A distributed intelligent driving and control integrated logistics equipment control system and method

Abstract

The application discloses a kind of distributed intelligent drive control integrated logistics equipment control system and method, system includes several drive control integrated machine, is arranged on the equipment of logistics system;Adjacent drive control integrated machine is formed by daisy chain type physical connection and constitutes equipment communication network;Adjacent drive control integrated machine exchanges state information, control intent and scheduling request directly through preset decentralized communication protocol;Drive control integrated machine includes drive module, control module, I / O module, intelligent gateway module and AI acceleration module;Multiple AI agents directly communicate by decentralized communication protocol, constitute multi-agent system, and autonomously complete conveying line linkage, path planning and conflict resolution;The application greatly reduces control cabinet and wiring, realizes equipment level intelligentization and decentralized collaborative control.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and more specifically to a distributed intelligent integrated drive and control system and method for logistics equipment. Background Technology

[0002] Current logistics automation control systems generally adopt a centralized, hierarchical architecture of "PLC + I / O modules + drive units (frequency converters / servo drives) + field sensors". Specifically, the programmable logic controller (PLC) acts as the central control unit, connecting local and remote I / O modules via fieldbus or hardwiring. The I / O modules receive signals from sensors such as photoelectric switches and proximity switches and output control signals to actuators such as frequency converters and contactors. The frequency converters drive conveyor motors, elevator motors, stacker crane motors, and other equipment to complete material handling operations. Network communication (such as PROFINET, EtherCAT) and power supply (three-phase 380V and DC 24V) are laid through independent cable networks to each field device. This traditional architecture has the following significant problems in practical engineering:

[0003] (1) Numerous and large control cabinets: Each conveyor line or functional area usually requires one or more control cabinets, which are equipped with PLC, I / O modules, circuit breakers, contactors, frequency converters and other equipment, resulting in a large electrical room area and high civil engineering costs.

[0004] (2) Complex wiring and long construction period: Power cables and signal cables need to be laid from the control cabinet to each motor and each sensor. The amount of cables used is large, the cable tray space is tight, and the construction and maintenance are difficult.

[0005] (3) Poor system flexibility: When the equipment layout or process changes, it is necessary to rewire, modify the PLC program and I / O address mapping, which results in a long transformation cycle and high cost.

[0006] (4) Difficulty in fault location: The centralized architecture makes the impact of a single point of failure (such as PLC shutdown) large, and the fault diagnosis requires checking from the control cabinet to the field equipment segment by segment.

[0007] Therefore, there is an urgent need to design a distributed intelligent integrated drive and control system and method for logistics equipment, which integrates drive, control, I / O, network communication and edge computing capabilities into a single field device, eliminating centralized control cabinets and redundant wiring, and realizing a true distributed control architecture. Summary of the Invention

[0008] The purpose of this invention is to provide a distributed intelligent integrated drive and control system for logistics equipment to solve the problems existing in the prior art.

[0009] To achieve the above objectives, the present invention provides the following solution: The present invention provides a distributed intelligent integrated drive and control logistics equipment control system, comprising:

[0010] Several drive and control integrated machines are deployed on the equipment of the logistics system; adjacent drive and control integrated machines are physically connected by a daisy chain to form an inter-device communication network; adjacent drive and control integrated machines directly exchange status information, control intentions and scheduling requests through a preset decentralized communication protocol;

[0011] The integrated drive and control unit includes a drive module, a control module, an I / O module, a smart gateway module, and an AI acceleration module. The drive module controls the drive motor, the I / O module connects to and transmits signal feedback from sensors and actuators, the AI ​​acceleration module performs neural network model inference locally, and the smart gateway module enables data conversion and communication between various industrial bus protocols. The control module is electrically connected to the I / O module, the smart gateway module, the AI ​​acceleration module, and the drive module, and is used for AI inference scheduling calculations and execution of coordinated motor control algorithms.

[0012] Preferably, the communication network adopts a layered design, including a sensor bus layer, a device communication layer, and an industrial Ethernet layer; the industrial Ethernet layer realizes protocol conversion through the intelligent gateway module, and an external PLC or host controller can directly control the drive and control integrated machine through the standard industrial bus protocol;

[0013] The device communication layer adopts the decentralized communication protocol, enabling a single integrated drive and control machine to autonomously complete inter-device linkage control, path planning, and conflict resolution.

[0014] The sensor bus layer enables digital communication between the drive and control unit and the sensors.

