A hook remote intelligent control method based on an internet of things

Abstract

The application discloses a kind of based on Internet of Things's hook remote intelligent control method, it is related to remote intelligent control technical field, the present application is applied in the hooking of this method, the self-acting cooperation of multiple hooking hoist is applied in tower crane, tire crane, gate machine operation The self-acting level of level is significantly improved;In efficiency dimension, dynamic priority arbitration and local path planning eliminate the operation delay caused by traditional centralization scheduling, make multiple hooks seamless cooperation in cross operation, reduce task interruption;At the same time, the real-time fuse response of sudden obstacle avoids the hysteresis of artificial intervention, maintains operation continuity;Safety level, motion envelope prediction is combined with wind speed compensation, enhances the robustness of path to dynamic interference, still maintains millimeter level obstacle avoidance precision under strong wind or hook mutation movement scene, greatly reduces collision risk.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote intelligent control, and in particular to a hook remote intelligent control method based on an Internet of Things. BACKGROUND

[0002] At the port container terminal and large construction site, multiple hook lifting devices are densely operated under the tower crane, the tire crane and the portal crane; such scenes require the hook to complete high-speed lifting within a few millimeter accuracy, and frequent cross movement; with the popularization of 5G edge computing and hook lifting device anti-collision technology, the single machine automation level is significantly improved, but the dynamic cooperation of multiple hook lifting devices still depends on centralized scheduling; the operator needs to monitor multiple hook trajectories at the remote control console, and the mental load increases dramatically, and when a sudden path conflict occurs, the operator is often forced to stop work, resulting in high overall efficiency loss.

[0003] The current mainstream scheme issues avoidance instructions through the cloud, but the cloud-edge data transmission and calculation takes more than 200ms, which cannot respond to sudden turning of the hook, such as avoiding AGV, and such delay significantly increases the risk of collision; in addition, the obstacle avoidance algorithm only processes static obstacles such as building structures, and does not fuse the real-time motion intention of other hooks; when multiple hook lifting devices under the tower crane, the tire crane and the portal crane move towards each other, the system cannot automatically generate an interleaved path and needs manual intervention to stop, causing work flow interruption.

[0004] Some schemes introduce digital twin pre-play lifting paths, but the update frequency of the twin model is insufficient, ≤1Hz, which is difficult to track the second-level turning of the hook; some manufacturers use reinforcement learning to optimize the path, but the training relies on a large amount of site data, which is difficult to reuse for small and medium-sized projects, and essentially still belongs to post-correction, which does not solve the problem of active cooperation in dense scenes, and becomes a key bottleneck for the hook remote intelligent control to move towards fully autonomous operation. SUMMARY

[0005] In view of the above existing problems, the present application is proposed.

[0006] The present application provides a hook remote intelligent control method based on an Internet of Things, which solves the problem of high cloud scheduling delay, weak dynamic obstacle avoidance capability, low cooperation efficiency and high collision risk of multiple hook lifting devices in operation under the tower crane, the tire crane and the portal crane.

[0007] To solve the above technical problems, the present application provides the following technical scheme:

[0008] The present application provides a hook remote intelligent control method based on an Internet of Things, which includes,

[0009] Step S1, each hook controller broadcasts the target spatial coordinates of the next action period through 5G communication;

[0010] Step S2, competing for path passing right according to dynamic priority rule, which generates weight value based on real-time load weight, motion speed and task urgency;

[0011] Step S3, the hook controller without passing right autonomously generates a smooth obstacle avoidance curve path;

[0012] Step S4, all hooks synchronously execute action instructions.

[0013] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, in the dynamic priority rule:

[0014] The task urgency is automatically classified according to the type of hoisted goods, and the weight of dangerous goods transportation task is increased to 3 times of the reference value;

[0015] When the motion speed is higher than a threshold value, the weight is linearly attenuated to avoid high-speed conflict.

[0016] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, the generation of the smooth obstacle avoidance curve path includes:

[0017] The motion envelope region of other hooks is predicted based on the broadcast coordinates, and the region is a fan-shaped space with the target point as the top of the cone;

[0018] A continuously derivable path containing at least two inflection points is generated outside the envelope region, and the path curvature radius is greater than the minimum turning radius of the hook.

