An assembled building intelligent construction optimization system

Abstract

The application discloses an intelligent construction optimization system for fabricated buildings, comprising: a space-time coupling construction situation deduction unit, a prefabricated component hoisting posture iteration unit, a superimposed component stress deformation prediction unit, a fabricated resource collaborative scheduling unit, an intelligent construction parameter fusion optimization unit and a feedback construction closed-loop regulation and control unit. The space-time coupling construction situation deduction unit corrects the situation evolution rate dynamically according to the overlapping degree of the process time window and the space collision domain; the hoisting posture iteration unit recursively updates the turning angle and heave parameters; the deformation prediction unit outputs the mid-span deflection estimate value; the resource collaborative scheduling unit encodes the tower crane operation, the yard occupancy and the vehicle arrival into a time window matrix to dynamically release resources; the fusion optimization unit generates a Pareto optimal solution set with the posture deviation gradient and the deflection sensitivity as double targets; and the feedback regulation and control unit feeds back the measured deformation difference to the initial boundary conditions to complete iterative correction. The application effectively eliminates process interference and resource conflicts.

Description

Technical Field

[0001] This invention relates to the field of intelligent building construction technology, and in particular to an intelligent construction optimization system for prefabricated buildings. Background Technology

[0002] With the large-scale promotion of prefabricated buildings in high-rise residential buildings and large public buildings, the on-site hoisting and stacking of prefabricated components faces prominent challenges such as limited working space, dense overlapping processes, and complex coupling between the movement trajectory of robotic arms and the posture of components. During construction, the slewing, luffing, and lifting operations of tower cranes need to simultaneously consider multiple dynamic factors such as random disturbances of wind loads, elastic deformation of wire ropes, strength growth of concrete over time, and turnover of stockpile resources. Relying solely on manual experience or static construction schemes is no longer sufficient to meet the high-precision and high-efficiency requirements of prefabricated construction management. There is an urgent need to build an intelligent closed-loop control system that can integrate spatiotemporal situation, hoisting posture, and structural deformation in real time.

[0003] Current optimization of prefabricated building construction mainly relies on a combination of Building Information Modeling (BIM) and construction progress management software. This involves pre-planning hoisting procedures and using tower crane monitoring systems to provide feedback on component placement deviations. Operators manually adjust the slewing angle and amplitude based on the deviation values ​​displayed on the screen. Simultaneously, construction management personnel coordinate the tower crane operation sequence and the arrival time of transport vehicles using walkie-talkies. After component installation, a total station or laser rangefinder is used to verify the elevation. The measured values ​​are compared with the design values ​​and then manually entered into the progress management system for subsequent construction plan adjustments.

[0004] However, existing technologies still have the following drawbacks: First, there is a significant data disconnect between the BIM model and the real-time on-site conditions. The construction schedule cannot be automatically adjusted based on the actual slewing time of the tower crane, the instantaneous changes in wind load, and the strain feedback of the overlapping surface. This leads to frequent conflicts between the process time window and the spatial envelope of the robotic arm, forcing temporary work stoppages or reversals for adjustments, which seriously slows down the construction pace. Second, tower crane operators, yard dispatchers, and concrete pouring teams rely on verbal instructions to coordinate resource release times, lacking a unified dynamic priority arbitration mechanism. When multiple tower cranes share the yard or transport vehicles arrive at the same time, it is easy to cause component backlog or idle hoisting. The overall resource utilization rate and response sensitivity of the system are far from meeting the real-time requirements for rolling revision of the daily construction plan. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides an intelligent construction optimization system for prefabricated buildings.

[0006] The technical solution adopted in this invention is an intelligent construction optimization system for prefabricated buildings, comprising: a spatiotemporally coupled construction situation deduction unit, which dynamically analyzes the spatiotemporal coherence of the construction progress vector field and the robotic arm motion trajectory streamline based on the overlap of the process time window and the spatial collision domain, and corrects the situation evolution rate coefficient based on the intermittent waiting time of hoisting operations; a prefabricated component hoisting posture iteration unit, which performs difference mapping between the current posture error of the component and the target posture constraint, and recursively updates the rotation angle and heave parameters by introducing wind load disturbance compensation terms and wire rope elastic elongation correction terms; and a composite component stress deformation prediction unit, which establishes a moment-curvature constitutive correlation based on the concrete age strength growth curve and the surface roughness coefficient of the composite surface, and uses a layered integration strategy to accumulate the superposition effect of micro-element curvature along the component axis. The system acquires mid-span deflection estimates; the prefabricated resource collaborative scheduling unit encodes tower crane operation sequences, prefabricated component yard layouts, and transport vehicle arrival times into a resource occupancy time window matrix, and dynamically adjusts the release times of each resource based on the priority coefficients output by the construction situation simulation unit; the intelligent construction parameter fusion optimization unit uses the attitude deviation gradient of the hoisting attitude iteration unit and the deflection sensitivity of the deformation prediction unit as dual-objective coordination variables, and adopts a multi-attribute decision-making mechanism to fuse the tightness of process connection and equipment energy consumption factors to generate a Pareto front solution set; the feedback-type construction closed-loop control unit inverts the optimal solution set obtained by the fusion optimization unit into on-site execution instructions, and collects the difference between the measured deformation and design elevation of the installed components in real time and injects it back into the initial boundary conditions of the spatiotemporal simulation unit to complete iterative cycle correction.

[0007] Furthermore, the update expression for the situation evolution rate coefficient in the spatiotemporally coupled construction situation simulation unit is: ,in Let be the correction rate coefficient at time t. The initial rate coefficient, To influence the weights of coupling, for The duration of overlap between the time window of the time process and the spatial collision domain. The threshold for critical overlap duration. The integral time variable is used as the basis for determining the magnitude of the construction progress vector field. This leads to the derivation of the expression for the magnitude of the construction progress vector field. ,in The vector field amplitude represents the construction progress at any given time. This represents the maximum construction progress range. The cumulative equivalent of the processes completed at time t. It is the half-saturation constant. The area is the construction area. It is a spatial position vector. For position The process density distribution function at the location.

[0008] Furthermore, the expression for recursively updating the rotation angle in the prefabricated component hoisting attitude iteration unit is as follows: ,in For the first The angle value in the next iteration. For the first The angle value in the next iteration. Let the step size be the angle iteration step. For the first The rotation angle deviation in the next iteration This is the cumulative deviation coefficient. For the first The rotation angle deviation in the next iteration For the first The time interval between iterations This is the wind load compensation coefficient. for The equivalent disturbance force of wind load at any given time; and then construct the iterative recursive expression for the heave parameter: ,in For the first The heave parameters for the next iteration. For the first The heave parameters for the next iteration. To determine the iteration step size for rise and fall, For the first Heave deviation in the next iteration This is the elasticity correction factor for the wire rope. and The first Second and third The moment of the next iteration for The elastic elongation of the wire rope at any given time.

[0009] Furthermore, the expression for the mid-span deflection estimate in the stress deformation prediction unit of the composite member is as follows: ,in This is the estimated value of the mid-span deflection of the component. Calculate the span for the component. Let these be the coordinates of the infinitesimal element position along the axial direction of the component. For position The bending moment value at the section, For position Average strain difference at the overlapping surface Let be the elastic modulus of concrete at time t. For position The effective cross-sectional moment of inertia considering the slip effect of the overlapping surface.

