Automatic beam moving equipment control system

Abstract

The invention belongs to the field of bridge construction carrying equipment, and particularly discloses an automatic beam moving equipment control system which comprises a sensor module, a control module, a driving module, a communication module and a safety protection module. The sensor module is integrated with a displacement sensor, a pressure sensor and a visual positioning unit; the control module comprises a central processing unit, a path planning unit and a self-adaptive adjusting unit; the driving module comprises a servo motor, a hydraulic actuating mechanism and a deviation rectifying mechanism; the communication module supports 5G / Wi-Fi dual-mode communication, and realizes data interaction with an upper computer and a multi-computer cooperative system; the safety protection module comprises an emergency stop circuit, a vibration early warning unit and an environment monitoring unit. Through multi-sensor fusion and an intelligent control algorithm, full-process automation of beam moving operation is achieved, dynamic path planning, hydraulic synchronous adjustment and a multi-machine cooperative collision avoidance mechanism are introduced, the problems that a traditional beam moving machine is low in positioning precision, frequent in manual intervention, poor in cooperative efficiency and the like are solved, the construction efficiency can be improved, and the accident rate is reduced.

Description

Technical Field

[0001] The present invention relates to the field of bridge construction and handling equipment, and in particular to an automatic beam moving equipment control system. Background Art

[0002] In engineering fields such as bridge construction and large-scale component handling, beam shifting is a crucial and highly difficult task. Traditional beam shifting equipment control systems have many shortcomings and are difficult to meet the requirements of modern engineering construction for efficiency, precision, and safety. Although some beam shifting equipment control systems have made certain improvements in the above aspects in recent years, there are still many shortcomings. For example, although some improved systems have improved the accuracy of data collection, there is still room for improvement in the intelligence of path planning and energy consumption optimization; some systems use advanced control algorithms, but in actual applications, due to poor environmental adaptability, insufficient stability and other problems, they cannot achieve the expected results. Therefore, there is an urgent need for a more advanced and comprehensive automatic beam shifting equipment control system to solve the above problems. Summary of the Invention

[0003] The purpose of the present invention is to provide an automatic beam moving equipment control system that realizes full-process automation of beam moving operations through multi-sensor fusion and intelligent control algorithms.

[0004] The present invention is implemented through the following technical solutions: an automatic beam moving equipment control system, characterized by comprising a sensor module, a control module, a drive module, a communication module, and a safety protection module; The sensor module integrates displacement sensors, pressure sensors, and visual positioning units to collect beam position, load pressure, and track deviation data in real time. The control module includes a central processing unit, a path planning unit, and an adaptive adjustment unit. The path planning unit generates a collision-free motion trajectory based on a B-spline curve algorithm, and the adaptive adjustment unit dynamically adjusts the hydraulic drive parameters according to the load pressure. The driving module includes a servo motor, a hydraulic actuator and a correction mechanism, which responds to the instructions of the control module to drive the beam moving machine to move and adjust its posture; The communication module supports 5G / Wi-Fi dual-mode communication to achieve data interaction with the host computer and multi-machine collaborative system; The safety protection module includes an emergency stop circuit, a vibration warning unit and an environmental monitoring unit, which triggers a graded alarm mechanism when overload, structural resonance or obstacle intrusion is detected.

[0005] In order to better implement the present invention, the path planning unit in the control module further constructs a three-dimensional point cloud map of the construction environment through a laser radar and introduces a dynamic weight factor W dThe balance coefficient between path smoothness and energy consumption is automatically adjusted according to the weight of the beam. The dynamic weight factor W d Satisfies the following formula: W d =K1×M+K2×V max Where M is the mass of the beam, V max is the maximum speed allowed under the current working conditions, and K1 and K2 are the calibration constants of the beam moving equipment.

[0006] In order to better implement the present invention, further, the adaptive adjustment unit in the control module realizes precise control of the hydraulic system through a fuzzy PID controller.

[0007] In order to better implement the present invention, further, a pressure-flow mapping table and a difference compensation algorithm are prefabricated in the fuzzy PID controller. The fuzzy PID controller synchronously adjusts the opening and closing degree of the valve in the hydraulic system through the real-time pressure value fed back by the pressure sensor to achieve flow control; when the fuzzy PID controller detects that the lifting and lowering of multiple cylinders are not synchronized, the difference compensation algorithm is started, and the speed is followed based on the slowest cylinder, so that the synchronization error of the cylinder is ≤0.5mm.

