An IoT-based collaborative control method for hoisting

Abstract

This invention relates to the fields of industrial automation and the Internet of Things (IoT) technology, and discloses an IoT collaborative control method for hoisting, comprising the following steps: Step 1, collecting multi-source heterogeneous data; Step 2, obtaining the dynamic load force and attitude fluctuation variance of the hoisted object; Step 3, dynamically allocating the collaborative weights of each device during the hoisting process; Step 4, obtaining control input and introducing an improved cost function to perform MPC collaborative control on the hoisted object; By including steps 2, 3, and 4, it is beneficial to introduce a rigid body dynamics compensation model in step 2 to provide a real load state benchmark, rather than traditional manual estimation, effectively avoiding weight allocation errors caused by misjudgment; by dynamically allocating weights to collaborative devices in step 3, the traditional fixed division of labor mode of equipment is replaced, effectively improving the robustness of the system and ensuring the rhythm and efficiency of hoisting operations.

Description

Technical Field

[0001] This invention relates to the fields of industrial automation and Internet of Things (IoT) technology, and more specifically to an IoT collaborative control method for hoisting. Background Technology

[0002] Lifting operations are a core component in engineering construction, logistics and transportation, port terminals, energy and power, and their safety and efficiency are directly related to the success or failure of the entire project. With the trend of engineering projects becoming larger and more complex, a single lifting device is often unable to complete certain tasks independently, and the demand for multiple lifting devices to lift large components, equipment or modules in a coordinated manner is increasing.

[0003] Currently, traditional multi-crane collaborative operations mainly rely on the experience, gestures, and walkie-talkies of ground command personnel for communication and coordination. However, this approach has several drawbacks: 1. Information silos exist; the operational data of each hoisting device is not shared, making it difficult for ground command personnel to fully grasp the overall operational situation and resulting in insufficient decision-making basis; 2. Communication efficiency is low; communication via walkie-talkies is susceptible to interference, and information transmission is prone to delays and distortions, especially in noisy environments, where instructions are not issued and feedback is not timely, thus affecting the operational rhythm; 3. High safety risks; due to the lack of precise position and attitude perception, collisions are highly likely to occur when multiple devices are operating collaboratively, posing significant safety hazards.

[0004] In view of this, the present invention proposes an IoT collaborative control method for hoisting, which breaks down information barriers and enables intelligent collaboration among multiple devices to ensure operational safety and improve operational efficiency. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an Internet of Things collaborative control method for hoisting, so as to solve the problems existing in the background art.

[0006] This invention provides the following technical solution: an IoT collaborative control method for hoisting, comprising the following steps: Step 1: Collect multi-source heterogeneous data and unify the timestamps of the collected multi-source heterogeneous data through a spatiotemporal alignment protocol; Step 2: Obtain the dynamic load force and attitude fluctuation variance of the hoisted object based on the collected multi-source heterogeneous data; the dynamic load force of the hoisted object is introduced into a rigid body dynamics compensation model; Step 3: Obtain the target power based on the dynamic load force and attitude fluctuation variance of the hoisted object in Step 2, and dynamically allocate the collaborative weights of each piece of equipment during the hoisting process based on the target power; Step 4: Obtain control input based on the dynamically allocated equipment weights, and introduce an improved cost function to perform MPC collaborative control on the hoisted object.

[0007] Preferably, the multi-source heterogeneous data includes triaxial acceleration, angular velocity, point cloud data, and equipment position data; triaxial acceleration and angular velocity are obtained by deploying sensors at the hook, point cloud data is obtained by scanning the hoisting area with lidar, and position data of each device is obtained by positioning tags; the target power is the power required to complete the current hoisting task.

