parameters, and wind speed vector value at the grid location of the moving part includes: Based on the equipment attitude data, equipment operation data, equipment size, and maximum working radius, a three-dimensional spatial geometric model of the construction equipment in its current state is established to determine the wind load action surface and its normal vector direction. Based on the parameters of the moving parts, the three-dimensional wind speed vector value of the grid position of the moving parts, the wind load surface and its normal vector direction, calculate the normal wind pressure on the wind load surface; The normal wind pressure on the wind load surface is converted into force and moment on the main structure of the construction equipment according to the location of the point of application; Based on the structural damping coefficient and the three-dimensional spatial geometric model, a multibody dynamics model of the equipment is constructed. Using the effects and torques on the main structure of the construction equipment as input, the multibody dynamics model is solved to simulate and calculate the structural response generated by wind loads acting on the moving parts of the construction equipment in real time; the structural response includes the displacement response and vibration amplitude of the moving parts.

3. The intelligent scheduling risk assessment method for bridge construction based on artificial intelligence as described in claim 1, characterized in that, The three-dimensional wind field model is also used to output the wind load characteristics of each grid point; the wind load characteristics include average wind speed, wind speed fluctuation intensity, and prevailing wind direction; the steps for calculating the collision risk index and spatial disturbance risk index caused by the overlap of dynamic safe operating domains between construction equipment, based on the three-dimensional wind field model and the wind load characteristics of the overlapping area of ​​the dynamic safe operating domains, when there is an overlap of dynamic safe operating domains between construction equipment, include: When there is an overlap of dynamic safety operation domains between construction equipment, calculate the volume of the overlapping area of ​​the dynamic safety operation domains and its proportion in each dynamic safety operation domain. Wind load characteristics of the overlapping areas of the dynamic safe operating domain are extracted from the three-dimensional wind field model; Based on the proportion of the volume of the overlapping area of ​​the dynamic safety operation domain in their respective dynamic safety operation domains, the construction equipment type identification, the equipment location data of the construction equipment, and the wind load characteristics of the overlapping area of ​​the dynamic safety operation domains, the potential collision severity score between construction equipment is determined through a preset risk mapping rule table. Based on equipment attitude data and equipment operation data, assess the current movement trend of the equipment; By combining the potential collision severity score and motion trend, a collision risk index is generated; Based on the volume of the overlapping areas of the dynamic safety operation domains and their proportion in their respective dynamic safety operation domains, as well as the wind speed fluctuation intensity, the probability of construction equipment interfering with each other in the overlapping areas is predicted to generate a spatial interference risk index.

4. The intelligent scheduling risk assessment method for bridge construction based on artificial intelligence as described in claim 3, characterized in that, The method further includes: Establish a library of construction equipment type combinations; For each equipment combination, the hazard level is determined under various relative positions, various wind load conditions, and various overlapping ratios of dynamic safe operating domains through historical accident analysis or simulation. The hazard level is converted into a potential collision severity score, forming the risk mapping rule table.

5. The intelligent scheduling risk assessment method for bridge construction based on artificial intelligence as described in claim 1, characterized in that, The step of weightedly fusing the collision risk index, the spatial disturbance risk index, and the operational risk index of the construction equipment itself to obtain the comprehensive risk value of the construction equipment group includes: For each piece of construction equipment, its own operational risk index is calculated based on its load status, continuous working time, and operator status. Assign device weights based on the importance of device type and location; The collision risk index between construction equipment and the spatial disturbance risk index are weighted and averaged to obtain the corresponding risk contribution value. The risk contribution values ​​of all construction equipment with overlapping dynamic safety operation domains are summed and then added to the individual operational risk indices of all construction equipment after equipment weighting to obtain the comprehensive risk value of the construction equipment group.

6. The intelligent scheduling risk assessment method for bridge construction based on artificial intelligence as described in claim 1, characterized in that, The step of generating a corresponding optimized scheduling scheme based on the current construction data of the construction equipment, wind speed vector value, and preset construction task progress constraints when the comprehensive risk value exceeds the preset risk threshold, in order to avoid conflicts between the dynamic safe operation domain of the risky wind field area and the construction equipment, includes: Extract high-risk construction equipment that currently exhibits overlapping and conflicting dynamic safety operation domains, as well as the overlapping areas between high-risk construction equipment. Acquire future prediction data from a 3D wind field model, map overlapping areas onto the 3D wind field model, and identify risky wind field areas where the wind field intensity is greater than a preset intensity within a preset prediction period. Construct a scheduling optimization model with the objective function of minimizing project delay, and impose constraints on it: Multiple high-risk construction equipment must not operate simultaneously or have their movement paths intersect in the high-risk wind field area during conflicting periods; When construction equipment operates in high-risk wind field areas, a safety buffer zone should be reserved in the adjacent area; An improved genetic algorithm is used to solve the scheduling optimization model, generating a scheduling optimization scheme that reallocates construction equipment tasks, adjusts operation time windows, and operation locations.

7. The intelligent scheduling risk assessment method for bridge construction based on artificial intelligence as described in claim 6, characterized in that, The improved genetic algorithm includes: Initialize the population, where each chromosome encodes a construction equipment task allocation scheme and the corresponding time window and work location sequence; Calculate the individual fitness value, which is obtained by weighted summation of the project duration objective function value, resource utilization score, and penalty value for violating the constraints; The individual selection operation that implements the elite retention strategy retains individuals whose fitness ranking in each generation reaches the preset requirements and directly enters the next generation; Perform multi-point crossover operations to exchange consecutive segments of the construction equipment task sequence in the parent chromosome, while ensuring that the offspring chromosomes satisfy the task logical dependencies. Perform mutation operations, including the following types: Under the condition of satisfying task dependencies, randomly swap the execution order of two non-critical tasks in the same construction equipment task sequence; For tasks involving high-risk combinations of construction equipment prohibited by the aforementioned constraints, their original work times are moved to a conflict-free time window; For tasks that require entry into high-risk wind field areas, designate alternative work locations that meet the safety buffer zone requirements of the aforementioned constraints; Iterate and evolve until the preset termination condition is met, and output the scheduling scheme with the highest fitness and that satisfies all constraints.

8. A bridge construction intelligent scheduling and risk assessment system based on artificial intelligence, characterized in that, The system includes: a memory, a processor, and an AI-based bridge construction intelligent scheduling risk assessment program stored in the memory and executable on the processor, wherein the AI-based bridge construction intelligent scheduling risk assessment program is configured to implement the steps of the AI-based bridge construction intelligent scheduling risk assessment method as described in any one of claims 1 to 7.

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