An automated material conveying system for a door and window processing workshop

Abstract

The present application belongs to the technical field of logistics transportation, and specifically relates to an automatic material conveying system for a door and window processing workshop, aiming to solve the problem of low efficiency caused by the lack of automation, flexibility and intelligence of the existing system. The system includes modules such as data acquisition and perception, digital twin, task management and scheduling, intelligent path planning and obstacle avoidance, multi-agent collaborative control, execution and feedback, and human-computer interaction and visualization. Through an integrated intelligent scheduling and control platform, the system realizes multi-modal collaborative operation, real-time path optimization and dynamic task allocation of material conveying equipment. The present application significantly improves the production efficiency, flexibility and intelligence level of the door and window processing workshop.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of logistics transportation, and particularly relates to an automatic material transportation system for a door and window processing workshop. BACKGROUND

[0002] Industrial automation and intelligent manufacturing have become the core driving force for the transformation and upgrading of modern manufacturing. Among them, an efficient material transportation system is a key link to ensure smooth production processes and improve overall production efficiency. Such a system aims to optimize the flow path and management of materials within a factory, thereby reducing operating costs and shortening production cycles, and plays a fundamental role in achieving lean and intelligent production.

[0003] Among them, the door and window processing industry has higher requirements for the automation, flexibility and precision of the material transportation system due to the diversity of its products, the non-standardization of profile sizes and the complexity of processing technology. The automatic material transportation system emerged as the times required, and its core is to realize the whole-process optimization management and efficient flow of materials from raw material storage to finished product delivery in the door and window processing workshop by integrating various automated equipment and intelligent control technology.

[0004] The existing technology still faces significant challenges in the material transportation of door and window processing workshops. The production characteristics of door and window products, such as multi-variety, small batch and customization, as well as the different forms of materials such as profiles and glass, make the traditional material handling method labor-intensive, low-efficiency and prone to material damage or mismatch due to human factors. Although some deployed automated systems have introduced mechanical equipment, they often lack integration, lack effective coordination and information sharing between subsystems, forming data silos, making it difficult to achieve precise positioning and real-time tracking of materials. In addition, their scheduling strategies are usually fixed, making it difficult to adapt to dynamic changes in production tasks and temporary order requirements, resulting in material delays, extended processing unit waiting times or imbalanced production rhythms. These problems not only constrain the overall production efficiency and flexibility of the door and window processing workshop, but also significantly increase operating costs and management difficulties, and an advanced, efficient and intelligent automatic material transportation system is urgently needed. SUMMARY

[0005] The present application aims to solve the deficiencies of the existing material conveying system in door and window processing workshops in terms of automation, flexibility, intelligence, and response to complex and variable production requirements. The existing material conveying system usually relies on fixed routes or relatively simple scheduling logic, which is difficult to effectively adapt to dynamic adjustments of production orders, real-time changes of equipment status, and complex conveying requirements brought by differences in material types, sizes, and weights. This leads to low material flow efficiency, frequent path conflicts, low resource utilization, and strong dependence on manual intervention, thereby limiting the improvement of the overall production efficiency and automation level of door and window processing workshops. In order to solve the above technical problems, the present application proposes an automated material conveying system for door and window processing workshops.

[0006] According to one aspect of the present application, an automated material conveying system for door and window processing workshops is provided, which realizes multi-modal collaborative operation of various material conveying equipment, real-time path optimization, and dynamic task allocation by constructing an integrated intelligent scheduling and control platform. First, the system comprehensively perceives the material state, equipment operation, and production task demand in the workshop through multi-source data fusion technology. Then, based on the digital twin model, the physical workshop is mirrored in real time to provide accurate context information for intelligent decision-making. Subsequently, the core intelligent task management and scheduling module uses advanced artificial intelligence algorithms to dynamically allocate and prioritize conveying tasks, and combines predictive analysis to optimize material flow path and timing. At the same time, the multi-agent collaborative control module ensures the conflict-free and efficient operation of different types of conveying equipment in the shared space. Finally, through precise execution and feedback mechanisms, the automated and precise conveying of materials is realized, thereby significantly improving the production efficiency, flexibility level, and intelligence degree of door and window processing workshops.

[0007] As an embodiment of the present application, the automated material conveying system comprises:

[0008] A data acquisition and perception module for acquiring real-time production state data, material information data, and conveying equipment operation data in the door and window processing workshop;

[0009] A digital twin and state maintenance module for constructing and updating the digital twin model of the physical entities in the workshop based on the data acquired by the data acquisition and perception module;

[0010] A task management and scheduling module for generating, allocating, and optimizing material conveying tasks based on real-time information provided by production orders, inventory status, and the digital twin model;

[0011] An intelligent path planning and obstacle avoidance module for planning the optimal travel path for each conveying unit in the material conveying task and performing real-time dynamic obstacle avoidance;

[0012] A multi-agent collaborative control module is configured to coordinate the conflict-free operation of multiple conveying units in a shared space, and to optimize traffic flow.

[0013] An execution and feedback module is configured to receive scheduling instructions and control specific material conveying equipment to perform conveying tasks, and to feed back execution results to the data acquisition and perception module.

[0014] A human-computer interaction and visualization module is configured to provide real-time monitoring of system status, manual intervention interface, and historical data query function.

[0015] As an embodiment of the present application, the data acquisition and perception module comprises:

[0016] A production line state sensor group is configured to monitor the working state, processing progress, and fault information of each processing equipment in real time.

[0017] A material information recognition device is configured to identify the type, specification, quantity, current location, and target destination of materials such as profile, glass, hardware, etc.

[0018] A conveying equipment state sensor group is configured to obtain the position, speed, attitude, power, and load state of conveying units such as automated guided vehicles, mechanical arms, and conveying belts in real time.

[0019] An environmental perception sensor group is configured to monitor environmental parameters such as temperature, humidity, and possible dynamic obstacle information in the workshop.

[0020] Further, the production line state sensor group comprises photoelectric sensors, proximity switches, encoders, and industrial cameras for detecting the position, speed, and processing state of workpieces on the production line.

[0021] Further, the material information recognition device comprises a two-dimensional code scanner, a radio frequency identification reader, and a machine vision system for automatic identification and tracking of materials. The machine vision system uses image processing technology to identify the shape, size, surface defects, and type characteristics of materials, and encodes them into digital information for system processing.

[0022] Further, the conveying equipment state sensor group comprises a global positioning system receiver, an inertial measurement unit, a laser radar sensor, and an ultrasonic sensor for providing accurate position, speed, attitude, and obstacle information of the surrounding environment of the conveying unit. The laser radar sensor constructs a high-precision two-dimensional or three-dimensional point cloud map of the workshop environment for path planning and obstacle avoidance.

[0023] Further, the environmental perception sensor group includes temperature sensors, humidity sensors, and additional laser radars or cameras for auxiliary monitoring of changes in the workshop environment, such as detecting smoke, abnormal temperature, or sudden personnel intrusion.

[0024] As an embodiment of the present application, the digital twin and state maintenance module includes:

[0025] A workshop layout modeling unit for constructing a static three-dimensional model of the door and window processing workshop, including the geometric topology of processing equipment, storage area, conveying channel, and safety area;

[0026] A dynamic entity modeling unit for creating a dynamic digital model of each material conveying unit and the material to be processed, including its attributes, state, and behavior rules;

[0027] A real-time data mapping unit for mapping real-time data obtained by the data acquisition and perception module to the corresponding digital twin entity, updating its position, state, and attributes;

[0028] A state prediction and analysis unit for predicting potential events such as equipment failure, material consumption, and traffic congestion based on historical data and real-time state, and conducting trend analysis.