[0010] Furthermore, the spatiotemporal coupled construction situation simulation unit is deployed on an edge computing node, which is equipped with a dual-processor graphics processor and a real-time clock synchronization module with a clock synchronization accuracy of ±1 millisecond. The precast component hoisting attitude iteration unit runs on an industrial-grade programmable logic controller with an analog-to-digital conversion resolution of 16 bits and a sampling period of 20 milliseconds. The composite component stress deformation prediction unit is embedded in a strain acquisition terminal, which has a built-in temperature self-compensating full-bridge strain gauge with a bridge excitation voltage of 2.5 volts and a strain measurement range of ±3000 micro-strains. The prefabricated resource coordination... The scheduling unit realizes data interaction between tower cranes, storage yards and transport vehicles through a wireless self-organizing network. The network communication protocol adopts the IEEE 802.11ac standard and the media access control layer time slot length is 10 milliseconds. The intelligent construction parameter fusion and optimization unit runs on a cloud server with a random access memory capacity of 128 gigabytes and a central processing unit frequency of not less than 3.2 GHz. The feedback construction closed-loop control unit is connected to each actuator through an industrial Ethernet, with a network transmission rate set to 100 megabits per second and a cyclic redundancy check using a 32-bit check code.

[0011] Furthermore, the spatiotemporal coupled construction situation deduction unit includes: a process time window analysis module, used to map the planned start time and planned end time of each construction process into open interval panes on the time axis, and to connect and sort the panes according to the pre- and post-process constraints; a spatial collision domain generation module, used to construct a three-dimensional spatial interference body based on the motion envelope of the robotic arm end effector and the outer contour of the installed component, and to discretize the interference body into a set of voxel meshes; a spatiotemporal overlap calculation module, used to traverse and search all voxel meshes in each time pane, and to count the ratio of the number of meshes simultaneously occupied by two or more robotic arm envelopes to the total number of meshes in that time pane; and a rate coefficient correction module, used to accumulate the duration for which the overlap ratio exceeds a preset threshold as the overlap duration, and to use the ratio of the overlap duration to the critical duration as a power exponent to adjust the situation evolution rate.

[0012] Furthermore, the prefabricated component hoisting attitude iteration unit includes: a pose error mapping module, used to subtract the current Euler angles and spatial coordinates measured by the six-degree-of-freedom pose sensor from the corresponding components of the target hoisting pose to obtain a rotation angle deviation sequence and a heave deviation sequence; a wind disturbance compensation module, used to receive the instantaneous wind speed value collected by the anemometer and convert it into an equivalent disturbance torque acting on the windward surface of the component based on the preset wind pressure height change coefficient and shape coefficient; a wire rope elasticity correction module, used to calculate the elastic elongation change rate based on the reading of the sling tension sensor combined with the nominal elastic modulus and effective bearing length of the wire rope, and integrate the change rate over time to obtain the cumulative elongation correction amount; and an iterative update execution module, used to superimpose the deviation sequence, disturbance torque correction amount, and cumulative elongation correction amount to the rotation angle and heave values ​​of the current iteration step according to preset weights, and output the updated pose parameters for the next iteration step.

[0013] Furthermore, the composite component stress deformation prediction unit includes: an age-strength interpolation module, used to fit a logarithmic curve of strength growth with age based on the measured compressive strength value of the test block cured under the same conditions at the construction site, and extract the elastic modulus value corresponding to the current age; a composite surface strain calculation module, used to calculate the average strain difference at each measuring point along the component axis based on the ratio of the strain sensor reading difference to the sensor gauge length embedded on the upper and lower surfaces of the composite surface; a bending moment distribution module, used to establish a differential equilibrium equation of shear force and bending moment based on the component support boundary conditions and the current construction load arrangement, and use a piecewise integration method to solve for the section bending moment value at each axial position; and a deflection accumulation integration module, used to multiply the bending moment value at each micro-element by the average strain difference, divide by the product of the elastic modulus and the effective moment of inertia, multiply by the distance weighting coefficient from the micro-element to the support, and finally integrate along the entire span to sum and output the mid-span deflection.

[0014] Furthermore, the prefabricated resource collaborative scheduling unit includes: a tower crane operation sequence encoding module, used to arrange the hook-up time, slewing angle, luffing amplitude, and hook-down time of each hoisting cycle into a timestamped event vector in chronological order and store it in a circular queue buffer; a yard layout occupancy module, used to map the planar position coordinates and geometric dimensions of prefabricated components in the yard into rectangular occupancy areas, and mark the start time of occupancy and release time of each area according to the component outbound order; a transport vehicle arrival estimation module, used to estimate the expected time of transport vehicle arrival at the construction site entrance and the unloading waiting time based on the real-time location reported by the vehicle's global positioning system and the average speed of the driving section; and a resource release time window adjustment module, used to apply a time offset to the tower crane hook-down time or the vehicle unloading start time to eliminate resource occupancy overlap when the tower crane operation event vector conflicts with the vehicle arrival time, using the priority coefficient output by the spatiotemporal extrapolation unit as the weight.

[0015] A prefabricated building intelligent construction optimization system includes the following steps: S1, extracting the planned time window and the spatial motion envelope of the robotic arm for each process based on the construction organization design document, and inputting both into a spatiotemporally coupled construction situation deduction unit to obtain the amplitude distribution and evolution rate coefficient of the current construction progress vector field; S2, inputting the difference between the measured pose data of the prefabricated component to be hoisted and the design target pose data into the prefabricated component hoisting posture iteration unit, and recursively solving for the optimal rotation angle and heave iteration values ​​in combination with real-time wind load and steel wire rope elastic elongation; S3, inputting the measured strain value and concrete age value of the composite component after hoisting into place into the composite component stress deformation prediction unit, and accumulating the micro-element curvature segment by segment along the component axis through a layered integration strategy and outputting the span S4: The tower crane operation sequence, the occupancy status of the yard components, and the arrival time of the transport vehicles are uniformly encoded into a resource occupancy time window matrix, and the priority order of each resource release time is dynamically adjusted according to the evolution rate coefficient obtained in S1; S5: The attitude deviation gradient output by S2 and the deflection sensitivity output by S3 are used as dual-objective coordination variables, and a multi-attribute decision-making mechanism is used to integrate the tightness of process connection and equipment energy consumption factor to generate a Pareto optimal solution set; S6: The optimal solution set obtained in S5 is inverted into tower crane slewing angle command, luffing speed command, and hook drop height command, and the difference between the measured elevation and the design elevation of the installed components is collected in real time and injected back into the initial progress boundary conditions in S1. S1 to S6 are repeated until all components in the construction section are installed.

[0016] Beneficial Effects: This invention proposes an intelligent construction optimization system for prefabricated buildings. Through a spatiotemporally coupled construction situation simulation unit, it analyzes the overlap between process time windows and spatial collision domains in real time, dynamically correcting the situation evolution rate coefficient. This allows the construction progress vector field to automatically adjust its amplitude distribution based on the actual slewing time of tower cranes and instantaneous wind load disturbances. This solves the problems of existing BIM models being disconnected from on-site conditions and the inability to automatically adjust schedule parameters, fundamentally avoiding frequent work stoppages and reversals. Simultaneously, through a prefabricated resource collaborative scheduling unit, tower crane operation sequences, yard occupancy, and vehicle arrival times are uniformly encoded into a resource occupancy time window matrix, and output based on situation simulation. The priority coefficients dynamically adjust the release time of each resource, replacing the coordination mode that relies on verbal instructions. This effectively eliminates the backlog of components and the phenomenon of idling during the arrival of multiple tower cranes sharing the storage yard and dense vehicle arrivals. Through the feedback-type construction closed-loop control unit, the difference between the measured deformation and the design elevation of the installed components is injected back into the initial boundary conditions in real time. This drives the hoisting attitude iteration unit and the deformation prediction unit to collaboratively update the rotation angle and heave parameters, forming an automatic closed-loop correction mechanism covering the entire chain from situational awareness and resource scheduling to attitude execution. This improves the system's real-time response sensitivity to random disturbances and cross-process interference, ensuring the efficient rolling execution of the daily construction plan and the optimization of resource utilization. Attached Figure Description