[0019] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, in step S3, the hook controller without passing right generates a smooth obstacle avoidance curve path, including two sub-steps: predicting the motion envelope region of other hooks, and generating a continuously derivable obstacle avoidance path based on the region;

[0020] The step of predicting the motion envelope region of other hooks includes:

[0021] Based on the target space coordinates and the current motion state broadcast by other hooks, the motion envelope region is calculated as a fan-shaped space with the target point as the top of the cone. The specific logic is: input the broadcast coordinates and speed data of other hooks, calculate the envelope region parameters using the fan-shaped model formula, and output the region boundary for obstacle avoidance. The formula is:

[0022] ,

[0023] Wherein, represents the current speed vector of other hooks, with the unit of meter / second, represents the module of the speed vector, with the unit of meter / second, denotes the fan half-angle width, dimensionless;

[0024] Calculate the fan half-angle width:

[0025] ,

[0026] wherein, denotes the maximum acceleration of the hook, in meters per second squared, denotes the prediction time, in seconds, denotes the inverse sine function, denotes the safety factor, dimensionless, denotes the fan half-angle width, in radians;

[0027] The step of generating a smooth obstacle avoidance curve path comprises:

[0028] Outside the motion envelope region, a continuous and derivable path is generated using a cubic Bezier curve;

[0029] The logical closed loop is: input the current hook position, speed and obstacle data, calculate the control point position, generate the curve equation, and verify the curvature constraint;

[0030] Calculate the control point of the Bezier curve:

[0031] ,

[0032] wherein, denotes the starting position coordinates of the current hook, in meters, denotes the distance from the current hook to the nearest obstacle, in meters, denotes the unit normal vector from the starting point to the obstacle, dimensionless, denotes the velocity vector of the current hook, in meters per second, denotes the speed compensation factor, in seconds;

[0033] Meanwhile, the second control point is calculated:

[0034] ,

[0035] wherein, denotes the adjusted end point coordinates, in meters, obtained by offsetting the original target point outward by the minimum safety distance;

[0036] Speed compensation factor wherein, denotes the modulus of the current hook velocity vector, in meters per second;

[0037] Generate the Bezier curve path equation:

[0038] ,

[0039] wherein, represents coordinates of a point on the curve path, in meters, represents a parameterized variable, dimensionless, ranging from 0 to 1;

[0040] Verify the path curvature radius constraint:

[0041] ,

[0042] wherein, represents the path curvature radius, in meters, represents the first-order derivative of the curve, in meters / second, represents the second-order derivative of the curve, in meters / second², represents the vector cross product, represents the vector module, calculated and verify so that the path satisfies the curvature constraint, wherein represents the minimum turning radius of the hook, in meters, which is an inherent parameter.

[0043] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, the method further comprises the following steps before the synchronization execution:

[0044] When a sudden obstacle intrudes, the action cycle is compressed to 1 / 5 of the original cycle, and steps S1-S3 are retriggered.

[0045] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, the method further comprises the following steps before the synchronization execution:

[0046] Trajectory prediction module: performs the motion envelope area calculation in the generation of the smooth obstacle-avoiding curve path;

[0047] Dynamic arbitration module: performs the weight value generation in the dynamic priority rule;

[0048] Communication coordination module: manages the 5G broadcast and data reception in step S1.

[0049] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, the method further comprises the following steps before the synchronization execution:

[0050] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, in the edge computing node, the step of introducing real-time wind speed direction compensation includes data acquisition, vector decomposition, path correction and constraint verification, including:

[0051] Real-time wind speed data is read from the meteorological sensor, and the wind speed vector is decomposed into a tangent component parallel to the current hook movement direction and a normal component perpendicular to the current hook movement direction :

[0052] ,

[0053] Wherein, represents the real-time wind speed vector, with the unit of meter / second, represents the current hook speed vector, with the unit of meter / second, represents the vector dot product operation, represents the wind speed tangent component, with the unit of meter / second, represents the wind speed normal component, with the unit of meter / second;

[0054] Based on the normal wind speed component and the hook physical parameters, the compensation offset of the trajectory tangent angle is calculated :

[0055] ,

[0056] Wherein, represents the wind speed normal component module, with the unit of meter / second, represents the hook speed module, with the unit of meter / second, represents the inverse tangent function, represents the vector cross product, represents the sign function, outputting -1 or 1, represents the wind resistance coupling coefficient, dimensionless, represents the trajectory tangent angle compensation, with the unit of radian;

[0057] The compensation is embedded into the obstacle avoidance curve generation process to adjust the Bezier curve control point position:

[0058] ,

[0059] ,

[0060] The rotation matrix is defined as:

[0061] ,

[0062] wherein, represents the original first control point coordinate, unit: meter, represents the original second control point coordinate, unit: meter, represents the hook starting position, unit: meter, represents the adjusted end position, unit: meter, represents a two-dimensional rotation matrix, dimensionless, represents the compensated first control point, unit: meter, represents the compensated second control point, unit: meter;

[0063] The final obstacle avoidance path is generated using the corrected control points, and the curvature constraint is verified:

[0064] ,

[0065] Curvature radius Must meet:

[0066] ,

[0067] wherein, represents the compensated path coordinate, unit: meter, represents the curve parameter, dimensionless, represents the compensated path curvature radius, represents the minimum turning radius of the hook.

[0068] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, before step S1, it further includes:

[0069] Preload the three-dimensional digital map of the construction site, and convert the static obstacle coordinates into a set of no-fly zone coordinates for local path planning of each controller.

[0070] As a preferred scheme of the hook remote intelligent control method based on the Internet of Things, when the retriggering fails:

[0071] Force the hook to maintain a uniform straight-line motion in the current speed direction;

[0072] Send an emergency avoidance instruction containing the conflict coordinates to the remote monitoring end.

[0073] The present application has the beneficial effects that: the present application significantly improves the autonomy level of multiple hook lifting devices in tower cranes, tire cranes, and portal cranes through a distributed hook cooperative control mechanism; in the efficiency dimension, dynamic priority arbitration and local path planning eliminate the operation delay caused by traditional centralized scheduling, enabling seamless cooperation of multiple hooks in cross operations and reducing task interruptions; at the same time, real-time fuse response to sudden obstacles avoids the lag of manual intervention and maintains operation continuity; in terms of safety, motion envelope prediction combined with wind speed compensation enhances the robustness of the path to dynamic interference, maintaining millimeter-level obstacle avoidance accuracy in strong wind or sudden hook movement scenarios and significantly reducing the risk of collision; in terms of technical implementation, edge nodes integrate trajectory calculation and communication coordination, utilizing the 5G low-latency feature to ensure real-time decision-making, and static no-fly zone preloading further optimizes adaptability to complex environments; overall, the present application promotes the transition of hook control from single-machine automation to group intelligence, providing reliable technical support for high-risk scenarios such as ports and construction. BRIEF DESCRIPTION OF DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creating any inventive labor.

[0075] Figure 1 The flowchart of the hook remote intelligent control method based on the Internet of Things in Embodiment 1. DETAILED DESCRIPTION

[0076] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0077] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0078] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent or alternative to other embodiments.

[0079] Embodiment 1, refer to Figure 1 The embodiment provides a hook remote intelligent control method based on the Internet of Things, comprising the following steps:

[0080] Step S1, each hook controller broadcasts the target space coordinates of the next action cycle through 5G communication;

[0081] Step S2, compete for path access according to dynamic priority rules, which generate weight values based on real-time load weight, motion speed, and task urgency;

[0082] In the dynamic priority rules:

[0083] Task urgency is automatically classified according to the type of hoisted goods, and the weight of dangerous goods transportation task is increased to 3 times the reference value;

[0084] When the motion speed is higher than the threshold, the weight is linearly attenuated to avoid high-speed conflict;

[0085] Step S3, the hook controller that does not obtain access autonomously generates a smooth obstacle avoidance curve path;

[0086] Generating a smooth obstacle avoidance curve path includes:

[0087] Based on the broadcast coordinates of other hooks, the motion envelope region is predicted, which is a sector space with the target point as the top of the cone;

[0088] Generate a continuously derivable path containing at least two inflection points outside the envelope region, and the path curvature radius is greater than the minimum turning radius of the hook;

[0089] In step S3, the hook controller that does not obtain access generates a smooth obstacle avoidance curve path, including two sub-steps: predicting the motion envelope region of other hooks, and generating a continuously derivable obstacle avoidance path based on the region;

[0090] The step of predicting the motion envelope region of other hooks includes:

[0091] Based on the target space coordinates and the current motion state broadcast by other hooks, the motion envelope region is calculated as a sector space with the target point as the top of the cone; The specific logic is: input the broadcast coordinates and speed data of other hooks, calculate the envelope region parameters using the sector model formula, output the region boundary for obstacle avoidance, and the formula is:

[0092] ,

[0093] Where, represents the current speed vector of other hooks, with the unit of meter / second, represents the modulus of the speed vector, with the unit of meter / second, represents the direction vector of the sector center line, dimensionless;

[0094] Calculate the sector half-angle width:

[0095] ,

[0096] where, represents the maximum acceleration of the hook, in meters per second squared, represents the prediction time, in seconds, represents the inverse sine function, represents the safety factor, dimensionless, represents the sector half-angle width, in radians; this area covers the maximum possible displacement of other hooks within the prediction time, ensuring that dynamic motion uncertainty is included;

[0097] The steps for generating a smooth obstacle-avoiding curved path include:

[0098] Outside the motion envelope area, a continuous and derivable path is generated using a cubic Bezier curve, such that the path contains at least two inflection points and the radius of curvature satisfies the physical constraints of the hook;

[0099] The logical closed loop is: input the current hook position, velocity, and obstacle data, calculate the control point position, generate the curve equation, and verify the curvature constraint;

[0100] Calculate the control points of the Bezier curve:

[0101] ,

[0102] where, represents the starting position coordinates of the current hook, in meters, represents the distance from the current hook to the nearest obstacle (including the envelope area boundary of other hooks), in meters, represents the unit normal vector pointing from the starting point to the obstacle, dimensionless, represents the velocity vector of the current hook, in meters per second, represents the velocity compensation coefficient, in seconds;

[0103] Calculate the second control point simultaneously:

[0104] ,

[0105] where, represents the adjusted end point coordinates, in meters, obtained by offsetting the original target point outward by the minimum safety distance;

[0106] Velocity compensation coefficient where, represents the modulus of the current hook velocity vector, in meters per second;

[0107] Generate the Bezier curve path equation:

[0108] ,

[0109] wherein, represents coordinates of a point on the curve path, in meters, represents a parameterized variable, dimensionless, ranging from 0 to 1;

[0110] Verify the path curvature radius constraint:

[0111] ,

[0112] wherein, represents the path curvature radius, in meters, represents the first derivative of the curve, in meters per second, represents the second derivative of the curve, in meters per second squared, represents the vector cross product, represents the vector modulus, calculated , and verify so that the path satisfies the curvature constraint, wherein represents the minimum turning radius of the hook, in meters, which is an inherent parameter;

[0113] Specifically, this step effectively solves the real-time obstacle avoidance problem of hook path conflicts in the use scenarios of tower cranes, tire cranes, and gantry cranes by dynamically predicting the sector envelope region and generating a Bezier curve path; the prediction model covers the possible motion range of other hooks, reducing false positives caused by sudden position changes; the obstacle avoidance curve combines speed and acceleration constraints to ensure smooth and executable paths, avoiding the risk of loss of control caused by sharp turns; overall, it improves the efficiency and safety of hook autonomous collaboration, adapts to high-dynamic environments in ports or construction sites, and can handle unexpected obstacles without human intervention;

[0114] Step S4, all hooks synchronize to execute the action instruction;

[0115] Before synchronization, it also includes:

[0116] When an unexpected obstacle intrudes, the action period is compressed to 1 / 5 of the original period, and steps S1-S3 are retriggered;

[0117] When the renegotiation fails:

[0118] Force the hook to maintain a uniform linear motion in the current speed direction;

[0119] Send an emergency avoidance instruction containing the conflict coordinates to the remote monitoring end;

[0120] The hook controller is deployed on an edge computing node, which includes:

[0121] Trajectory prediction module: performs motion envelope region calculation in generating smooth obstacle avoidance curve paths;

[0122] Dynamic arbitration module: execute weight value generation in dynamic priority rule;

[0123] Communication coordination module: manage 5G broadcast and data reception in step S1;

[0124] The edge computing node accesses the meteorological sensor, and introduces the real-time wind speed direction as a compensation amount of a trajectory tangent angle when planning an obstacle avoidance curve.