[0008] Preferably, the dynamic load capacity of the hoisted object is expressed by the formula: ; in, Indicates the dynamic load capacity of the hoisted object; The mass of the hoisted object is indicated by dynamic calibration using a pressure sensor; The linear acceleration of the center of gravity of the hoisted material; Represents the gravitational acceleration vector; The tensor representing the moment of inertia of the hoisted object; This represents the angular velocity vector of the hoisted object; express The inverse matrix; The attitude fluctuation variance is obtained by the following formula: ;in, Indicates the target power; This indicates the target power before the correction; This represents the correction factor related to the variance of attitude fluctuations. satisfy The greater the fluctuation, the larger the correction coefficient; attitude fluctuation variance is used to correct the target power. When the attitude fluctuation variance exceeds the threshold, it indicates that the hoisted object is swinging violently. At this time, additional power is needed to suppress the swing and improve the safety factor. ;in, This indicates the dynamic load force value of the hoisted object; Indicates the hoisting speed; This indicates the efficiency of a mechanical system.

[0009] Preferably, the dynamic allocation of the coordination weights of each piece of equipment during the hoisting process specifically involves: ;in, Indicates the first Each device at time The weights; Indicates the first The current available power of each device; Indicates the distance attenuation coefficient; Indicates the first The Euclidean distance from each piece of equipment to the hoisting load; Indicates the first The historical task success rate of each device, with a value range of [value missing]. ; are the corresponding weight coefficients, satisfying If there are a total of during the hoisting process Each collaborative device, Preferably, the control input is represented as ;in, for The set of action commands for each device at any given time. Indicates the first Each device at time Time control input, This represents the total number of collaborative devices; The control input obtained based on the dynamically allocated device weights is specifically as follows: Based on the dynamically allocated weights from step 3, all collaborative devices are sorted in descending order, categorized into master devices, slave devices, and auxiliary devices; if the ideal control input is... ; The improved cost function, derived by introducing dynamically allocated weights, is expressed as: ; in, Represents the improved cost function; Represented by the minimum value; Indicates control input; express The actual state of the device at any given time. Indicates the device reference state quantity; Represents the state weighting matrix. This represents the weighted L2 norm for state tracking; Indicates the first Each device at time The target control input at that time; Indicates the oscillation suppression term; This represents the weighted L2 norm of the control input; ;in, This represents the scaling factor. Preferably, the correction factor related to the attitude fluctuation variance... The specific method of obtaining it is as follows: For geometrically regular suspended objects, the swing angle and angular velocity are obtained from the attitude fluctuation variance, and the torque required to suppress the swing is obtained. Converting torque into power and the foundation hoisting power The correction coefficient is obtained by superposition, and the formula is expressed as: ;in, This represents the power after torque conversion; Indicates the foundation hoisting power; in, This represents the angular velocity vector value of the hoisted object. For irregularly shaped components, by changing the lifting speed and wind load, different degrees of attitude fluctuations are generated in the hoisted object. The corresponding attitude fluctuation variance is recorded, and the actual power used to complete the hoisting task under each working condition is measured. and the reference power when there is no fluctuation. In comparison, the fitted functional relationship using regression analysis is expressed as follows: ; are the fitting parameters, respectively.

[0010] The technical effects and advantages of this invention are as follows: This invention, through steps 2, 3, and 4, facilitates the acquisition of the dynamic load force and attitude fluctuation variance of the hoisted object by introducing a rigid body dynamics compensation model, and dynamically allocates the collaborative weights of each piece of equipment during the hoisting process. Control inputs are obtained based on the dynamically allocated equipment weights, and an improved cost function is introduced. Step 2, by introducing the rigid body dynamics compensation model, provides a realistic load state benchmark, rather than traditional manual estimation, effectively avoiding weight allocation errors caused by misjudgment. Step 3, by dynamically allocating weights to collaborative equipment, replaces the traditional fixed division of labor mode, effectively improving the robustness of the system and ensuring the rhythm and efficiency of the hoisting operation. The improved cost function, based on the dynamic weight allocation, effectively reduces the safety risks of multi-equipment collaborative operation, while simultaneously improving operational safety, efficiency, and intelligence. Attached Figure Description