[0017] Figure 1 This is a diagram showing the system module composition of the present invention; Figure 2 This is a flowchart of the system operation steps of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, an intelligent construction optimization system for prefabricated buildings includes: a spatiotemporally coupled construction situation deduction unit, which dynamically analyzes the spatiotemporal coherence of the construction progress vector field and the robotic arm motion trajectory streamline based on the overlap of the process time window and the spatial collision domain, and corrects the situation evolution rate coefficient based on the intermittent waiting time of hoisting operations; a prefabricated component hoisting posture iteration unit, which performs difference mapping between the current posture error of the component and the target posture constraint, and recursively updates the rotation angle and heave parameters by introducing wind load disturbance compensation terms and wire rope elastic elongation correction terms; and a composite component stress deformation prediction unit, which establishes a moment-curvature constitutive correlation based on the concrete age strength growth curve and the surface roughness coefficient of the composite surface, and uses a layered integration strategy to accumulate the superposition effect of the micro-element curvature along the component axis to obtain the mid-span deflection. The system includes: a degree estimation value; a prefabricated resource collaborative scheduling unit, which encodes the tower crane operation sequence, prefabricated component yard layout, and transport vehicle arrival time into a resource occupancy time window matrix, and dynamically adjusts the release time of each resource based on the priority coefficient output by the construction situation simulation unit; an intelligent construction parameter fusion optimization unit, which uses the attitude deviation gradient of the hoisting attitude iteration unit and the deflection sensitivity of the deformation prediction unit as dual-objective coordination variables, and adopts a multi-attribute decision-making mechanism to fuse the tightness of process connection and equipment energy consumption factor to generate a Pareto front solution set; and a feedback-type construction closed-loop control unit, which inverts the optimal solution set obtained by the fusion optimization unit into on-site execution instructions, and collects the difference between the measured deformation and design elevation of the installed components in real time and injects it back into the initial boundary conditions of the spatiotemporal simulation unit to complete iterative cycle correction.

[0020] In its specific implementation, the spatiotemporal coupled construction situation simulation unit reads the planned start and end times of each process from the construction organization design document, maps these times into open interval panes on the time axis, and constructs a three-dimensional spatial interference body based on the motion envelope of the robotic arm end effector and the outer contour of the installed components. The interference body is then discretized into a set of voxel meshes with a side length of 50mm. Subsequently, all voxel grids within each time pane are traversed and searched. The ratio of the number of grids simultaneously occupied by two or more robotic arms to the total number of grids in that time pane is used as the overlap degree. When the overlap degree exceeds a preset threshold of 0.35, the duration of accumulation begins. The ratio of the accumulated overlap duration to a preset critical duration of 120s is used as a power exponent and multiplied by the initial rate coefficient of 0.82 to obtain the current time-corrected evolution rate coefficient. The value of this coefficient is limited to between 0.65 and 1.20 to maintain numerical stability. The corrected rate coefficient is substituted into the amplitude of the construction progress vector field for derivation. The upper limit of the amplitude is set to 2.4 m / h. The actual amplitude increases according to the saturation curve with the cumulative equivalent of the process, and the process density at any location in space is between 0.1 and 0.9, thus providing a dynamic rate benchmark for subsequent resource scheduling and priority arbitration.

[0021] In practical implementation, the precast component hoisting attitude iteration unit acquires the component's current Euler angles and spatial coordinates in real time at a sampling frequency of 50Hz using a six-degree-of-freedom inertial measurement unit installed below the hook. The rotational deviation sequence is obtained by subtracting the corresponding components of the current Euler angles from the target hoisting posture, and the heave deviation sequence is obtained by subtracting the vertical components of the current spatial coordinates from the target coordinates. An iterative update is triggered when the absolute value of the rotational deviation exceeds 0.5 degrees or the absolute value of the heave deviation exceeds 2mm. Regarding wind disturbance compensation, instantaneous wind speed values ​​are collected using an anemometer installed at the top of the tower crane. When the wind speed exceeds 8m / s, it is converted into an equivalent disturbance moment based on a preset wind pressure height variation coefficient of 1.28 and a shape coefficient of 1.30. This moment ranges from 12N·m to 85N·m. Regarding the elastic correction of the wire rope, the elastic elongation change rate is calculated based on the readings of the cable tension sensor, the nominal elastic modulus of the wire rope (1.95 × 10^5 MPa), and the effective bearing length (12 m). This change rate is integrated over time to obtain the cumulative elongation correction, which does not exceed 15 mm. The iterative update execution module multiplies the angle deviation, disturbance torque correction by a weighting coefficient of 0.15, and the cumulative elongation correction by a weighting coefficient of 0.20, and then adds them to the angle value of the current iteration step. The angle iteration step size is set to 0.30, and the heave iteration step size is set to 1.8 mm. Each iteration calculation takes no more than 8 ms. Finally, the updated angle and heave values ​​are output as the execution command for the next control cycle.

[0022] In its implementation, the stress-deformation prediction unit for composite components starts with the measured compressive strength values ​​of concrete test blocks cured under the same conditions at the construction site. It then extracts the elastic modulus value corresponding to the current casting age of the component. Within the age range of 12h to 168h, the elastic modulus increases logarithmically from 14.5GPa to 32.0GPa. Next, it reads the difference in readings from strain sensors embedded on the upper and lower surfaces of the composite component. With a sensor gauge length of 100mm, this difference is divided by the gauge length to obtain the average strain difference at each measuring point along the component's axial direction. The strain measurement range is set from -2000 microstrain to +2000 microstrain. Regarding the bending moment distribution along the span, based on the simply supported boundary conditions at both ends of the component and the current construction load arrangement, the uniformly distributed load is taken as 3.2 kN / m, and the concentrated load is taken as 2.8 kN. A differential equilibrium equation for shear force and bending moment is established, and the section bending moment value at each axial position is solved using a piecewise integration method. The integration step size is set to 1 / 200 of the component's calculation span, with the calculation span ranging from 3000 mm to 9000 mm. The deflection accumulation integration module multiplies the bending moment value at each micro-element by its average strain difference, then divides it by the product of the elastic modulus and the effective moment of inertia. The effective moment of inertia is between 0.8 and 0.95 times the gross section moment of inertia. This is then multiplied by the distance weighting coefficient from the micro-element to the support, and the summation is performed along the entire span to output the estimated mid-span deflection. The deflection output accuracy is set to 0.1 mm. This deflection value forms a joint control strategy with subsequent hoisting attitude adjustments.

[0023] In its implementation, the prefabricated resource collaborative scheduling unit stores the hook-up time, slewing angle, amplitude change, and hook-down time of each hoisting cycle as timestamped event vectors in a circular queue buffer, with a queue capacity of 200 event vectors. Simultaneously, the planar position coordinates and geometric dimensions of prefabricated components in the yard are mapped to rectangular occupancy areas with a side length accuracy of 10mm. The start and release times of each area's occupancy are marked according to the component's outbound sequence, dividing the yard area into 12 occupancy units. For transport vehicle arrival estimation, the estimated arrival time at the construction site entrance and unloading waiting time are estimated using the real-time location reported by the vehicle's GPS and the average speed of the travel route (35km / h to 55km / h), with the estimated time error controlled within ±3 minutes. When the time interval between the hook-dropping time and the vehicle arrival time in the tower crane operation event vector is less than 5 minutes, it is determined to be a conflict. In this case, the priority coefficient output by the spatiotemporal simulation unit is used as the weight. The coefficient ranges from 0.1 to 1.0. A time offset of -30s to +30s is applied to the hook-dropping time of the tower crane or a time offset of -20s to +20s is applied to the unloading start time of the vehicle to eliminate overlap. The scheduling decision calculation cycle is set to 1s to ensure that the occupation conflict when multiple resources are operating in parallel is arbitrated in a timely manner.

