Cable coil rapid transfer monitoring method and system based on industrial Internet of Things

Abstract

The invention belongs to the technical field of logistics monitoring, and discloses a cable coil rapid transfer monitoring method and system based on the industrial Internet of Things, and the method comprises the steps: obtaining real-time data, building a space model, predicting a vehicle track, calculating space overlapping, and recognizing potential conflicts. A cooperative control instruction is generated according to the conflict type, the task priority, the cable coil specification and the regional traffic condition, an instruction is issued to adjust the vehicle motion state, and potential conflicts are avoided; potential conflicts can be recognized prospectively, a cooperative control instruction is generated based on multiple factors, conflicts are avoided by adjusting the vehicle motion state, vehicle pauses are reduced, the continuity of the transfer process is guaranteed, the transfer speed is increased, and the logistics efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of logistics monitoring technology, and more specifically, to a cable roll rapid transportation monitoring method and system based on the Industrial Internet of Things. Background Art

[0002] In modern large-scale cable manufacturing bases, the transfer of cable reels is an important component connecting key links such as production, warehousing, and delivery. The base usually has a large warehouse area for storing a large number of cable reels of different specifications and different completion states. In order to improve the efficiency and automation level of transfer, the base widely uses automated transfer vehicles, such as automatic guided vehicles (AGVs) or forklifts equipped with automated navigation systems, which are responsible for quickly and accurately transferring cable reels from the end of the production line or temporary buffer zone to the designated storage location or shipping area. Transfer tasks are usually generated, optimized, and assigned to idle transfer vehicles by the central scheduling system based on real-time production plans, inventory status, and delivery needs.

[0003] In a multi-roll coordinated transport scenario, a large project may require the simultaneous transport of dozens or even hundreds of cable rolls to a designated area for assembly or distribution. A central dispatch system assigns these tasks to multiple transport vehicles for parallel execution. These vehicles must perform intensive pickup, driving, and unloading operations within the same area. The Industrial Internet of Things (IIoT) system must simultaneously track all participating vehicles, the cable rolls they carry, and the environmental conditions. Potential conflicts in this scenario extend beyond simple vehicle collisions to encompass a variety of complex interactions: for example, a vehicle blocking the path of other vehicles while waiting to enter the loading and unloading area; multiple vehicles competing to enter the same narrow passageway or intersection; vehicles carrying extra-long or extra-wide cable rolls, with their rear or side safety envelopes interfering with nearby stationary cable rolls, equipment, or structures; and human activity within the work area overlapping with the movement of transport vehicles, creating the risk of human-machine conflicts.

[0004] Existing transshipment monitoring methods have significant limitations when dealing with these complex, dynamic, and multi-volume conflicts. They are often only able to identify a single type of conflict (such as two vehicles being too close together), lacking comprehensive consideration and forward-looking analysis of multiple factors, such as multiple vehicles, multi-volume coordination, and dynamic environmental changes. Therefore, there is an urgent need for a method that can quickly and accurately identify multi-volume coordinated transshipment conflicts in complex scenarios based on real-time data from the Industrial Internet of Things (IIoT) and generate effective collaborative control strategies to ensure the continuity and efficiency of the transshipment process.

[0005] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] The purpose of this application is to provide a cable roll rapid transfer monitoring method and system based on the industrial Internet of Things, which can proactively identify potential conflicts, generate collaborative control instructions based on multiple factors, avoid conflicts by adjusting the vehicle's motion state, reduce vehicle pauses, ensure the continuity of the transfer process, increase the transfer speed, and improve logistics efficiency.

[0007] In a first aspect, the present application provides a method for monitoring the rapid transport of cable rolls based on the Industrial Internet of Things. The method monitors the coordinated transport of cable rolls by multiple automated transport vehicles based on the Industrial Internet of Things system. The method comprises the following steps:

[0008] A1. Obtain the real-time position, speed, acceleration, planned path, and load status of each automated transfer vehicle within the transfer area, along with cable reel specifications and the location of environmental obstacles, to create a real-time data view of the transfer environment.

[0009] A2. Based on the real-time data view, a joint space occupancy model is developed for each automated transfer vehicle and its cable rolls, as well as an obstacle space occupancy model for each environmental obstacle.

[0010] A3. Predict the spatial trajectory sequence of each automated transfer vehicle within a future time window based on its real-time position, speed, acceleration, planned path, load status, and the combined spatial occupancy model.

[0011] A4. Calculate the spatial overlap between different spatial occupancy models within the future time window and identify potential conflicts;

[0012] A5. When a potential conflict is identified, a coordinated control instruction is generated based on the conflict type, the mission priorities of the vehicles involved, the cable reel specifications, and regional traffic conditions. The coordinated control instruction includes at least one of a fine-tuning of the route, a speed adjustment, and a timing adjustment.

[0013] A6. Send the generated collaborative control instructions to the relevant automated transfer vehicles to adjust the vehicle's motion status and avoid potential conflicts.

[0014] Preferably, step A1 includes:

[0015] A101. Multiple wireless sensor nodes deployed within the transfer area collect real-time position coordinates, velocity vectors, and acceleration vectors of each automated transfer vehicle, as well as load weight data fed back from the vehicle load sensors on each automated transfer vehicle.

[0016] A102 extracts the cable roll ID from each automated transfer vehicle's task list to obtain the corresponding specification information from the cable roll specification database;

[0017] A103. Use environmental sensors to obtain the three-dimensional coordinate information of static and dynamic obstacles within the transfer area;

[0018] A104. Clean and time-synchronize the acquired data, encapsulate it in JSON format, and construct a real-time data view of the transit environment.

[0019] Preferably, step A2 includes:

[0020] A201. According to the model of each automated transfer vehicle, retrieve the corresponding vehicle 3D model from the 3D model library;

[0021] A202. According to the cable roll ID in the task list of each automated transport vehicle, the corresponding cable roll 3D model is retrieved from the 3D model library;

[0022] A203. Determine the position of the cable reel relative to the vehicle coordinate system based on the load status of each automated transfer vehicle;

[0023] A204. Based on the position of the cable reel relative to the vehicle coordinate system, rigidly combine the retrieved cable reel 3D model and the vehicle 3D model to construct a joint 3D model as the joint space occupancy model;

[0024] A205. For environmental obstacles, based on the obstacle type, retrieve the corresponding typical 3D spatial envelope from the 3D model library as the corresponding obstacle space occupancy model;

[0025] A206. Based on the real-time data view, place the obtained space occupancy models in corresponding positions.

