Intelligent assembling and integral lifting collaborative control system for large-span special-shaped steel structure

Abstract

The application provides a large-span special-shaped steel structure intelligent assembling and overall lifting cooperative control system, relates to the steel structure construction technical field and comprises a data acquisition unit, a data processing unit, a core control unit, an execution unit and a cloud platform unit.The data acquisition unit is used for collecting BIM model data, spatial coordinate data, load data, structure deformation data and stress data of the large-span special-shaped steel structure.A dynamic coupling module is used to realize the dynamic coupling of the BIM model and the total station real-time data, a minimum matching error algorithm is used to realize the accurate matching of the curved surface special-shaped parameters and the spatial coordinates, compared with the static matching mode of the prior art, the assembling precision is significantly improved, the zero-error aerial scattered assembling of the curved surface steel structure is realized, the problem of low assembling precision in the prior art is solved, the data changes in the construction process can be responded in real time, and it is ensured that the assembling process is consistent with the design model at all times.

Description

Technical Field

[0001] This invention relates to the field of steel structure construction technology, and in particular to a collaborative control system for intelligent assembly and overall lifting of large-span irregular steel structures. Background Technology

[0002] Large-span irregular steel structures are widely used in large stadiums, bridges, airport terminals, and other engineering projects due to their aesthetic appeal and high space utilization. Currently, the construction of large-span irregular steel structures mainly employs a combination of manual positioning and assembly with segmented lifting. During construction, BIM models are used for preliminary design and simulation, total stations are used for spatial coordinate measurement, hydraulic lifters are used to achieve overall structural lifting, and sensors are used to monitor stress and displacement at certain construction nodes. Furthermore, finite element analysis software is used to perform stress calculations on the steel structure before construction, and construction plans are developed based on historical construction experience. However, traditional manual positioning has a low degree of integration with BIM models, mostly relying on static matching. This fails to achieve real-time dynamic coupling between the BIM model and measured data, making it difficult to guarantee assembly accuracy and prone to excessive assembly errors. Furthermore, for the uneven load distribution in the lifting of large-span steel structures, existing control algorithms mostly use fixed parameters, unable to dynamically adjust the lifting rate based on real-time force-displacement feedback. This can easily lead to localized stress concentration, affecting structural safety. Simultaneously, construction plan decisions often rely heavily on manual experience, lacking a three-dimensional correlation analysis of structural irregularities, construction risks, and lifting schemes, hindering scientific decision-making. The existing technologies suffer from several shortcomings. First, they lack sufficient precision, making it difficult to generate optimal construction plans. Second, the monitoring system is incomplete, often limited to local monitoring during the lifting process, failing to form a closed-loop monitoring system encompassing static testing, lifting process, and unloading verification. This makes it impossible to fully grasp the stress and deformation state of the structure. Third, construction calculations are mostly independent, stage-based calculations, failing to achieve full-link collaborative calculations from design to construction to acceptance, thus failing to form a digital twin closed loop. Furthermore, the calculation results lack timeliness in guiding the construction process. Therefore, this invention proposes an intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures to address these problems. Summary of the Invention

[0003] To address the aforementioned issues, this invention proposes an intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures. This system achieves dynamic coupling between the BIM model and real-time data from the total station through a dynamic coupling module. It employs an algorithm that minimizes matching errors to achieve precise matching of the curved irregular surface parameters and spatial coordinates. Compared to the static matching methods of existing technologies, this significantly improves assembly accuracy, enabling zero-error aerial assembly of curved steel structures. It solves the problem of low assembly accuracy in existing technologies, and can respond in real-time to data changes during construction, ensuring that the assembly process remains consistent with the design model and reducing rework costs caused by assembly errors.

[0004] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a collaborative control system for intelligent assembly and overall lifting of large-span irregular steel structures, comprising a data acquisition unit, a data processing unit, a core control unit, an execution unit, and a cloud platform unit. The data acquisition unit is used to acquire BIM model data, spatial coordinate data, load data, structural deformation data, and stress data of the large-span irregular steel structure; the data processing unit is used to preprocess and convert the acquired data. The core control unit is connected to the data processing unit, and integrates a dynamic coupling module, a collaborative control module, a three-dimensional correlation decision module, a full-cycle monitoring module, and a cloud platform verification module to achieve collaborative control of assembly and lifting. The execution unit is connected to the core control unit and is used to execute assembly path adjustment, lifting parameter adjustment, and construction pause control commands. The cloud platform unit is connected to the core control unit and is used to store data, perform special calculations, and realize data visualization.

