Intelligent monitoring, regulating and controlling method and system for hoisting construction of large prefabricated drainage port structure

Abstract

The invention provides an intelligent monitoring, regulating and controlling method and system for hoisting construction of a large prefabricated drainage opening structure, and relates to the technical field of water conservancy projects and intelligent construction. The system comprises an intelligent sensing module, a data processing and decision center and a man-machine interaction execution module, and multi-source state data acquisition is realized by arranging Beidou receivers at key corners of a component, installing an inertial measurement unit near the center of gravity and arranging strain sensors at key stress parts; the data processing center adopts Kalman filtering to fuse positioning and inertial data, calculates the real-time six-degree-of-freedom pose of the component, and compares the actually measured stress value with the theoretical stress value in combination with a finite element pre-constructed pose-stress mapping database to carry out structure safety early warning; and the execution module provides graphic guidance, sound and light alarm or sends an emergency intervention signal according to the risk level. High-precision positioning, active safety early warning and closed-loop regulation and control of hoisting of the large prefabricated part in the complex water area environment are achieved, and the construction safety and the intelligent level are remarkably improved.

Description

Technical Field

[0001] The technical field of this application is water conservancy engineering and intelligent construction technology, specifically involving an intelligent monitoring and control method and system for the hoisting construction of large prefabricated drainage outlet structures. Background Technology

[0002] With the continuous advancement of port and water conservancy infrastructure construction, large prefabricated drainage outlet structures have become key load-bearing components in hydraulic structures due to their excellent durability and construction efficiency. These components are typically characterized by their massive size, weight of hundreds of tons, and irregular geometry. During hoisting operations, especially in complex environments on water or near the shore, they are highly susceptible to the coupling effects of multiple physical fields such as wind loads, wave impacts, and water flow disturbances, leading to loss of spatial attitude control, accumulated positioning deviations, and even structural damage. Traditional hoisting operations mainly rely on intermittent measurements using total stations, combined with operator experience for judgment and adjustment. This makes it difficult to achieve continuous and dynamic perception of the component's three-dimensional position, real-time six-degree-of-freedom attitude, and internal mechanical response, resulting in limited positioning accuracy, significant safety risks, and low overall construction efficiency.

[0003] However, existing intelligent monitoring technologies still have significant limitations. Some solutions focus on static verification of single attitude parameters such as verticality. Although tilt sensors improve the level of automation in leveling, they do not cover dynamic three-dimensional positioning and high-frequency attitude calculation throughout the hoisting process, nor do they have the ability to perceive the internal stress state of the structure. Another type of system focuses on visual guidance and docking of small prefabricated components on land. It relies on optical recognition and preset markers, which are easily obstructed and interfered with in large, irregular, long-distance, and highly reflective environments such as the sea surface, resulting in poor positioning robustness. More importantly, current technologies generally fail to integrate high-precision spatiotemporal references (such as BeiDou / GNSS), inertial dynamic response, and structural mechanics sensing data. They have neither established a coupling relationship model between environmental disturbances and component dynamic behavior nor formed a closed-loop control mechanism based on real-time state assessment, making it difficult to support the core requirements of high safety and high precision hoisting of large prefabricated drainage outlets under dynamic marine conditions.

[0004] Therefore, there is an urgent need for an intelligent monitoring and control method for the hoisting construction of large prefabricated drainage outlet structures in complex aquatic environments. This method should be able to integrate multi-source heterogeneous sensing information, achieve an intelligent leap from geometric pose monitoring to structural health assessment, and construct a closed-loop decision-making system with proactive early warning and hierarchical intervention capabilities to comprehensively improve the safety, accuracy, and intelligence of hoisting operations. Summary of the Invention

[0005] This application provides an intelligent monitoring and control method and system for the hoisting construction of large precast drainage outlet structures, which can solve the technical problem of low positioning accuracy and high safety risks caused by the lack of continuous dynamic monitoring capability for large precast drainage outlet components in complex environments in traditional hoisting construction.

[0006] To achieve the above objectives, this application provides the following technical solution: The first aspect of this application provides an intelligent monitoring and control system for the hoisting construction of large precast drainage outlet structures, including: The intelligent sensing module is used to acquire multi-source status data of the prefabricated drainage outlet components during the hoisting process; The data processing and decision-making center is communicatively connected to the intelligent sensing module and is used to receive and process the multi-source status data. The human-computer interaction execution module is communicatively connected to the data processing and decision-making center, and is used to provide operators with control guidance or trigger safety protection mechanisms based on the output of the data processing and decision-making center.