[0026] Preferably, step A3 includes:

[0027] A301. According to the planned path of each automated transfer vehicle, extract the key path point sequence on the path, based on the real-time position of the automated transfer vehicle, determine the current target path point of the automated transfer vehicle in the key path point sequence;

[0028] A302. Based on the speed, acceleration, and load status of each automated transfer vehicle, use the vehicle dynamics model to predict the vehicle's velocity change sequence within consecutive time steps. Based on the velocity change sequence, calculate the vehicle's displacement increment within each time step.

[0029] A303. Based on the current target path points and displacement increments of each automated transfer vehicle, predict the position coordinates of the automated transfer vehicle at each time step. Combined with the joint space occupancy model, the space occupancy sequence of the vehicle in the future time window is obtained.

[0030] A304. The predicted spatial occupancy sequence is simplified into a circumscribed sphere. The center position of the circumscribed sphere is adjusted according to the movement direction of the automated transfer vehicle to obtain the spatial trajectory sequence of the automated transfer vehicle in the future time window.

[0031] Preferably, step A302 includes:

[0032] For the vehicle dynamics model, the vehicle's speed, acceleration, load state, and center of gravity are considered to establish a six-degree-of-freedom vehicle dynamics equation set that includes the vehicle's longitudinal, lateral, and vertical motions.

[0033] The six-degree-of-freedom vehicle dynamics equations are numerically solved using the Runge-Kutta algorithm to obtain a velocity variation sequence of the automated transfer vehicle in consecutive time steps;

[0034] According to the speed change sequence of the automated transfer vehicle in each time step and combined with the real-time attitude angle of the automated transfer vehicle, the displacement increment of the automated transfer vehicle in each time step is calculated; among them, the calculation of the displacement increment takes into account the influence of the vehicle's roll angle and pitch angle on the displacement direction.

[0035] Preferably, step A304 includes:

[0036] Extracting the circumscribed sphere parameters of each joint space occupancy model in the predicted space occupancy sequence; the circumscribed sphere parameters include the coordinates of the sphere center and the radius;

[0037] According to the vehicle motion direction vector, the product of the vector and the adjustment factor is calculated to obtain the sphere center offset;

[0038] The center offset is added to the coordinates of the center of the corresponding circumscribed sphere, and the radius of the circumscribed sphere is constrained to remain unchanged to obtain the adjusted spatial trajectory sequence.

[0039] Preferably, step A4 includes:

[0040] A401. Identify the types of environmental obstacles within the transfer area and determine the static obstacle set and dynamic obstacle set;

[0041] A402. For the static obstacle set, calculate the spatial overlap between the spatial trajectory sequence of each automated transfer vehicle in the future time window and the static obstacle spatial occupancy model. If overlap exists, determine it as a static obstacle interference risk.

[0042] A403. For a set of dynamic obstacles, using the Kalman filter algorithm, based on the historical position and velocity information of the dynamic obstacles, the motion trajectory of each dynamic obstacle within a future time window is predicted. The minimum distance between the spatial trajectory sequence of each automated transfer vehicle within the future time window and the motion trajectory of the dynamic obstacle is calculated. If the minimum distance is less than a preset safety threshold, a dynamic obstacle collision risk is determined.

[0043] A404. For each automated transfer vehicle, calculate the spatial overlap between its spatial trajectory sequence and the spatial occupancy models of other vehicles. If overlap exists, it is determined to be a vehicle-to-vehicle collision risk.

[0044] Preferably, step A5 includes:

[0045] A501. Real-time collection of vehicle density at each channel within the transfer area and the number of vehicles waiting at intersections to assess traffic congestion levels.

[0046] A502. Determine the priority of tasks involving vehicles based on the generation time and task type of the task order involving the vehicle;

[0047] A503. Based on the traffic congestion level, combined with the conflict type, the task priority of the vehicles involved, and the cable roll specifications, the preset collaborative control strategy library is searched to obtain the initial collaborative control instruction set;

[0048] A504. For each instruction in the initial coordinated control instruction set, evaluate its potential impact on the involved vehicle and surrounding vehicles; potential impacts include the degree of path deviation, speed change, and expected delay time;

[0049] A505. Select the instruction with the least impact on the overall transfer efficiency as the final collaborative control instruction.

[0050] Preferably, step A503 includes:

[0051] Constructing a multidimensional query vector containing conflict type, task priority of involved vehicles, cable roll specifications, and traffic congestion level;

[0052] Calculate the similarity between the multidimensional query vector and the attribute labels of each strategy in the collaborative control strategy library; each strategy in the collaborative control strategy library is pre-labeled with multidimensional attribute labels, including conflict type, task priority range, cable roll size range, and traffic congestion level range;

[0053] Select multiple strategies with the highest similarity as the initial collaborative control instruction set.

[0054] In a second aspect, the present application provides a cable roll rapid transport monitoring system based on the Industrial Internet of Things, which monitors the collaborative transport of cable rolls by multiple automated transport vehicles based on the Industrial Internet of Things system. The system includes:

[0055] The data acquisition module is used to obtain the real-time position, speed, acceleration, planned path and load status of each automated transfer vehicle in the transfer area, the specifications of each cable reel, and the location information of each environmental obstacle, and build a real-time data view of the transfer environment;

[0056] A spatial modeling module that creates a joint spatial occupancy model for each automated transfer vehicle and its cable reels, and an obstacle spatial occupancy model for each environmental obstacle based on real-time data views;

[0057] The trajectory prediction module is used to predict the spatial trajectory sequence of each automated transfer vehicle in the future time window based on the real-time position, speed, acceleration, planned path, load status and joint space occupancy model of each automated transfer vehicle;

[0058] a conflict detection module, configured to calculate spatial overlap between different spatial occupancy models within the future time window and identify potential conflicts;

[0059] a strategy generation module for generating, when a potential conflict is identified, a coordinated control instruction based on the conflict type, the mission priorities of the vehicles involved, the cable roll specifications, and the regional traffic conditions; the coordinated control instruction including at least one of a path fine-tuning, a speed adjustment, and a timing adjustment;

[0060] The instruction issuing module is used to issue the generated collaborative control instructions to relevant automated transfer vehicles, adjust the vehicle movement status, and avoid potential conflicts.