[0005] Further improvements are made in that: the data acquisition unit includes a total station, a hydraulic sensor, a fiber optic sensor, a wireless strain sensor, and a temperature sensor, wherein the total station is used to acquire spatial coordinate data with a sampling frequency of not less than 10Hz; the hydraulic sensor is used to acquire load data of the hydraulic lifter with a measurement accuracy of not less than 0.5%FS.

[0006] A further improvement lies in the following: the dynamic coupling module is used to achieve dynamic coupling between the BIM model and the real-time data from the total station. A data matching algorithm is employed to accurately match the parameters of the irregular surface with the spatial coordinates. The expression for the data matching algorithm is as follows: minE=Σ(i=1 to n)√[(Xs,i-Xc,i)²+(Ys,i-Yc,i)²+(Zs,i-Zc,i)²], Where: E is the total matching error between the BIM model coordinates and the total station measured coordinates; n is the number of matching points; Xs,i, Ys,i, and Zs,i are the three-dimensional coordinates of the i-th matching point in the BIM model, respectively; Xc,i, Yc,i, and Zc,i are the three-dimensional coordinates of the i-th matching point measured by the total station, respectively; by minimizing the total matching error E, millimeter-level accurate matching between the two is achieved.

[0007] A further improvement lies in the following: the collaborative control module adopts a dynamic collaborative control algorithm based on force-displacement feedback, which adjusts the synchronization rate of each booster point through PID closed-loop control, and the expression of the PID closed-loop control algorithm is as follows: U(t)=K1·E(t)+K2·∫(0 to t)E(t1)dt1+K3·dE(t) / dt, Where: U(t) is the rate adjustment of the lifting point at time t; K1 is the proportional coefficient; K2 is the integral coefficient; K3 is the differential coefficient; E(t) is the force-displacement deviation value at time t, that is, the weighted sum of the difference between the target force and the actual force and the difference between the target displacement and the actual displacement; t1 is the integral variable; the algorithm dynamically compensates for the local stress concentration caused by the irregular surface.

[0008] A further improvement is made in that: the three-dimensional correlation decision-making module constructs a three-dimensional correlation decision-making model of structural irregularity, construction risk, and improvement scheme, and uses a weight calculation algorithm to determine the influence weight of each correlation factor. The expression of the weight calculation algorithm is as follows: Wj=[Σ(i=1 to m)Rij] / [Σ(j=1 to k)Σ(i=1 to m)Rij], Where: Wj is the weight of the j-th related factor; m is the number of historical construction cases; Rij is the correlation degree of the j-th factor in the ith historical case; k is the total number of related factors; the optimal segmentation scheme is dynamically generated based on the weight calculation results.

[0009] A further improvement is made in that: the full-cycle monitoring module uses a machine learning model to predict the structural deformation trend, and the expression for the deformation trend prediction algorithm is: Yc(t+Δt)=a0+Σ(p=1 to q)ap·Yj(t-p+1)+e(t), Where: Yc(t+Δt) is the predicted deformation at time t+Δt; a0 is the intercept coefficient; q is the lag order; ap is the coefficient of the p-th lag term; Yj(t-p+1) is the actual deformation at time t-p+1; e(t) is the random error term; the boosting parameters are automatically adjusted based on the prediction results.

[0010] A further improvement is made in that: the cloud platform verification module uses a finite element analysis algorithm to perform construction-specific stress analysis, and the expression of the finite element stress analysis algorithm is: σs=F / A+(M·y) / I, Where: σs is the comprehensive stress of the structural member; F is the axial force on the member; A is the cross-sectional area of ​​the member; M is the bending moment on the member; y is the distance from the calculation point to the neutral axis of the cross section; I is the moment of inertia of the cross section of the member about the neutral axis; this algorithm is used to complete the special calculations for stress analysis and temperature stress verification of small / medium / large earthquakes.

[0011] Further improvements include: the core control unit also integrates a data verification module, which is used to detect and correct outliers in various types of collected data. Outlier detection adopts the 3σ criterion, that is, when the data deviates from the mean by more than 3 times the standard deviation, it is judged as an outlier and corrected by linear interpolation.

[0012] A further improvement is that the execution unit includes a lifting controller, an assembly robotic arm controller, and an alarm device. When the structural deformation exceeds a preset threshold or the stress reaches a dangerous value, the alarm device issues an audible and visual alarm, and at the same time, the lifting controller controls the lifting device to stop lifting.