[0125] In the edge computing node, the step of introducing the real-time wind speed direction compensation includes data acquisition, vector decomposition, path correction, and constraint verification, including:

[0126] Real-time wind speed data is read from the meteorological sensor, and the wind speed vector is decomposed into a tangent component parallel to the current hook movement direction and a normal component :

[0127] ,

[0128] wherein, represents the real-time wind speed vector, in meters per second, represents the current hook speed vector, in meters per second, represents a vector dot product operation, represents the wind speed tangent component, in meters per second, represents the wind speed normal component, in meters per second; the normal component directly affects the trajectory deviation and needs to be compensated;

[0129] Based on the normal wind speed component and the hook physical parameters, the compensation deviation of the trajectory tangent angle is calculated :

[0130] ,

[0131] wherein, represents the wind speed normal component module, in meters per second, represents the hook speed module, in meters per second, represents an inverse tangent function, represents a vector cross product, represents a sign function, outputting -1 or 1, represents a wind resistance coupling coefficient, dimensionless, represents the trajectory tangent angle compensation amount, in radians; the formula quantifies the wind speed influence as a path direction correction angle;

[0132] The compensation amount is embedded in the obstacle avoidance curve generation process, and the Bezier curve control point position is adjusted:

[0133] ,

[0134] ,

[0135] Rotation matrix is defined as:

[0136] ,

[0137] wherein, represents the original first control point coordinate, in meters, represents the original second control point coordinate, in meters, represents the hook starting position, in meters, represents the adjusted end position, in meters, represents a two-dimensional rotation matrix, dimensionless, represents the compensated first control point, in meters, represents the compensated second control point, in meters; control point rotation causes the overall path to deflect by an angle, offsetting the influence of wind pressure;

[0138] The final obstacle avoidance path is generated using the corrected control points, and the curvature constraint is verified:

[0139] ,

[0140] Curvature radius must satisfy:

[0141] ,

[0142] wherein, represents the compensated path coordinate, in meters, represents the curve parameter, dimensionless, represents the compensated path curvature radius, represents the minimum turning radius of the hook;

[0143] Specifically, this step converts the wind speed into a tangent angle offset through vector decomposition, dynamically correcting the obstacle avoidance path control points; the normal wind speed component drives the path direction compensation, and the rotation matrix ensures smooth adaptation of the trajectory to wind pressure, with the final path maintaining continuous and derivable characteristics and satisfying the physical constraints of the hook; in strong wind scenarios at the port, positioning deviations caused by crosswinds can be reduced, improving operation accuracy in complex environments;

[0144] Before step S1, it also includes:

[0145] Preload the three-dimensional digital map of the construction site, and convert the static obstacle coordinates into a set of no-fly zone coordinates for local path planning by each controller.

[0146] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and all modifications and equivalents should be included in the scope of the claims of the present application.

Claims

1. A method for remotely intelligent control of a hook based on the Internet of Things, characterized in that, Comprising, Step S1, each hook controller broadcasts the target space coordinates of the next action cycle through 5G communication; Step S2, compete for path access according to dynamic priority rules, which generate weight values based on real-time load weight, movement speed and task urgency; In the dynamic priority rules: Task urgency is automatically classified according to the type of hoisted goods, and the weight of dangerous goods transportation task is increased to 3 times the reference value; When the movement speed is higher than the threshold, the weight is linearly attenuated to avoid high-speed conflict; Step S3, the hook controller that fails to obtain access autonomously generates a smooth obstacle avoidance curve path; The smooth obstacle avoidance curve path includes: Predict the movement envelope area of other hooks based on the broadcast coordinates, which is a sector space with the target point as the cone top; Generate a continuously derivable path with at least two inflection points outside the envelope area, and the path curvature radius is greater than the minimum turning radius of the hook; In step S3, the hook controller that fails to obtain access generates a smooth obstacle avoidance curve path, including two sub-steps: predicting the movement envelope area of other hooks, and generating a continuously derivable obstacle avoidance path based on the area; The step of predicting the movement envelope area of other hooks includes: Based on the target space coordinates and the current movement state broadcast by other hooks, calculate the movement envelope area as a sector space with the target point as the cone top; The specific logic is: input the broadcast coordinates and speed data of other hooks, calculate the envelope area parameters using the sector model formula, output the area boundary for obstacle avoidance, and the formula is: ， wherein, Vcurrentrepresents the current speed vector of the other hook in meters / second, |V|represents the modulus of the speed vector in meters / second, Vcenterrepresents the fan center line directional vector, dimensionless; Calculate the sector half-angle width: ， wherein, denotes the maximum acceleration of the hook in meters per second², denotes the prediction time in seconds, denotes the inverse sine function, denotes the safety factor, dimensionless, denotes the half-angle width of the sector in radians; The step of generating a smooth obstacle avoidance curve path includes: Outside the movement envelope area, use a cubic Bezier curve to generate a continuously derivable path; The logic closed loop is: input the current hook position, speed and obstacle data, calculate the control point position, generate the curve equation, and verify the curvature constraint; Calculate the control points of the Bezier curve: ， wherein, represents the starting position coordinate of the current hook in meters, represents the distance from the current hook to the nearest obstacle in meters, represents the unit normal vector from the origin to the obstacle, dimensionless, represents the velocity vector of the current hook in meters per second, represents the velocity compensation coefficient in seconds; Calculate the second control point: ， wherein, represents the adjusted end point coordinate in meters, obtained by offsetting the original target point outward by the minimum safety distance; Speed compensation coefficient wherein, denotes the modulus of the current hook speed vector in meters per second; Generate the Bezier curve path equation: ， wherein, represents the coordinates of a point on the curve path in meters, represents the parameterization variable, dimensionless, ranging from 0 to 1; Verify the path curvature radius constraint: ， wherein, represents the path curvature radius in meters, represents the first derivative of the curve in meters per second, represents the second derivative of the curve in meters per second squared, represents the vector cross product, represents the vector modulus, calculated as and verified that the path satisfies the curvature constraint, wherein represents the minimum turning radius of the hook in meters, which is an intrinsic parameter; Step S4, all hooks synchronize to execute the action instruction.