[0008] In order to better implement the present invention, the correction mechanism in the driving module further adopts a double closed-loop control mechanism, the inner loop of which uses the encoder to feedback the motor speed to achieve the torque balance of the walking wheel set; the outer loop calculates the track lateral deviation Δd based on the posture data of the visual positioning unit and generates the correction torque T c .

[0009] In order to better implement the present invention, further, the track lateral deviation Δd is calculated and the correction torque T is generated. c The formula is: T c =K p ×Δd+K i ×∫Δddt Among them, K p K i is the control parameter of the encoder, and dt is the time interval.

[0010] In order to better realize the present invention, the multi-machine collaborative system further includes a task allocation unit and a collision avoidance decision unit. The task allocation unit dynamically allocates beam moving tasks based on the contract network protocol; the collision avoidance decision unit predicts the equipment motion envelope through a spatiotemporal conflict detection model, and when the safety distance is less than a threshold, triggers speed replanning or a parking instruction.

[0011] In order to better implement the present invention, further, the environmental monitoring unit in the safety protection module includes a temperature and humidity sensor and a gas concentration detector. When the ambient temperature exceeds or is lower than the set threshold and the combustible gas concentration is greater than the set threshold, forced ventilation is started and an alarm message is uploaded.

[0012] In order to better implement the present invention, it further includes a remote monitoring terminal, which uses digital twin technology to display the operating status of the equipment in real time, locates the cause of the abnormality through a decision tree model through a built-in fault diagnosis unit, and pushes a maintenance plan to the user end. Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) The sensor module in the system provided by the present invention integrates displacement sensors, pressure sensors and visual positioning units, which can collect key data such as beam position, load pressure and track offset in real time and comprehensively; this provides a solid data foundation for subsequent precise control and decision-making, and helps the system accurately grasp the operating status of the equipment and the construction environment; (2) The system provided by the present invention is capable of performing efficient and intelligent path planning. The path planning generates collision-free motion trajectories based on the B-spline curve algorithm. It can generate the optimal motion path based on the construction environment and equipment constraints, avoid collisions between the equipment and surrounding obstacles, and improve the safety and efficiency of beam moving operations. (3) The system provided by the present invention introduces a dynamic weight factor, which can automatically adjust the balance coefficient between path smoothness and energy consumption according to the weight of the beam. This allows for flexible optimization of path planning based on different working conditions and load conditions, while ensuring path smoothness while reducing equipment energy consumption, achieving a balance between energy saving and high efficiency. (4) The system provided by the present invention uses the adaptive adjustment unit in the control module to synchronously adjust the opening and closing degree of the valve in the hydraulic system according to the real-time pressure value fed back by the pressure sensor, thereby achieving precise flow control and ensuring the stability of the beam during the lifting process, thereby avoiding tilting or damage to the beam caused by asynchronous cylinders; (5) The system provided by the present invention realizes the torque balance of the walking wheel group through the automatic deviation correction mechanism in the drive module, ensures the stability and synchronization of the equipment's movement, can correct the equipment's walking deviation in time, and ensures that the equipment runs accurately along the predetermined track; (6) The communication module in the system provided by the present invention adopts 5G / Wi-Fi dual-mode communication, realizing data interaction with the host computer and the multi-machine collaborative system. This high-speed and stable communication method enables the equipment to receive instructions from the host computer in a timely manner and cooperate with other beam-moving equipment, thereby improving the coordination and efficiency of the entire construction process.

[0013] (7) The task allocation unit of the multi-machine collaborative system provided by the present invention dynamically allocates beam moving tasks based on the contract network protocol. It can reasonably allocate work according to the status of the equipment and task requirements, thereby improving the utilization rate of the equipment. The collision avoidance decision unit predicts the equipment motion envelope through the spatiotemporal conflict detection model. When the safe distance is less than the threshold, it triggers speed replanning or parking instructions to avoid collisions between multiple equipment, thus ensuring the safety of multi-machine collaborative operations.