[0029] Further, the workshop layout modeling unit uses building information modeling technology or industrial computer-aided design data to generate a high-precision three-dimensional geometric model containing the positions of all fixed infrastructure and equipment. This model serves as the basic framework of the digital twin.

[0030] Further, the dynamic entity modeling unit establishes an independent digital agent for each automated guided vehicle, each robotic arm, and each batch of material to be processed. These digital agents contain real-time attributes such as speed, acceleration, power, load, task ID, destination, and material ID, type, size, and weight.

[0031] Further, the real-time data mapping unit periodically receives data streams from the data acquisition and perception module through a high-speed data bus and standard communication protocols such as Ethernet or industrial Internet of Things protocols, and binds and updates them with the corresponding entities in the digital twin model. The data update frequency can reach real-time level.

[0032] Further, the state prediction and analysis unit uses machine learning models such as recurrent neural networks or long short-term memory networks to analyze historical operation data and sensor data. By learning equipment wear patterns, traffic flow rules, and material consumption trends, this unit can predict equipment remaining life, potential traffic bottlenecks, and future material demand, and send these predictions to the task management and scheduling module for forward-looking decision-making.

[0033] As an embodiment of the present application, the task management and scheduling module comprises:

[0034] A task generation unit for automatically generating material transport tasks based on production orders issued by the production management system, current inventory levels, and material requirements;

[0035] A task decomposition and priority sorting unit for decomposing complex transport tasks into smaller subtasks and assigning priorities to each subtask based on the urgency of the production plan, the importance of the material, and the status of the equipment;

[0036] A resource allocation unit for assigning the most suitable material transport tasks to the transport units based on their current location, availability, load capacity, and task priority;

[0037] A scheduling strategy optimization unit for using multi-objective optimization algorithms to consider factors such as transport efficiency, energy consumption, equipment utilization, and waiting time to optimize the overall scheduling scheme.

[0038] Further, the task generation unit receives production instructions from the manufacturing execution system and enterprise resource planning system, and combines inventory information in the digital twin model to automatically identify material shortages or impending material requirements, and create corresponding task descriptions such as "transport C material from point A to point B".

[0039] Further, the task decomposition and priority sorting unit decomposes large-volume material transport tasks into multiple batches or single-item transport subtasks. Priority sorting is calculated based on a configurable weight function that considers task deadlines, production line downtime risks, material value, and current production load.

[0040] Further, the resource allocation unit uses algorithms based on reinforcement learning or auction mechanisms to select the most suitable transport unit from multiple available units. For example, for heavy materials, the system will preferentially select automated guided vehicles with strong carrying capacity; for urgent tasks, the closest and currently idle unit is selected. The allocation process takes into account the current task queue and estimated completion time of the transport unit.

[0041] Further, the scheduling strategy optimization unit uses heuristic search methods such as genetic algorithms or ant colony algorithms to find the optimal solution in a vast scheduling scheme space. The optimization objective function includes minimizing total transport time, minimizing transport energy consumption, maximizing average equipment utilization, and minimizing production line waiting time. This unit periodically re-evaluates the scheduling scheme to adapt to dynamic changes in the state of the workshop.

[0042] As an embodiment of the present application, the intelligent path planning and obstacle avoidance module comprises:

[0043] a global path planning unit for calculating an initial optimal path from a start point to a target point for a transport unit assigned with a task on the plant layout topology of the digital twin model;

[0044] a local obstacle avoidance and path correction unit for detecting dynamic obstacles around the transport unit in real time, such as other transport units in motion, temporarily stacked materials or personnel, and immediately adjusting the current travel path to avoid collision;

[0045] a multi-path pre-computation and selection unit for pre-computing multiple alternative paths, which can quickly switch to a sub-optimal path when the main path is blocked;

[0046] a path conflict prediction and resolution unit for predicting potential path intersections or congestion between different transport units, and adjusting their speed or path in advance to avoid conflicts.

[0047] Further, the global path planning unit uses an improved A* algorithm or a fast search random tree algorithm to generate a theoretically optimal path in open space based on consideration of the size of the transport unit, the maximum speed, and the static obstacles in the plant. The cost function of this algorithm considers path length and estimated transit time.

[0048] Further, the local obstacle avoidance and path correction unit combines laser radar or visual sensor data to construct a local environment grid map around the transport unit. This unit uses a dynamic window method or a potential field-based method to calculate the required steering angle and speed adjustment for obstacle avoidance in real time. When an obstacle is detected, the path correction unit will slow down, stop or detour within a safe distance.

[0049] Further, the multi-path pre-computation and selection unit, in addition to generating an optimal path, also simultaneously computes and stores several alternative paths that meet sub-optimal conditions during the global path planning phase. These alternative paths can be quickly activated through a simple path evaluation function when the main path fails, reducing decision-making delay.

[0050] Further, the path conflict prediction and resolution unit receives motion prediction information from neighboring transport units of the multi-agent collaborative control module, and uses a time window algorithm or game theory method to identify potential collision or congestion points. For predicted conflicts, the unit sends instructions to relevant transport units to slow down, stop and wait, or detour, to ensure smooth traffic flow.

[0051] As an embodiment of the present application, the multi-agent collaborative control module comprises:

[0052] Traffic flow management unit, for allocating and managing the passing right of multiple transport units at the shared transport channel and intersections between workshops, to prevent traffic congestion;

[0053] Formation and coordination unit, for coordinating multiple transport units to form a cooperative formation when large or heavy materials need to be transported simultaneously;

[0054] Fault tolerance and task reassignment unit, for automatically reassigning the unfinished tasks of a failed transport unit to other available units and adjusting the path of the affected unit;

[0055] Energy management and charging scheduling unit, for monitoring the power status of all automated guided vehicles and intelligently scheduling them to go to the charging station for charging to ensure the continuous operation of the system.

[0056] Further, the traffic flow management unit controls the priority of passing through the main roads and intersections of the workshop by implementing virtual traffic lights or time slice rotation mechanism. For example, when multiple transport units approach an intersection at the same time, the system will allocate the passing right according to the task priority or first-come-first-served principle.

[0057] Further, the formation and coordination unit enables multiple automated guided vehicles to accurately maintain the preset relative position and speed through cooperative sensing and distributed control algorithms. This is particularly important for scenarios where multiple vehicles need to be synchronized to lift or carry super-long profiles or super-large glass panels.

[0058] Further, the fault tolerance and task reassignment unit continuously receives device status information from the execution and feedback module. Once a hardware failure or communication interruption is detected in a transport unit, the unit will immediately mark its in-transit task status as abnormal and start an emergency scheduling process to dispatch the remaining tasks to the nearest idle transport unit that matches the capability.

[0059] Further, the energy management and charging scheduling unit predicts the charging needs of the automated guided vehicles based on their real-time power, estimated task volume, and available status of the charging station. It plans the charging tasks in advance and guides the low-power automated guided vehicles to idle charging piles during task gaps or off-peak periods to avoid task interruption due to power depletion.