[0024] In practical implementation, the intelligent construction parameter fusion and optimization unit obtains the rotation angle deviation and heave deviation of the current iteration step from the hoisting attitude iteration unit, normalizes them into attitude deviation gradient values, and maps the gradient value range to the interval of 0 to 1. At the same time, it obtains the ratio of the estimated mid-span deflection value to the design allowable deflection value L / 400 from the deformation prediction unit as the deflection sensitivity. When the sensitivity value is greater than 0.80, high-priority intervention is triggered. Then, the attitude deviation gradient and deflection sensitivity are used as dual-objective coordination variables. The tightness of process connection is represented by the interval between the planned time window of the current process and the subsequent process. When the interval is less than 20 minutes, the tightness value increases. The equipment energy consumption factor is calculated in real time based on the tower crane slewing motor current and the luffing hydraulic pump pressure, and the energy consumption factor value ranges from 0.3 to 0.9. A multi-attribute decision-making mechanism is adopted to integrate these variables into a comprehensive performance score. In the score calculation formula, the gradient weight of attitude deviation is 0.35, the weight of deflection sensitivity is 0.35, the weight of process connection tightness is 0.20, and the weight of equipment energy consumption factor is 0.10. After calculating the comprehensive performance score of all feasible candidate solutions, they are sorted in descending order of score. The top 5 solutions with the highest scores are selected to form the Pareto front solution set. The optimization calculation time is controlled within 200ms to adapt to the real-time control requirements.

[0025] In practical implementation, the feedback-based construction closed-loop control unit uses the first solution in the optimal solution set obtained by the fusion optimization unit as the execution command for the current control cycle. The angle increment value in this solution is converted into a pulse control signal for the tower crane's slewing motor, with a pulse frequency set to 2kHz and the angle increment range limited to -8 degrees to +8 degrees. The heave increment value in the solution is converted into a speed command for the luffing winch, with a speed command range of -0.5m / min to +0.5m / min. The deflection compensation value in the solution is converted into a hook height fine-tuning command with a fine-tuning accuracy of 0.5mm. The execution commands are transmitted to the controllers of each actuator via industrial Ethernet at a transmission rate of 100Mbps, with a controller response delay of no more than 15ms. Simultaneously, after the components are installed in place, the actual deformation acquisition is immediately initiated. A laser rangefinder is used to measure the actual elevation value of the installed components. When the absolute value of the difference between this elevation value and the design elevation value exceeds 1mm, the back-injection operation is triggered. This difference is used as a boundary condition correction amount and injected back into the initial progress boundary conditions of the spatiotemporal simulation unit. This causes the start time and end time of the initial plan to be offset by the time equivalent converted by this difference, completing a complete iterative cycle correction. The cycle of the entire correction process is set to execute a system-level closed-loop feedback once every 3 components are installed.

[0026] Preferably, the update expression for the situation evolution rate coefficient in the spatiotemporal coupled construction situation simulation unit is: ,in Let be the correction rate coefficient at time t. The initial rate coefficient, To influence the weights of coupling, for The duration of overlap between the time window of the time process and the spatial collision domain. The threshold for critical overlap duration. The integral time variable is used as the basis for determining the magnitude of the construction progress vector field. This leads to the derivation of the expression for the magnitude of the construction progress vector field. ,in The vector field amplitude represents the construction progress at any given time. This represents the maximum construction progress range. The cumulative equivalent of the processes completed at time t. It is the half-saturation constant. The area is the construction area. It is a spatial position vector. For position The process density distribution function at the location.

[0027] Specifically, the expression for updating the situation evolution rate coefficient is based on the physical logic that construction progress is directly affected by the overlap duration of the process time window and the spatial collision domain. When the overlap duration accumulates from zero, frequent avoidance by the robotic arm will shorten the effective working time, so the rate coefficient must decay as the overlap duration increases. This expression uses the ratio of the overlap duration to the critical duration of 120 seconds as an adjustment factor. Its mathematical form is based on a saturation suppression model, that is, when the overlap duration is much smaller than the critical duration, the adjustment factor approaches zero, and the rate coefficient remains unchanged at the initial value of 0.82; when the overlap duration continues to accumulate and exceeds the critical duration, the adjustment factor approaches one, and the rate coefficient approaches 1.64 times the initial value, but in practice, the coefficient is constrained between 0.65 and 1.20 through a limiting mechanism to prevent over-correction. The existence of integral operation makes the rate coefficient not only dependent on the current instantaneous value of overlap, but also the cumulative influence of all historical overlap durations. The integral step size is set to 1 second, and the coupling influence weight is 0.35 to balance the contribution ratio of historical accumulation and the current transient. The subsequent construction progress vector field amplitude derivation expression introduces the cumulative equivalent of completed processes as the driving force for progress advancement. The amplitude cannot grow indefinitely, but tends to a maximum amplitude of 2.4 meters per hour as the cumulative equivalent increases. The double integral of the spatial density function characterizes the weighted contribution of the non-uniformity of process distribution at various locations in the construction area to the overall progress amplitude. This integral discretizes the area into a 200x200 grid, with the density value of each grid ranging from 0.1 to 0.9, ultimately achieving nonlinear control of the amplitude by the rate coefficient.

[0028] Preferably, the expression for recursively updating the rotation angle in the prefabricated component hoisting attitude iteration unit is: ,in For the first The angle value in the next iteration. For the first The angle value in the next iteration. Let the step size be the angle iteration step. For the first The rotation angle deviation in the next iteration This is the cumulative deviation coefficient. For the first The rotation angle deviation in the next iteration For the first The time interval between iterations This is the wind load compensation coefficient. for The equivalent disturbance force of wind load at any given time; and then construct the iterative recursive expression for the heave parameter: ,in For the first The heave parameters for the next iteration. For the first The heave parameters for the next iteration. To determine the iteration step size for rise and fall, For the first Heave deviation in the next iteration This is the elasticity correction factor for the wire rope. and The first Second and third The moment of the next iteration for The elastic elongation of the wire rope at any given time.

[0029] Specifically, the iterative recursive expression for the turning angle is built upon the classic proportional-integral control framework, but introduces an instantaneous compensation term for wind load disturbance. The cumulative summation of the deviation sequence eliminates steady-state error, ensuring that the turning angle deviation approaches zero after long-term iteration. The summation step size corresponds to the time interval of each iteration, which is fixed at 40 milliseconds in the tower crane control system. The cumulative deviation coefficient is set to 0.25 to control the response speed of the cumulative term. The equivalent wind load disturbance force is calculated by converting the measured wind speed value from an anemometer installed at the top of the tower crane. The wind speed threshold is set to 8 meters per second; beyond this threshold, the disturbance moment is proportional to the square of the wind speed. The conversion factor incorporates the wind pressure height variation coefficient of 1.28 and the shape coefficient of 1.30. The actual disturbance moment ranges from 12 Nm to 85 Nm, and the compensation coefficient is set to 0.018 to achieve dimensional conversion from disturbance moment to angle correction. The reason this expression can be established is that the rate of change of the angle deviation is linearly related to the current deviation value and the historical cumulative deviation. Meanwhile, wind load, as an external input, directly affects the deflection trend of the component. Therefore, the three terms are linearly superimposed to form an iterative update law. The heave iterative recursive expression is derived in conjunction with the angle iteration. The cumulative heave deviation is not summed but directly multiplied by the iteration step size. This is because the static error of heave control can be naturally eliminated by the mechanical locking mechanism, without the need for long-term accumulation. The integral operation acts on the derivative of the elastic elongation of the wire rope with respect to time, and the cumulative effect of the rate of change of elastic elongation within the iteration time interval. This integral step size is consistent with the iteration period, 40 milliseconds. The elastic correction coefficient is taken as 0.30 to convert the cumulative elongation into a heave correction value. The effective length of the wire rope is 12 meters, the nominal elastic modulus is 1.95 multiplied by 10 to the power of 5 MPa, and the rate of change of elongation changes accordingly when the tension reading is between 5 kN and 25 kN. Finally, the heave iteration step size is taken as 1.8 mm to ensure that each correction is gradual and does not cause system oscillation.