[0015] Preferably, the daisy-chain physical connection method is that adjacent drive and control units are connected in series through two ports.

[0016] Preferably, it also includes an edge server, which is deployed in a local area network to receive non-real-time data and global optimization requests uploaded by the drive and control integrated machine, run a large-scale AI model to perform global scheduling optimization and energy consumption optimization, and distribute the optimization strategy to each of the drive and control integrated machines.

[0017] Preferably, the intelligent gateway module supports, but is not limited to, the following protocols: PROFINET, EtherCAT, CC-LinkIE, Modbus TCP, Modbus RTU, IO-Link, and AS-i, and is capable of bidirectional data conversion between any two protocols.

[0018] Preferably, the digital input / output channels of the I / O module are switched via DIP switches, and the same physical channel can be configured as a digital input or a digital output.

[0019] Preferably, the AI ​​acceleration module is an NPU.

[0020] A method for controlling distributed intelligent drive and control integrated logistics equipment, comprising a distributed intelligent drive and control integrated logistics equipment control system, including the following steps:

[0021] Multiple drive and control units are installed on the racks of corresponding equipment in the logistics system. Each drive and control unit drives a motor and forms a communication network between the devices through a daisy-chain physical connection.

[0022] In graphical configuration software, users can complete the topology configuration of the logistics system by dragging and dropping device icons and connecting lines. The software automatically parses the device connection relationship, generates the corresponding control logic and material scheduling strategy, and automatically generates the HMI interface.

[0023] The control logic and scheduling strategy are compiled and downloaded to each drive and control unit, and each drive and control unit runs as an independent intelligent agent.

[0024] During operation, each intelligent agent perceives the local state in real time through integrated I / O modules and sensor interfaces, performs real-time inference through AI acceleration modules, and autonomously decides the control behavior of the motor.

[0025] Adjacent AI agents directly exchange control intentions and scheduling requests through a decentralized communication protocol, and autonomously complete inter-device linkage control, path planning and conflict resolution through a distributed negotiation algorithm.

[0026] Preferably, the distributed negotiation algorithm in step S5 adopts a conflict resolution strategy based on priority, estimated arrival time, and energy consumption weighting. When there is a spatiotemporal conflict in the paths of multiple device agents, each agent independently calculates the path priority locally and compares them with each other. The path priority is calculated and scored by a combination of factors such as the current task priority of the goods being carried, directional consistency, segment occupancy time, estimated arrival time, waiting time limit, energy consumption, and fault status. The agent with the best score obtains priority passage.

[0027] The present invention discloses the following technical effects: significantly reducing control cabinets and wiring: the distributed deployment and daisy-chain connection of the drive and control integrated machine reduces the number of field control cabinets by more than 75%, the total length of power cables and signal cables by more than 60%, and shortens the construction period by more than 50%.

[0028] Decentralization brings robustness: multi-agent distributed decision-making eliminates the risk of single point of failure—the failure of a single device only affects a local workstation, and adjacent devices can automatically renegotiate material routes, greatly improving the overall availability of the system.

[0029] Real-time intelligent operation and maintenance: The built-in AI inference capabilities of each device enable fault prediction and anomaly detection to be implemented at the device level, reducing response time from the traditional minutes (relying on the host computer polling) to the millisecond level, significantly improving equipment utilization and maintenance efficiency. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0031] Figure 1 This is a diagram showing the overall architecture of the integrated drive and control logistics control system of the present invention.

[0032] Figure 2 This is a schematic diagram of the integrated drive and control unit of the present invention.

[0033] Figure 3 This is a schematic diagram of the multi-agent distributed cooperative control architecture of the present invention;

[0034] Figure 4 This is a schematic diagram of the graphical configuration software interface of the present invention;

[0035] Figure 5 This is a diagram of the system communication layered architecture of the present invention;

[0036] Figure 6 This is a schematic diagram of the daisy-chain physical topology connection of the present invention;

[0037] Figure 7 This is a flowchart of the multi-agent conveyor line linkage control in an embodiment of the present invention;

[0038] Figure 8 This is a flowchart of the multi-agent path planning and conflict resolution algorithm in an embodiment of the present invention;

[0039] Figure 9 This is a schematic diagram of the internal data flow of the integrated drive and control unit of the present invention; Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] This invention discloses a distributed intelligent drive and control integrated logistics equipment control system, such as... Figure 1 As shown, it includes an integrated drive and control unit; the integrated drive and control unit adopts a rack-mounted structure and can be directly installed on the equipment rack of a conveyor line, elevator, or stacker crane; each integrated drive and control unit drives one motor per pair, with rated power covering asynchronous motors below 7.5kW, and also supports servo motor drive.