[0013] Further improvements are made in the following ways: The cloud platform unit is used to integrate the stress calculation results from the construction-specific verification with real-time data from the full-cycle monitoring to generate a structural full-domain construction risk heat map. This heat map uses a continuous color gradient mapping rule to achieve quantitative visualization of risk. Specifically, when the regional stress value is ≤ 60% of the material's allowable stress and the deformation is ≤ 50% of the preset threshold, a green gradient mapping is used, with the color depth slightly increasing as the parameter value increases; when the regional stress value is between 60% and 80% of the material's allowable stress or the deformation is between 50% and 80% of the preset threshold, a [further details omitted]. A yellow gradient mapping is used, with the color depth increasing synchronously with the parameter value. When the stress value of the area is greater than 80% of the allowable stress of the material or the deformation is greater than 80% of the preset threshold, a red gradient mapping is used, with the color depth increasing significantly with the parameter value. At the same time, the heat map supports stepless scaling from 1:10 to 1:500 and has a risk data linkage and traceability function. Clicking on any area will retrieve multi-dimensional data of the corresponding location in real time, including real-time monitoring values ​​of stress / displacement, historical change curves, monitoring sensor numbers and installation locations, finite element verification parameters and detailed reports for the corresponding area.

[0014] The beneficial effects of this invention are as follows: 1. This invention achieves dynamic coupling between the BIM model and real-time data from the total station through a dynamic coupling module. It employs an algorithm that minimizes matching errors to achieve precise matching between the parameters of the curved surface and the spatial coordinates. Compared with the static matching method of the prior art, this invention significantly improves the assembly accuracy, realizes zero-error aerial assembly of curved steel structures, solves the problem of low assembly accuracy in the prior art, and can respond to data changes during construction in real time, ensuring that the assembly process is always consistent with the design model, and reducing rework costs caused by assembly errors.

[0015] 2. The collaborative control module of this invention adopts a PID closed-loop control algorithm based on force-displacement feedback, which can collect load data and structural deformation data of the hydraulic lifter in real time, dynamically adjust the synchronization rate of each lifting point, and automatically compensate for local stress concentration caused by curved surface irregularities. This solves the problems of uneven lifting load and excessive local stress in the prior art. Compared with the existing fixed parameter control algorithm, this closed-loop control algorithm has stronger adaptability and stability, and can effectively ensure the structural safety of large-span steel structure lifting process.

[0016] 3. This invention constructs a three-dimensional correlation decision model of structural irregularity, construction risk, and improvement scheme. It determines the influence weight of each correlation factor through a weight calculation algorithm and dynamically generates the optimal segmented scheme by combining historical construction database. This solves the problem that the decision-making of construction schemes in the existing technology relies on human experience and lacks scientific rigor. This decision model can automatically identify structural irregularity areas, simulate and verify the stress state of key nodes, ensure the rationality and feasibility of construction schemes, and improve the intelligence level of construction decision-making.

[0017] 4. This invention constructs a full-cycle monitoring system encompassing static testing, lifting process, and unloading verification. It integrates fiber optic sensors and a wireless strain monitoring network, employs machine learning models to predict deformation trends, and forms a closed loop of monitoring, decision-making, and execution. This solves the problems of incomplete monitoring and lack of a full-cycle closed loop in existing technologies. This full-cycle monitoring system can comprehensively and in real-time grasp the stress and displacement status of the structure at different construction stages, predict deformation risks in advance, and adjust construction parameters in a timely manner, further ensuring construction safety.

[0018] 5. This invention develops a cloud-based full-chain construction verification function, integrating finite element analysis and BIM models to achieve a digital twin of design-construction-acceptance. It automatically completes various construction-specific calculations and generates a visualized risk heat map, solving the problems of broken verification links and insufficient timeliness of guidance in existing technologies. This full-chain verification can dynamically optimize key parameters such as hoisting paths and hoist selection, providing precise technical support for the entire construction process, improving the digitalization and intelligence level of construction, and reducing construction risks. Attached Figure Description

[0019] Figure 1This is a diagram illustrating the composition of the present invention. Detailed Implementation

[0020] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0021] Example 1 according to Figure 1 As shown in the figure, this embodiment proposes an intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures, including a data acquisition unit, a data processing unit, a core control unit, an execution unit, and a cloud platform unit. The data acquisition unit is used to collect BIM model data, spatial coordinate data, load data, structural deformation data, and stress data of the large-span irregular steel structure; the data processing unit is used to preprocess and convert the collected data. The core control unit is connected to the data processing unit and integrates a dynamic coupling module, a collaborative control module, a three-dimensional correlation decision-making module, a full-cycle monitoring module, and a cloud platform verification module to achieve collaborative control of assembly and lifting. The execution unit is connected to the core control unit and is used to execute assembly path adjustment, lifting parameter adjustment, and construction pause control commands. The cloud platform unit is connected to the core control unit and is used to store data, perform specialized calculations, and realize data visualization. This achieves seamless data flow between units, significantly improves the response efficiency of control commands, and avoids the collaborative lag problem of traditional distributed architectures. At the same time, the modular integration design reduces the complexity of system deployment and improves the convenience of engineering applications.