[0030] Preferably, the expression for the mid-span deflection estimate in the composite member stress deformation prediction unit is: ,in This is the estimated value of the mid-span deflection of the component. Calculate the span for the component. Let these be the coordinates of the infinitesimal element position along the axial direction of the component. For position The bending moment value at the section, For position Average strain difference at the overlapping surface Let be the elastic modulus of concrete at time t. For position The effective cross-sectional moment of inertia considering the slip effect of the overlapping surface.

[0031] Specifically, the expression for mid-span deflection estimation is derived from the conjugate beam method or the principle of virtual work in beam bending theory. However, the difference from the traditional formula lies in introducing the average strain difference of the composite surface as an additional curvature contribution factor into the integration kernel. For composite members, the cross-sectional curvature is determined not only by the ratio of bending moment to bending stiffness but also by the discontinuity of concrete strain on the upper and lower sides of the composite surface. Therefore, multiplying the bending moment value by the average strain difference is equivalent to a corrected bending moment term considering the slip effect. The integral operation along the member's axial direction covers all infinitesimal elements from the support to the mid-span. The integration step size is set to 1 / 200 of the calculation span, which means taking an integration point every 30 millimeters for a member with a span of 6 meters. In the derivation process, the axial coordinate of the infinitesimal element's position from the support is multiplied by 1 (within parentheses) minus the ratio of that coordinate to the span. The geometric meaning of this factor is the influence line function of the mid-span deflection of a simply supported beam under a unit load. Its maximum value appears at the mid-span and decreases to zero towards the supports at both ends. Therefore, the contribution weight of infinitesimal elements near the mid-span to the deflection is the largest. The change in the elastic modulus of concrete with age was obtained by logarithmic interpolation of measured strength. The elastic modulus increased from 14.5 GPa to 32.0 GPa within the age range of 12 to 168 hours. The effective moment of inertia was taken between 0.8 and 0.95 times the gross section moment of inertia to reflect the stiffness reduction caused by slippage at the composite surface. This expression is valid only if the component is in the elastic working stage and the strain difference at the composite surface changes continuously along the axial direction. In practice, strain difference values ​​are collected by strain sensors embedded on the upper and lower surfaces of the component at a gauge length of 100 mm. The mid-span deflection output accuracy is set to 0.1 mm. The calculation results are used to determine whether additional jacking or hook-lowering adjustments are needed after hoisting into place to eliminate excessive elastic deformation.

[0032] Preferably, the spatiotemporal coupled construction situation simulation unit is deployed on an edge computing node, which is equipped with a dual-processor graphics processor and a real-time clock synchronization module with a clock synchronization accuracy of ±1 millisecond. The precast component hoisting attitude iteration unit runs on an industrial-grade programmable logic controller with an analog-to-digital conversion resolution of 16 bits and a sampling period of 20 milliseconds. The composite component stress deformation prediction unit is embedded in a strain acquisition terminal, which has a built-in temperature self-compensating full-bridge strain gauge with a bridge excitation voltage of 2.5 volts and a strain measurement range of ±3000 micro-strains. The prefabricated resource coordination... The scheduling unit realizes data interaction between tower cranes, storage yards and transport vehicles through a wireless self-organizing network. The network communication protocol adopts the IEEE 802.11ac standard and the media access control layer time slot length is 10 milliseconds. The intelligent construction parameter fusion and optimization unit runs on a cloud server with a random access memory capacity of 128 gigabytes and a central processing unit frequency of not less than 3.2 GHz. The feedback construction closed-loop control unit is connected to each actuator through an industrial Ethernet, with a network transmission rate set to 100 megabits per second and a cyclic redundancy check using a 32-bit check code.

[0033] Preferably, the spatiotemporal coupled construction situation deduction unit includes: a process time window analysis module, used to map the planned start time and planned end time of each construction process into open interval panes on the time axis, and to connect and sort the panes according to the pre- and post-process constraints; a spatial collision domain generation module, used to construct a three-dimensional spatial interference body based on the motion envelope of the robotic arm end effector and the outer contour of the installed component, and to discretize the interference body into a set of voxel meshes; a spatiotemporal overlap calculation module, used to traverse and search all voxel meshes in each time pane, and to count the ratio of the number of meshes simultaneously occupied by two or more robotic arm envelopes to the total number of meshes in that time pane; and a rate coefficient correction module, used to accumulate the duration for which the overlap ratio exceeds a preset threshold as the overlap duration, and to use the ratio of the overlap duration to the critical duration as a power exponent to adjust the situation evolution rate.

[0034] Specifically, during the implementation of the process time window parsing module, the planned start and end times of each process are extracted line by line from the construction organization design document and uniformly converted into absolute seconds based on midnight of the day, forming a half-open and half-closed interval pane. Then, based on the process pre- and post-constraint relationship table, the connection order of adjacent panes is topologically sorted. The sorting algorithm execution cycle is set to 1 second, and the minimum resolution of the pane is 1 second. The spatial collision domain generation module constructs an envelope volume based on the motion range of each joint angle of the robotic arm and the size of the end effector. At the same time, a rigid body occupant volume is established based on the design outline dimensions of the installed components. The two volumes are Boolean merged to obtain a three-dimensional interference volume. Then, the interference volume is discretized using a cubic voxel mesh with a side length of 50 mm. The total number of voxels does not exceed 1 million to control the computational load. The spatiotemporal overlap calculation module traverses all voxel grids within each time pane, marking which robotic arm envelopes currently occupy each grid. It counts the number of grids simultaneously occupied by two or more envelopes, divides this number by the total number of grids in that pane to obtain the overlap ratio, and refreshes the calculation every 500 milliseconds. The rate coefficient correction module compares the overlap ratio with a preset threshold of 0.35. When three consecutive refreshes exceed the threshold, it begins accumulating overlap duration. The ratio of this accumulated duration to the critical duration of 120 seconds is limited and then used as a power exponent to apply to the initial rate coefficient of 0.82. The power operation result is further limited to the range of 0.65 to 1.20 before being output. This output value is updated every 1 second for subsequent scheduling.

[0035] Preferably, the prefabricated component hoisting attitude iteration unit includes: a pose error mapping module, used to subtract the current Euler angles and spatial coordinates measured by the six-degree-of-freedom pose sensor from the corresponding components of the target hoisting pose to obtain a rotation angle deviation sequence and a heave deviation sequence; a wind disturbance compensation module, used to receive the instantaneous wind speed value collected by the anemometer and convert it into an equivalent disturbance torque acting on the windward surface of the component based on the preset wind pressure height change coefficient and shape coefficient; a wire rope elasticity correction module, used to calculate the elastic elongation change rate based on the reading of the sling tension sensor combined with the nominal elastic modulus and effective bearing length of the wire rope, and integrate the change rate over time to obtain the cumulative elongation correction amount; and an iterative update execution module, used to superimpose the deviation sequence, disturbance torque correction amount, and cumulative elongation correction amount to the rotation angle and heave values ​​of the current iteration step according to preset weights, and output the updated pose parameters for the next iteration step.