[0042] Furthermore, the integrated drive and control unit can also control and drive 2-3 motors simultaneously.

[0043] In one embodiment of the present invention, such as Figure 9 As shown, each drive and control unit is defined as an independent AI agent; the agent has the following functions:

[0044] Local sensing: Through integrated I / O modules and sensor interfaces, the device's own status (current, temperature, vibration, speed) and surrounding environmental information (photoelectric switch status, encoder status, material position, etc.) are collected in real time.

[0045] Real-time inference: Utilizes the AI ​​acceleration module to run lightweight AI models, enabling edge intelligence functions such as control parameter self-tuning, fault prediction, energy efficiency optimization, and anomaly detection; the inference results are directly applied to the local control loop without needing to be uploaded to the cloud.

[0046] Autonomous decision-making: Based on preset control rules and AI inference results, it autonomously decides on the motor's start-stop, forward and reverse rotation, speed adjustment and other control behaviors, rather than passively waiting for instructions from the superior controller.

[0047] Inter-agent communication: State information, control intentions and scheduling requests are exchanged directly with neighboring intelligent agents through a decentralized communication protocol (E-Link protocol) without going through a central controller.

[0048] In one embodiment of the present invention, such as Figure 3 As shown, multiple agents constitute a multi-agent system with L3 (conditional autonomy) collaborative decision-making capabilities: each agent makes decisions autonomously through the E-Link protocol; when communication is interrupted or the edge server is unreachable, each agent degrades to fully local autonomous mode, and automatically synchronizes its state and resumes negotiation after communication is restored.

[0049] Conveyor line linkage: After the upstream intelligent agent detects the arrival of materials, it automatically notifies the downstream intelligent agent to prepare to receive them; the downstream intelligent agent decides autonomously whether to allow material transfer based on its own status (ready / busy / faulty), realizing automatic coordination of the conveyor line without a central PLC.

[0050] Path planning and conflict resolution: When multiple RGVs or stacker cranes share the same track or intersecting area, the relevant agents conduct distributed negotiation through the E-Link protocol and use optimization algorithms based on priority, estimated arrival time and energy consumption to autonomously achieve a conflict-free passage sequence.

[0051] Fault self-healing: When a device agent detects its own fault or communication interruption, neighboring agents automatically renegotiate task allocation and reroute affected materials to alternative paths, achieving system-level fault tolerance.

[0052] The multi-agent computing architecture adopts a hybrid "local + edge" mode: each drive and control unit is responsible for real-time inference and control within 100 milliseconds; when more complex global optimization is required (such as plant-wide scheduling strategy optimization, global energy consumption optimization), the agent can send non-real-time data and inference requests to the edge server deployed in the local area network, and the edge server runs a larger-scale AI model to solve the global problem and distributes the optimization strategy to each agent for execution.

[0053] In one embodiment of the present invention, such as Figure 2 As shown, the integrated drive and control unit includes the following modules:

[0054] Drive module: High-performance inverter unit is built using IGBT power modules, supporting three modes: V / F control, sensorless vector control, and closed-loop vector control, to realize frequency conversion speed regulation and servo control of 0.55kW to 7.5kW three-phase asynchronous motors and permanent magnet synchronous servo motors; the switching frequency is adjustable from 4-12kHz to meet the dynamic response and energy efficiency requirements under different operating conditions.

[0055] Control module: It has a built-in high-performance embedded processor (ARM Cortex-A series or equivalent), runs a real-time operating system (RTOS), and is responsible for the closed-loop control algorithm of the motor and the inference calculation of the local AI agent.

[0056] I / O Module: Integrated configurable digital input / output channels (basic model 4DI / 2DO, extended model 6DI / 4DO). Some DI / DO channels can be freely switched between input and output functions via DIP switches to adapt to the access requirements of different field sensors.

[0057] Intelligent gateway module: integrates multi-protocol industrial Ethernet interfaces, supports mainstream industrial bus protocols such as PROFINET, EtherCAT, CC-Link, Modbus TCP / RTU (RS-485), realizes bidirectional conversion between protocols (such as AS-i to PROFINET, IO-Link to PROFINET), and ensures seamless integration with third-party PLCs and host systems.