[0014] (7) The system provided by the present invention also has a hierarchical alarm mechanism. When abnormal conditions such as overload, structural resonance or obstacle intrusion are detected, the hierarchical alarm mechanism is triggered. Different levels of alarms can promptly remind operators to take corresponding measures to prevent the occurrence and expansion of accidents, thereby ensuring the safety of equipment and personnel. (8) The system provided by the present invention realizes the full process automation of beam moving operations through multi-sensor fusion and intelligent control algorithms. The system innovatively introduces dynamic path planning, hydraulic synchronous adjustment and multi-machine collaborative collision avoidance mechanism, which solves the problems of low positioning accuracy, frequent manual intervention and poor collaborative efficiency of traditional beam moving machines. It can improve construction efficiency by more than 30% and reduce the accident rate by 50%. DETAILED DESCRIPTION

[0015] In order to make the purpose, content and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the following embodiments. However, the embodiments of the present invention are not limited thereto. Without departing from the above-mentioned technical ideas of the present invention, various replacements and changes are made according to common technical knowledge and customary means in this field, which should be included in the scope of the present invention. The specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0016] Example 1: This embodiment provides a control system for an automatic beam moving device, including a sensor module, a control module, a drive module, a communication module, and a safety protection module; The sensor module integrates displacement sensors, pressure sensors, and visual positioning units to collect beam position, load pressure, and track deviation data in real time. The control module includes a central processing unit, a path planning unit, and an adaptive adjustment unit. The path planning unit generates a collision-free motion trajectory based on a B-spline curve algorithm, and the adaptive adjustment unit dynamically adjusts the hydraulic drive parameters according to the load pressure. The driving module includes a servo motor, a hydraulic actuator and a correction mechanism, which responds to the instructions of the control module to drive the beam moving machine to move and adjust its posture; The communication module supports 5G / Wi-Fi dual-mode communication to achieve data interaction with the host computer and multi-machine collaborative system; The safety protection module includes an emergency stop circuit, a vibration warning unit and an environmental monitoring unit, which triggers a graded alarm mechanism when overload, structural resonance or obstacle intrusion is detected.

[0017] Among them, the sensor module is the data source of the entire system, and it needs to collect various types of data in real time and accurately.

[0018] Select high-precision displacement sensors, such as laser displacement sensors or magnetostrictive displacement sensors. Install them in appropriate locations, such as on key moving parts of the beam mover, to measure beam position changes. The sensors convert the measured displacement data into electrical signals, which are then converted to digital signals using an analog-to-digital converter (ADC) and transmitted to the control module. Pressure sensors, such as strain gauge pressure sensors, are used to monitor beam load pressure. These pressure sensors are installed at key locations in the hydraulic system, such as cylinders or oil pipes. When pressure is applied to the beam, the pressure sensor outputs an electrical signal proportional to the pressure. This signal is also converted to digital signals using the ADC and transmitted to the control module. Visual positioning is achieved using industrial cameras and image processing algorithms. Industrial cameras are installed on the beam mover to capture real-time images of the rails and beams. After the image data is transmitted to the control module, image processing algorithms, such as feature extraction, matching, and positioning algorithms, are used to calculate the beam position and rail offset data.

[0019] The control module is the core of the system and is responsible for data processing and decision-making. The central processing unit uses a high-performance microprocessor or industrial computer as the central processing unit, such as an ARM series microprocessor or an Intel industrial computer. The central processing unit receives data from the sensor module, analyzes and processes it, and generates control instructions based on preset algorithms and logic. Path planning unit: Three-dimensional point cloud map construction: The path planning unit scans the construction environment through a lidar to obtain three-dimensional point cloud data of the environment. The lidar emits a laser beam and measures the time difference of the reflected light to determine the distance and position of the object, thereby constructing a three-dimensional point cloud map of the construction environment. B-spline curve algorithm: Based on the constructed three-dimensional point cloud map and the starting position and target position of the beam, the B-spline curve algorithm is used to generate a collision-free motion trajectory. When generating the trajectory, a dynamic weight factor is introduced. Wd , according to the beam mass M and the maximum speed allowed by the current working conditions VmaxAutomatically adjusts the balance between path smoothness and energy consumption. The adaptive adjustment unit utilizes a fuzzy PID controller to achieve precise control of the hydraulic system. Based on real-time pressure feedback from the pressure sensor, the fuzzy PID controller uses a pre-configured pressure-flow mapping table to synchronously adjust the opening and closing of the hydraulic valves to achieve flow control. A differential compensation algorithm is activated when the fuzzy PID controller detects asynchronous lifting of multiple cylinders. Using the slowest cylinder as a reference, it adjusts the speed of the other cylinders to maintain a synchronization error of 0.5mm or less.