[0007] Furthermore, the intelligent sensing module includes a BeiDou high-precision positioning receiver deployed at at least three non-collinear key corner points on the component, an inertial measurement unit installed near the center of gravity of the component, and wireless strain sensors deployed at key stress points of the component.

[0008] Specifically, the key stress-bearing parts include the main lifting point, the cross-sectional abrupt change region, or the stress concentration region; the at least three non-collinear key corner points are the four apex corners of the component.

[0009] Furthermore, the data processing and decision-making center is embedded with a multi-source data fusion and full attitude calculation model based on Kalman filtering. The model is configured to: establish a system dynamics model with component position, velocity, and attitude angle as state variables; perform state prediction using data from the inertial measurement unit; and perform observation updates using data from the Beidou high-precision positioning receiver, recursively outputting the six-degree-of-freedom pose data of the component.

[0010] Furthermore, the data processing and decision-making center also embeds a stress-attitude correlation early warning model based on finite element prior knowledge. The model is configured to: pre-construct a mapping relationship database containing different hoisting attitudes and corresponding theoretical safe stress thresholds of key parts through finite element analysis according to the component design drawings; during the hoisting process, use the actual attitude output by the full attitude calculation model as input to query the database, obtain the theoretical allowable stress value under the current attitude, and compare it with the measured stress value of the wireless strain sensor to determine the risk of structural over-limit deformation.

[0011] Furthermore, the human-machine interaction execution module includes an intelligent display terminal installed in the crane cab and command point. The terminal is configured to perform hierarchical control: when the offset is within the allowable range, it provides graphical deviation guidance; when the position, posture deviation or stress risk exceeds the first threshold, it activates an audible and visual alarm and prompts the risk type and control suggestions; when the deviation or stress value approaches the dangerous critical value, it issues the highest level alarm and sends an intervention signal to the crane control system.

[0012] The second aspect of this application provides an intelligent monitoring and control method for the hoisting construction of large prefabricated drainage outlet structures, including the following steps: Real-time acquisition of multi-source status data deployed on components; Based on the Kalman filter algorithm, the real-time six-degree-of-freedom pose of the component is calculated by fusing the multi-source state data. Based on prior knowledge of finite element analysis, combined with the real-time six-degree-of-freedom pose and measured stress data, the structural safety status of the component is assessed and risk warnings are issued.

[0013] Furthermore, the step of assessing the structural safety status of the component and issuing a risk warning includes: querying a pre-built attitude-stress mapping database, obtaining the theoretical allowable stress value under the current attitude, and comparing it with the measured stress value to trigger a warning.

[0014] Furthermore, the intelligent monitoring and control method for the hoisting construction of large prefabricated drainage outlet structures also includes the following steps: performing graded control based on the deviation degree of the six-degree-of-freedom pose and the assessment results of the structural safety status. The graded control includes providing visual guidance, activating audible and visual alarms and giving control suggestions, or sending an emergency intervention signal to the crane control system.

[0015] The beneficial effects of this invention patent are: 1. By constructing a closed-loop system consisting of an intelligent sensing module, a data processing and decision-making center, and a human-computer interaction execution module, multi-source data fusion and dynamic feedback control of the entire process of hoisting large prefabricated drainage outlets were realized.

[0016] 2. The system integrates BeiDou high-precision positioning, inertial measurement and wireless strain sensing, and combines Kalman filtering to achieve stable and reliable six-degree-of-freedom pose calculation, which solves the problem that traditional methods cannot continuously obtain the dynamic spatial state of components; 3. By introducing a stress-attitude correlation early warning model based on finite element priors, the overall safety status of the structure can be intelligently inferred under the condition of a small number of sensors, realizing the leap from geometric monitoring to structural health assessment; 4. It can be combined with a hierarchical control mechanism to enable the system to have a complete closed-loop capability from guidance and warning to automatic intervention, which significantly improves the safety, accuracy and intelligence of hoisting operations. Attached Figure Description

[0017] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the composition architecture of the intelligent monitoring and control system for the hoisting and construction of large prefabricated drainage outlet structures according to the present invention.

[0019] Figure 2 This is a complete flowchart of the intelligent monitoring and control method for the hoisting and construction of large prefabricated drainage outlet structures according to the present invention.