[0058] AI acceleration module: Built-in NPU (Neural Processing Unit) or equivalent AI inference acceleration hardware, supporting the local running of lightweight deep learning models (such as anomaly detection, lifetime prediction, control parameter self-optimization, etc.) with inference latency of less than 100 milliseconds.

[0059] Furthermore, it also includes an integrated communication and power supply interface: it adopts a standard industrial Ethernet RJ45 interface to achieve daisy-chain connection; each drive and control unit has two Ethernet ports, which can transmit real-time control data, equipment status information and negotiation messages between AI agents simultaneously through a single network cable, while powering the equipment through a 24V DC power bus.

[0060] In one embodiment of the present invention, adjacent drive and control integrated machines are physically connected in a daisy chain to form an inter-device communication network; adjacent drive and control integrated machines directly exchange status information, control intentions and scheduling requests through a preset decentralized communication protocol;

[0061] Furthermore, such as Figure 5 As shown, the communication network adopts a layered design, including a sensor bus layer, a device communication layer, and an industrial Ethernet layer;

[0062] Industrial Ethernet Layer: The integrated drive and control unit supports multiple mainstream industrial Ethernet protocols and can convert between protocols through the intelligent gateway module to ensure seamless integration with third-party PLCs and upper-level SCADA / MES systems. Third-party devices can directly control the integrated drive and control unit (transparent transmission mode) through the standard industrial bus protocol. In this mode, the integrated drive and control unit operates as a standard actuator, while the internal AI inference module keeps running to monitor the status. Through the intelligent gateway module, protocol conversion is achieved, and external PLCs or upper-level controllers can directly control the integrated drive and control unit through the standard industrial bus protocol.

[0063] Device communication layer: Employs the E-Link protocol defined in this invention, a lightweight, decentralized inter-agent communication protocol based on real-time Ethernet; the core technical features of the E-Link protocol include:

[0064] Message type definition: The E-Link protocol defines standard message frames;

[0065] Status broadcast frame (periodically sends device operating status, including motor speed, current, temperature, I / O status, etc., with a default period of 500 microseconds);

[0066] Control intent frame (event-driven, used to transmit control signals such as material arrival, readiness, and completion of transmission);

[0067] Scheduling request / response frame (used for path occupancy requests and negotiation responses, containing fields such as segment ID, priority, and estimated occupancy duration);

[0068] Anomaly alarm frame (broadcast immediately after fault detection, containing fault code and context data).

[0069] Addressing and routing mechanism: Each drive and control unit is assigned a unique device identifier (Device ID, 48 bits) at the factory. E-Link adopts relative addressing based on topological location - each agent only maintains the address table of its directly adjacent devices (previous hop and next hop). Messages from non-adjacent devices are forwarded hop by hop, avoiding the overhead of maintaining a global routing table.

[0070] Real-time performance guarantee: The E-Link protocol is based on the IEEE 802.1Qav Time-Sensitive Network (TSN) priority scheduling mechanism, which maps control messages to the highest priority queue (Priority 7) to ensure that the end-to-end communication delay is less than 1 millisecond and the jitter is less than 100 microseconds, meeting the strict real-time requirements of multi-motor linkage control.

[0071] Reliability and fault tolerance: E-Link adopts a message confirmation and timeout retransmission mechanism (ACK / NAK, retransmission timeout of 500 microseconds, maximum retransmission of 3 times) and automatically triggers local autonomous degradation mode when communication is interrupted; after the agent does not receive heartbeat frames from neighboring agents for 3 consecutive cycles, it enters independent operation state, relies on local sensors and AI inference to maintain safe operation, and automatically resumes collaboration through state synchronization frames after communication is restored.

[0072] Sensor bus layer: Primarily using IO-Link and secondarily using AS-i, it enables digital communication between the integrated drive and control unit and field sensors (photoelectric switches, proximity switches, encoders, RFID, etc.). IO-Link provides point-to-point bidirectional digital communication capabilities (maximum 230.4kbps), supporting remote configuration of sensor parameters and feedback of diagnostic information; AS-i is used for low-cost access of simple switching sensors. The integrated drive and control unit housing features an M12 standard quick-connect interface, compatible with IO-Link sensors from mainstream brands on the market.