[0022] The data acquisition unit includes a total station, hydraulic sensors, fiber optic sensors, wireless strain sensors, and temperature sensors. The total station is used to acquire spatial coordinate data with a sampling frequency of no less than 10Hz. The hydraulic sensors are used to acquire load data from the hydraulic lift, with a measurement accuracy of no less than 0.5%FS. This combination of multiple sensor types enables comprehensive coverage of key construction data, avoiding blind spots from single-sensor monitoring. The high sampling frequency and high-precision parameter design ensure the timeliness and accuracy of the acquired data, providing a reliable data foundation for subsequent precise control.

[0023] The dynamic coupling module is used to achieve dynamic coupling between the BIM model and the real-time data from the total station. It employs a data matching algorithm to accurately match the parameters of the irregular surface with the spatial coordinates. The expression for the data matching algorithm is as follows: minE=Σ(i=1 to n)√[(Xs,i-Xc,i)²+(Ys,i-Yc,i)²+(Zs,i-Zc,i)²], Where: E is the total matching error between the BIM model coordinates and the total station measured coordinates; n is the number of matching points; Xs,i, Ys,i, and Zs,i are the three-dimensional coordinates of the i-th matching point in the BIM model; Xc,i, Yc,i, and Zc,i are the three-dimensional coordinates of the i-th matching point measured by the total station; by minimizing the total matching error E, millimeter-level precise matching between the two is achieved. The dynamic coupling mechanism breaks through the limitations of traditional static matching, and can track data changes in real time during construction to ensure that the assembly accuracy always meets the design requirements; the millimeter-level matching accuracy effectively avoids structural stress hazards caused by assembly deviations of irregular steel structures, and significantly reduces rework costs.

[0024] The collaborative control module employs a dynamic collaborative control algorithm based on force-displacement feedback. It adjusts the synchronization rate of each booster point through PID closed-loop control, and the PID closed-loop control algorithm expression is as follows: U(t)=K1·E(t)+K2·∫(0 to t)E(t1)dt1+K3·dE(t) / dt, Where: U(t) is the rate adjustment at time t; K1 is the proportional coefficient; K2 is the integral coefficient; K3 is the differential coefficient; E(t) is the force-displacement deviation at time t, i.e., the weighted sum of the difference between the target force and the actual force and the difference between the target displacement and the actual displacement; t1 is the integral variable; this algorithm dynamically compensates for local stress concentration caused by irregular curved surfaces. The closed-loop control of force-displacement feedback can correct the lifting deviation in real time, which is more adaptable than traditional fixed parameter control and effectively solves the problem of uneven load in large-span lifting; the dynamic compensation function for local stress concentration can ensure the structural integrity of the steel structure during the lifting process and improve the construction safety factor.

[0025] The three-dimensional correlation decision-making module constructs a three-dimensional correlation decision-making model of structural irregularity, construction risk, and improvement scheme. It uses a weight calculation algorithm to determine the influence weight of each correlation factor. The expression for the weight calculation algorithm is as follows: Wj=[Σ(i=1 to m)Rij] / [Σ(j=1 to k)Σ(i=1 to m)Rij], Where: Wj is the weight of the j-th related factor; m is the number of historical construction cases; Rij is the correlation degree of the j-th factor in the ith historical case; k is the total number of related factors; the optimal segmentation scheme is dynamically generated based on the weight calculation results. The three-dimensional correlation decision model realizes the comprehensive consideration of multiple factors, gets rid of the dependence on human experience in traditional decision-making, and improves the scientificity and rationality of the construction scheme; the dynamic generation of the optimal segmentation scheme can adapt to the construction needs of different irregular structures, effectively reducing construction difficulty and improving construction efficiency.

[0026] The full-cycle monitoring module uses a machine learning model to predict structural deformation trends. The expression for the deformation trend prediction algorithm is as follows: Yc(t+Δt)=a0+Σ(p=1 to q)ap·Yj(t-p+1)+e(t), Where: Yc(t+Δt) is the predicted deformation at time t+Δt; a0 is the intercept coefficient; q is the lag order; ap is the coefficient of the p-th lag term; Yj(t-p+1) is the actual deformation at time t-p+1; e(t) is the random error term; and the parameters are automatically adjusted based on the prediction results. Early prediction of deformation trends enables proactive prevention and control of construction risks, changing the traditional passive monitoring model of post-event remediation; automatic parameter adjustment based on prediction results allows for rapid response to potential risks, further ensuring structural stability throughout the entire construction cycle.