[0060] As an embodiment of the present application, the execution and feedback module includes:

[0061] Instruction analysis and driving unit, for receiving instructions from the task management and scheduling module and the multi-agent cooperative control module and converting them into bottom-layer control signals recognizable by the transport device;

[0062] A motion control unit for precisely controlling the driving speed, steering angle, lifting mechanism, and motion trajectory of the automated guided vehicle;

[0063] An execution state monitoring unit for real-time monitoring of the actual execution state of the conveying device, such as position, speed, load sensor data, and fault indication;

[0064] A data upload unit for packaging the data obtained by the execution state monitoring unit and sending it to the data acquisition and perception module through the communication interface.

[0065] Further, the instruction analysis and driving unit translates abstract instructions such as "move to X, Y coordinates" or "grab material" into specific motor speed, steering angle, or hydraulic system pressure instructions, and sends them to the corresponding actuator controller.

[0066] Further, the motion control unit ensures that the conveying device can accurately move according to the planned speed and trajectory through a closed-loop control system, such as a proportional-integral-derivative controller. For the robotic arm, its motion control involves inverse kinematics calculation and torque control to achieve precise grabbing and placing.

[0067] Further, the execution state monitoring unit continuously obtains the running parameters and state flags of the device using the encoders, current sensors, limit switches, and visual feedback systems carried by the conveying device itself.

[0068] Further, the data upload unit uses low-latency wireless communication protocols such as Wi-Fi or the fifth generation mobile communication technology to efficiently transmit real-time execution data streams back to the data acquisition and perception module, ensuring the freshness of the digital twin model data.

[0069] As an embodiment of the present application, the human-computer interaction and visualization module includes:

[0070] A real-time monitoring interface for graphically displaying the digital twin model of the door and window processing workshop, including the real-time position, state, material flow direction, and production line operating conditions of all conveying units;

[0071] A task management interface for manually creating, modifying, or canceling material conveying tasks and adjusting task priorities;

[0072] An alarm and event recording unit for generating and displaying alarms when the system detects abnormal conditions such as device failure, material jamming, or security area intrusion, while recording all event logs;

[0073] A historical data query and report generation unit for querying and analyzing historical data of system operation, generating reports on efficiency, energy consumption, failure rate, etc.

[0074] Further, the real-time monitoring interface employs three-dimensional rendering technology to reproduce the physical layout of the workshop on the display. Icons or animation effects of different colors represent different states of the conveying units and materials, such as green for normal operation and red for failure or abnormality.

[0075] Further, the task management interface provides intuitive drag-and-drop operations, allowing operators to simply click and drag materials from one location to another and automatically trigger task generation and scheduling.

[0076] Further, the alarm and event recording unit stores all system events according to timestamps and classifies them as warnings, errors, or emergency events. Emergency alarms can notify relevant managers through audio-visual signals or SMS / email.

[0077] Further, the historical data query and report generation unit allows users to set time ranges and query conditions, such as querying the utilization rate of a specific conveying unit or the average conveying time of a certain type of material. Reports can be exported in multiple formats to support decision analysis.

[0078] Compared with the prior art, the advantages and positive effects of the present application are:

[0079] The present application constructs an automatic material conveying system for a door and window processing workshop. The system integrates multi-source heterogeneous data acquisition, digital twin technology, and multi-agent collaborative control, fundamentally improving the intelligence, flexibility, and efficiency of material conveying.

[0080] Specifically, the present application realizes real-time and accurate perception of the physical world of the workshop through comprehensive data acquisition and perception modules, covering multiple dimensions such as production lines, materials, conveying equipment, and environment. This provides a solid data foundation for subsequent intelligent decision-making, overcoming the defects of information isolation and strong lag in traditional systems.

[0081] Further, the present application introduces a digital twin and state maintenance module that accurately maps the physical workshop in the digital space and performs real-time updates and state prediction. This technological breakthrough enables the system to monitor, analyze, and predict trends of the workshop running state, providing forward-looking support for scheduling decisions, significantly superior to existing systems that rely only on historical data or simple rules.

[0082] In addition, the task management and scheduling module in the present application can dynamically generate, decompose, prioritize, and optimize material conveying tasks based on production orders, inventory, and real-time workshop state by using advanced artificial intelligence algorithms. This enables the system to flexibly respond to frequent adjustments in production plans and unexpected events, achieving truly flexible production and avoiding the inefficiency and rigidity caused by traditional fixed scheduling.

[0083] As an important advantage of the present application, the intelligent path planning and obstacle avoidance module not only calculates the globally optimal path, but more importantly, it has the ability of real-time local obstacle avoidance, multi-path pre-computation, and path conflict prediction and resolution. This ensures that the conveying unit can operate safely, efficiently and without conflict in a complex dynamic environment, greatly reducing the risk of collision and traffic congestion, thereby improving the material turnover efficiency and equipment safety.

[0084] Further, the multi-agent collaborative control module realizes seamless collaboration of heterogeneous conveying equipment and system-level optimization through traffic flow management, formation and cooperation, fault tolerance and task redistribution, and energy management and charging scheduling. This solves the conflict and resource waste problems that may occur when multiple devices operate in a shared space, improves the overall system utilization and robustness, and ensures the continuity and stability of the production process.

[0085] The human-computer interaction and visualization module of the present application provides an intuitive, comprehensive real-time monitoring interface and flexible task management interface, reduces the difficulty of operation, improves the management efficiency, and supports historical data analysis, providing strong data support for production management decision-making.

[0086] In summary, the present application builds a highly integrated, intelligent and self-adaptive automated material conveying system, which can significantly improve the material flow efficiency in the door and window processing workshop, reduce operating costs, improve production flexibility and the ability to cope with complex production environments, thereby providing key support for intelligent manufacturing. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1 is the overall technical scheme architecture schematic diagram of the present application;

[0088] Figure 2 is the core principle framework schematic diagram of intelligent task management and scheduling in the present application. DETAILED DESCRIPTION

[0089] Please refer to Figure 1 and Figure 2 , the present application discloses an automated material conveying system applied to a door and window processing workshop, which aims to revolutionize the efficiency, accuracy and intelligence level of material flow in traditional door and window manufacturing process. By integrating advanced sensing technology, digital twin modeling, artificial intelligence scheduling and multi-agent collaborative control, the system can realize adaptive management of complex and variable production environment, thereby significantly improving the overall production efficiency and flexibility.

[0090] The automatic material conveying system mainly includes a data acquisition and perception module, a digital twin and state maintenance module, a task management and scheduling module, an intelligent path planning and obstacle avoidance module, a multi-agent collaborative control module, an execution and feedback module, and a human-computer interaction and visualization module. These modules closely cooperate to form a highly integrated, fully functional intelligent ecosystem that collectively supports the full-process automatic material conveying of the door and window processing workshop.

[0091] The data acquisition and perception module is the "senses" of the entire system, and its core function is to obtain real-time, comprehensive, and accurate key production state data, material information data, and conveying equipment operation data inside the door and window processing workshop. This module deploys multiple types of sensors and recognition devices to build a digital information network covering the physical entities of the workshop. Specifically, the data acquisition and perception module is divided into a production line state sensor group, a material information recognition device, a conveying equipment state sensor group, and an environmental perception sensor group.