[0061] Beneficial effects: The present application provides a cable roll rapid transfer monitoring method and system based on the industrial Internet of Things, which identifies potential conflicts by acquiring real-time data, constructing spatial models, predicting vehicle trajectories, and calculating spatial overlaps, and generates collaborative control instructions based on the conflict type, task priority, cable roll specifications, and regional traffic conditions, and issues instructions to adjust the vehicle motion state to avoid potential conflicts; it can proactively identify potential conflicts, generate collaborative control instructions based on multiple factors, avoid conflicts by adjusting the vehicle motion state, reduce vehicle pauses, ensure the continuity of the transfer process, increase the transfer speed, and improve logistics efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of a method for monitoring the rapid transfer of cable rolls based on the Industrial Internet of Things provided in an embodiment of the present application.

[0063] Figure 2A schematic diagram of the structure of a cable roll rapid transfer monitoring system based on the Industrial Internet of Things provided in an embodiment of the present application.

[0064] Explanation of numbers: 1. Data acquisition module; 2. Spatial modeling module; 3. Trajectory prediction module; 4. Conflict detection module; 5. Strategy generation module; 6. Instruction issuance module. DETAILED DESCRIPTION

[0065] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0066] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0067] Application Scenario: To achieve refined management and real-time monitoring of the entire transfer process, Industrial Internet of Things (IIoT) systems are being widely deployed. This system utilizes wireless sensor networks distributed throughout the transfer area, high-precision positioning modules (such as those based on UWB or laser SLAM) installed on vehicles, environmental perception sensors (such as obstacle avoidance radar and visual sensors), and identification readers (such as RFID readers or QR code scanners) for cable reels. These systems collect massive amounts of operational data in real time and comprehensively. This data includes, but is not limited to, critical information such as each transfer vehicle's precise three-dimensional position, instantaneous speed, acceleration, current load status, unique cable reel identifiers, and their target locations. Furthermore, environmental sensors provide dynamic information within the area, such as the locations of temporary obstacles and areas of human activity. All collected data is aggregated into an IIoT platform for preliminary data cleaning, processing, and storage, creating a real-time data view of the transfer environment. Based on this real-time data, along with production plans, inventory status, and delivery orders, a central dispatch system generates a series of specific transfer tasks. Each task specifies the cable reel to be moved, its starting location, and its target location. The system assigns these tasks to appropriate transfer vehicles and plans a path for each vehicle from its current location to the mission's starting location and then to the destination. Ideally, these paths are pre-calculated and designed to avoid known static obstacles. However, in complex scenarios where multiple automated transfer vehicles are performing tasks simultaneously, especially in high-traffic areas such as main corridors, intersections, and loading and unloading areas within a warehouse, the vehicles' planned paths may potentially conflict in time or space.

[0068] In a multi-roll coordinated transport scenario, a large project may require the simultaneous transport of dozens or even hundreds of cable rolls to a designated area for assembly or distribution. A central dispatch system assigns these tasks to multiple transport vehicles for parallel execution. These vehicles must perform intensive pickup, driving, and unloading operations within the same area. Industrial IoT systems must simultaneously track all participating vehicles, the cable rolls they carry, and the environmental conditions. Potential conflicts in this scenario extend beyond simple collisions between two vehicles to encompass a variety of complex interactions: for example, a vehicle blocking the path of other vehicles while waiting to enter the loading and unloading area; multiple vehicles competing for access to the same narrow passageway or intersection; vehicles carrying extra-long or extra-wide cable rolls, with their rear or side safety envelopes interfering with nearby stationary cable rolls, equipment, or structures; and human activity within the work area overlapping with the transport vehicle's movement zone, creating the risk of human-machine conflicts. Existing transport monitoring methods have significant limitations in handling these complex, dynamic, and multiple conflicts. They are often only able to identify a single type of conflict (such as two vehicles being too close together), lacking comprehensive consideration and forward-looking analysis of multiple factors, including multiple vehicles, multi-volume coordination, and dynamic environmental changes. To address these issues, existing technologies urgently need to be improved.

[0069] refer to Figure 1 This application proposes a method for monitoring the rapid transfer of cable rolls based on the Industrial Internet of Things. The method monitors the collaborative transfer of cable rolls by multiple automated transfer vehicles based on the Industrial Internet of Things system. The steps of the method include:

[0070] A1. Obtain the real-time position, speed, acceleration, planned path, and load status of each automated transfer vehicle within the transfer area, along with cable reel specifications and the location of environmental obstacles, to create a real-time data view of the transfer environment.

[0071] A2. Based on the real-time data view, a joint space occupancy model is developed for each automated transfer vehicle and its cable rolls, as well as an obstacle space occupancy model for each environmental obstacle.

[0072] A3. Predict the spatial trajectory sequence of each automated transfer vehicle within a future time window based on its real-time position, speed, acceleration, planned path, load status, and the combined spatial occupancy model.

[0073] A4. Calculate the spatial overlap between different spatial occupancy models within the future time window and identify potential conflicts;

[0074] A5. When a potential conflict is identified, a coordinated control instruction is generated based on the conflict type, the mission priorities of the vehicles involved, the cable reel specifications, and regional traffic conditions. The coordinated control instruction includes at least one of a fine-tuning of the route, a speed adjustment, and a timing adjustment.

[0075] A6. Send the generated collaborative control instructions to the relevant automated transfer vehicles to adjust the vehicle's motion status and avoid potential conflicts.

[0076] In step A1, real-time data is acquired and a real-time data view of the transshipment environment is established. This provides a comprehensive picture of the current transshipment status and serves as the basis for subsequent analysis.

[0077] In step A2, based on the real-time data view, spatial occupancy models are created for the vehicle, its payload, and environmental obstacles. These models transform physical entities into spatial representations and are used to detect spatial interference. A joint model of the vehicle and payload is created, accounting for the impact of cable reels on the actual space occupied by the vehicle.

[0078] In step A3, the vehicle's spatial trajectory over the next period of time is predicted based on the vehicle's current state and spatial model. Predicting the vehicle's future location and the space it will occupy enables the system to proactively identify problems.

[0079] In step A4, the spatial overlap between different space occupancy models within the predicted future time window is calculated. By calculating the spatial overlap, the system identifies potential conflicts between vehicles or between vehicles and obstacles.

[0080] In step A5, when a potential conflict is identified, coordinated control instructions are generated based on the specific circumstances of the conflict, the mission importance of the vehicles involved, the size of the cable roll, and traffic congestion in the area. These instructions may adjust the vehicle's path, speed, or timing. This step generates a refined control strategy based on multiple factors, avoiding simple stops and improving efficiency.