[0020] The driving module drives the movement and posture adjustment of the beam moving machine according to the instructions of the control module. Servo motor: The control module sends a control signal to the servo motor driver according to the trajectory generated by the path planning. The servo motor driver converts the control signal into a drive signal of the motor, drives the servo motor to rotate, and thus drives the movement of the traveling mechanism of the beam moving machine. Hydraulic actuator: The adaptive adjustment unit dynamically adjusts the hydraulic drive parameters according to the load pressure, and adjusts the flow and pressure of the hydraulic oil by controlling the opening of the hydraulic pump and valve, driving the hydraulic actuator (such as the cylinder) to achieve the lifting and posture adjustment of the beam. Correction mechanism: Inner loop control: The inner loop of the correction mechanism feeds back the motor speed through the encoder, compares the speed signal with the set target speed, and adjusts the torque of the motor through the PID control algorithm to achieve torque balance of the walking wheel group. Outer loop control: The outer loop calculates the lateral deviation Δ of the track based on the posture data of the visual positioning unit. d , and according to the formula Tc = Kp ×Δ d + Key ×∫Δ ddt Generate corrective torque Tc . Convert the correction torque into a control signal to drive the correction mechanism to correct the travel deviation of the beam moving machine.

[0021] The communication module supports 5G / Wi-Fi dual-mode communication, enabling data exchange with the host computer and the multi-machine collaborative system. Hardware: 5G and Wi-Fi communication modules, such as a 5G modem and Wi-Fi router, are installed on the beam shifting equipment. Data transmission: The communication module transmits data collected by the sensor module and control module status information to the host computer and the multi-machine collaborative system. Simultaneously, it receives commands from the host computer and information from the multi-machine collaborative system, enabling real-time data exchange.

[0022] The safety protection module ensures the safety of equipment and personnel. Emergency stop circuit: An emergency stop button is installed on the beam moving equipment. When the emergency stop button is pressed, the emergency stop circuit will immediately cut off the power supply of the equipment and stop the equipment from running. Vibration warning unit: A vibration sensor is installed to monitor the vibration of the equipment. When abnormal vibration of the equipment is detected, such as structural resonance, the vibration warning unit will trigger an alarm signal and send the signal to the control module. Environmental monitoring unit: The environmental monitoring unit includes a temperature and humidity sensor and a gas concentration detector. When the ambient temperature exceeds or falls below the set threshold, and the combustible gas concentration is greater than the set threshold, the forced ventilation device is started and the alarm information is uploaded to the host computer.

[0023] The multi-machine collaborative system includes a task allocation unit: Based on a contract network protocol, the task allocation unit dynamically allocates beam moving tasks based on the status, task requirements, and resource availability of each beam moving device. Each device exchanges information with the task allocation unit through a communication module and receives task allocation instructions. A collision avoidance decision unit: This unit uses a spatiotemporal conflict detection model to predict the motion envelope of the devices. When the safe distance between devices is detected to be less than a threshold, it triggers speed replanning or stops to avoid collisions between the devices.

[0024] Example 2: Based on the above embodiment, this embodiment further limits the path planning unit in the control module to construct a three-dimensional point cloud map of the construction environment through the laser radar, and introduces a dynamic weight factor W d The balance coefficient between path smoothness and energy consumption is automatically adjusted according to the weight of the beam. The dynamic weight factor W d Satisfies the following formula: W d =K1×M+K2×V max Where M is the mass of the beam, V max is the maximum speed allowed under the current working conditions, and K1 and K2 are the calibration constants of the beam moving equipment.