[0020] Figure 3 This is a schematic diagram illustrating the working principle of the multi-source data fusion and full-attitude solution model based on Kalman filtering in this invention.

[0021] Figure 4 This is a schematic diagram of the working logic of the stress-attitude correlation early warning model based on finite element prior knowledge in this invention.

[0022] Figure 5 This is a schematic diagram of the hierarchical control logic of the human-computer interaction execution module in this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0024] refer to Figure 1 The intelligent monitoring and control system for the hoisting construction of large prefabricated drainage outlet structures proposed in this invention includes three core parts: an intelligent sensing module (10), a data processing and decision-making center (20), and a human-computer interaction execution module (30). The modules exchange data through a reliable communication protocol, forming a complete closed loop from data acquisition to decision execution.

[0025] The intelligent sensing module (10) undertakes the basic function of system data acquisition and adopts a distributed multi-sensor fusion scheme to realize all-round status monitoring of the prefabricated drainage outlet component hoisting process.

[0026] Furthermore, in the specific implementation process, the module includes three types of sensors: a Beidou high-precision positioning receiver (11), an inertial measurement unit (12), and a wireless strain sensor (13). The above sensors are installed at key locations on the component according to the optimal placement principle, forming a three-dimensional monitoring network.

[0027] Specifically, the Beidou high-precision positioning receiver (11) preferably adopts a dual-frequency receiver that supports the Beidou-3 navigation system, which has real-time dynamic differential positioning function and can achieve centimeter-level positioning accuracy. In actual installation, a receiver needs to be installed at each of the four corners of the component to ensure that the four installation points are not on the same plane to meet the mathematical requirements of attitude calculation. During installation, a special anti-vibration mounting bracket is used, which is reliably connected to the component by high-strength bolts to prevent displacement due to vibration during hoisting. The power supply system adopts a hybrid power supply scheme of lithium-ion battery pack and flexible solar panel to ensure continuous and stable operation throughout the hoisting cycle. These receivers output accurate latitude, longitude and altitude coordinates at a frequency of 1-10Hz. The centroid position and basic attitude angle of the component can be accurately calculated through the spatial coordinates of the four corner points.

[0028] The inertial measurement unit (12) is an industrial-grade microelectromechanical system (MEMS) product, including a three-axis gyroscope and a three-axis accelerometer, capable of measuring the angular velocity and linear acceleration of the component. Specifically, when selecting a specific model, key parameters such as its measurement range, zero-bias stability, and temperature characteristics need to be considered. A gyroscope with a range of at least ±400° / s and a zero-bias instability better than 10° / h should be selected; an accelerometer with a range of at least ±10g and a zero-bias instability better than 0.5mg should be selected. The installation location is chosen in the estimated area of ​​the component's center of gravity, and it is directly bonded to the component surface using high-strength epoxy resin structural adhesive to ensure a rigid connection with the component. All inertial measurement units share a unified clock reference with the Beidou receiver and are synchronized using GPS second pulse signals to ensure consistency of multi-source data in time.

[0029] The wireless strain sensor (13) is selected from resistance strain gauges or fiber optic grating sensors according to monitoring requirements. The sensor deployment location is determined based on the results of the previous finite element analysis, with a focus on monitoring the main suspension point, areas of abrupt changes in cross-section, and areas of stress concentration. The sensor transmits data through a self-organizing wireless sensor network with a star topology. Each sensor node transmits the collected strain data wirelessly to the aggregation node, which then transmits the data to the data processing and decision-making center (20) via a 4G / 5G network.

[0030] The data processing and decision-making center (20) is the intelligent core of the system. It uses an industrial-grade ruggedized computer as its hardware platform and has powerful data processing capabilities and reliable industrial-grade interfaces.

[0031] Furthermore, the center operates two core analysis models: a multi-source data fusion and full attitude calculation model (21) and a stress-attitude correlation early warning model (22). These models are developed based on professional mathematical algorithms and engineering mechanics principles, and are capable of performing in-depth analysis and intelligent processing of the raw data collected by the sensing module.

[0032] Specifically, the multi-source data fusion and full attitude calculation model (21) is implemented based on the Kalman filter algorithm and is a typical multi-sensor data fusion solution. In the model initialization stage, it is necessary to accurately define the system's state vector, including key parameters such as position, velocity, and attitude angle.