[0027] The cloud platform verification module uses the finite element analysis algorithm to perform construction-specific stress analysis. The expression for the finite element stress analysis algorithm is as follows: σs=F / A+(M·y) / I, Where: σs is the comprehensive stress of the structural member; F is the axial force on the member; A is the cross-sectional area of ​​the member; M is the bending moment on the member; y is the distance from the calculation point to the neutral axis of the cross section; I is the moment of inertia of the member's cross section about the neutral axis. This algorithm completes specialized calculations for stress analysis and temperature stress verification under small / medium / large earthquakes. The application of the finite element analysis algorithm improves the accuracy of stress verification, providing a reliable basis for construction safety under different working conditions; the comprehensive coverage of specialized verifications can thoroughly identify potential construction risks and avoid structural safety problems caused by missing verifications for single working conditions.

[0028] The core control unit also integrates a data verification module for outlier detection and correction of various types of collected data. Outlier detection employs the 3σ criterion: when data deviates from the mean by more than three times the standard deviation, it is considered an outlier and corrected using linear interpolation. The application of the 3σ criterion accurately identifies outlier data, preventing erroneous data from interfering with control decisions; the linear interpolation correction method preserves the continuity and validity of the data to the greatest extent possible, ensuring the accuracy of subsequent algorithm analysis.

[0029] The execution unit includes a hoist controller, an assembly robotic arm controller, and an alarm device. When structural deformation exceeds a preset threshold or stress reaches a dangerous level, the alarm device emits an audible and visual alarm, and simultaneously, the hoist controller stops the hoist from lifting. The audible and visual alarm function can immediately alert on-site construction personnel to avoid risks, improving the timeliness of construction safety warnings; the automatic stop control of the hoist can quickly prevent the risk from escalating, avoiding structural damage or safety accidents caused by delayed human response.

[0030] The cloud platform unit integrates stress calculation results from construction-specific verification with real-time data from full-cycle monitoring to generate a structural full-domain construction risk heatmap. This heatmap uses a continuous color gradient mapping rule to achieve quantitative visualization of risk. Specifically: when the regional stress value is ≤ 60% of the material's allowable stress and the deformation is ≤ 50% of a preset threshold, a green gradient mapping is used, with the color depth slightly increasing as the parameter value increases; when the regional stress value is between 60% and 80% of the material's allowable stress or the deformation is between 50% and 80% of the preset threshold, a yellow gradient mapping is used. The gradient mapping feature increases color depth synchronously with parameter values. When the stress value of a region exceeds 80% of the material's allowable stress or the deformation exceeds 80% of a preset threshold, a red gradient mapping is used, with the color depth significantly increasing with parameter values. Simultaneously, the heatmap supports stepless scaling from 1:10 to 1:500 and features risk data linkage and traceability. Clicking on any region allows for real-time retrieval of multi-dimensional data for the corresponding location, including real-time stress / displacement monitoring values, historical change curves, monitoring sensor numbers and installation locations, finite element verification parameters for the corresponding region, and detailed reports. Continuous color gradient mapping makes risk distribution intuitively identifiable, facilitating quick location of high-risk areas by construction personnel and improving risk management efficiency. The data linkage and traceability function provides complete data support for risk investigation and problem analysis, reducing the difficulty and time cost of fault location.

[0031] Example 2 according to Figure 1 As shown in the figure, this embodiment proposes a collaborative control system for intelligent assembly and overall lifting of large-span irregular steel structures, applied to the roof project of a large stadium. This roof is a large-span curved irregular steel structure with a maximum span of 68m, and the irregular area is mainly a curved trapezoidal outline. The system of this invention is used for intelligent assembly control. The data acquisition unit collects the three-dimensional coordinate data of the roof steel structure assembly nodes in real time using a total station, with a sampling frequency set to 15Hz. Simultaneously, it collects the curved trapezoidal outline parameters from the BIM model. The data processing unit preprocesses the collected coordinate data to remove noise interference. The dynamic coupling module uses the data matching algorithm in claim 2, setting the number of matching points n=50. By minimizing the total matching error E, it achieves millimeter-level matching between the BIM model and the measured coordinates, with a matching accuracy of ±0.5mm. The core control unit automatically generates assembly path optimization instructions based on the matching results using an AI algorithm, controlling the assembly robotic arm to perform "zero-error" aerial assembly. During construction, the full-cycle monitoring module collects stress data of the assembly nodes in real time to ensure the structural stability of the assembly process. This embodiment achieves precise assembly of curved irregular roof steel structures through this system, improving assembly efficiency by 30% and controlling assembly error within 0.5mm.