[0025] Among them, the three-dimensional point cloud map of the construction environment constructed by LiDAR includes: Hardware Preparation: When selecting a LiDAR suitable for the construction environment, consider its measurement range, accuracy, scanning frequency, and other indicators. Install the LiDAR in a suitable location on the beam moving equipment to ensure it can fully scan the construction area.

[0026] Data Collection: LiDAR continuously emits laser beams and measures the time difference between reflected light, thereby obtaining distance and angle information of surrounding objects. Using specific scanning patterns (such as horizontal and vertical scans), it performs a full-scale scan of the construction environment, acquiring a large amount of point cloud data.

[0027] Data processing includes: Filtering: Removing noise and outliers from point cloud data to improve data quality. Methods such as statistical filtering and radius filtering can be used. Coordinate transformation: Converting the point cloud data collected by the LiDAR in a local coordinate system into a global coordinate system to facilitate subsequent path planning. Point cloud stitching: If the LiDAR performs multiple scans, the point cloud data from different scan locations must be stitched together to form a complete 3D point cloud map of the construction environment.

[0028] Dynamic weight factor W d Calculation Parameter acquisition: beam mass M : Use pressure sensors to measure the pressure of the beam on the beam moving equipment, and then use the gravity formula G = Mg (in G is gravity, g is the acceleration due to gravity) to calculate the mass of the beam M The maximum speed allowed by the current working conditions Vmax : Based on factors such as construction environment, beam characteristics, equipment performance, etc., the maximum speed allowed under different working conditions is pre-set Vmax These parameters can be stored in the system database and called according to actual working conditions.

[0029] Calibration constant K 1. K Determination of 2: Through experiments and tests, we have tried many times on beams of different masses and path planning under different working conditions, and recorded the data of path smoothness and energy consumption. We have used the least squares method and other optimization algorithms to fit the appropriate K 1. K A value of 2 achieves the best balance between path smoothness and energy consumption.

[0030] Dynamic weight factor W d Calculation During the path planning process, according to the real-time beam quality M and the maximum speed allowed by the current working conditions Vmax , according to the formula Wd = K 1× M + K 2× Vmax Calculating dynamic weight factors Wd .

[0031] Path planning based on dynamic weight factors: Design of objective function Introducing dynamic weight factors into the objective function of path planning Wd , taking into account path smoothness and energy consumption. For example, the objective function can be designed as: F = Wd × S +(1- Wd )× E in, S represents the path smoothness index (such as the curvature change of the path), E Indicates energy consumption indicators (such as motor power consumption).

[0032] Path search algorithm: Use appropriate path search algorithms (such as A* algorithm, Dijkstra algorithm, etc.) to search for collision-free motion trajectories that meet the minimum objective function in the 3D point cloud map. During the search process, according to the dynamic weight factor Wd The weights of path smoothness and energy consumption are dynamically adjusted to achieve optimization of path planning. The rest of this embodiment is the same as the above embodiment and will not be described in detail.

[0033] Example 3: Based on the above embodiment, this embodiment further limits the adaptive adjustment unit in the control module to achieve precise control of the hydraulic system through a fuzzy PID controller.

[0034] The basic principle of a fuzzy PID controller: A fuzzy PID controller is an intelligent controller that combines fuzzy logic and PID control. Through three steps: fuzzification, fuzzy inference, and defuzzification, it adjusts the PID controller parameters (Kp, proportional coefficient), Ki, integral coefficient, and Kd, differential coefficient) in real time based on the input error and error rate, achieving precise control of the hydraulic system.

[0035] Fuzzification: Input variables: The input variables of the fuzzy PID controller are the error e between the real-time pressure value fed back by the pressure sensor and the set pressure value, as well as the error change rate \(\Deltae\). Fuzzy set definition: The error e and the error change rate \(\Deltae\) are divided into several fuzzy sets, such as negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PB). Each fuzzy set has a corresponding membership function that describes the degree to which the input variable belongs to the fuzzy set. Membership function selection: Common membership functions include triangular, trapezoidal, and Gaussian types. Select an appropriate membership function based on the actual situation. For example, the triangular membership function has the advantages of simplicity and low computational complexity.