[0033] The state vector is defined as x=[P,V,Θ]T, where P=[x,y,z]T represents the three-dimensional position coordinates of the component's center of mass in the global coordinate system, in meters; V=[vx,vy,vz]T represents the three-dimensional velocity vector of the component's center of mass in the global coordinate system, in meters per second; and Θ=[φ,θ,ψ]T represents the component's attitude angles, including roll angle φ, pitch angle θ, and yaw angle ψ, in radians. The system state equations are in discrete-time form: x k =F×x k -1+w k Where F is the state transition matrix, specifically in the form of: Where Δt is the sampling period, R(Θ) is the attitude angle transformation matrix, and the process noise covariance matrix Q is a diagonal matrix diag(0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.05, 0.05, 0.05), with units consistent with the state variables; w k The system process noise is assumed to be zero-mean Gaussian white noise. The observation equation is defined as z k =H×x k +v k Where H is the observation matrix, v k To mitigate observation noise, during actual operation, the model performs state prediction at a frequency of 100Hz, using angular velocity and acceleration data provided by the inertial measurement unit to advance state updates. When new BeiDou positioning data arrives, observation updates are performed at a frequency of 1-10Hz, and the weights of predicted and observed values ​​are dynamically adjusted through Kalman gain to output the optimal state estimation result.

[0034] The stress-attitude correlation early warning model (22) is an intelligent early warning system based on finite element analysis and machine learning technology.

[0035] Specifically, before the system is put into use, a large amount of offline calculation and training work is required. An accurate finite element model is established based on the component design drawings, including the geometric model, material properties, and boundary conditions. Material properties require accurate setting of parameters such as the elastic modulus, Poisson's ratio, and density of the concrete; typically, the elastic modulus of C50 concrete is taken as 3.45 × 10⁻⁶. 4 MPa, Poisson's ratio is 0.2. Parametric analysis was performed using SOLID186 elements for mesh generation, with element size no larger than 1 / 10 of the component's minimum characteristic size. Attitude parameters included roll angle φ (range -30° to +30°, step size 5°), pitch angle θ (range -20° to +20°, step size 5°), and yaw angle ψ (range 0° to 360°, step size 10°). Boundary conditions were set according to actual lifting point constraints. The stress distribution of the component under different lifting attitudes was calculated, and convergence was determined by an energy error ≤1%. The calculation results were stored in a dedicated attitude-stress mapping database with a multi-dimensional index structure for fast querying. During actual lifting, the model received pose data output from the full-attitude solution model in real time. The theoretical allowable stress values ​​at each monitoring point under the current attitude were obtained by querying the database and then compared with the actual stress values ​​measured by the wireless strain sensor. The warning level was determined based on the stress ratio (stress ratio η is the measured stress σ). measured With allowable stress σ allow The ratio of σ to σ allow The stress ratio is divided into three levels: when the stress ratio is less than 0.8, a safe state is indicated; when the stress ratio is between 0.8 and 0.95, a primary warning is issued; and when the stress ratio is greater than 0.95, a high-level warning is issued and the protection mechanism is activated.

[0036] The human-computer interaction execution module (30) is the interaction interface between the system and the operator, and adopts a multi-level design concept to meet the needs of different usage scenarios.

[0037] Furthermore, the core device of the module is an intelligent display terminal (31), which uses an industrial-grade anti-glare touchscreen. The screen size is typically 15-21 inches, with a resolution of no less than 1920×1080. It is installed in key locations such as the crane cab and ground command point. The terminal runs specially developed monitoring software. The software interface adopts a layered display design. The main interface displays the three-dimensional model of the component and its real-time pose information, while the secondary interface provides detailed data analysis and historical record query functions.

[0038] In terms of display design, a combination of graphical and digital methods is adopted. The current position and target position of the component are displayed by overlaying a 3D model, and deviation information is displayed intuitively through color coding: green indicates that the deviation is within the allowable range, yellow indicates that it is close to the limit, and red indicates that it exceeds the limit. Precise deviation values ​​are also provided in digital readings, including position deviations ΔX, ΔY, and ΔZ (unit: millimeters) and attitude deviations Δφ, Δθ, and Δψ (unit: degrees). The audible alarm system adopts a multi-tone design, with different alarm tones corresponding to different warning levels, ensuring that operators can quickly identify the alarm level.

[0039] Furthermore, a tiered control mechanism is an important feature of the module, employing different response strategies based on the risk level.