[0092] The production line status sensor group is responsible for real-time monitoring of the working status, processing progress, and fault information of each processing equipment. This sensor group is composed of photoelectric sensors, proximity switches, encoders, and industrial cameras, etc. Photoelectric sensors detect the presence, position, and passage of workpieces by emitting and receiving light beams, such as detecting whether the profile is in place at the profile cutting machine entrance or confirming that the workpiece has been moved out at the drilling machine exit. Its output signal is a binary state value, accurately reflecting the presence or absence of the workpiece, with a typical response time of milliseconds, ensuring real-time monitoring on high-speed production lines. Proximity switches are used for non-contact detection of the approach of metal or non-metal workpieces, such as monitoring whether the mechanical arm gripping point is ready or detecting the limit position of the feeding mechanism. Its detection range is generally a few millimeters to a few tens of millimeters, and the output signal is also a switch value. Encoders are installed on the conveyor belt motor or rotating parts of processing equipment to accurately measure their rotational speed, angular displacement, or linear displacement, thereby calculating the running speed of the processing equipment, the conveying distance of the workpiece, and the completion degree of the work cycle. The pulse signal output by the encoder can be converted into linear speed or position data after being processed by the counter, with an accuracy of microns. Industrial cameras use image acquisition and processing technology to monitor key processing links on the production line in real time, such as the accuracy of profile cutting, the positional deviation of drilling, and the quality defects of glass surface. Industrial cameras capture high-resolution image sequences, which are analyzed by the image processing unit to extract the geometric dimensions, surface features, and defect information of the workpiece, and then quantified into digital indicators and compared with the preset standards to evaluate the processing quality and progress. All these sensor data are uploaded in real time to the central processing unit of the data acquisition and perception module through industrial Ethernet or fieldbus protocols (such as Profinet or EtherCAT). The data will be preliminarily formatted and time-stamped before uploading to ensure the synchronization and integrity of the data. Abnormal data or data exceeding the preset threshold will be immediately marked and trigger the alarm mechanism for the system to handle the exception.

[0093] The material information recognition device focuses on identifying the type, specification, quantity, current location, and target destination of various materials such as processed profiles, glass, hardware, etc. The core components of the device include a two-dimensional code scanner, a radio frequency identification reader, and a machine vision system. The two-dimensional code scanner reads the two-dimensional code label attached to the material or material tray through optical reading, quickly obtaining pre-stored data such as batch number, production date, supplier information, and material code. The scanner usually uses CMOS or CCD image sensors, with fast decoding speed and extremely low error rate, and can adapt to different lighting conditions. The radio frequency identification reader uses radio waves to exchange data with the radio frequency identification tag embedded in the material or tray, automatically identifying material information. Radio frequency identification technology has strong penetration and can identify without alignment, making it particularly suitable for batch identification and material tracking in harsh environments. The reader transmits radio frequency signals through the antenna, receives the unique identification code and stored material attribute data from the tag, and parses these data into a structured format. The machine vision system uses high-resolution industrial cameras to capture material images and uses image processing algorithms (such as edge detection, feature extraction, pattern recognition, etc.) to identify the shape and size of the material, surface defects, and type characteristics. For example, by analyzing the cross-sectional profile of the profile to identify the specific model, and by detecting scratches or bubbles on the surface of the glass to determine its quality grade. The recognition results output by the machine vision system include the physical dimensions (length, width, height, diameter) of the material, color, texture, defect location and type, etc., which are encoded into digital information for system processing. These identification information and the current location of the material (usually provided by the encoder on the conveyor belt or external positioning system) are combined with the material requirements in the production order to automatically infer its target destination, such as entering the cutting station, drilling station, or glass cleaning station. All recognition data is transmitted through standard communication interfaces (such as RS232, Ethernet), and data verification and encryption processing are performed to ensure the accuracy and security of information transmission.

[0094] The conveyor equipment state sensor group is used to obtain the position, speed, attitude, power, and load state of the automated guided vehicle, mechanical arm, conveyor belt, and other conveying units in real time. The sensor group is composed of a global positioning system receiver, an inertial measurement unit, a laser radar sensor, and an ultrasonic sensor. The global positioning system receiver (which can be replaced by a UWB ultra-wideband positioning system or a wireless local area network fingerprint positioning system in an indoor environment) provides accurate three-dimensional position coordinates of the conveying unit. The system calculates its own position by receiving signals from multiple base stations, with an accuracy of centimeters. The inertial measurement unit includes a three-axis accelerometer and a three-axis gyroscope, which are used to measure the linear acceleration and angular velocity of the conveying unit. Through integration, real-time speed and attitude (pitch, roll, and yaw angle) information can be obtained. Its data update frequency is as high as hundreds of hertz, providing high dynamic response for high-speed motion control. The laser radar sensor obtains distance information of the surrounding environment by emitting a laser beam and measuring the time of flight of the reflected beam, constructing a high-precision two-dimensional or three-dimensional point cloud map of the workshop environment. Laser radar can accurately identify static obstacles (such as walls and fixed equipment) and dynamic obstacles (such as other conveying units and personnel), with a distance measurement accuracy of millimeters and a high scanning frequency, making it a key data source for path planning and real-time obstacle avoidance. The ultrasonic sensor detects close-range obstacles by emitting ultrasonic pulses and measuring their echo time, such as detecting whether there are sudden obstacles in the conveying unit's forward direction or whether the distance to other units is too close. The detection range of the ultrasonic sensor is usually tens of centimeters to several meters, with fast response and relatively low cost. As an auxiliary obstacle avoidance means for laser radar, it is used for precise collision avoidance at close range. In addition, the conveying equipment also integrates current sensors and voltage sensors to monitor power status, as well as weighing sensors or torque sensors to obtain load information. After preprocessing by the internal controller, all these sensor data are uploaded in real time to the central data bus of the data acquisition and perception module through wireless local area networks or cellular networks (such as the fifth generation mobile communication technology) using MQTT or HTTP protocols, ensuring data freshness and reliability.

[0095] The environmental perception sensor group is used to monitor environmental parameters in the workshop, such as temperature, humidity, and possible dynamic obstacle information. This sensor group includes temperature sensors, humidity sensors, and additional lidar or cameras. Temperature sensors and humidity sensors monitor the temperature and relative humidity of the workshop environment in real time, which is crucial for the stable operation of certain environmentally sensitive materials (such as glass storage conditions) and equipment. The data is periodically uploaded in digital signals, with a typical sampling period of seconds. Additional lidar or cameras are used for auxiliary monitoring of changes in the workshop environment. For example, when unexpected areas are intruded by personnel, cameras detect abnormal personnel activity through visual analysis and trigger safety alarms; lidar can be used to construct a real-time three-dimensional environmental model of the entire workshop, detect temporarily stacked materials or equipment, and ensure the safety and orderliness of the entire workshop environment. These environmental perception data are fused with other data to form a comprehensive and high-precision real-time perception of the physical world of the workshop, laying a data foundation for subsequent digital twin construction and intelligent scheduling decisions.

[0096] The digital twin and state maintenance module serves as the "brain" of the system, responsible for constructing and updating the digital twin model of the physical entities in the workshop based on the rich data obtained by the data acquisition and perception module. This module accurately maps the static structure and dynamic behavior of the physical world in the digital space and conducts state maintenance and predictive analysis based on this. The digital twin and state maintenance module consists of a workshop layout modeling unit, a dynamic entity modeling unit, a real-time data mapping unit, and a state prediction and analysis unit.

[0097] The workshop layout modeling unit is responsible for constructing a static three-dimensional model of the door and window processing workshop, covering the geometric topology of processing equipment, storage areas, conveying channels, and safety areas. This unit uses building information modeling technology or industrial computer-aided design data to generate high-precision three-dimensional geometric models containing the positions of all fixed infrastructure and equipment. These models are stored in parameterized three-dimensional geometric data (such as STEP, IGES format) and describe the precise positions, sizes, and geometric shapes of fixed elements such as walls, columns, production line equipment (such as cutting machines, milling machines, welding machines), workbenches, material shelves, charging stations, and safety fences. Each element is attached with its physical properties (such as material, load-bearing capacity) and logical properties (such as equipment number, functional area division). This high-precision three-dimensional model is the foundation skeleton of the entire digital twin, providing accurate physical references for subsequent path planning, space management, and visualization.