[0081] In step A6, the generated coordinated control instructions are sent to the relevant vehicles. Upon receiving the instructions, the vehicles adjust their motion to avoid the predicted conflict. This step translates the system's decisions into actual vehicle behavior, achieving conflict avoidance.

[0082] This method uses the real-time data and computing power provided by the industrial Internet of Things system to achieve refined monitoring and intelligent control of complex multi-vehicle collaborative transportation scenarios, improving the safety, efficiency and reliability of cable roll transportation.

[0083] In some embodiments, step A1 comprises:

[0084] A101. Multiple wireless sensor nodes deployed within the transfer area collect real-time position coordinates, velocity vectors, and acceleration vectors of each automated transfer vehicle, as well as load weight data fed back from the vehicle load sensors on each automated transfer vehicle.

[0085] A102 extracts the cable roll ID from each automated transfer vehicle's task list to obtain the corresponding specification information from the cable roll specification database;

[0086] A103. Use environmental sensors to obtain the three-dimensional coordinate information of static and dynamic obstacles within the transfer area;

[0087] A104. Clean and time-synchronize the acquired data, encapsulate it in JSON format, and construct a real-time data view of the transit environment.

[0088] Specifically, this solution addresses the problem of collecting data from various sources and performing data cleansing, time synchronization, and format encapsulation to construct a data view that supports subsequent steps. First, a wireless sensor network deployed within the transfer area, such as ultra-wideband (UWB)-based positioning nodes, acquires the precise location coordinates of the automated transfer vehicles in real time. Simultaneously, the vehicle's own sensors, such as the inertial measurement unit (IMU) and wheel speed sensors, provide the vehicle's velocity and acceleration vectors, which are uploaded via the vehicle communication bus (e.g., CAN bus) or wirelessly. Load sensors on the vehicle measure the weight of the cable roll currently being carried and transmit this data to the monitoring system. Second, the system retrieves the task orders assigned to each vehicle from the central dispatch system and extracts the unique cable roll identifier from it. Using this identifier, the system queries the cable roll specification database to obtain specifications such as the cable roll's diameter, width, and weight. Third, environmental sensors deployed within the area, such as fixed or mobile LiDAR scanners, acquire the three-dimensional positional information of static obstacles such as walls and pillars, as well as dynamic obstacles such as people and temporary storage. Finally, the collected data, including various types of data such as position, velocity, acceleration, load, cable reel specifications, and obstacle locations, is aggregated into a data processing unit. Within this unit, data cleaning algorithms, such as statistically based outlier detection and removal, are executed. Next, time synchronization processing is performed, such as interpolation or alignment based on the data's timestamp. The processed data is organized into unified JSON-formatted data packets, each containing status information for all relevant objects at a specific point in time. This constructs a real-time data view of the transport environment. This structured, synchronized data view provides a reliable data foundation for subsequent spatial modeling, trajectory prediction, and conflict detection, improving the effectiveness of the monitoring method.

[0089] In some embodiments, step A2 comprises:

[0090] A201. According to the model of each automated transfer vehicle, retrieve the corresponding vehicle 3D model from the 3D model library;

[0091] A202. According to the cable roll ID in the task list of each automated transport vehicle, the corresponding cable roll 3D model is retrieved from the 3D model library;

[0092] A203. Determine the position of the cable reel relative to the vehicle coordinate system based on the load status of each automated transfer vehicle;

[0093] A204. Based on the position of the cable reel relative to the vehicle coordinate system, rigidly combine the retrieved cable reel 3D model and the vehicle 3D model to construct a joint 3D model as the joint space occupancy model;

[0094] A205. For environmental obstacles, based on the obstacle type, retrieve the corresponding typical 3D spatial envelope from the 3D model library as the corresponding obstacle space occupancy model;

[0095] A206. Based on the real-time data view, place the obtained space occupancy models in corresponding positions.

[0096] Specifically, this method addresses the issue of spatial representation of the vehicle and payload, as well as that of environmental obstacles, impacting subsequent trajectory prediction and collision detection. When handling cable reels of varying sizes, considering vehicle load states, and handling environmental obstacles, the spatial representation must reflect physical entities; otherwise, collision identification will be affected. First, based on the vehicle model and cable reel ID obtained from the real-time data view, the corresponding 3D geometric models of the vehicle and cable reel are retrieved from a pre-established 3D model library. These models provide accurate shape information for the vehicle and cable reel. Next, based on the vehicle load state obtained from the real-time data view, such as the cable reel's placement and orientation on the vehicle platform, the translation and rotation of the cable reel relative to the vehicle's coordinate system are calculated, thereby determining its pose. Using this pose information, the 3D cable reel model is transformed and combined in 3D space relative to the vehicle's 3D model, forming a single, combined 3D model representing the overall shape of the vehicle and its loaded cable reel. This combined 3D model, known as the combined spatial occupancy model, accurately reflects the actual spatial extent occupied by the vehicle when carrying its payload. At the same time, for environmental obstacles identified in the real-time data view, a predefined typical 3D spatial envelope representing the spatial extent of that obstacle is retrieved from the 3D model library based on its type. These volumes, such as cubes, cylinders, gantry structures, or more complex mesh models, serve as the obstacle space occupancy model. Finally, using the global position and posture information of each vehicle and obstacle acquired from the real-time data view, the established joint space occupancy model and the obstacle space occupancy model are placed at corresponding locations in the global coordinate system of the transfer environment. This creates a real-time 3D scene that reflects the physical space occupancy of the current transfer environment, providing an accurate spatial foundation for subsequent trajectory prediction and spatial conflict detection. Compared to real-time scanning or modeling, retrieving and combining pre-existing models improves the efficiency of spatial model construction.

[0097] In some embodiments, step A3 comprises:

[0098] A301. According to the planned path of each automated transfer vehicle, extract the key path point sequence on the path, based on the real-time position of the automated transfer vehicle, determine the current target path point of the automated transfer vehicle in the key path point sequence;

[0099] A302. Based on the speed, acceleration, and load status of each automated transfer vehicle, use the vehicle dynamics model to predict the vehicle's velocity change sequence within consecutive time steps. Based on the velocity change sequence, calculate the vehicle's displacement increment within each time step.

[0100] A303. Based on the current target path points and displacement increments of each automated transfer vehicle, predict the position coordinates of the automated transfer vehicle at each time step. Combined with the joint space occupancy model, the space occupancy sequence of the vehicle in the future time window is obtained.