[0011] Figure 1 This is a flowchart of the IoT collaborative control method for hoisting according to the present invention. Detailed Implementation

[0012] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The IoT collaborative control method for hoisting involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] like Figure 1 As shown, the present invention provides an IoT collaborative control method for hoisting, comprising the following steps: Step 1: Collect multi-source heterogeneous data. Use a spatiotemporal alignment protocol to unify the timestamps of the collected multi-source heterogeneous data to address the issue of asynchronous equipment. The multi-source heterogeneous data includes, but is not limited to, triaxial acceleration, angular velocity, point cloud data, and equipment position data. Triaxial acceleration and angular velocity are acquired by deploying sensors at the hook, point cloud data is acquired by scanning the hoisting area with LiDAR, and position data of each device is acquired through positioning tags. The purpose is to provide a data foundation for subsequent collaborative control by collecting multi-source heterogeneous data. Step 2: Obtain the dynamic load force and attitude fluctuation variance of the hoisted object based on the collected multi-source heterogeneous data; the purpose is to eliminate the virtual force interference caused by the swing of the hoisted object by introducing rigid body dynamics compensation, accurately quantify the mechanical state of the load, and avoid the errors of traditional models. Step 3: Obtain the target power based on the dynamic load force and attitude fluctuation variance of the hoisted object in Step 2, and dynamically allocate the coordination weight of each piece of equipment during the hoisting process based on the target power; the target power is the power required to complete the current hoisting task. Step 4: Obtain control inputs based on dynamically allocated equipment weights, and introduce an improved cost function to perform MPC collaborative control on the hoisted object; the purpose is to adjust the deviation penalty between the control inputs of different equipment and the target value by introducing weights into the improved cost function, thereby realizing the allocation of control authority according to weights.

[0014] In this embodiment, it should be specifically noted that the dynamic load force of the hoisted object is introduced into a rigid body dynamics compensation model, which is expressed by the following formula: in, It represents the dynamic load force of the hoisted object to eliminate interference from oscillating virtual forces; The mass of the hoisted object can be dynamically calibrated using a pressure sensor; The linear acceleration of the center of gravity of the hoisted material; Represents the gravitational acceleration vector; The tensor representing the moment of inertia of the hoisted object is used to describe its rotational inertial characteristics. The vector representing the angular velocity of the hoisted object, used to describe the oscillation angular velocity; express The inverse matrix is ​​used to calculate the inertial force related to angular momentum; The attitude fluctuation variance is obtained by the following formula: This represents the variance of the attitude fluctuation of the hoisted object, used to measure the degree of attitude fluctuation. This represents the total number of attitude samplings; Indicates the first Secondary sampling; Indicates the first Attitude angle deviation at the next sampling time; Denotes the Euclidean norm; The dynamic load force is calculated accurately using rigid body dynamics compensation, eliminating virtual force interference caused by swaying; the attitude fluctuation variance is used to quantify the attitude stability of the hoisted object, providing real-time feedback for safety control. In this embodiment, it should be specifically noted that the target power is expressed by the formula: ;in, Indicates the target power; This indicates the target power before the correction; This represents the correction factor related to the variance of attitude fluctuations. satisfy The greater the fluctuation, the larger the correction coefficient; attitude fluctuation variance is used to correct the target power. When the attitude fluctuation variance exceeds the threshold, it indicates that the hoisted object is swinging violently. At this time, additional power is needed to suppress the swing or increase the safety factor. In real-time control, if the attitude fluctuation is too large, the control coefficient will optimize the allocation of more power to the anti-sway device or attitude stabilization system, thereby indirectly affecting the target power allocation of the main hoisting task. Therefore, it is necessary to correct the target power based on the attitude fluctuation variance; represents the dynamic load force value of the hoisted object; Indicates the hoisting speed; Indicates the efficiency of a mechanical system; The dynamic allocation of the coordination weights of each piece of equipment during the hoisting process specifically refers to the following: ; in, Indicates the first Each device at time The weight; used to switch the priority of master and slave devices; Indicates the first The current available power of each device; This represents the distance decay coefficient, used to control the degree of influence of distance on the weights; Indicates the first The Euclidean distance from each piece of equipment to the hoisting load; ; are the corresponding weight coefficients, satisfying If there are a total of during the hoisting process Each collaborative device, .