[0098] The dynamic entity modeling unit is dedicated to creating dynamic digital models of each material conveying unit and the material to be processed, including its attributes, state, and behavior rules. This unit establishes independent digital agents for each automated guided vehicle, each robotic arm, and each batch of material to be processed. These digital agents exist in the form of objects or entities in the digital twin model, with a series of real-time attributes that can be updated. For automated guided vehicles, their digital agents include attributes such as real-time speed vector, acceleration vector, current three-dimensional position coordinates, attitude (yaw angle, pitch angle, roll angle), battery power, current load mass, task identifier, target destination, and estimated arrival time. For robotic arms, their digital agents include joint angles, end effector position and attitude, grasping state, current load, etc. For materials to be processed, their digital agents include material identifier, type, size (length, width, height), weight, batch information, current position, target processing procedure, and priority, etc. These dynamic entity models not only store real-time data, but also encapsulate their behavior rules, such as the kinematics and dynamics models of automated guided vehicles, the inverse kinematics constraints of robotic arms, and the state transition logic of materials between different stations. These digital agents can accurately reflect the instantaneous state and potential behavior of physical entities, and are the key to achieving dynamic scheduling and control.

[0099] The real-time data mapping unit is responsible for mapping real-time data obtained by the data acquisition and perception module to the corresponding digital twin entities and updating their positions, states, and attributes. This unit periodically receives various real-time data streams from the data acquisition and perception module through high-speed data buses and standard communication protocols, such as Ethernet or industrial Internet of Things protocols (such as OPC UA, MQTT). These data streams include but are not limited to production line status, material identification information, conveying equipment position, speed, attitude, power, load, and environmental parameters. The real-time data mapping unit contains a data analysis and verification engine inside, which performs format conversion, noise reduction, outlier detection, and consistency checking on the received raw data. The verified data is bound to the corresponding entities in the digital twin model through predefined mapping rules. For example, the position data from the automated guided vehicle global positioning system receiver will be accurately updated to the "current three-dimensional position coordinates" attribute of the corresponding automated guided vehicle digital agent; the material type and quantity data from the material information identification device will update the "type" and "quantity" attributes of the corresponding batch of material digital agents. The data update frequency can reach real-time level, with a typical update period of 50 to 100 milliseconds, ensuring that the digital twin model is always highly synchronized with the physical world and provides high-freshness data support.

[0100] The state prediction and analysis unit predicts potential events such as equipment failure, material consumption, and traffic congestion based on historical data and real-time states, and performs trend analysis. This unit uses machine learning models such as recurrent neural networks or long short-term memory networks to analyze historical operation data and sensor data. By learning equipment wear patterns (such as abnormal motor current, vibration spectrum changes), traffic flow patterns (such as the efficiency of a specific intersection during peak hours), and material consumption trends (such as daily consumption of different types of profiles), this unit can predict equipment remaining life, potential traffic bottlenecks, and future material requirements. For example, by analyzing historical battery discharge curves and task loads, it can predict when an automated guided vehicle will have a battery level below a preset threshold under the current task queue. These predictions are sent to the task management and scheduling module in structured data form (such as predicted events, probability of occurrence, and prediction time window) to make forward-looking decisions, such as scheduling equipment maintenance in advance, adjusting material procurement plans, or optimizing delivery routes to avoid production interruptions or efficiency declines. This unit also performs trend analysis to identify long-term operating patterns and potential risks, generating reports for management reference.

[0101] The task management and scheduling module is the "decision center" of the system, generating, assigning, and optimizing material delivery tasks based on production orders, inventory status, and real-time information provided by the digital twin model. The degree of intelligence of this module directly determines the flexibility and efficiency of the entire system. The task management and scheduling module consists of a task generation unit, a task decomposition and priority sorting unit, a resource allocation unit, and a scheduling strategy optimization unit.

[0102] The task generation unit automatically generates material delivery tasks based on production orders issued by the production management system, current inventory levels, and material requirements. This unit receives production instructions from the manufacturing execution system and enterprise resource planning system, such as "Production Order No. XXX, 100 sets of window frames need to be processed, materials A, B, and C are needed." At the same time, it combines real-time inventory information in the digital twin model (such as the real-time quantity of profile A in the warehouse and the quantity of material B in the current production line buffer area) to automatically identify material shortages or upcoming material requirements. Once it detects that the material requirements do not match the existing inventory, or that a new material is needed for a certain processing station to complete the current batch processing, the task generation unit will automatically create a task description for "transporting material C from point A to point B." This task description includes detailed information such as material identifier, quantity, starting location (current material location or warehouse location), target location (such as cutting machine loading port), required completion time, material type, size, weight, and task creation time.

[0103] The task decomposition and prioritization unit breaks down complex conveying tasks into smaller sub-tasks and assigns a priority to each sub-task based on the urgency of the production plan, the importance of the material, and the state of the equipment. Large quantity material conveying tasks can be broken down into multiple batches or single item conveying sub-tasks to accommodate the load capacity of the conveying units and the rhythm of the production line. For example, a task of conveying 100 profiles can be broken down into 10 sub-tasks of conveying 10 profiles each. The prioritization is calculated based on a configurable weight function that considers multiple dimensions such as task deadline, risk of production line downtime, material value, and current production load. For example, an urgent material demand that would cause production line downtime is significantly boosted in priority. The priority score of a task can be calculated by the following formula:

[0104]

[0105] where, represents the priority score of the task; represents the urgency of the task, such as the inverse of the deadline or the quantified value of the risk of production line downtime; represents the criticality of the material to the production process, such as whether it is a unique material required for a bottleneck process; represents the value of the material; represents the demand of the current production load on the task; , , , are configurable weight parameters to balance the importance of different factors. The unit periodically re-evaluates the priority of generated tasks to adapt to the dynamic changes in the production environment.

[0106] The resource allocation unit assigns the most suitable material conveying tasks to the conveying units based on their current location, available state, load capacity, and task priority. The unit employs algorithms based on reinforcement learning or auction mechanisms to select the most suitable unit among multiple available conveying units for the current task demand. The reinforcement learning model learns and optimizes the task allocation strategy through continuous interaction with the digital twin environment to maximize the long-term cumulative reward (such as conveying efficiency, equipment utilization). The auction mechanism allows each conveying unit (agent) to "bid" for unassigned tasks based on its current state (such as location, power, current task queue length, load capacity), and the task management and scheduling module selects the optimal bidder as the "auctioneer". For example, for heavy materials, the system will preferentially select automated guided vehicles with strong carrying capacity; for urgent tasks, the closest and currently idle unit is selected. The allocation process considers the current task queue and estimated completion time of the conveying unit to avoid overloading or idling of individual vehicles, ensuring balanced and efficient use of resources.