[0073] Furthermore, such as Figure 6As shown, the daisy-chain physical connection method uses two RJ45 ports to achieve a "one-in, one-out" serial topology for each device, significantly reducing the number of switch ports and cable length. At the same time, the power bus (DC 24V or AC 380V) is laid along the same physical path, and power supply is cascaded through the power interface on the device casing.

[0074] In one embodiment of the present invention, the distributed intelligent drive and control integrated logistics equipment control method based on the above system includes the following steps:

[0075] Multiple drive and control units are installed on the racks of corresponding equipment in the logistics system. Each drive and control unit drives a motor and forms a communication network between the devices through a daisy-chain physical connection.

[0076] In graphical configuration software, users can complete the topology configuration of the logistics system by dragging and dropping device icons and connecting lines. The software automatically parses the device connection relationship, generates the corresponding control logic and material scheduling strategy, and automatically generates the HMI interface.

[0077] The control logic and scheduling strategy are compiled and downloaded to each drive and control unit, and each drive and control unit runs as an independent intelligent agent.

[0078] During operation, each intelligent agent perceives the local state in real time through integrated I / O modules and sensor interfaces, performs real-time inference through AI acceleration modules, and autonomously decides the control behavior of the motor.

[0079] Adjacent AI agents directly exchange control intentions and scheduling requests through a decentralized communication protocol, and autonomously complete inter-device linkage control, path planning and conflict resolution through a distributed negotiation algorithm.

[0080] Preferably, the distributed negotiation algorithm in step S5 adopts a conflict resolution strategy based on priority, estimated arrival time, and energy consumption weighting. When there is a spatiotemporal conflict in the paths of multiple device agents, each agent independently calculates a weighted score locally and compares them with each other. The agent with the best score obtains priority passage.

[0081] In one embodiment of the present invention, such as Figure 4 As shown, a graphical configuration software platform is also provided, fundamentally changing the engineering deployment method of traditional logistics control systems. The core features of this software platform include:

[0082] Equipment-level drag-and-drop configuration: The software provides a component library containing standard logistics equipment elements such as "belt conveyor", "lifting platform", "RGV", "elevator" and "stall crane". Users can drag and drop equipment icons from the component library onto the canvas, and the system will automatically identify the equipment type and match the required number and model of drive and control units.

[0083] Connecting devices defines the control logic: Users connect device icons on the canvas according to their physical layout using the connection tool. The software automatically parses the connection relationships and generates the corresponding control logic. For example, connecting "Conveyor Line A" to "Conveyor Line B" automatically generates a handshake protocol for material transfer (the upstream sends a "material arrived" signal, and the downstream responds with a "ready" signal) and an automatic matching algorithm for the conveyor speed.

[0084] Automatic HMI Interface Generation: After the configuration canvas is completed, the software automatically generates the corresponding HMI (Human Machine Interface) screen, which includes equipment status indicators, operating parameter displays, alarm information, and manual operation buttons, without the need for additional HMI programming.

[0085] WCS scheduling functionality integration: The software incorporates a WCS (Warehouse Control System) scheduling engine. Users can define the origin, destination, and path constraints of materials on the configuration canvas. The software automatically generates material scheduling strategies and distributes them to each device agent for execution. The scheduling strategies support priority queuing, path optimization, and dynamic rerouting.

[0086] WMS Interface: The software provides a standardized WMS (Warehouse Management System) interface (RESTful API / WebSocket) to receive inbound and outbound task instructions issued by the upper-layer WMS and to transmit the execution status and equipment operation data back in real time.

[0087] One-click deployment: After configuration, the software automatically compiles and downloads the control logic, HMI configuration and scheduling strategy to each drive and control unit on site, realizing the "design as deployment" engineering closed loop.

[0088] Software license management: The system controls software licenses based on the actual number of drive and control unit hardware units sold. Each device is bound with a unique hardware identification code (Device ID) at the factory. The software platform activates the licenses for the corresponding number of devices through online verification or offline license files.

[0089] In Embodiment 1 of the present invention, this embodiment describes the hardware configuration of a basic model integrated drive and control unit. This integrated drive and control unit uses a die-cast aluminum alloy shell with an IP65 protection rating, a rack-mounted structure, and external dimensions of approximately 300mm × 180mm × 120mm. It internally includes the following core components:

[0090] Processor: Uses TI AM6548 or NXP i.MX 8M Plus SoC, integrating a quad-core ARM Cortex-A53 (1.5GHz) for control logic and AI inference scheduling, paired with a Cortex-M7 coprocessor (800MHz) for real-time motor control algorithms (FOC vector control), and the two exchange data through internal shared memory.