[0015] In this embodiment, it should be specifically noted that the correction coefficient related to the attitude fluctuation variance... The specific method of obtaining it is as follows: For geometrically regular suspended objects, the swing angle and angular velocity are obtained from the attitude fluctuation variance, and the torque required to suppress the swing is obtained. Converting torque into power and the foundation hoisting power The correction coefficient is obtained by superposition, and the formula is expressed as: This represents the power after torque conversion; Indicates the foundation hoisting power; ;in, This represents the vector value of the angular velocity of the hoisted object; For lifting irregularly shaped components, by changing the lifting speed and wind load, the degree of attitude fluctuation of the lifted object is controlled, the corresponding attitude fluctuation variance is recorded, and the actual power to complete the lifting task under each working condition is measured. and the reference power when there is no fluctuation. In comparison, the fitted functional relationship using regression analysis is expressed as follows: ; are the fitting parameters, respectively.

[0016] In this embodiment, it should be specifically noted that the control input is represented as The set of action commands for each device at any given time. Indicates the first Each device at time Time control input, Total number of collaborative devices The control input obtained based on the dynamically allocated device weights is specifically as follows: Based on the weights dynamically allocated in step 3, all collaborative devices are sorted in descending order and divided into master devices, slave devices, and auxiliary devices. Master devices are devices with higher weights, slave devices are devices with medium weights, and auxiliary devices are devices with lower weights. The weight boundaries of master devices, slave devices, and auxiliary devices can be reasonably set by those skilled in the art based on the actual hoisting situation. This embodiment does not specifically limit this. There can be one or more master devices, slave devices, and auxiliary devices. If the ideal control input is ,but ; The improved cost function, derived by introducing dynamically allocated weights, is expressed as: ; in, Represents the improved cost function; Represented by the minimum value; Indicates control input; express The actual state of the device at any given time. Indicates the device reference state quantity; Represents the state weighting matrix. This represents the weighted L2 norm for state tracking; Indicates the first Each device at time The target control input at that time; Indicates the oscillation suppression term; This represents the weighted L2 norm of the control input; ;in, This represents the proportionality coefficient; by using the swing suppression term, passive compensation is transformed into active optimization, thereby effectively reducing energy consumption.

[0017] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0018] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An Internet of Things collaborative control method for hoisting, characterized in that: Includes the following steps: Step 1: Collect multi-source heterogeneous data and unify the timestamps of the collected multi-source heterogeneous data through a spatiotemporal alignment protocol; Step 2, acquiring the dynamic load force of the hoisted object based on the collected multi-source heterogeneous data and attitude fluctuation variance; the dynamic load force of the hoisted object introduces a rigid body dynamics compensation model; Step 3: Obtain the target power based on the dynamic load force and attitude fluctuation variance of the hoisted object in Step 2, and dynamically allocate the collaborative weights of each piece of equipment during the hoisting process based on the target power; Step 4: Obtain control input based on the dynamically allocated equipment weights, and introduce an improved cost function to perform MPC collaborative control on the hoisted object.

2. The IoT collaborative control method for hoisting according to claim 1, characterized in that: The multi-source heterogeneous data includes three-axis acceleration, angular velocity, point cloud data, and device position data The three-axis acceleration and angular velocity are obtained by deploying sensors at the hooks, the point cloud data is obtained by scanning the hoisting area by a laser radar, and the device position data is obtained by positioning tags. The target power is the power required to complete the current hoisting task.