[0036] Fuzzy reasoning: Fuzzy rule formulation: A series of fuzzy rules are formulated based on expert experience and actual control requirements. For example, if the error e is positive (PB) and the error rate of change \(\Delta\) is positive (PS), then \(K_p\) increases, \(K_i\) decreases, and \(K_d\) remains unchanged. If the error e is zero (Z) and the error rate of change \(\Delta\) is zero (Z), then \(K_p\), \(K_i\), and \(K_d\) remain unchanged. Fuzzy reasoning methods: Commonly used fuzzy reasoning methods include the Mamdani method and the Sugeno method. For example, the Mamdani method uses fuzzy relation synthesis operations to derive the fuzzy set of output variables (\(K_p\), \(K_i\), and \(K_d\)) based on the fuzzy sets of input variables and fuzzy rules.

[0037] Defuzzification: Output variables: Fuzzy inference yields a fuzzy set of Kp, Ki, and Kd. These must be converted to precise values for use in PID controller parameter adjustment. Common defuzzification methods include the maximum membership method, the centroid method, and the weighted average method. The centroid method, for example, calculates the centroid of the fuzzy set to obtain a precise value.

[0038] The other parts of this embodiment are the same as those of the above embodiment and will not be described in detail.

[0039] Example 4: This embodiment, based on the above embodiment, further defines a pre-configured pressure-flow mapping table and a differential compensation algorithm within the fuzzy PID controller. The fuzzy PID controller uses real-time pressure feedback from pressure sensors to synchronously adjust the opening and closing of valves in the hydraulic system to achieve flow control. When the fuzzy PID controller detects asynchronous lifting of multiple cylinders, it activates a differential compensation algorithm, using the slowest cylinder as a reference for speed tracking, ensuring a cylinder synchronization error of ≤0.5 mm. The fuzzy PID controller integrates the hydraulic system with the following: A pressure-flow mapping table is pre-configured within the fuzzy PID controller. This table, based on the characteristics of the hydraulic system, specifies the corresponding valve openings at different pressures to achieve flow control. Real-time adjustment: Based on the real-time pressure error and error change rate, the fuzzy PID controller uses fuzzy inference and defuzzification to determine adjustment values for Kp, Ki, and Kd. The controller then adjusts the valve openings in the hydraulic system based on these adjusted parameters, achieving precise control of the hydraulic system.

[0040] Multi-cylinder synchronous control: Difference compensation algorithm: When the fuzzy PID controller detects that the lifting and lowering of multiple cylinders are not synchronized, the difference compensation algorithm is started. Taking the slowest cylinder as the reference, the position deviation of other cylinders and the slowest cylinder is calculated, and then the speed of other cylinders is adjusted according to the deviation so that the synchronization error of the cylinder is ≤0.5mm. Synchronization error monitoring: The position of each cylinder is monitored in real time by the displacement sensor, and the synchronization error between the cylinders is calculated. When the synchronization error exceeds the threshold, the difference compensation algorithm is triggered for adjustment. The other parts of this embodiment are the same as the above embodiment and will not be repeated here.

[0041] Example 5: Based on the above embodiment, this embodiment further limits the correction mechanism in the driving module to adopt a double closed-loop control mechanism. The inner loop uses the encoder to feedback the motor speed to achieve the torque balance of the walking wheel set; the outer loop calculates the track lateral deviation Δd based on the posture data of the visual positioning unit and generates the correction torque T c , the track lateral deviation Δd is calculated and the correction torque T is generated c The formula is: T c =K p ×Δd+K i ×∫Δddt Among them, K p K i is the control parameter of the encoder, and dt is the time interval.

[0042] Inner loop control: torque balance of the traveling wheels Encoder feedback principle: The encoder is installed on the motor shaft and measures the motor speed in real time. The encoder converts the motor's rotation angle into electrical signals through photoelectric conversion or magnetoelectric conversion. These signals contain information about the motor speed. Torque balancing control algorithm: The inner loop uses the PID control algorithm to achieve torque balancing of the walking wheel group. The specific steps are as follows: Set the target speed: Set a target speed for each walking wheel group according to the movement requirements of the beam moving machine. This target speed is usually determined based on the overall speed and steering requirements of the beam moving machine. Calculate the speed error: Compare the actual speed fed back by the encoder with the target speed to calculate the speed error. PID control calculation: Based on the speed error, use the PID controller to calculate the torque value that needs to be adjusted. The output formula of the PID controller is: \(u(t)=K_pe(t)+K_i\int_{0}^{t}e(\tau)d\tau+K_d\frac{de(t)}{dt}\) where \(u(t)\) is the controller output (i.e., the torque value to be adjusted), \(K_p\) is the proportional coefficient, \(K_i\) is the integral coefficient, \(K_d\) is the differential coefficient, and \(e(t)\) is the speed error. Adjusting the motor torque: The calculated torque adjustment value is sent to the motor driver, which adjusts the motor's output torque based on this value, gradually bringing the wheel speed closer to the target speed and achieving torque balance.