[0101] A304. The predicted spatial occupancy sequence is simplified into a circumscribed sphere. The center position of the circumscribed sphere is adjusted according to the movement direction of the automated transfer vehicle to obtain the spatial trajectory sequence of the automated transfer vehicle in the future time window.

[0102] Specifically, to address the potential inaccuracy of trajectory predictions based solely on the vehicle's real-time position, speed, acceleration, and planned path, this solution utilizes a vehicle dynamics model and considers load status to predict vehicle motion. First, key path points (such as the starting point, end point, path turning points, loading and unloading points, and speed limits) are identified from the vehicle's planned path. The vehicle's current target path point (e.g., the nearest key path point in front of the vehicle) is determined based on the vehicle's real-time position. This anchors the prediction process to the vehicle's planned path. Next, using a vehicle dynamics model that incorporates information about vehicle speed, acceleration, and load status, the vehicle's velocity change and resulting displacement increments are calculated over successive future time steps. The vehicle dynamics model reflects the vehicle's actual motion characteristics, such as the impact of load on acceleration, braking, and steering performance, improving the accuracy of displacement predictions. Based on the current target path point and predicted displacement increments, the vehicle's position coordinates at each future time point are inferred. Combining these predicted positions with a joint spatial occupancy model of the vehicle and its load yields a time-varying sequence of the vehicle's spatial occupancy range within the future time window. Finally, to simplify subsequent collision detection calculations, the spatial occupancy range at each time point is approximated by a circumscribed sphere. The center position of the circumscribed sphere is adjusted according to the vehicle's direction of motion to generate a sequence of future spatial trajectories for the vehicle. Through these steps, this solution can generate future spatial trajectory predictions that better reflect the vehicle's actual motion, providing more reliable input data for subsequent collision detection and improving the accuracy of collision identification.

[0103] Preferably, step A302 may include:

[0104] For the vehicle dynamics model, the vehicle's speed, acceleration, load state, and center of gravity are considered to establish a six-degree-of-freedom vehicle dynamics equation set that includes the vehicle's longitudinal, lateral, and vertical motions.

[0105] The six-degree-of-freedom vehicle dynamics equations are numerically solved using the Runge-Kutta algorithm to obtain a velocity variation sequence of the automated transfer vehicle in consecutive time steps;

[0106] According to the speed change sequence of the automated transfer vehicle in each time step and combined with the real-time attitude angle of the automated transfer vehicle, the displacement increment of the automated transfer vehicle in each time step is calculated; among them, the calculation of the displacement increment takes into account the influence of the vehicle's roll angle and pitch angle on the displacement direction.

[0107] The vehicle dynamics model considers the vehicle's speed, acceleration, load state, and center of gravity position, establishing a six-degree-of-freedom vehicle dynamics equation system encompassing the vehicle's longitudinal, lateral, and vertical motion. This system of equations describes the vehicle's translational and rotational motion in three-dimensional space, including linear velocities along the three coordinate axes and angular velocities about the three coordinate axes. The load state and center of gravity position directly affect the vehicle's mass distribution and inertial characteristics. These parameters are incorporated into the equation system to reflect the vehicle's dynamic response under varying load conditions. This enables the dynamics model to more accurately simulate the relationship between forces and motion during acceleration, deceleration, cornering, or hill driving.

[0108] Furthermore, the Runge-Kutta algorithm is used to numerically solve the six-degree-of-freedom vehicle dynamics equations. Vehicle dynamics equations are typically nonlinear differential equations, making analytical solutions difficult to obtain. The Runge-Kutta algorithm is a high-precision numerical integration method that effectively calculates the change in vehicle state variables (such as speed and angular velocity) over consecutive time steps by performing multiple function evaluations within each time step, thereby obtaining a sequence of vehicle speed changes (the specific solution method is prior art and will not be described in detail here). This numerical solution method provides more accurate state predictions than simplified models.

[0109] Specifically, the displacement increment of the automated transfer vehicle within each time step is calculated based on the velocity change sequence of the automated transfer vehicle within each time step, combined with the automated transfer vehicle's real-time attitude angle. The vehicle dynamics model typically yields the vehicle's velocity or velocity change in its own coordinate system. To obtain the vehicle's displacement increment in the global coordinate system, a coordinate transformation is required. The real-time attitude angles, including roll and pitch angles, provide information about the rotation of the vehicle's own coordinate system relative to the global coordinate system. When calculating the displacement increment, the velocity or displacement increment in the vehicle's own coordinate system is transformed to the global coordinate system using a rotation matrix that accounts for roll and pitch angles. This calculated displacement increment reflects the vehicle's actual motion in three-dimensional space, avoiding prediction errors caused by attitude changes and improving the accuracy of displacement increment calculation. This technical approach overcomes the problem of simplified dynamics models that fail to fully account for the complex effects of load state, vehicle attitude, and multi-directional motion. It improves the prediction accuracy of velocity change sequences and displacement increments, thereby enhancing the accuracy of subsequent spatial trajectory prediction and collision detection.

[0110] Preferably, step A304 may include:

[0111] Extracting the circumscribed sphere parameters of each joint space occupancy model in the predicted space occupancy sequence; the circumscribed sphere parameters include the coordinates of the sphere center and the radius;

[0112] According to the vehicle motion direction vector, the product of the vector and the adjustment factor is calculated to obtain the sphere center offset;

[0113] The center offset is added to the coordinates of the center of the corresponding circumscribed sphere, and the radius of the circumscribed sphere is constrained to remain unchanged to obtain the adjusted spatial trajectory sequence.