3. The IoT collaborative control method for hoisting according to claim 2, characterized in that: The dynamic load force of the hoisted object is expressed by the formula: in, Indicates the dynamic load capacity of the hoisted object; The mass of the hoisted object is indicated by dynamic calibration using a pressure sensor; The linear acceleration of the center of gravity of the hoisted material; Represents the gravitational acceleration vector; The tensor representing the moment of inertia of the hoisted object; This represents the angular velocity vector of the hoisted object; express The inverse matrix; The attitude fluctuation variance is obtained by the following formula: ;in, This represents the variance of the attitude fluctuation of the hoisted object; This represents the total number of attitude samplings; Indicates the first Secondary sampling; Indicates the first Attitude angle deviation at the next sampling time; This represents the Euclidean norm.

4. The IoT collaborative control method for hoisting according to claim 3, characterized in that: The target power is obtained by the following formula: ;in, Indicates the target power; indicates the target power before correction; This represents the correction factor related to the variance of attitude fluctuations. satisfy The greater the fluctuation, the larger the correction coefficient; attitude fluctuation variance is used to correct the target power. When the attitude fluctuation variance exceeds the threshold, it indicates that the hoisted object is swinging violently. At this time, additional power is needed to suppress the swing and improve the safety factor. ;in, This indicates the dynamic load force value of the hoisted object; Indicates the hoisting speed; This indicates the efficiency of a mechanical system.

5. The IoT collaborative control method for hoisting according to claim 4, characterized in that: The specific steps for dynamically allocating the collaborative weights of each piece of equipment during the hoisting process are as follows: in, Indicates the first Each device at time The weights; Indicates the first The current available power of each device; Indicates the distance attenuation coefficient; Indicates the first The Euclidean distance from each piece of equipment to the hoisting load; Indicates the first The historical task success rate of each device, with a value range of [value missing]. ; are the corresponding weight coefficients, satisfying If there are a total of during the hoisting process Each collaborative device, .

6. The IoT collaborative control method for hoisting according to claim 5, characterized in that: The control input is represented as in, The set of action commands for each device at any given time. Indicates the first Each device at time Time control input, This represents the total number of collaborative devices; The control input obtained based on the dynamically allocated device weights is specifically as follows: Based on the weights dynamically allocated in step 3, all collaborative devices are sorted in descending order and divided into master devices, slave devices, and auxiliary devices. If the ideal control input is, then The improved cost function, derived by introducing dynamically allocated weights, is expressed as: in, Represents the improved cost function; Represented by the minimum value; Indicates control input; express The actual state of the device at any given time. Represents the device reference state quantity; represents the state weighting matrix. This represents the weighted L2 norm for state tracking; Indicates the first The target control input of each device at any given time; Indicates the oscillation suppression term; This represents the weighted L2 norm of the control input; ;in, This represents the proportionality coefficient.

7. The IoT collaborative control method for hoisting according to claim 6, characterized in that: The correction coefficient related to attitude fluctuation variance The specific method of obtaining it is as follows: For geometrically regular suspended objects, the swing angle and angular velocity are obtained from the attitude fluctuation variance, and the torque required to suppress the swing is obtained. Converting torque into power and the foundation hoisting power The correction coefficient is obtained by superposition, and the formula is expressed as: ;in, This represents the power after torque conversion; Indicates the foundation hoisting power; ;in, This represents the vector value of the angular velocity of the hoisted object; For lifting irregularly shaped components, by changing the lifting speed and wind load, the degree of attitude fluctuation of the lifted object is controlled, the corresponding attitude fluctuation variance is recorded, and the actual power to complete the lifting task under each working condition is measured. and the reference power when there is no fluctuation. In comparison, the fitted functional relationship using regression analysis is expressed as follows: ; are the fitting parameters, respectively.

Images