[0091] AI Accelerator: Built-in NPU (or equivalent GPU acceleration unit) with 2.3 TOPS computing power, supporting lightweight model inference for TensorFlow Lite Micro and ONNX Runtime.

[0092] Drive module: Supports three-phase asynchronous motors from 0.55kW to 7.5kW and permanent magnet synchronous servo motors. The inverter uses IGBT power modules with an adjustable switching frequency of 4-16kHz, supporting three modes: V / F control, sensorless vector control, and closed-loop vector control.

[0093] I / O module: 6-channel digital input (24V DC, NPN / PNP compatible, opto-isolated), 2-channel digital output (24V DC, 500mA per channel), where the first 2 DI channels can be switched to DO function via DIP switches to achieve flexible configuration of 6DI / 2DO or 4DI / 4DO.

[0094] Communication interfaces: 2 RJ45 Gigabit Ethernet ports (supporting PROFINET RT / IRT, EtherCAT slave, Modbus TCP), 1 RS-485 interface (supporting Modbus RTU), 1 IO-Link master interface (compliant with IEC 61131-9 standard), 1 AS-i master interface (compliant with EN 50295 standard).

[0095] Sensor expansion interface: The device housing has two M12 standard quick-connect interfaces, which are compatible with IO-Link sensors from mainstream brands on the market (such as Yike, Pepperl+Fuchs, Turck, etc.). When using proprietary brand sensors, automatic recognition can be achieved without configuration through the preset sensor configuration file.

[0096] Power supply: Supports two power supply methods: 24V DC power supply (maximum 60W) and three-phase 380V AC independent power supply. Users can choose according to the site conditions.

[0097] To verify the effect of reducing control cabinets, a typical logistics conveying system with 40 drive points (30 conveyor lines, 4 lifting platforms, 3 RGVs, 2 hoists, and 1 stacker crane) is used as an example for comparative analysis: In the traditional solution, one local control cabinet is required for every 20 drive points, with cabinet dimensions of 1200mm width * 500mm depth * 2000mm height (including PLC slave station, I / O modules, circuit breakers, contactors, frequency converters, etc.). The stacker crane and hoist require independent control cabinets, totaling 3 control cabinets. The master station PLC is integrated into one of the control cabinets, for a total of 4 control cabinets.

[0098] After adopting the integrated drive and control unit solution of this invention, each integrated drive and control unit is directly installed on the equipment rack, completely replacing the functions of the frequency converter, I / O module, and PLC slave station. Only one master station edge server cabinet is needed (for global optimization scheduling, not required for real-time control). The total number of control cabinets is reduced from 4 to 1, a reduction of approximately 75%. Considering that some special function cabinets may be retained on site (such as main power supply distribution, DC power distribution, and network distribution), the actual reduction rate remains stable at over 80%, consistent with the discussion of the beneficial effects. At the same time, the power cable is changed from being laid separately at each drive point to a daisy-chain series power supply, reducing the total cable length by about 65% and shortening the construction period from 30-45 days in the traditional solution to 10-15 days.

[0099] In Embodiment 2 of the present invention, as Figure 7 As shown, multi-agent conveyor line linkage control;

[0100] This embodiment uses a three-section conveyor line driven by three integrated drive and control units as an example to describe the process of autonomous linkage control of multiple intelligent agents.

[0101] Conveyor lines A, B, and C are connected in series, with materials transferred from A to C. In traditional solutions, a PLC is needed for centralized programming to implement the interlocking logic of the three conveyor lines. In this invention:

[0102] Step 1: In the configuration software, connect the icons of the three equipment lines, "Conveyor Line A", "Conveyor Line B" and "Conveyor Line C", sequentially, and set the material flow direction to A→B→C. The software automatically generates the control logic and downloads it to the three integrated drive and control units.

[0103] Step 2: The drive and control unit (intelligent agent A) of conveyor line A detects that the photoelectric switch is blocked (material has arrived at the end of A) through the I / O module. The NPU of intelligent agent A runs the material detection model to confirm the material type and size.

[0104] Step 3: Agent A sends a material delivery request message (including material size, current speed, and estimated arrival time) to Agent B via the E-Link protocol.