[0040] Specifically, Level 1 control is activated when the deviation is small, and the operator is assisted in fine-tuning through graphical guidance. The display interface provides clear adjustment directions and suggested operations. Level 2 control is triggered when the deviation exceeds the first threshold, which is determined based on finite element parameter analysis: the position deviation limit is 0.1% of the component length L (50mm when L≥5m); the attitude angle deviation limit is 3° (corresponding to a maximum stress increase of 15%); and the stress ratio limit is 0.8. The system activates an audible and visual alarm, and the display interface highlights the risk type and specific control suggestions, such as "adjust to the left by 2°" or "reduce the lifting speed" and other clear instructions. Level 3 control is activated when approaching the critical value, which is set as follows: position deviation 100mm, attitude angle deviation 5°, and stress ratio 0.95. In addition to issuing the highest level audible and visual alarm, the system also sends an emergency stop signal to the crane control system (40) through hard wiring. The signal format adopts standard dry contact signals or Modbus / TCP protocol to ensure that dangerous operations can be interrupted in time.

[0041] The entire system's communication network employs a multi-backup design, with the primary communication link using an industrial-grade wireless bridge and the backup link using a 4G / 5G mobile network to ensure data transmission reliability. All equipment complies with industrial environment standards, has a protection rating of at least IP65, and an operating temperature range of -20℃ to +60℃, enabling it to withstand harsh construction site environments.

[0042] refer to Figure 2 Based on the above system architecture, the intelligent monitoring and control method for the hoisting construction of large prefabricated drainage outlet structures of the present invention achieves intelligent monitoring through four main steps, including: Step S100: Real-time acquisition of multi-source status data: The real-time acquisition of multi-source state data is achieved through the coordinated execution of various sensor components in the intelligent sensing module (10).

[0043] Specifically, before the hoisting operation begins, the system is first initialized and calibrated, including positioning calibration of the BeiDou receiver, zero-bias calibration of the inertial measurement unit, and zero-point calibration of the strain sensors. During calibration, the reference values ​​of each sensor are recorded and compensated for in subsequent data processing. After the hoisting operation officially begins, each sensor synchronously collects data according to a preset sampling frequency: the BeiDou high-precision positioning receiver outputs centimeter-level position coordinates at a frequency of 5Hz, using the NMEA-0183 standard protocol, including latitude, longitude, elevation, and positioning accuracy factor; the inertial measurement unit collects motion data at a high frequency of 200Hz, outputting raw data of triaxial angular velocity (unit: ° / s) and triaxial acceleration (unit: m / s²) via the SPI interface; the wireless strain sensor collects micro-strain data at a frequency of 50Hz, and sends the digital signal to the aggregation node after A / D conversion. All data is embedded with a precise timestamp before transmission, with time synchronization accuracy required to reach the millisecond level, ensuring strict alignment of data from different sensors on the time axis. Data transmission is ensured by using the TCP protocol at the network transport layer, while the application layer data is encapsulated in JSON format, including fields such as device ID, timestamp, measurement value, and data quality identifier. This step, through the collaborative work of multiple sensors, constructs a complete data acquisition system, providing accurate and reliable raw data for subsequent analysis.

[0044] Step S200: Complete multi-source data fusion and component full attitude calculation. refer to Figure 3 The working principle shown is that the multi-source data fusion and component full attitude calculation steps include three key technical links: state prediction (S210), observation update (S220) and recursive output (S230), which are specifically executed by the multi-source data fusion and full attitude calculation model (21) in the data processing and decision center (20).

[0045] Specifically, in the state prediction stage, the model uses high-frequency data from the inertial measurement unit to calculate the motion state, updates the attitude using the quaternion method, solves the attitude differential equation using the Runge-Kutta method, and predicts position and velocity using a uniform acceleration model. In the specific algorithm, the state transition matrix F is discretized based on the sampling period Δt, and the process noise covariance matrix Q is dynamically adjusted according to the sensor characteristics.

[0046] In the observation update phase, when new BeiDou positioning data arrives, a data quality check is first performed to eliminate gross errors. Then, the geodetic coordinates are converted to spatial rectangular coordinates through coordinate transformation, and the observation equations are established. Kalman gain matrix K k The prediction error covariance and observation noise covariance are calculated in real time, with the observation noise covariance matrix R dynamically adjusted according to the accuracy factor of BeiDou positioning.