[0107] The scheduling strategy optimization unit utilizes a multi-objective optimization algorithm to consider factors such as transportation efficiency, energy consumption, equipment utilization, and waiting time to optimize the overall scheduling plan. This unit uses heuristic search methods such as genetic algorithms or ant colony algorithms to search for the optimal solution in a vast scheduling plan space. The optimization objective function can be represented as:

[0108]

[0109] where, represents the total transportation time, represents the total transportation energy consumption, represents the average equipment utilization, represents the average production line waiting time; , , , are weight coefficients used to balance the importance of different optimization objectives. This optimization algorithm generates a series of scheduling plans that meet the constraint conditions through iterative search, and selects the optimal plan that minimizes the objective function value. The scheduling plan includes the task sequence, start time, end time, and expected path of each transportation unit. This unit periodically re-evaluates the scheduling plan to adapt to dynamic changes in the workshop state, such as new emergency task insertion, equipment failure, or traffic congestion, thereby achieving adaptive optimization.

[0110] The intelligent path planning and obstacle avoidance module serves as the "navigation system" for the transportation units, planning the optimal travel path for each transportation unit in the material transportation task and performing real-time dynamic obstacle avoidance to ensure the safety and efficiency of the transportation process. This module consists of a global path planning unit, a local obstacle avoidance and path correction unit, a multi-path pre-computation and selection unit, and a path conflict prediction and resolution unit.

[0111] The global path planning unit calculates an initial optimal path from the starting point to the target point for the transportation unit assigned with a task on the workshop layout topology of the digital twin model. This unit uses an improved A algorithm or a fast search random tree algorithm. The A algorithm uses a heuristic function to guide the search and can quickly find the shortest path in a complex static environment. Its cost function not only considers the path length, but also incorporates estimated transit time, transportation unit size, and maximum speed constraints. The fast search random tree algorithm explores the state space through random sampling and is suitable for high-dimensional continuous spaces, with advantages for nonholonomic constraint systems (such as the minimum turning radius of automated guided vehicles). This algorithm models fixed obstacles (walls, equipment) in the workshop layout as non-passable areas, generating a theoretically optimal path in open space. The path is represented as a series of waypoints and corresponding speed instruction sequences.

[0112] The local obstacle avoidance and path correction unit detects dynamic obstacles around the transport unit in real-time, such as other moving transport units, temporarily stacked materials, or personnel, and immediately adjusts the current travel path to avoid collisions. This unit combines lidar or vision sensor data to construct a local environmental grid map around the transport unit. This local map is centered on the transport unit and updates the surrounding environment information in real-time. This unit uses dynamic window approach or potential field-based method to calculate the required steering angle and speed adjustment for obstacle avoidance in real-time. The dynamic window approach samples within the current speed space of the transport unit, predicts the future trajectory of the transport unit under different speed instructions, and evaluates the safety, distance to the target point, and distance to the obstacle of these trajectories, thereby selecting the optimal speed instruction. The potential field-based method treats the target point as a source of attractive force and obstacles as a source of repulsive force, and the transport unit moves under the action of these virtual forces to avoid obstacles and approach the target. When obstacles are detected, the path correction unit will slow down, stop, or detour within a safe distance. The obstacle avoidance strategy needs to ensure that while the path is corrected, the stability of the transport unit and the continuity of the task are maintained. A simplified dynamic obstacle avoidance strategy can be represented as:

[0113]

[0114] where, is the command speed vector of the transport unit, including linear speed and angular speed; is the target speed vector; is the repulsive force vector from the obstacle, whose size is inversely proportional to the distance between the transport unit and the obstacle; is the attractive force vector from the target point; , , are weight coefficients used to adjust the influence of different factors on the command speed.

[0115] The multi-path pre-computation and selection unit, in the global path planning stage, not only generates the optimal path, but also simultaneously calculates and stores several alternative paths that meet the sub-optimal conditions. These alternative paths can be quickly activated through a simple path evaluation function when the main path is blocked, reducing decision-making delay. The generation of alternative paths can be achieved by modifying the cost function of the A* algorithm or introducing random perturbations to generate paths with different topologies. Each pre-computed path is accompanied by metadata such as estimated transit time, risk level, and required energy consumption. When the main path is blocked due to sudden obstacles or traffic congestion, the system can quickly select a currently optimal alternative path from the alternative path library based on real-time conditions.

[0116] The path conflict prediction and resolution unit receives motion prediction information from the multi-agent collaborative control module for adjacent conveying units and identifies potential collision or congestion points using time window algorithms or game theory methods. The time window algorithm assigns an estimated time window to each conveying unit for each road segment in the shared space, predicting conflicts by detecting overlaps in time windows. The game theory method treats each conveying unit as a rational agent, predicting and avoiding potential conflicts by analyzing their interactions. For predicted conflicts, the unit sends instructions to related conveying units to slow down, stop and wait, or detour to ensure smooth traffic flow. For example, at an intersection, if two automated guided vehicles are expected to arrive at the same time, the system will instruct one to slow down or stop and wait based on task priority or first-come-first-served principles, while the other passes through first.

[0117] The multi-agent collaborative control module is the key to realizing group intelligence in the system, coordinating the conflict-free operation of multiple conveying units in the shared space, optimizing traffic flow, forming formations, fault-tolerant, and energy management. The module consists of a traffic flow management unit, a formation formation and collaboration unit, a fault tolerance and task redistribution unit, and an energy management and charging scheduling unit.

[0118] The traffic flow management unit allocates and manages the passage rights of multiple conveying units at the conveying channel shared by the workshop and the intersection, preventing traffic congestion. The unit implements virtual traffic lights or time slice rotation mechanisms to control the priority of passing through the main roads and intersections of the workshop. Virtual traffic lights can dynamically adjust the red and green light cycle according to real-time traffic flow and the task priority of each conveying unit. The time slice rotation mechanism divides the passing time of the shared road segment into multiple time slices and assigns them to different conveying units. For example, when multiple conveying units approach an intersection at the same time, the system will allocate passage rights to them based on their task priority, distance to the intersection, or first-come-first-served principles. Units that fail to obtain passage rights will be instructed to wait outside a safe distance.

[0119] The formation formation and collaboration unit is used to coordinate multiple conveying units to form a collaborative formation when conveying large or heavy materials simultaneously. For example, when conveying ultra-long profiles or ultra-large glass panels, two or more automated guided vehicles may need to be synchronized to lift or carry. The unit uses collaborative sensing and distributed control algorithms to enable multiple automated guided vehicles to accurately maintain the preset relative position and speed. Collaborative sensing allows each unit in the formation to share its sensor data (such as position, speed, and obstacle information), establishing a common understanding of the environment and formation members. Distributed control algorithms ensure that each unit makes local decisions based on the overall goal of the formation and its own sensor data, while maintaining synchronization with other members, such as through a leader-following control strategy or virtual structure method to achieve precise control of the formation posture.

[0120] The fault tolerance and task reassignment unit is used to automatically reassign the unfinished tasks of a failed transport unit to other available units and adjust the path of the affected units when a certain transport unit fails. This unit continuously receives device status information from the execution and feedback module. Once a hardware failure (such as motor stall, sensor failure) or communication interruption of a certain transport unit is detected, this unit immediately marks the status of its in-transit tasks as abnormal and initiates an emergency scheduling process. First, it notifies other units around the failed unit to avoid secondary accidents. Then, the remaining tasks of the failed unit, including unfinished transport sub-tasks, are assigned to the nearest idle transport unit that matches the capabilities. At the same time, the path of the affected transport unit is updated to guide it to the nearest safe parking area or maintenance station.