[0114] Specifically, this technical solution aims to address the problem that simplifying a complex joint space occupancy model of a vehicle and its payload into a simple circumscribed sphere may not accurately reflect the dynamic spatial extent of the vehicle's actual movement. First, from the predicted space occupancy sequence of the vehicle within a future time window, the center coordinates and radius of the circumscribed sphere corresponding to the joint space occupancy model are calculated and extracted for each time step in the sequence. These circumscribed spheres provide a simplified spatial envelope representation of the vehicle and its payload at a specific moment. However, as the vehicle moves, the space it occupies is a swept volume, which cannot be fully captured by a simple instantaneous circumscribed sphere. To more accurately represent the space that the vehicle may occupy in the direction of motion, a sphere center offset is calculated based on the vehicle's current direction of motion. The magnitude of this offset is determined by the direction of motion vector and an adjustment factor. The adjustment factor can be pre-set based on vehicle type, payload characteristics, or empirical experience. Using a lookup table with pre-set adjustment factors based on vehicle type and payload characteristics, the adjustment factor can be retrieved based on the actual vehicle type and payload characteristics. The calculated center offset is then added to the center coordinates of the original circumscribed sphere, shifting the sphere's center forward in the direction of vehicle motion. When the center of the sphere is shifted, the radius of the circumscribed sphere remains unchanged, ensuring that the basic size of the spatial envelope does not change. The space swept by the circumscribed sphere during the shift process is updated with the corresponding occupied space in the spatial trajectory sequence. By repeating the above extraction, calculation, and superposition process for the circumscribed sphere corresponding to each time step in the future time window, a series of adjusted circumscribed spheres are obtained. These adjusted circumscribed spheres constitute the spatial trajectory sequence of the automated transfer vehicle in the future time window. This adjusted sequence can more effectively simulate the spatial area actually swept by the vehicle during movement, especially when considering the impact of vehicle length or load size on the space ahead. As a result, the generated spatial trajectory sequence can be more accurately used in subsequent collision detection steps, improving the reliability of identifying potential spatial overlaps and reducing the risk of false positives or omissions.

[0115] In some embodiments, step A4 comprises:

[0116] A401. Identify the types of environmental obstacles within the transfer area and determine the static obstacle set and dynamic obstacle set;

[0117] A402. For the static obstacle set, calculate the spatial overlap between the spatial trajectory sequence of each automated transfer vehicle in the future time window and the static obstacle spatial occupancy model. If overlap exists, determine it as a static obstacle interference risk.

[0118] A403. For a set of dynamic obstacles, using the Kalman filter algorithm, based on the historical position and velocity information of the dynamic obstacles, the motion trajectory of each dynamic obstacle within a future time window is predicted. The minimum distance between the spatial trajectory sequence of each automated transfer vehicle within the future time window and the motion trajectory of the dynamic obstacle is calculated. If the minimum distance is less than a preset safety threshold, a dynamic obstacle collision risk is determined.

[0119] A404. For each automated transfer vehicle, calculate the spatial overlap between its spatial trajectory sequence and the spatial occupancy models of other vehicles. If overlap exists, it is determined to be a vehicle-to-vehicle collision risk.

[0120] Specifically, within a large cable manufacturing base, the transfer of cable reels involves multiple automated transfer vehicles moving within a warehouse area. This area contains static obstacles such as fixed equipment and walls, as well as dynamic obstacles such as temporarily parked vehicles and pedestrians. Existing monitoring methods are limited in handling this complex, dynamic, and multi-conflict situation. They often only identify a single type of conflict and lack comprehensive consideration of factors such as the coordination of multiple vehicles, multiple reels, and dynamic environmental changes. This can lead to missed or false conflict reports, impacting the effectiveness of subsequent coordinated control. This technical solution distinguishes environmental obstacle types. For static obstacles, the overlap between the vehicle's future spatial trajectory and the obstacle's spatial model is calculated to directly determine interference risk. For dynamic obstacles, the Kalman filter is used to predict their motion trajectory (the Kalman filter algorithm is currently available and will not be described in detail here). The minimum distance between the vehicle's trajectory and the predicted trajectory is calculated, and the collision risk is determined by comparing it with a safety threshold, improving the accuracy of dynamic obstacle risk assessment. For vehicles, the spatial overlap between their future spatial trajectories is calculated to identify inter-vehicle collision risk. By adopting detection methods for different types of obstacles and vehicles, this solution can more comprehensively and accurately identify potential conflicts during the transfer process, including interference with static obstacles, collisions with dynamic obstacles, and collisions between vehicles. This solves the problem of inaccurate and incomplete conflict detection in existing methods and provides a reliable basis for the subsequent generation of effective collaborative control instructions.

[0121] In some embodiments, step A5 comprises:

[0122] A501. Real-time data collection of vehicle density and the number of vehicles waiting at intersections within the transit area is used to assess traffic congestion levels. For example, the mean vehicle density for each channel and the mean number of vehicles waiting at each intersection are calculated, and a weighted sum of the two means is taken. The corresponding traffic congestion level is determined based on the numerical range within which the weighted sum falls.

[0123] A502. Determine the priority of the task involving the vehicle based on the generation time and task type of the task order. For example, the task type is queried to obtain the corresponding preset initial task priority. The initial task priority is then adjusted based on the time interval between the current time and the generation time of the task order to obtain the final task priority. The longer the time interval, the higher the task priority is adjusted.

[0124] A503. Based on the traffic congestion level, combined with the conflict type, the task priority of the vehicles involved, and the cable roll specifications, the preset collaborative control strategy library is searched to obtain the initial collaborative control instruction set;

[0125] A504. For each instruction in the initial coordinated control instruction set, evaluate its potential impact on the involved vehicle and surrounding vehicles; potential impacts include the degree of path deviation, speed change, and expected delay time;

[0126] A505. Select the instruction with the least impact on the overall transfer efficiency as the final collaborative control instruction.

[0127] Specifically, within large-scale cable manufacturing bases, the transfer of cable reels is a critical link connecting production, warehousing, and shipping. These cable reels need to be quickly transported to designated storage locations or shipping areas by automated transfer vehicles. When multiple vehicles are performing tasks simultaneously, especially in traffic-intensive areas, their planned routes may conflict in time or space. Existing monitoring methods are limited in handling these complex, dynamic, and multiple conflicts. They lack forward-looking conflict prediction and intelligent coordinated control, resulting in frequent vehicle stops and waiting times, which reduces transfer speed, increases energy consumption, and increases equipment wear. To address the problem of how to comprehensively consider multiple factors, including conflict type, task priority, cable reel specifications, and regional traffic conditions, and generate coordinated control instructions that effectively avoid conflicts while minimizing the impact on overall transfer efficiency, this application proposes a method. This method first collects real-time vehicle density and the number of vehicles waiting at intersections in each channel within the transfer area to assess the regional traffic congestion level, providing a data foundation for evaluating the traffic environment. Furthermore, the priority of the tasks involving the vehicles is determined based on the generation time of the task orders and the task type, providing a basis for handling conflicts involving tasks of varying importance. Priority information is a key input for generating appropriate control instructions. Then, using the acquired traffic congestion level and task priority, combined with the conflict type and cable reel specifications obtained from conflict detection, a pre-set policy library is queried. The policy library contains pre-stored control solutions for different scenarios. Through the query mechanism, a set of possible response instructions can be quickly obtained, forming an initial instruction set. Next, each instruction in the initial instruction set is evaluated for its effectiveness. This evaluation includes the potential path change, speed adjustment, and estimated delay caused by the instruction. This evaluation step predicts the potential consequences of implementing different instructions, providing data support for selecting the optimal instruction. Finally, based on the evaluation results, the instruction with the least impact on overall transfer efficiency is selected as the final execution instruction. This ensures that the generated instructions not only avoid conflicts but also select the one with the lowest impact on system operation and the highest efficiency among multiple feasible options. This paper details how to generate coordinated control instructions that effectively address conflicts while maintaining overall transfer efficiency, starting with acquiring environmental and task information, through policy query, effectiveness evaluation, and optimization.