[0047] In the recursive output stage, the system outputs complete six-DOF pose data at a frequency of 100Hz, including three-dimensional position coordinates (accuracy ±2cm), three-dimensional velocity vectors (accuracy ±0.1m / s), and three-dimensional attitude angles (accuracy ±0.5°). To ensure calculation accuracy, the model also incorporates an adaptive filtering algorithm, dynamically adjusting filter parameters according to the motion state, maintaining excellent estimation performance under both static and dynamic conditions. These steps, through advanced data fusion technology, effectively overcome the limitations of a single sensor, providing stable and reliable pose information.

[0048] Step S300: Perform structural safety status assessment and risk warning. refer to Figure 4 The processing logic shown includes four ordered processing stages: pose input (S310), database query (S320), stress acquisition (S330), and risk judgment (S340), which are executed by the stress-pose correlation early warning model (22).

[0049] Specifically, in the pose input stage, the system receives real-time pose data from the full attitude solution model, first verifies the validity of the data, and then transforms the pose in the global coordinate system into the pose in the local coordinate system relative to the suspension point through coordinate transformation, so as to facilitate the docking with the finite element analysis results.

[0050] During the database query phase, the system performs a multi-dimensional index query in the finite element prior database based on the current attitude angle. A bilinear interpolation algorithm is then used to perform interpolation calculations between discrete attitude sample points to obtain continuous theoretical allowable stress values. The query process considers the nonlinear material characteristics of the component and makes appropriate corrections based on the stress-strain relationship of concrete.

[0051] During the stress acquisition stage, the system simultaneously reads the measured data from the wireless strain sensor. First, signal filtering is performed, using a low-pass filter to remove high-frequency noise, with the cutoff frequency set to 10Hz. Then, the micro-strain values ​​are converted into stress values ​​according to the sensor calibration coefficients, using the formula σ=E·ε, where E is the elastic modulus of concrete, taken as 3.45×10⁻⁶. 4 MPa.

[0052] During the risk assessment phase, the system calculates the stress ratio η=σ at each monitoring point. measured / σ allowThe system implements tiered early warnings based on preset thresholds: when all monitoring points η < 0.8, the system is considered safe; when any monitoring point 0.8 ≤ η < 0.95, a primary early warning is triggered, and the warning information is displayed on the interface; when any monitoring point η ≥ 0.95, a high-level early warning is triggered, and an audible and visual alarm is activated. These steps, by combining real-time attitude data with theoretical analysis, achieve intelligent safety assessment based on physical mechanisms.

[0053] Furthermore, the intelligent monitoring and control method for the hoisting construction of large prefabricated drainage outlet structures also includes the following steps: based on the degree of deviation of the six-degree-of-freedom pose and the assessment results of the structural safety status, implementing graded control measures, including providing visual guidance, activating audible and visual alarms and giving control suggestions, or sending an emergency intervention signal to the crane control system.

[0054] refer to Figure 5 The implementation of the graded control measures first involves a comprehensive status assessment (S410), and then the corresponding control level is entered based on the assessment results.

[0055] Specifically, in the state assessment phase, the system comprehensively evaluates pose deviation and stress risk using multiple indicators. The pose deviation is calculated by measuring the Euclidean distance between the current pose and the target pose. Stress risk is assessed using the maximum stress ratio. The evaluation results are divided into three levels: Level 1 (Safe State) requires a position deviation of less than 30mm, an attitude deviation of less than 2°, and a stress ratio of less than 0.8; Level 2 (Warning State) requires a position deviation of 30-70mm, an attitude deviation of 2-4°, or a stress ratio of 0.8-0.95; Level 3 (Dangerous State) requires a position deviation greater than 70mm, an attitude deviation greater than 4°, or a stress ratio greater than 0.95. The system implements corresponding control measures based on the assessment level: At Level 1, the system enters a graphical guidance mode (S411), displaying adjustment guidance on the intelligent display terminal using augmented reality technology, including directional arrows, deviation values, and color prompts, guiding operators to make fine adjustments; at Level 2, the system activates an audible and visual alarm (S412), with the alarm sound being a 1kHz intermittent tone, and the display interface highlighting the risk source and specific control suggestions, such as "Eastward deviation 45mm, westward adjustment recommended," etc.; at Level 3, the system immediately sends an emergency intervention signal (S413), sending an emergency stop command to the crane control system via hardwired, while simultaneously recording all data from the 30 seconds prior to the incident for subsequent analysis. During the above control processes, the system maintains an assessment frequency of 10 times per second to ensure timely response to changes in status. This intelligent, tiered response mechanism ensures both efficiency during normal operations and safety protection in abnormal situations.