[0121] The energy management and charging scheduling unit is used to monitor the power status of all automated guided vehicles and intelligently schedule their charging at charging stations to ensure the continuous operation of the system. Based on the real-time power of the automated guided vehicles, the estimated task volume (obtained from the task management and scheduling module), and the available status of the charging stations, this unit predicts the charging needs. It plans ahead to guide low-power automated guided vehicles to idle charging piles during task gaps or off-peak periods. The charging scheduling strategy takes into account the capacity of the charging stations, charging time, and the impact on overall transport efficiency, aiming to maximize the availability of automated guided vehicles without affecting the production plan. For example, the system will preferentially arrange charging at night or during lunch breaks, or after a short task is completed, a quick power-up will be done before going to the next task point.

[0122] The execution and feedback module is the "executor" of the system, which receives scheduling instructions and controls specific material transport equipment to execute transport tasks, while feeding back the execution results to the data collection and perception module. This module ensures that scheduling decisions in the digital space can be accurately executed in the physical world. The execution and feedback module consists of an instruction parsing and driving unit, a motion control unit, an execution state monitoring unit, and a data uploading unit.

[0123] The instruction parsing and driving unit receives high-level instructions from the task management and scheduling module and the multi-agent collaborative control module, and converts them into bottom-level control signals that can be recognized by the transport equipment. For example, abstract instructions such as "move to X, Y coordinates" or "grab materials" are translated into specific motor speed, servo angle, hydraulic system pressure instructions, or pneumatic valve switch instructions, and are sent to the corresponding actuator controller. This unit is responsible for unpacking, checking, and format conversion of instructions to ensure that instructions can be correctly understood and executed by transport equipment hardware.

[0124] The motion control unit precisely controls the travel speed, steering angle, lifting mechanism, and motion trajectory of the automated guided vehicle. This unit ensures that the transport equipment moves accurately according to the planned speed and trajectory through a closed-loop control system, such as a proportional-integral-derivative controller. For the automated guided vehicle, the motion control unit adjusts the output of the drive motor based on real-time position and speed feedback to achieve precise speed tracking, path tracking, and posture maintenance. For the robotic arm, its motion control involves inverse kinematics solving and torque control. Inverse kinematics solving converts the target position and posture of the end effector into the required angles of each joint, and torque control ensures that the robotic arm can accurately grasp, place, and transport materials with the required force and speed, while avoiding overload and vibration.

[0125] The execution state monitoring unit monitors the actual execution state of the transport equipment in real time, such as position, speed, load sensor data, and fault indications. This unit uses the transport equipment's own on-board encoders (to measure motor speed and position), current sensors (to monitor motor load and abnormalities), limit switches (to detect mechanism movement range), force sensors (to measure gripping force or load weight), and visual feedback systems (such as cameras installed at the end of the robotic arm for visual servoing and grasping confirmation) to continuously obtain the equipment's operating parameters and state indicators. These data are collected in real time, preliminarily processed, and time-stamped to form a continuous equipment operation state data stream.

[0126] The data upload unit encapsulates the data obtained by the execution state monitoring unit and sends it to the data acquisition and perception module through the communication interface. This unit uses low-latency wireless communication protocols such as Wi-Fi or the fifth generation mobile communication technology to efficiently transmit real-time execution data streams back to the data acquisition and perception module, ensuring the freshness of the digital twin model data. Data compression and encryption techniques are used during data transmission to reduce bandwidth usage and ensure data security. This real-time feedback mechanism is the basis for synchronously updating the digital twin model with the physical world and is a key input for state prediction and analysis, fault tolerance, and task redistribution.

[0127] The human-machine interaction and visualization module is a bridge between the user and the system, providing real-time monitoring of system status, manual intervention interfaces, and historical data query functions. This module aims to improve the usability, manageability, and transparency of the system. The human-machine interaction and visualization module consists of a real-time monitoring interface, a task management interface, an alarm and event recording unit, and a historical data query and report generation unit.

[0128] The real-time monitoring interface displays the digital twin model of the door and window processing workshop in a graphical manner, including the real-time positions, states, material flow directions, and production line operating conditions of all conveying units. The interface uses three-dimensional rendering technology to highly replicate the physical layout of the workshop on the display, including production equipment, shelves, conveying channels, etc. Icons or animation effects of different colors represent conveying units and materials in different states, such as green icons representing normal operation and task execution, yellow representing standby or idle, and red representing failure or abnormality. The material flow direction is intuitively displayed through dynamic arrows or path tracks, allowing operators to easily grasp the operating situation and material distribution of the entire workshop. The monitoring interface also provides zooming, panning, and rotating functions, allowing users to observe the workshop from different angles and levels of detail.

[0129] The task management interface provides functions for manually creating, modifying, or canceling material conveying tasks, and adjusting task priorities. The interface provides intuitive drag-and-drop operations, allowing operators to simply click and drag material icons from one storage area or processing station to another target location, and automatically trigger task generation and scheduling processes. Operators can also directly modify the targets, quantities, or priorities of existing tasks to respond to sudden production changes or urgent needs. All manual interventions are verified by the system to ensure the effectiveness and safety of the operations.

[0130] The alarm and event recording unit generates and displays alarms when the system detects abnormal conditions, such as device failure, material jamming, or security area intrusion, and records all event logs. All system events are stored according to timestamps and classified as warnings, errors, or emergency events, such as low battery level warnings for conveying units, mechanical arm grabbing failure errors, security grating triggered emergency shutdowns, etc. Emergency alarms can notify relevant management personnel through sound and light signals, interface pop-ups, SMS notifications, or email notifications to ensure that abnormal conditions can be discovered and handled in a timely manner. Event logs record the time, type, location, involved devices, processing results, and relevant operator information of the event, providing a basis for subsequent problem analysis and system optimization.

[0131] The historical data query and report generation unit allows users to query and analyze historical data of system operation and generate reports on efficiency, energy consumption, failure rate, etc. Users can set time ranges and query conditions, such as querying the utilization rate of a specific conveying unit in the past week, the average conveying time of a certain type of material, or the material turnover frequency of a certain processing station. Reports can be displayed in various visual forms such as charts and tables, and can be exported in formats such as PDF and Excel to support production management decision analysis and continuous improvement. These reports can help managers evaluate system performance, identify bottlenecks, and optimize production processes.

[0132] To sum up, the automatic material conveying system of the embodiment, through multi-dimensional data acquisition, digital twin model construction and real-time updating, task management and scheduling based on artificial intelligence, intelligent path planning and dynamic obstacle avoidance, multi-agent collaborative control, and intuitive human-computer interaction and visualization, builds a highly integrated, intelligent and self-adaptive material conveying solution for door and window processing workshops. The system fundamentally solves the shortcomings of traditional systems in automation, flexibility and intelligence, significantly improves material flow efficiency, reduces operating costs, improves production flexibility and the ability to cope with complex production environments, thereby providing key support for realizing intelligent manufacturing in the door and window processing industry. The system can effectively improve the overall production efficiency, automation level and intelligence level of the door and window processing workshop, bringing significant economic and social benefits to the enterprise.