[0032] Example 3 according to Figure 1As shown, this embodiment proposes an intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures, applied to the overall lifting project of a large-span bridge steel structure. This bridge steel structure has a span of 56m and a lifting weight of 800t, and uneven load is prone to occur during the lifting process. The system of this invention is used for collaborative lifting control. The data acquisition unit collects load data at each lifting point in real time through hydraulic sensors and structural deformation data through displacement sensors. The collaborative control module uses the PID closed-loop control algorithm of claim 3, setting the proportional coefficient K1=5.2, integral coefficient K2=0.8, and derivative coefficient K3=1.5, calculating the force-displacement deviation value E(t) in real time, and dynamically adjusting the synchronization rate of each lifting point according to the algorithm output adjustment amount U(t). During the lifting process, when the load deviation at a certain lifting point exceeds 10%, the system automatically adjusts the rate of that lifting point to compensate for local stress concentration. This embodiment achieves load balance control during the lifting process through this system, controlling the synchronization error of the rate at each lifting point within 2%, and reducing local stress concentration by 60%.

[0033] Example 4 according to Figure 1 As shown in the figure, this embodiment proposes a collaborative control system for intelligent assembly and overall lifting of large-span irregular steel structures, applied to an irregular steel structure project of an airport terminal. This project's steel structure includes multiple irregular areas with curved trapezoidal boundaries, and the construction plan needs to be determined based on the structural irregularity and construction risks. Using the three-dimensional association decision module of this invention, the weights of the association factors are determined through the weight calculation algorithm in claim 4. The number of historical construction cases is selected (m=30), and the association factors include six factors such as structural irregularity, construction difficulty, and risk level (k=6). The weight Wj of each factor is calculated. Based on the weight results, the system automatically identifies irregular areas, combines them with the historical construction database, and generates a combined segmented scheme of "aerial assembly + local lifting." The stress state of key nodes is verified through BIM model simulation. Simulation results show that this scheme can effectively reduce construction risks, shorten the construction period by 25%, and reduce construction costs by 18% compared to traditional schemes.

[0034] Example 5 according to Figure 1As shown, this embodiment proposes an intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures, applied to the construction of a large-span steel structure in an exhibition center. The system employs a full-cycle monitoring system to monitor the entire construction process. The data acquisition unit integrates fiber optic sensors and a wireless strain monitoring network to collect stress and displacement data of the structure, with a sampling frequency set to 12Hz. The full-cycle monitoring module uses the deformation trend prediction algorithm from claim 5, setting the lag order q=4. Through training with historical deformation data, the intercept coefficient a0=0.2 and lag coefficients a1=0.6, a2=0.3, a3=0.1, and a4=0.05 are obtained. The system uses this algorithm to predict the deformation trend of the structure in subsequent construction stages. When the predicted deformation Yc(t+Δt) exceeds a preset threshold of 3mm, it automatically triggers the adjustment of lifting parameters, suspends lifting, and fine-tunes the load. In this embodiment, the system successfully predicted three potential deformation risks, adjusted construction parameters in a timely manner, ensured the safety of the construction process, and controlled the maximum deformation of the structure within 2mm during construction.

[0035] Example 6 according to Figure 1 As shown, this embodiment proposes a collaborative control system for intelligent assembly and overall lifting of large-span irregular steel structures, applied to the steel structure construction of a super high-rise office building. The system utilizes the cloud platform full-link verification system of this invention for specialized construction verification. This system integrates finite element analysis and BIM modeling, employing the finite element stress analysis algorithm from claim 6 to perform 12 types of specialized calculations, including small / medium / large earthquake stress analysis and temperature stress verification. In the temperature stress verification, the axial force on the component is known to be F = 500 kN, the cross-sectional area A = 0.02 m², the bending moment M = 80 kN·m, the distance from the calculation point to the neutral axis is y = 0.1 m, and the moment of inertia of the cross-section about the neutral axis is I = 1.2 × 10⁻⁻⁻⁶. 4 m 4 The algorithm calculated the comprehensive stress of the structural components to be σs = 25MPa, which is less than the allowable stress of the material of 30MPa, meeting the construction requirements. The system generated a visual risk heat map, dynamically optimized the hoisting path and the selection of lifting devices, and selected 16 hydraulic lifting devices with a rated load of 50t to ensure the rationality and safety of the construction.

[0036] Validation data: This invention has been verified through multiple engineering examples, and its performance indicators are as follows: Assembly accuracy: Achieves millimeter-level matching, with an average assembly error of ≤0.5mm, reducing the error by 85% compared to existing technologies; Improved load balance: Synchronous rate error at each lifting point ≤2%, local stress concentration phenomenon reduced by more than 60%; Decision-making efficiency: The time for generating construction plans is reduced by 70%, and the efficiency is increased by 5 times compared to manual decision-making; Monitoring response speed: Monitoring data acquisition frequency ≥10Hz, risk identification response time ≤0.5s, and accuracy rate of successful deformation risk prediction ≥95%; Verification efficiency: The total calculation time for 12 types of construction special projects is ≤2 hours, which is 80% more efficient than the traditional independent verification method; Overall benefits of construction: The construction period is shortened by an average of 25%, the construction cost is reduced by an average of 18%, and the incidence of construction safety accidents is reduced by more than 90%.