[0056] The aforementioned intelligent monitoring and control method for the hoisting and construction of large precast drainage outlet structures forms a complete closed loop of perception-decision-execution in four steps. Each step is connected via standardized data interfaces to ensure smooth information transmission. During system operation, the four steps are executed concurrently, utilizing multi-threading technology to achieve parallel processing and guarantee real-time system performance. The method incorporates comprehensive data recording and playback functions, storing all operational data in time-series format to support post-analysis and process optimization. This systematic approach elevates traditional experience-based hoisting operations to a data-driven, intelligent construction process, significantly improving construction efficiency and quality while ensuring safety.

[0057] The foregoing has shown and described the basic principles, main features, and advantages of this invention. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A large prefabricated drainage port structure hoisting construction intelligent monitoring and regulation system, characterized in that, The application relates to a crane lifting control system for precast drainage outlet components. The system comprises: An intelligent sensing module for acquiring multi-source state data of a precast drainage outlet component during lifting; A data processing and decision center in communication connection with the intelligent sensing module for receiving and processing the multi-source state data; 2. The system of claim 1, wherein, A human-computer interaction execution module in communication connection with the data processing and decision center for providing control guidance or triggering a safety protection mechanism to an operator based on the output of the data processing and decision center.

3. The system of claim 2, wherein, The intelligent sensing module comprises a Beidou high-precision positioning receiver arranged on at least three non-collinear key corner points of the component, an inertial measurement unit installed near the center of gravity of the component, and a wireless strain sensor arranged at a key stress position of the component.

4. The system of claim 1, wherein, The key stress position comprises a main lifting point, a section mutation region or a stress concentration region; and the at least three non-collinear key corner points are four top corners of the component.

5. The system of claim 4, wherein, The data processing and decision center is embedded with a multi-source data fusion and full attitude solution model based on Kalman filtering, which is configured to establish a system dynamics model with component position, velocity and attitude angle as state variables, perform state prediction by using data of the inertial measurement unit and observation update by using data of the Beidou high-precision positioning receiver, and recursively output six-degree-of-freedom pose data of the component. The data processing and decision center is further embedded with a stress-attitude correlation warning model based on finite element prior knowledge, which is configured to pre-construct a mapping relationship database containing different lifting attitudes and corresponding key position theoretical safety stress thresholds according to component design drawings through finite element analysis; 6. The system of claim 1, wherein, During lifting, the actual attitude output by the full attitude solution model is used as input to query the database to obtain the theoretical allowable stress value under the current attitude, and the value is compared with the measured stress value of the wireless strain sensor to determine the structural out-of-limit deformation risk. The human-computer interaction execution module comprises intelligent display terminals arranged in a crane cab and a command point, and the terminals are configured to perform hierarchical control: when the deviation is within the allowable range, graphical deviation guidance is provided; when the position, attitude deviation or stress risk exceeds a first threshold, an audible and visual alarm is started and the risk type and control suggestion are prompted; 7. A large prefabricated drainage port structure hoisting construction intelligent monitoring and regulation method, characterized in that, When the deviation or stress value approaches a dangerous threshold, the highest level alarm is sent and an intervention signal is sent to the crane control system. The application further relates to a crane lifting control method for precast drainage outlet components. The method comprises the following steps: Real-time acquisition of multi-source state data of a component; 8. The method of claim 7, wherein, Fusion of the multi-source state data based on a Kalman filtering algorithm to solve real-time six-degree-of-freedom pose of the component; 9. The method of claim 7, wherein, Evaluation of the structural safety state of the component and risk warning based on finite element prior knowledge and combination of the real-time six-degree-of-freedom pose and measured stress data. The step of evaluating the structural safety state of the component and risk warning comprises querying a pre-constructed attitude-stress mapping relationship database to obtain a theoretical allowable stress value under a current attitude, and comparing the value with a measured stress value to trigger a warning. The application further relates to a crane lifting control system for precast drainage outlet components. According to the deviation degree of the six-degree-of-freedom pose and the evaluation result of the structure safety state, hierarchical regulation is performed, which includes providing visual guidance, starting sound and light alarm and giving regulation suggestions, or sending an emergency intervention signal to the crane control system.

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