Claims

1. An automated material conveying system for a door and window processing workshop, characterized in that, include: The data acquisition and sensing module acquires production line status data, material information identification data, conveying equipment operation data, and environmental sensing data within the door and window processing workshop. The digital twin and status maintenance module is used to construct and dynamically update digital twin models of physical entities in the workshop based on real-time multi-source data acquired by the data acquisition and sensing module, and to perform status prediction and trend analysis of potential events. The task management and scheduling module, in which the digital twin and status maintenance module sends the results of status prediction and trend analysis to the task management and scheduling module in the form of structured data, enables the task management and scheduling module to make forward-looking decisions. Based on production orders, current inventory status and precise context information provided by the digital twin model, it intelligently generates, decomposes, prioritizes and optimizes the allocation and scheduling of material transportation tasks with multiple objectives. The intelligent path planning and obstacle avoidance module plans the globally optimal travel path for each conveying unit in the material conveying task, and detects dynamic obstacles in real time by combining sensor data; The multi-agent collaborative control module is used to coordinate the conflict-free and efficient operation of multiple heterogeneous transport units in a shared space, specifically realizing traffic flow management, multi-unit formation grouping and cooperation, fault tolerance and task redistribution, as well as energy management and charging scheduling. The execution and feedback module is used to receive scheduling instructions and convert them into low-level control signals, thereby precisely controlling specific material conveying equipment to perform conveying tasks. The human-computer interaction and visualization module is used to provide real-time graphical monitoring of the system's operating status and a manual intervention interface for manually creating or modifying delivery tasks. The intelligent path planning and obstacle avoidance module includes: A global path planning unit is used to calculate an initial optimal path from the starting point to the target point for the assigned transport units on the workshop layout topology of the digital twin model. The global path planning unit employs an improved A / B algorithm. Algorithm or fast search random tree algorithm; The local obstacle avoidance and path correction unit is used to detect dynamic obstacles around the delivery unit in real time and immediately adjust the current travel path to avoid collisions. A multi-path pre-calculation and selection unit is used to pre-calculate multiple alternative paths, enabling rapid switching to a suboptimal path when the primary path is blocked; and The path conflict prediction and resolution unit is used to predict potential path intersections or congestion between different transport units and adjust their speeds or paths in advance to avoid conflicts. The multi-agent collaborative control module includes: The traffic flow management unit is used to allocate and manage the access rights of multiple transport units at shared transport channels and intersections in the workshop by implementing virtual traffic lights or time-slice rotation mechanisms to prevent traffic congestion. Formation grouping and collaborative units are used to coordinate multiple conveying units to form a collaborative formation when large or heavy materials need to be conveyed simultaneously, through collaborative sensing and distributed control algorithms, to jointly complete the conveying task. The fault-tolerance and task reassignment unit is used to automatically reassign unfinished tasks of a transport unit to other available units and adjust the paths of the affected units when a failure occurs; and The energy management and charging scheduling unit monitors the battery status of all automated guided vehicles and intelligently schedules them to charging stations to ensure the system's continuous operation.

2. The automated material conveying system for a door and window processing workshop according to claim 1, characterized in that, The data acquisition and sensing module includes: The production line status sensor group is used to monitor the working status, processing progress and fault information of each processing equipment in real time. The production line status sensor group includes photoelectric sensors, proximity switches, encoders and industrial cameras. The material information identification device is used to identify the type, specifications, quantity, current location, and target destination of the profiles, glass, and hardware materials to be processed. The material information identification device includes a QR code scanner, an RFID reader, and a machine vision system. A status sensor array for conveying equipment is used to acquire in real time the position, speed, attitude, power level, and load status of the automated guided vehicle, robotic arm, and conveyor belt unit. The sensor array includes a GPS receiver, an inertial measurement unit, a lidar sensor, and an ultrasonic sensor. An environmental sensing sensor group is used to monitor environmental parameters and dynamic obstacle information in the workshop. The environmental sensing sensor group includes a temperature sensor, a humidity sensor, and additional lidar or camera.

3. The automated material conveying system for a door and window processing workshop according to claim 1, characterized in that, The digital twin and state maintenance module includes: The workshop layout modeling unit is used to construct a static three-dimensional model of the door and window processing workshop, including the geometric topology of processing equipment, storage area, conveyor channel and safety area; The dynamic entity modeling unit is used to create a dynamic digital model of each material conveying unit and the material to be processed, including its attributes, state, and behavior rules. The real-time data mapping unit is used to map the real-time data acquired by the data acquisition and sensing module onto the corresponding digital twin entity, updating its position, status, and attributes; and The status prediction and analysis unit is used to predict equipment failures, material consumption, and potential traffic congestion events based on historical data and real-time status using machine learning models, and to perform trend analysis.

4. The automated material conveying system for a door and window processing workshop according to claim 1, characterized in that, The task management and scheduling module includes: The task generation unit is used to automatically generate material delivery tasks based on production orders issued by the production management system, current inventory levels, and material requirements. The task decomposition and priority sorting unit is used to break down complex conveying tasks into smaller sub-tasks and assign priorities to each sub-task based on the urgency of the production plan, the importance of materials, and the status of equipment. The resource allocation unit is used to allocate the most suitable material conveying task to the conveying unit based on its current location, availability, load capacity, and task priority, using an algorithm based on reinforcement learning or an auction mechanism; and The scheduling strategy optimization unit is used to optimize the overall scheduling scheme by adopting a multi-objective optimization algorithm, taking into account factors such as transportation efficiency, energy consumption, equipment utilization, and waiting time.

5. The automated material conveying system for a door and window processing workshop according to claim 1, characterized in that, The execution and feedback module includes: The instruction parsing and driving unit is used to receive instructions from the task management and scheduling module and the multi-agent collaborative control module, and convert them into low-level control signals that the conveying equipment can recognize. The motion control unit is used to precisely control the driving speed, steering angle, lifting mechanism, and movement trajectory of the robotic arm of the automated guided vehicle through a closed-loop control system. The execution status monitoring unit is used to monitor the actual execution status of the conveying equipment in real time, including position, speed, load sensor data, and fault indications; and The data uploading unit is used to encapsulate the data acquired by the execution status monitoring unit and send it to the data acquisition and sensing module via a low-latency wireless communication protocol.

6. The automated material conveying system for a door and window processing workshop according to claim 1, characterized in that, The human-computer interaction and visualization module includes: The real-time monitoring interface is used to graphically display the digital twin model of the door and window processing workshop using 3D rendering technology, including the real-time location, status, material flow direction, and production line operation status of all conveying units. The task management interface provides an intuitive drag-and-drop operation, allowing users to manually create, modify, or cancel material handling tasks and adjust task priorities. The alarm and event logging unit is used to generate and display alarms when the system detects abnormal conditions, and simultaneously log all events; and The historical data query and report generation unit is used to query and analyze historical data of system operation and generate reports on efficiency, energy consumption, and failure rate.

7. The automated material conveying system for a door and window processing workshop according to claim 4, characterized in that, The task decomposition and priority ranking unit calculates the priority ranking based on a configurable weight function, which comprehensively considers multiple dimensions such as task deadline, downtime risk of the involved production line, material value, and current production load. The scheduling strategy optimization unit uses a genetic algorithm or ant colony algorithm heuristic search method to find the optimal scheduling scheme through a multi-objective optimization function, which includes minimizing total transport time, minimizing transport energy consumption, maximizing average equipment utilization, and minimizing production line waiting time.

8. The automated material conveying system for a door and window processing workshop according to claim 1, characterized in that, The local obstacle avoidance and path correction unit, in conjunction with LiDAR or visual sensor data, constructs a local environmental grid map around the transport unit and uses a dynamic window method or a potential field-based method to calculate the steering angle and speed adjustment required for obstacle avoidance in real time. When an obstacle is detected, it decelerates, stops, or detours within a safe distance. The path conflict prediction and resolution unit receives motion prediction information from adjacent transport units from the multi-agent cooperative control module and uses a time window algorithm or game theory method to identify potential collisions or congestion points. For predicted conflicts, this unit sends instructions to the relevant transport units to decelerate, stop, or detour.

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