2. The method for remote intelligent control of a lifting hook based on the Internet of Things according to claim 1, characterized in that, Before synchronization, it also includes: When a sudden obstacle intrudes, compress the action cycle to 1 / 5 of the original cycle, and retrigger steps S1-S3.

3. The method of claim 1, wherein the method is characterized by: The hook controller is deployed on an edge computing node, which includes: Trajectory prediction module: performs the movement envelope area calculation in the smooth obstacle avoidance curve path generation; Dynamic arbitration module: performs weight value generation in the dynamic priority rules; Communication coordination module: manages 5G broadcast and data reception in step S1.

4. The method for remote intelligent control of a lifting hook based on the Internet of Things according to claim 3, characterized in that, The edge computing node accesses meteorological sensors, and introduces real-time wind direction as a compensation amount for the trajectory tangent angle when planning the obstacle avoidance curve.

5. The method for remote intelligent control of a lifting hook based on the Internet of Things according to claim 4, characterized in that, In the edge computing node, the step of introducing real-time wind direction compensation includes data acquisition, vector decomposition, path correction and constraint verification, including: reading real-time wind speed data from a weather sensor, decomposing the wind speed vector into a tangential component parallel to the current hook movement direction and a normal component perpendicular to it : ， wherein, represents the real-time wind speed vector in meters / second, represents the current hook speed vector in meters / second, represents the vector dot product operation, represents the wind speed tangential component in meters / second, represents the wind speed normal component in meters / second; Based on the normal wind speed component and the physical parameters of the hook, a compensation offset of the trajectory tangent angle is calculated : ， wherein, denotes the wind speed normal component module, in meters per second, denotes the hook speed module, in meters per second, denotes the arctangent function, denotes the vector cross product, denotes the sign function, outputting -1 or 1, denotes the wind resistance coupling coefficient, dimensionless, denotes the trajectory tangent angle compensation quantity, in radians; Compensate the amount The embedded obstacle avoidance curve generation process adjusts the Bezier curve control point position: ， ， Rotation matrix is defined as: ， wherein, represents the original first control point coordinate, in meters, represents the original second control point coordinate, in meters, represents the hook starting position, in meters, represents the adjusted end position, in meters, represents the two-dimensional rotation matrix, dimensionless, represents the compensated first control point, in meters, represents the compensated second control point, in meters; Use the corrected control points to generate the final obstacle avoidance path and verify the curvature constraint: ， Radius of curvature Must be satisfied: ， wherein, represents the compensated path coordinate in meters, represents the curve parameter, dimensionless, represents the compensated path radius of curvature, represents the minimum turning radius of the hook.

6. The method for remote intelligent control of a lifting hook based on the Internet of Things according to claim 1, characterized in that, Before step S1, it also includes: Preload the three-dimensional digital map of the construction site, and convert the static obstacle coordinates into a set of no-fly zone coordinates for each controller's local path planning.

7. The method of claim 2, wherein the method further comprises: When retriggering fails: Force the hook to maintain a uniform linear motion in the current speed direction; Sending an emergency avoidance instruction containing the conflict coordinates to the remote monitoring end.

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