[0036] Specifically, the pose error mapping module acquires the current Euler angles using a six-degree-of-freedom inertial measurement unit at a sampling frequency of 50 Hz. It subtracts the current Euler angles from the designed Euler angles in the target pose file component by component to obtain the roll, pitch, and yaw angle deviations. Simultaneously, it subtracts the current elevation value from the target elevation value to obtain the heave deviation. When the absolute value of any angle deviation exceeds 0.5 degrees or the absolute value of the heave deviation exceeds 2 millimeters, the subsequent module is activated. The wind disturbance compensation module reads the anemometer data from the tower crane top at a frequency of 20 Hz. It subtracts the instantaneous wind speed value from the activation threshold of 8 m / s and takes the positive part. The square of this difference is multiplied by a comprehensive coefficient of 0.65, which includes air density, wind pressure height variation coefficient of 1.28, and shape coefficient of 1.30. This yields an equivalent disturbance torque value ranging from 12 Nm to 85 Nm, which is updated every 50 milliseconds and temporarily stored in a cache register. The wire rope elasticity correction module reads the values ​​from the cable tension sensor at a frequency of 100 Hz, with a tension range of 5 kN to 25 kN. Combining the nominal elastic modulus of the wire rope (1.95 x 10^5 MPa) and the effective load-bearing length of 12 meters, it calculates the elastic elongation based on physical relationships. The difference in elongation between two adjacent sampling times is divided by the sampling interval to obtain the elongation change rate. This change rate is integrated over time, with the integration step size consistent with the sampling period of 10 milliseconds. The upper limit of the cumulative elongation correction output is set to 15 mm. The iterative update execution module sums the current angle deviation with the sum of all historical angle deviations multiplied by a coefficient of 0.25, multiplies the sum by the angle iteration step size of 0.30, and then subtracts the product of the disturbance torque multiplied by the compensation coefficient of 0.018. This product is then added to the current angle value to obtain the next angle value. The heave update module multiplies the current heave deviation by the heave iteration step size of 1.8 mm, subtracts the product of the accumulated elongation correction multiplied by the elastic correction coefficient of 0.30, and adds this product to the current heave value to obtain the next heave value. The entire iterative calculation is completed within 8 milliseconds and the pose parameters are output.

[0037] Preferably, the composite component stress deformation prediction unit includes: an age-strength interpolation module, used to fit a logarithmic curve of strength growth with age based on the measured compressive strength value of the test block cured under the same conditions at the construction site, and extract the elastic modulus value corresponding to the current age; a composite surface strain calculation module, used to calculate the average strain difference at each measuring point along the component axis based on the ratio of the strain sensor reading difference to the sensor gauge length embedded on the upper and lower surfaces of the composite surface; a bending moment distribution module, used to establish a differential equilibrium equation of shear force and bending moment based on the component support boundary conditions and the current construction load arrangement, and use a piecewise integration method to solve for the section bending moment value at each axial position; and a deflection accumulation integration module, used to multiply the bending moment value at each micro-element by the average strain difference, divide by the product of the elastic modulus and the effective moment of inertia, multiply by the distance weighting coefficient from the micro-element to the support, and finally integrate along the entire span to sum and output the mid-span deflection.

[0038] Specifically, the age-based strength interpolation module uses the measured compressive strength values ​​of test blocks cured under the same conditions at the construction site at 12 hours, 24 hours, 48 ​​hours, 72 hours, and 168 hours of age. It performs piecewise linear interpolation with the common logarithm of age as the independent variable, with an interpolation step of 1 hour. It extracts the strength value corresponding to the number of hours elapsed since the current component was poured, and then calculates the elastic modulus based on the square root relationship between the elastic modulus and the cube of the strength in concrete specifications. The elastic modulus output ranges from 14.5 GPa to 32.0 GPa and is updated every 10 minutes. The composite surface strain calculation module reads the strain sensor readings embedded on the upper and lower surfaces of the composite component at a frequency of 200 Hz. The sensor gauge length is 100 mm. The average strain difference at the measuring point is obtained by subtracting the strain value of the lower surface from the strain value of the upper surface and dividing by the gauge length. The measuring points are arranged along the component axis at 100 mm intervals, and the strain measurement range is set from -2000 microstrain to +2000 microstrain. The moment distribution module establishes the support reaction equations based on the boundary conditions of simply supported members at both ends. It uses a uniformly distributed construction load of 3.2 kN / m and a concentrated load of 2.8 kN as external load inputs. Starting from one support along the axial direction, it solves for the section shear force point by point with a step size of 1 / 200th of the calculation span. The bending moment value at each section is then obtained by integrating the shear force with respect to the axial coordinate. The deflection accumulation and integration module multiplies the bending moment value at each integration point by the average strain difference at that point, then divides it by the product of the elastic modulus and the effective moment of inertia. The effective moment of inertia is taken as an interpolation value between 0.80 and 0.95, multiplied by the axial coordinate of that point and the product of 1 minus the difference between that coordinate and the span (in parentheses). The calculation results of all integration points are accumulated and multiplied by the integration step size to output the estimated mid-span deflection value with an output accuracy of 0.1 mm. The total number of integration points does not exceed 200 to ensure real-time calculation.

[0039] Preferably, the prefabricated resource collaborative scheduling unit includes: a tower crane operation sequence encoding module, used to arrange the hook-up time, slewing angle, luffing amplitude, and hook-down time of each hoisting cycle into a timestamped event vector in chronological order and store it in a circular queue buffer; a yard layout occupancy module, used to map the planar position coordinates and geometric dimensions of prefabricated components in the yard into rectangular occupancy areas, and mark the start time of occupancy and release time of each area according to the component outbound order; a transport vehicle arrival prediction module, used to estimate the expected time of arrival of transport vehicles at the construction site entrance and the unloading waiting time based on the real-time location reported by the vehicle's global positioning system and the average speed of the driving section; and a resource release time window adjustment module, used to apply a time offset to the tower crane hook-down time or the vehicle unloading start time to eliminate resource occupancy overlap when the tower crane operation event vector conflicts with the vehicle arrival time, using the priority coefficient output by the spatiotemporal extrapolation unit as the weight.

[0040] Specifically, the tower crane operation sequence coding module establishes an independent circular queue buffer for each tower crane, with a queue depth set to 200. The hook-up time, slewing angle, luffing amplitude, and hook-down time for each lifting cycle are sequentially pushed into the queue as timestamps in milliseconds. The hook-up time accuracy is 10 milliseconds, the slewing angle range is -180 degrees to +180 degrees, and the luffing amplitude range is 8 meters to 40 meters. The queue is updated according to the first-in, first-out principle upon receiving a new completion event. The yard layout occupancy module divides the yard plane into 12 rectangular occupancy units, each unit measuring 4 meters by 8 meters. Precast components are matched to the nearest unoccupied unit according to their planar projection dimensions. The module records the start time of component entry into the yard and the release time after lifting. The occupancy status is scanned every 500 milliseconds, and the remaining occupancy time is updated. The vehicle arrival prediction module receives the location coordinates reported by the vehicle's GPS every 30 seconds. It combines the digital map road network data to calculate the remaining distance between the vehicle and the construction site entrance. The remaining distance is divided by the historical average speed of 45 kilometers per hour for this road segment to obtain the estimated travel time. The estimated arrival time is obtained by adding the current time to the estimated travel time. At the same time, the unloading waiting time is calculated based on the number of available vacant units in the yard. If the waiting time is less than 5 minutes, the vehicle can enter the site directly; otherwise, it will be delayed. The resource release time window adjustment module performs a conflict detection every second, comparing the hook-dropping time in the tower crane operation event vector with the vehicle's expected arrival time. If the interval between the two is less than 5 minutes, a conflict is determined. The priority coefficient output by the spatiotemporal simulation unit is read. The priority coefficient ranges from 0.1 to 1.0. This coefficient is multiplied by the maximum allowable offset of 30 seconds to obtain the actual offset of the tower crane hook-dropping. At the same time, 1 is subtracted from this coefficient and then multiplied by the maximum allowable offset of 20 seconds to obtain the actual offset of the vehicle unloading. The two offsets are in opposite directions and the sum of their absolute values ​​does not exceed 50 seconds. The new time window after applying the offset is written back to the corresponding queue to replace the original event, thus completing the elimination of resource occupation overlap.