[0128] Preferably, step A503 may include:

[0129] Constructing a multidimensional query vector containing conflict type, task priority of involved vehicles, cable roll specifications, and traffic congestion level;

[0130] Calculate the similarity between the multidimensional query vector and the attribute labels of each strategy in the collaborative control strategy library; each strategy in the collaborative control strategy library is pre-labeled with multidimensional attribute labels, including conflict type, task priority range, cable roll size range, and traffic congestion level range;

[0131] Select multiple strategies with the highest similarity as the initial collaborative control instruction set.

[0132] Specifically, when the system identifies a potential conflict, it first collects the current conflict type (e.g., inter-vehicle collision risk, static obstacle interference risk, dynamic obstacle collision risk, or a combination thereof), the task priorities of the vehicles involved, the specifications of the cable rolls being carried, and the traffic congestion level in the transfer area. This information is used to construct a multidimensional query vector, which represents the current conflict scenario. Simultaneously, the system maintains a collaborative control strategy library. Each strategy in the library is pre-assigned with multidimensional attribute labels, describing the conflict type, task priority range, cable roll specification range, and traffic congestion level range to which the strategy applies. The system calculates the similarity between the constructed query vector and the attribute labels of each strategy in the strategy library. The similarity calculation results reflect the degree of match between the current conflict scenario and the strategy's applicable conditions. Based on these similarity calculation results, the system selects the strategies with the highest similarity as the initial collaborative control instruction set. This allows the system to quickly retrieve the most relevant response strategies from the strategy library based on the specific conflict scenario, improving the targeted nature of strategy selection.

[0133] refer to Figure 2 The present application provides a cable roll rapid transport monitoring system based on the Industrial Internet of Things, which monitors the collaborative transport of cable rolls by multiple automated transport vehicles based on the Industrial Internet of Things system. The system includes:

[0134] Data acquisition module 1 is used to obtain the real-time position, speed, acceleration, planned path and load status of each automated transfer vehicle in the transfer area, the specifications of each cable reel, and the location information of each environmental obstacle, and construct a real-time data view of the transfer environment (the specific process can be referred to step A1 above);

[0135] Spatial Modeling Module 2, which is used to establish a joint space occupancy model for each automated transfer vehicle and its cable rolls based on the real-time data view, and to establish an obstacle space occupancy model for each environmental obstacle (the specific process can be referred to in step A2 above);

[0136] Trajectory prediction module 3 is used to predict the spatial trajectory sequence of each automated transfer vehicle in the future time window based on the real-time position, speed, acceleration, planned path, load status and joint space occupancy model of each automated transfer vehicle (the specific process can be referred to step A3 above);

[0137] Conflict detection module 4, used to calculate the spatial overlap between different space occupancy models in the future time window and identify potential conflicts (the specific process can be referred to step A4 above);

[0138] Strategy generation module 5 is configured to generate a coordinated control instruction when a potential conflict is identified, based on the conflict type, the mission priorities of the vehicles involved, the cable reel specifications, and the regional traffic conditions; the coordinated control instruction includes at least one of path fine-tuning, speed adjustment, and timing adjustment (for details, refer to step A5 above);

[0139] The instruction issuing module 6 is used to issue the generated collaborative control instructions to the relevant automated transfer vehicles, adjust the vehicle movement status, and avoid potential conflicts (the specific process can be referred to step A6 above).

[0140] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A cable roll rapid transport monitoring method based on the industrial Internet of Things, which monitors the collaborative transport of cable rolls by multiple automated transport vehicles based on the industrial Internet of Things system, characterized in that: The steps of the method include: A1. Obtain the real-time position, speed, acceleration, planned path, and load status of each automated transfer vehicle within the transfer area, along with cable reel specifications and the location of environmental obstacles, to create a real-time data view of the transfer environment. A2. Based on the real-time data view, a joint space occupancy model is developed for each automated transfer vehicle and its cable rolls, as well as an obstacle space occupancy model for each environmental obstacle. A3. Predict the spatial trajectory sequence of each automated transfer vehicle within a future time window based on its real-time position, speed, acceleration, planned path, load status, and the combined spatial occupancy model. A4. Calculate the spatial overlap between different spatial occupancy models within the future time window and identify potential conflicts; A5. When a potential conflict is identified, a coordinated control instruction is generated based on the conflict type, the mission priorities of the vehicles involved, the cable reel specifications, and regional traffic conditions. The coordinated control instruction includes at least one of a fine-tuning of the route, a speed adjustment, and a timing adjustment. A6. Send the generated collaborative control instructions to the relevant automated transfer vehicles to adjust the vehicle's motion status and avoid potential conflicts.

2. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 1, characterized in that: Step A1 includes: A101. Multiple wireless sensor nodes deployed within the transfer area collect real-time position coordinates, velocity vectors, and acceleration vectors of each automated transfer vehicle, as well as load weight data fed back from the vehicle load sensors on each automated transfer vehicle. A102 extracts the cable roll ID from each automated transfer vehicle's task list to obtain the corresponding specification information from the cable roll specification database; A103. Use environmental sensors to obtain the three-dimensional coordinate information of static and dynamic obstacles within the transfer area; A104. Clean and time-synchronize the acquired data, encapsulate it in JSON format, and construct a real-time data view of the transit environment.

3. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 1, characterized in that: Step A2 includes: A201. According to the model of each automated transfer vehicle, retrieve the corresponding vehicle 3D model from the 3D model library; A202. According to the cable roll ID in the task list of each automated transport vehicle, the corresponding cable roll 3D model is retrieved from the 3D model library; A203. Determine the position of the cable reel relative to the vehicle coordinate system based on the load status of each automated transfer vehicle; A204. Based on the position of the cable reel relative to the vehicle coordinate system, rigidly combine the retrieved cable reel 3D model and the vehicle 3D model to construct a joint 3D model as the joint space occupancy model; A205. For environmental obstacles, based on the obstacle type, retrieve the corresponding typical 3D spatial envelope from the 3D model library as the corresponding obstacle space occupancy model; A206. Based on the real-time data view, place the obtained space occupancy models in corresponding positions.

4. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 1, characterized in that: Step A3 includes: A301. According to the planned path of each automated transfer vehicle, extract the key path point sequence on the path, based on the real-time position of the automated transfer vehicle, determine the current target path point of the automated transfer vehicle in the key path point sequence; A302. Based on the speed, acceleration, and load status of each automated transfer vehicle, use the vehicle dynamics model to predict the vehicle's velocity change sequence within consecutive time steps. Based on the velocity change sequence, calculate the vehicle's displacement increment within each time step. A303. Based on the current target path points and displacement increments of each automated transfer vehicle, predict the position coordinates of the automated transfer vehicle at each time step. Combined with the joint space occupancy model, the space occupancy sequence of the vehicle in the future time window is obtained. A304. The predicted spatial occupancy sequence is simplified into a circumscribed sphere. The center position of the circumscribed sphere is adjusted according to the movement direction of the automated transfer vehicle to obtain the spatial trajectory sequence of the automated transfer vehicle in the future time window.

5. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 4, characterized in that: Step A302 includes: For the vehicle dynamics model, the vehicle's speed, acceleration, load state, and center of gravity are considered to establish a six-degree-of-freedom vehicle dynamics equation set that includes the vehicle's longitudinal, lateral, and vertical motions. The six-degree-of-freedom vehicle dynamics equations are numerically solved using the Runge-Kutta algorithm to obtain a velocity variation sequence of the automated transfer vehicle in consecutive time steps; According to the speed change sequence of the automated transfer vehicle in each time step and combined with the real-time attitude angle of the automated transfer vehicle, the displacement increment of the automated transfer vehicle in each time step is calculated; among them, the calculation of the displacement increment takes into account the influence of the vehicle's roll angle and pitch angle on the displacement direction.

6. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 4, characterized in that: Step A304 includes: Extracting the circumscribed sphere parameters of each joint space occupancy model in the predicted space occupancy sequence; the circumscribed sphere parameters include the coordinates of the sphere center and the radius; According to the vehicle motion direction vector, the product of the vector and the adjustment factor is calculated to obtain the sphere center offset; The center offset is added to the coordinates of the center of the corresponding circumscribed sphere, and the radius of the circumscribed sphere is constrained to remain unchanged to obtain the adjusted spatial trajectory sequence.

7. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 6, characterized in that: Step A4 includes: A401. Identify the types of environmental obstacles within the transfer area and determine the static obstacle set and dynamic obstacle set; A402. For the static obstacle set, calculate the spatial overlap between the spatial trajectory sequence of each automated transfer vehicle in the future time window and the static obstacle spatial occupancy model. If overlap exists, determine it as a static obstacle interference risk. A403. For a set of dynamic obstacles, using the Kalman filter algorithm, based on the historical position and velocity information of the dynamic obstacles, the motion trajectory of each dynamic obstacle within a future time window is predicted. The minimum distance between the spatial trajectory sequence of each automated transfer vehicle within the future time window and the motion trajectory of the dynamic obstacle is calculated. If the minimum distance is less than a preset safety threshold, a dynamic obstacle collision risk is determined. A404. For each automated transfer vehicle, calculate the spatial overlap between its spatial trajectory sequence and the spatial occupancy models of other vehicles. If overlap exists, it is determined to be a vehicle-to-vehicle collision risk.

8. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 1, characterized in that: Step A5 includes: A501. Real-time collection of vehicle density at each channel within the transfer area and the number of vehicles waiting at intersections to assess traffic congestion levels. A502. Determine the priority of tasks involving vehicles based on the generation time and task type of the task order involving the vehicle; A503. Based on the traffic congestion level, combined with the conflict type, the task priority of the vehicles involved, and the cable roll specifications, the preset collaborative control strategy library is searched to obtain the initial collaborative control instruction set; A504. For each instruction in the initial coordinated control instruction set, evaluate its potential impact on the involved vehicle and surrounding vehicles; potential impacts include the degree of path deviation, speed change, and expected delay time; A505. Select the instruction with the least impact on the overall transfer efficiency as the final collaborative control instruction.

9. The method for monitoring the rapid transportation of cable rolls based on the industrial Internet of Things according to claim 8, characterized in that: Step A503 includes: Constructing a multidimensional query vector containing conflict type, task priority of involved vehicles, cable roll specifications, and traffic congestion level; Calculate the similarity between the multidimensional query vector and the attribute labels of each strategy in the collaborative control strategy library; each strategy in the collaborative control strategy library is pre-labeled with multidimensional attribute labels, including conflict type, task priority range, cable roll size range, and traffic congestion level range; Select multiple strategies with the highest similarity as the initial collaborative control instruction set.

10. A cable roll rapid transport monitoring system based on the industrial Internet of Things, which monitors the coordinated transport of cable rolls by multiple automated transport vehicles based on the industrial Internet of Things system, characterized in that: The system includes: The data acquisition module is used to obtain the real-time position, speed, acceleration, planned path and load status of each automated transfer vehicle in the transfer area, the specifications of each cable reel, and the location information of each environmental obstacle, and build a real-time data view of the transfer environment; A spatial modeling module that creates a joint spatial occupancy model for each automated transfer vehicle and its cable reels, and an obstacle spatial occupancy model for each environmental obstacle based on real-time data views; The trajectory prediction module is used to predict the spatial trajectory sequence of each automated transfer vehicle in the future time window based on the real-time position, speed, acceleration, planned path, load status and joint space occupancy model of each automated transfer vehicle; a conflict detection module, configured to calculate spatial overlap between different spatial occupancy models within the future time window and identify potential conflicts; a strategy generation module for generating, when a potential conflict is identified, a coordinated control instruction based on the conflict type, the mission priorities of the vehicles involved, the cable roll specifications, and the regional traffic conditions; the coordinated control instruction including at least one of a path fine-tuning, a speed adjustment, and a timing adjustment; The instruction issuing module is used to issue the generated collaborative control instructions to relevant automated transfer vehicles, adjust the vehicle movement status, and avoid potential conflicts.

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