[0037] This invention achieves dynamic coupling between the BIM model and real-time data from a total station through a dynamic coupling module. It employs an algorithm that minimizes matching errors to achieve precise matching of curved surface parameters and spatial coordinates. Compared to existing static matching methods, this significantly improves assembly accuracy, enabling zero-error aerial assembly of curved steel structures. It solves the problem of low assembly accuracy in existing technologies and can respond in real-time to data changes during construction, ensuring that the assembly process remains consistent with the design model and reducing rework costs caused by assembly errors. Furthermore, the collaborative control module of this invention uses a PID closed-loop control algorithm based on force-displacement feedback. This algorithm can collect load data and structural deformation data from the hydraulic lifter in real time, dynamically adjust the synchronization rate of each lifting point, and automatically compensate for local stress concentration caused by the curved surface. This solves the problems of uneven lifting load and excessive local stress in existing technologies. Compared to existing fixed-parameter control algorithms, this closed-loop control algorithm has stronger adaptability and stability, effectively ensuring the structural safety of large-span steel structures during the lifting process. Meanwhile, this invention constructs a three-dimensional correlation decision-making model of structural irregularity, construction risk, and improvement scheme. It determines the influence weight of each correlation factor through a weight calculation algorithm and dynamically generates the optimal segmented scheme by combining historical construction databases. This solves the problem of existing technologies relying on human experience and lacking scientific rigor in construction scheme decision-making. This decision-making model can automatically identify structural irregularity areas and simulate and verify the stress state of key nodes, ensuring the rationality and feasibility of the construction scheme and improving the intelligence level of construction decision-making. Furthermore, this invention constructs a full-cycle monitoring system encompassing static testing, the lifting process, and unloading verification. It integrates fiber optic sensors and a wireless strain monitoring network, employing machine learning models to predict deformation trends, forming a monitoring-decision-execution closed loop. This solves the problems of incomplete monitoring and lack of a full-cycle closed loop in existing technologies. This full-cycle monitoring system can comprehensively and in real-time grasp the stress and displacement state of the structure at different construction stages, predict deformation risks in advance, and adjust construction parameters in a timely manner, further ensuring construction safety. Finally, this invention develops a cloud-based full-chain verification function for construction, integrating finite element analysis and BIM models to achieve a digital twin of design-construction-acceptance. It automatically completes various construction-specific calculations and generates a visualized risk heat map, solving the problems of broken verification links and insufficient timeliness of guidance in existing technologies. This full-chain verification can dynamically optimize key parameters such as hoisting paths and hoist selection, providing precise technical support for the entire construction process, improving the digitalization and intelligence level of construction, and reducing construction risks.

[0038] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A large-span, irregularly shaped steel structure intelligent assembly and overall lifting collaborative control system, comprising a data acquisition unit, a data processing unit, a core control unit, an execution unit, and a cloud platform unit, characterized in that: The data acquisition unit is used to acquire BIM model data, spatial coordinate data, load data, structural deformation data, and stress data of large-span irregular steel structures; the data processing unit is used to preprocess and convert the acquired data. The core control unit is connected to the data processing unit, and integrates a dynamic coupling module, a collaborative control module, a three-dimensional correlation decision module, a full-cycle monitoring module, and a cloud platform verification module to achieve collaborative control of assembly and lifting. The execution unit is connected to the core control unit and is used to execute assembly path adjustment, lifting parameter adjustment, and construction pause control commands. The cloud platform unit is connected to the core control unit and is used to store data, perform special calculations, and realize data visualization.

2. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 1, characterized in that: The data acquisition unit includes a total station, a hydraulic sensor, a fiber optic sensor, a wireless strain sensor, and a temperature sensor. The total station is used to acquire spatial coordinate data with a sampling frequency of not less than 10Hz. The hydraulic sensor is used to acquire load data of the hydraulic lifter with a measurement accuracy of not less than 0.5%FS.

3. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 1, characterized in that: The dynamic coupling module is used to achieve dynamic coupling between the BIM model and the real-time data from the total station. It employs a data matching algorithm to accurately match the parameters of the irregular surface with the spatial coordinates. The expression for the data matching algorithm is as follows: minE=Σ(i=1 to n)√[(Xs,i-Xc,i)²+(Ys,i-Yc,i)²+(Zs,i-Zc,i)²], Where: E is the total matching error between the BIM model coordinates and the total station measured coordinates; n is the number of matching points; Xs,i, Ys,i, and Zs,i are the three-dimensional coordinates of the i-th matching point in the BIM model, respectively; Xc,i, Yc,i, and Zc,i are the three-dimensional coordinates of the i-th matching point measured by the total station, respectively; by minimizing the total matching error E, millimeter-level accurate matching between the two is achieved.

4. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 3, characterized in that: The collaborative control module employs a dynamic collaborative control algorithm based on force-displacement feedback. It adjusts the synchronization rate of each booster point through PID closed-loop control, and the PID closed-loop control algorithm expression is as follows: U(t)=K1·E(t)+K2·∫(0 to t)E(t1)dt1+K3·dE(t) / dt, Where: U(t) is the rate adjustment of the lifting point at time t; K1 is the proportional coefficient; K2 is the integral coefficient; K3 is the differential coefficient; E(t) is the force-displacement deviation value at time t, that is, the weighted sum of the difference between the target force and the actual force and the difference between the target displacement and the actual displacement; t1 is the integral variable; the algorithm dynamically compensates for the local stress concentration caused by the irregular surface.

5. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 4, characterized in that: The three-dimensional correlation decision-making module constructs a three-dimensional correlation decision-making model of structural irregularity, construction risk, and improvement scheme. It uses a weight calculation algorithm to determine the influence weight of each correlation factor. The expression for the weight calculation algorithm is as follows: Wj=[Σ(i=1 to m)Rij] / [Σ(j=1 to k)Σ(i=1 to m)Rij], Where: Wj is the weight of the j-th related factor; m is the number of historical construction cases; Rij is the correlation degree of the j-th factor in the ith historical case; k is the total number of related factors; the optimal segmentation scheme is dynamically generated based on the weight calculation results.

6. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 5, characterized in that: The full-cycle monitoring module uses a machine learning model to predict structural deformation trends. The expression for the deformation trend prediction algorithm is as follows: Yc(t+Δt)=a0+Σ(p=1 to q)ap·Yj(t-p+1)+e(t), Where: Yc(t+Δt) is the predicted deformation at time t+Δt; a0 is the intercept coefficient; q is the lag order; ap is the coefficient of the p-th lag term; Yj(t-p+1) is the actual deformation at time t-p+1; e(t) is the random error term; the boosting parameters are automatically adjusted based on the prediction results.

7. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 6, characterized in that: The cloud platform verification module uses the finite element analysis algorithm to perform construction-specific stress analysis. The expression for the finite element stress analysis algorithm is as follows: σs=F / A+(M·y) / I, Where: σs is the comprehensive stress of the structural member; F is the axial force on the member; A is the cross-sectional area of ​​the member; M is the bending moment on the member; y is the distance from the calculation point to the neutral axis of the cross section; I is the moment of inertia of the cross section of the member about the neutral axis; this algorithm is used to complete the special calculations for stress analysis and temperature stress verification of small / medium / large earthquakes.

8. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 1, characterized in that: The core control unit also integrates a data verification module, which is used to detect and correct outliers in various types of collected data. The outlier detection adopts the 3σ criterion, that is, when the data deviates from the mean by more than 3 times the standard deviation, it is judged as an outlier and corrected by linear interpolation.

9. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 1, characterized in that: The execution unit includes a lifting device controller, an assembly robotic arm controller, and an alarm device. When the structural deformation exceeds a preset threshold or the stress reaches a dangerous value, the alarm device issues an audible and visual alarm, and at the same time, the lifting device controller controls the lifting device to stop lifting.

10. The intelligent assembly and overall lifting collaborative control system for large-span irregular steel structures according to claim 1, characterized in that: The cloud platform unit integrates stress calculation results from construction-specific verification with real-time data from full-cycle monitoring to generate a structural full-domain construction risk heatmap. This heatmap uses a continuous color gradient mapping rule to achieve quantitative visualization of risk. Specifically: when the regional stress value is ≤ 60% of the material's allowable stress and the deformation is ≤ 50% of a preset threshold, a green gradient mapping is used, with the color depth slightly increasing as the parameter value increases; when the regional stress value is between 60% and 80% of the material's allowable stress or the deformation is between 50% and 80% of the preset threshold, a yellow gradient mapping is used. Gradient mapping, the color depth increases synchronously with the parameter value; when the stress value of the area is greater than 80% of the allowable stress of the material or the deformation is greater than 80% of the preset threshold, a red gradient mapping is adopted, and the color depth increases significantly with the parameter value; at the same time, the heat map supports stepless scaling within the range of 1:10 to 1:500, and has the function of risk data linkage and traceability. Clicking on any area can retrieve multi-dimensional data of the corresponding location in real time, including real-time monitoring values ​​of stress / displacement, historical change curves, monitoring sensor number and installation location, finite element verification parameters and detailed reports of the corresponding area.

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