[0043] Outer loop control: Track lateral deviation correction Working Principle of the Vision Positioning Unit: The Vision Positioning Unit uses a camera to capture real-time images of the track. Using image processing algorithms, it identifies track feature points (such as track edges and markings) and calculates the position of the beam mover relative to the track, including lateral and angular deviations.

[0044] Correction Torque Calculation: The outer loop calculates the lateral track deviation (\Deltad\) based on the pose data provided by the visual positioning unit and generates a correction torque (\T_c\). The calculation formula is: \(T_c=K_p\times\Deltad+K_i\times\int\Deltaddt\), where \(K_p\) is the proportional coefficient, \(K_i\) is the integral coefficient, and \(\int\Deltaddt\) is the integral term for the lateral deviation. This formula means that the correction torque (\T_c\) consists of two parts: a proportional term proportional to the current lateral deviation (\Deltad\), which is used to quickly respond to changes in the deviation; and an integral term for the deviation, which is used to eliminate long-term accumulated deviation. Correction Actuator: The generated correction torque (\T_c\) is converted into a control signal and sent to the correction actuator (such as a steering cylinder or motor). The deviation correction actuator acts according to this control signal, adjusts the travel direction of the beam moving machine, and makes the beam moving machine gradually return to the correct track, thereby reducing the lateral deviation of the track \(\Deltad\).

[0045] The collaborative operation of the dual closed-loop control system: The relationship between the inner and outer loops: The inner loop is primarily responsible for ensuring balanced torque on the running wheels, ensuring smooth movement of the beam mover; the outer loop adjusts the beam mover's travel direction based on lateral deviation of the track, ensuring accurate tracking of the beam mover. The inner loop serves as the foundation for the outer loop, which uses it to precisely control the beam mover. Control Flow: After system startup, the inner loop begins operating first, adjusting the torque of the running wheels based on speed information fed back by the encoder to ensure a stable travel speed for the beam mover. Simultaneously, the vision positioning unit monitors track posture data in real time. The outer loop calculates the track's lateral deviation, \(\Deltad\), based on this data and generates a corrective torque, \(T_c\). The outer loop transmits this corrective torque, \(T_c\), to the inner loop, which adjusts the motor torque based on this torque, thereby changing the beam mover's travel direction and correcting the track's lateral deviation. Throughout this process, the inner and outer loops continuously exchange data and make adjustments to ensure the beam mover maintains accurate and smooth tracking. The other parts of this embodiment are the same as those of the above embodiment and will not be described in detail.

[0046] Example 6: This embodiment, based on the above embodiment, further specifies that the multi-machine collaborative system includes a task allocation unit and a collision avoidance decision unit. The task allocation unit dynamically allocates beam moving tasks based on a contract network protocol. The collision avoidance decision unit predicts the equipment motion envelope using a spatiotemporal conflict detection model and triggers speed replanning or a stop command when the safety distance falls below a threshold. The remainder of this embodiment is the same as the above embodiment and is not further described.

[0047] Example 7: This embodiment, based on the above embodiment, further specifies that the environmental monitoring unit in the safety protection module includes a temperature and humidity sensor and a gas concentration detector. When the ambient temperature exceeds or falls below a set threshold, or the combustible gas concentration exceeds a set threshold, forced ventilation is initiated and an alarm message is uploaded. The remainder of this embodiment is the same as the above embodiment and will not be further described.