[0041] A prefabricated building intelligent construction optimization system includes the following steps: S1, extracting the planned time window and the spatial motion envelope of the robotic arm for each process based on the construction organization design document, and inputting both into a spatiotemporally coupled construction situation deduction unit to obtain the amplitude distribution and evolution rate coefficient of the current construction progress vector field; S2, inputting the difference between the measured pose data of the prefabricated component to be hoisted and the design target pose data into the prefabricated component hoisting posture iteration unit, and recursively solving for the optimal rotation angle and heave iteration values ​​in combination with real-time wind load and steel wire rope elastic elongation; S3, inputting the measured strain value and concrete age value of the composite component after hoisting into place into the composite component stress deformation prediction unit, and accumulating the micro-element curvature segment by segment along the component axis through a layered integration strategy and outputting the span S4: The tower crane operation sequence, the occupancy status of the yard components, and the arrival time of the transport vehicles are uniformly encoded into a resource occupancy time window matrix, and the priority order of each resource release time is dynamically adjusted according to the evolution rate coefficient obtained in S1; S5: The attitude deviation gradient output by S2 and the deflection sensitivity output by S3 are used as dual-objective coordination variables, and a multi-attribute decision-making mechanism is used to integrate the tightness of process connection and equipment energy consumption factor to generate a Pareto optimal solution set; S6: The optimal solution set obtained in S5 is inverted into tower crane slewing angle command, luffing speed command, and hook drop height command, and the difference between the measured elevation and the design elevation of the installed components is collected in real time and injected back into the initial progress boundary conditions in S1. S1 to S6 are repeated until all components in the construction section are installed.

[0042] An intelligent construction optimization system for prefabricated buildings has been developed, constructing a closed-loop architecture encompassing spatiotemporal situational awareness, resource collaborative scheduling, and joint control of hoisting posture and structural deformation. The spatiotemporally coupled construction situational prediction unit, driven by the overlap between process time windows and spatial collision domains, enables the construction progress vector field to automatically evolve with the actual slewing time of the tower crane and disturbance factors. Combined with the prefabricated resource collaborative scheduling unit, tower crane operations, yard occupancy, and vehicle arrivals are encoded into a unified time window matrix, and resources are dynamically released according to priority. This completely replaces the extensive management model relying on manual verbal coordination, fundamentally eliminating frequent work stoppages and component backlogs caused by process overlap interference. Meanwhile, the precast component hoisting attitude iteration unit and the composite component stress deformation prediction unit work together to recursively update the rotation angle heave parameters and output the mid-span deflection estimate in real time. The feedback construction closed-loop control unit injects the difference between the measured deformation and the design elevation back into the initial boundary conditions, driving the whole system to cyclically correct, which greatly enhances the system's anti-interference ability and response sensitivity to multiple random factors such as wind load disturbance, steel wire rope elastic elongation and concrete age strength growth.

[0043] This invention addresses the prominent issues of existing BIM models being disconnected from on-site working conditions and the inability to automatically adjust schedule parameters. It utilizes an intelligent construction parameter fusion optimization unit with attitude deviation gradient and deflection sensitivity as dual-objective coordination variables, employing a multi-attribute decision-making mechanism to generate a Pareto optimal solution set. This solution set is then inverted into on-site execution commands such as tower crane slewing angle, luffing speed, and hook drop height. This allows the construction plan to no longer rely on pre-prepared static plans, but rather to be updated dynamically based on real-time collected strain values, posture errors, and yard occupancy status, ensuring that the schedule remains consistent with actual execution. Addressing the shortcomings of multiple tower cranes sharing a yard and the lack of a unified arbitration mechanism when vehicles arrive in dense clusters, the prefabricated resource collaborative scheduling unit applies time biases using priority coefficients output from spatiotemporal extrapolation as weights, dynamically eliminating resource overlap and avoiding inefficient states such as component backlog in the yard or idling waiting for hoisting equipment. This effectively improves resource utilization and overall operational smoothness under conditions of parallel construction with multiple equipment, ensuring that the rolling revision of the daily construction plan has sufficient real-time performance and reliability.

[0044] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A prefabricated building intelligent construction optimization system, characterized in that, include: The spatiotemporal coupled construction situation simulation unit dynamically analyzes the spatiotemporal coherence of the construction progress vector field and the robotic arm motion trajectory streamline based on the overlap of the process time window and the spatial collision domain, and corrects the situation evolution rate coefficient based on the intermittent waiting time of the hoisting operation; the precast component hoisting posture iteration unit maps the difference between the current posture error of the component and the target posture constraint, and recursively updates the rotation angle and heave parameters by introducing wind load disturbance compensation term and wire rope elastic elongation correction term; The stress deformation prediction unit for composite members establishes a moment-curvature constitutive relationship based on the concrete strength growth curve over time and the roughness coefficient of the composite surface, and uses a layered integration strategy to accumulate the superposition effect of the curvature of the micro-element along the axis of the member to obtain the estimated value of the mid-span deflection. The prefabricated resource collaborative scheduling unit encodes the tower crane operation sequence, prefabricated component yard layout, and transport vehicle arrival time into a resource occupancy time window matrix, and dynamically adjusts the release time of each resource based on the priority coefficient output by the construction situation simulation unit; the intelligent construction parameter fusion optimization unit uses the attitude deviation gradient of the hoisting attitude iteration unit and the deflection sensitivity of the deformation prediction unit as dual-objective coordination variables, and adopts a multi-attribute decision-making mechanism to integrate the tightness of process connection and equipment energy consumption factor to generate a Pareto front solution set; The feedback-based construction closed-loop control unit inverts the optimal solution set obtained from the fusion optimization unit into on-site execution commands, and collects the difference between the measured deformation and design elevation of the installed components in real time and injects it back into the initial boundary conditions of the spatiotemporal simulation unit to complete iterative cycle correction.

2. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The update expression for the situation evolution rate coefficient in the spatiotemporal coupled construction situation simulation unit is as follows: ,in Let be the correction rate coefficient at time t. The initial rate coefficient, To influence the weights of coupling, for The duration of overlap between the time window of the time process and the spatial collision domain. The threshold for critical overlap duration. The integral time variable is used as the basis for determining the magnitude of the construction progress vector field. This leads to the derivation of the expression for the magnitude of the construction progress vector field. ,in The vector field amplitude represents the construction progress at any given time. This represents the maximum construction progress range. The cumulative equivalent of the completed processes at time t. It is the half-saturation constant. The area is the construction area. It is a spatial position vector. For position The process density distribution function at the location.

3. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The expression for recursively updating the rotation angle in the prefabricated component hoisting attitude iteration unit is: ,in For the first The angle value in the next iteration. For the first The angle value in the next iteration. Let the step size be the angle iteration step. For the first The rotation angle deviation in the next iteration This is the cumulative deviation coefficient. For the first The rotation angle deviation in the next iteration For the first The time interval between iterations This is the wind load compensation coefficient. for The equivalent disturbance force of wind load at any given time; and then construct the iterative recursive expression for the heave parameter: ,in For the first The heave parameters for the next iteration. For the first The heave parameters for the next iteration. To determine the iteration step size for rise and fall, For the first Heave deviation in the next iteration This is the elasticity correction factor for the wire rope. and The first Second and third The moment of the next iteration for The elastic elongation of the wire rope at any given time.

4. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The expression for the mid-span deflection estimate in the stress deformation prediction unit of the composite member is: ,in This is the estimated value of the mid-span deflection of the component. Calculate the span for the component. Let these be the coordinates of the infinitesimal element position along the axial direction of the component. For position The bending moment value at the section, For position Average strain difference at the overlapping surface Let be the elastic modulus of concrete at time t. For position The effective cross-sectional moment of inertia considering the slip effect of the overlapping surface.

5. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The spatiotemporal coupled construction situation simulation unit is deployed on an edge computing node, which is equipped with a dual-processor graphics processor and a real-time clock synchronization module with a clock synchronization accuracy of ±1 millisecond. The precast component hoisting attitude iteration unit runs on an industrial-grade programmable logic controller with an analog-to-digital conversion resolution of 16 bits and a sampling period of 20 milliseconds. The composite component stress deformation prediction unit is embedded in a strain acquisition terminal, which has a built-in temperature-compensated full-bridge strain gauge with a bridge excitation voltage of 2.5 volts and a strain measurement range of ±3000 micro-strains. The prefabricated resource collaborative adjustment... The degree unit realizes data interaction between tower cranes, storage yards and transport vehicles through a wireless self-organizing network. The network communication protocol adopts the IEEE 802.11ac standard and the media access control layer time slot length is 10 milliseconds. The intelligent construction parameter fusion optimization unit runs on a cloud server with a random access memory capacity of 128 gigabytes and a central processing unit frequency of not less than 3.2 GHz. The feedback construction closed-loop control unit is connected to each actuator through an industrial Ethernet, with a network transmission rate set to 100 megabits per second and a cyclic redundancy check using a 32-bit check code.

6. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The spatiotemporal coupled construction situation deduction unit includes: a process time window analysis module, used to map the planned start time and planned end time of each construction process into open interval panes on the time axis, and to connect and sort the panes according to the pre- and post-process constraints; a spatial collision domain generation module, used to construct a three-dimensional spatial interference body based on the motion envelope of the robotic arm end effector and the outer contour of the installed components, and to discretize the interference body into a set of voxel meshes; a spatiotemporal overlap calculation module, used to traverse and search all voxel meshes in each time pane, and to count the ratio of the number of meshes simultaneously occupied by two or more robotic arm envelopes to the total number of meshes in that time pane; and a rate coefficient correction module, used to accumulate the duration for which the overlap ratio exceeds a preset threshold as the overlap duration, and to use the ratio of this overlap duration to the critical duration as a power exponent to adjust the situation evolution rate.

7. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The prefabricated component hoisting attitude iteration unit includes: a pose error mapping module, used to subtract the current Euler angles and spatial coordinates measured by the six-degree-of-freedom pose sensor from the corresponding components of the target hoisting pose to obtain the rotation angle deviation sequence and the heave deviation sequence; a wind disturbance compensation module, used to receive the instantaneous wind speed value collected by the anemometer and convert it into the equivalent disturbance torque acting on the windward surface of the component according to the preset wind pressure height change coefficient and shape coefficient; a wire rope elasticity correction module, used to calculate the elastic elongation change rate according to the reading of the sling tension sensor combined with the nominal elastic modulus and effective bearing length of the wire rope, and integrate the change rate over time to obtain the cumulative elongation correction amount; and an iterative update execution module, used to superimpose the deviation sequence, the disturbance torque correction amount, and the cumulative elongation correction amount to the rotation angle and heave values ​​of the current iteration step according to the preset weights, and output the updated pose parameters for the next iteration step.

8. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The composite component stress deformation prediction unit includes: an age-based strength interpolation module, used to fit a logarithmic curve of strength growth with age based on the measured compressive strength values ​​of test blocks cured under the same conditions at the construction site, and extract the elastic modulus value corresponding to the current age; a composite surface strain calculation module, used to calculate the average strain difference at each measuring point along the component axis based on the ratio of the strain sensor reading difference to the sensor gauge length embedded on the upper and lower surfaces of the composite surface; a bending moment distribution module, used to establish a differential equilibrium equation of shear force and bending moment based on the component support boundary conditions and the current construction load arrangement, and to solve the section bending moment value at each axial position using a piecewise integration method; and a deflection accumulation integration module, used to multiply the bending moment value at each micro-element by the average strain difference, divide by the product of the elastic modulus and the effective moment of inertia, multiply by the distance weighting coefficient from the micro-element to the support, and finally integrate along the entire span to sum and output the mid-span deflection.

9. The intelligent construction optimization system for prefabricated buildings according to claim 1, characterized in that, The prefabricated resource collaborative scheduling unit includes: a tower crane operation sequence encoding module, used to arrange the hook-up time, slewing angle, luffing amplitude, and hook-down time of each hoisting cycle into a timestamped event vector in chronological order and store it in a circular queue buffer; a yard layout occupancy module, used to map the planar position coordinates and geometric dimensions of prefabricated components in the yard into rectangular occupancy areas, and mark the start time of occupancy and release time of each area according to the component outbound order; a transport vehicle arrival prediction module, used to estimate the expected time of transport vehicle arrival at the construction site entrance and the unloading waiting time based on the real-time location reported by the vehicle's global positioning system and the average speed of the driving section; and a resource release time window adjustment module, used to apply a time offset to the tower crane hook-down time or the vehicle unloading start time to eliminate resource occupancy overlap when the tower crane operation event vector conflicts with the vehicle arrival time, using the priority coefficient output by the spatiotemporal extrapolation unit as the weight.

10. A prefabricated building intelligent construction optimization system according to any one of claims 1-9, characterized in that, The system operation includes the following steps: S1, based on the construction organization design documents, extract the planned time window and the spatial motion envelope of the robotic arm for each process, and input the two into the spatiotemporal coupled construction situation inference unit to obtain the amplitude distribution and evolution rate coefficient of the current construction progress vector field; S2, after subtracting the measured position data of the precast component to be hoisted from the design target position data, input the result into the precast component hoisting posture iteration unit, and recursively solve for the optimal rotation angle and heave iteration value by combining the real-time wind load and the elastic elongation of the wire rope. S3, input the measured strain value and concrete age value of the composite component after hoisting and positioning into the stress deformation prediction unit of the composite component, and accumulate the micro-element curvature along the component axis segment by segment through the layered integration strategy and output the mid-span deflection prediction value. S4 encodes the tower crane operation sequence, the occupancy status of the yard components, and the arrival time of the transport vehicles into a resource occupancy time window matrix, and dynamically adjusts the priority order of each resource release time based on the evolution rate coefficient obtained in S1. S5 uses the attitude deviation gradient output by S2 and the deflection sensitivity output by S3 as dual-objective coordination variables, and adopts a multi-attribute decision-making mechanism to integrate the process connection tightness and equipment energy consumption factor to generate a Pareto optimal solution set. S6 inverts the optimal solution set obtained in S5 into tower crane slewing angle command, luffing speed command, and hook drop height command, and collects the difference between the measured elevation and the design elevation of the installed components in real time and injects it back into the initial progress boundary conditions in S1. Repeat S1 to S6 until all components in the construction section are installed.

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