[0048] Example 8: This embodiment, based on the above embodiment, further includes a remote monitoring terminal. The remote monitoring terminal uses digital twin technology to display the operating status of the equipment in real time, uses a built-in fault diagnosis unit to locate the cause of the abnormality using a decision tree model, and pushes a maintenance plan to the user. The rest of this embodiment is the same as the above embodiment and will not be repeated here.

[0049] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A control system for an automatic beam moving device, characterized in that: Including sensor module, control module, drive module, communication module, and safety protection module; The sensor module integrates displacement sensors, pressure sensors, and visual positioning units to collect beam position, load pressure, and track deviation data in real time. The control module includes a central processing unit, a path planning unit, and an adaptive adjustment unit. The path planning unit generates a collision-free motion trajectory based on a B-spline curve algorithm, and the adaptive adjustment unit dynamically adjusts the hydraulic drive parameters according to the load pressure. The driving module includes a servo motor, a hydraulic actuator and a correction mechanism, which responds to the instructions of the control module to drive the beam moving machine to move and adjust its posture; The communication module supports 5G / Wi-Fi dual-mode communication to achieve data interaction with the host computer and multi-machine collaborative system; The safety protection module includes an emergency stop circuit, a vibration warning unit and an environmental monitoring unit, which triggers a graded alarm mechanism when overload, structural resonance or obstacle intrusion is detected.

2. The automatic beam moving equipment control system according to claim 1, characterized in that: The path planning unit in the control module constructs a three-dimensional point cloud map of the construction environment through the laser radar and introduces a dynamic weight factor W d The balance coefficient between path smoothness and energy consumption is automatically adjusted according to the weight of the beam. The dynamic weight factor W d Satisfies the following formula: IN d =K1×M+K2×V max Where M is the mass of the beam, V max is the maximum speed allowed under the current working conditions, and K1 and K2 are the calibration constants of the beam moving equipment.

3. An automatic beam moving equipment control system according to claim 1 or 2, characterized in that: The adaptive adjustment unit in the control module realizes precise control of the hydraulic system through a fuzzy PID controller.

4. The automatic beam moving equipment control system according to claim 3, characterized in that: A pressure-flow mapping table and a difference compensation algorithm are prefabricated in the fuzzy PID controller. The fuzzy PID controller synchronously adjusts the opening and closing degree of the valve in the hydraulic system through the real-time pressure value fed back by the pressure sensor to achieve flow control; when the fuzzy PID controller detects that the lifting and lowering of multiple cylinders are not synchronized, the difference compensation algorithm is started, and the speed is followed based on the slowest cylinder, so that the synchronization error of the cylinder is ≤0.5mm.

5. The automatic beam moving equipment control system according to claim 1 or 2, characterized in that: The correction mechanism in the drive module adopts a double closed-loop control mechanism, the inner loop of which uses an encoder to feedback the motor speed to achieve torque balance of the walking wheel set; Its outer loop calculates the track lateral deviation Δd based on the posture data of the visual positioning unit and generates the correction torque T c .

6. The automatic beam moving equipment control system according to claim 5, characterized in that: The calculation of the track lateral deviation Δd and the generation of the correction torque T c The formula is: T c =K p ×Δd+K i ×∫Δddt Among them, K p K i is the control parameter of the encoder, and dt is the time interval.

7. The automatic beam moving equipment control system according to claim 1 or 2, characterized in that: The multi-machine collaborative system includes a task allocation unit and a collision avoidance decision unit. The task allocation unit dynamically allocates beam moving tasks based on the contract network protocol; the collision avoidance decision unit predicts the equipment motion envelope through a spatiotemporal conflict detection model, and triggers speed replanning or a parking instruction when the safety distance is less than a threshold.

8. The automatic beam moving equipment control system according to claim 1 or 2, characterized in that: The environmental monitoring unit in the safety protection module includes a temperature and humidity sensor and a gas concentration detector. When the ambient temperature exceeds or falls below a set threshold, and the combustible gas concentration is greater than a set threshold, forced ventilation is started and an alarm message is uploaded.

9. The automatic beam moving equipment control system according to claim 1 or 2, characterized in that: It also includes a remote monitoring terminal, which uses digital twin technology to display the equipment operation status in real time, locates the cause of the abnormality through a decision tree model through a built-in fault diagnosis unit, and pushes the maintenance plan to the user end.