[0105] Step 4: After receiving the request, agent B checks its own status (whether the conveyor line is running normally, whether there is material passing through, and whether there is congestion downstream). If the status allows, it replies to agent A with a "ready" confirmation message and adjusts its own speed to match the material transfer.

[0106] Step 5: After receiving the "ready" confirmation, agent A transfers the material to conveyor line B and sends a "transfer complete" message.

[0107] Step 6: Agent B negotiates with Agent C in the same way to complete the transfer of materials in the B→C segment.

[0108] Throughout the process, the three integrated drive and control units communicate and negotiate directly without going through any central controller. If agent B is in a fault state (such as motor overheat protection), it replies with an "unavailable" message to agent A. Agent A then performs local AI reasoning to determine whether conveyor line A should be stopped to prevent material accumulation, and reports the anomaly to the configuration software and WCS, requesting a global rerouting.

[0109] In Embodiment 3 of the present invention, as Figure 8 As shown, multi-agent path planning and conflict resolution

[0110] This embodiment uses two RGVs sharing the same circular track as an example to describe how multiple agents can autonomously negotiate and resolve path conflicts through the E-Link protocol.

[0111] Step 1: The RGV-A agent and the RGV-B agent each maintain their own "orbit occupancy schedule", recording the orbital segments they will occupy in the current and future periods.

[0112] Step 2: When the RGV-A is scheduled to enter a certain track segment, it will broadcast a "segment occupancy request" (including segment ID, entry time, estimated occupancy duration, and priority) via the E-Link protocol.

[0113] Step 3: After receiving the request, RGV-B compares the requested segment with its own "track occupancy schedule" to see if there is a spatiotemporal conflict. If there is no conflict, it replies "agree"; if there is a conflict, it runs a local conflict resolution algorithm (weighted by priority, waiting time, and energy consumption), proposes an offset time suggestion, and replies.

[0114] Step 4: RGV-A collects responses from all neighboring agents, runs a local decision-making algorithm to select the optimal passage time, updates its own "track occupancy schedule", and broadcasts confirmation.

[0115] Step 5: When the system scales up to multiple RGVs and multiple stacker cranes, each agent can further solve complex global optimization problems (such as global optimal scheduling of multiple vehicles) through the edge server. After receiving the optimization results, the agents adjust their local schedules.

[0116] In Embodiment 4 of the present invention, a third-party device can control the integrated drive and control unit through transparent transmission;

[0117] This embodiment describes the compatible working mode of the drive and control unit as a standard actuator when a third-party PLC system already exists at the user's site.

[0118] Step 1: Switch the working mode of the drive and control unit to "Transparent Mode" using the configuration software or the DIP switch on the device.

[0119] Step 2: In transparent transmission mode, the integrated drive and control unit appears as a standard PROFINET or EtherCAT slave device. Third-party PLCs can send control commands such as start / stop, forward / reverse rotation, and speed setting through standard bus protocols.

[0120] Step 3: The AI ​​inference module inside the drive and control unit continues to run in transparent mode, continuously performing fault prediction and status monitoring, but the inference results do not directly interfere with the control output (or can be limitedly interfered with through configurable permission policies, such as emergency shutdown protection).

[0121] Step 4: When the user subsequently chooses to migrate to the multi-agent distributed control mode of this invention, they only need to switch the mode and re-download the configuration in the configuration software, without replacing the hardware.

[0122] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0123] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A distributed intelligent integrated drive and control system for logistics equipment, characterized in that, include: Several integrated drive and control units are deployed on equipment in the logistics system; The adjacent drive and control integrated machines are physically connected in a daisy chain to form an inter-device communication network; Adjacent drive and control units directly exchange status information, control intentions, and scheduling requests through a preset decentralized communication protocol; The integrated drive and control unit includes a drive module, a control module, an I / O module, a smart gateway module, and an AI acceleration module. The drive module controls the drive motor, the I / O module connects to and transmits signal feedback from sensors and actuators, the AI ​​acceleration module performs neural network model inference locally, and the smart gateway module enables data conversion and communication between various industrial bus protocols. The control module is electrically connected to the I / O module, the smart gateway module, the AI ​​acceleration module, and the drive module, and is used for AI inference scheduling calculations and execution of coordinated motor control algorithms.

2. The distributed intelligent drive and control integrated logistics equipment control system according to claim 1, characterized in that: The communication network adopts a layered design, including a sensor bus layer, a device communication layer, and an industrial Ethernet layer; the industrial Ethernet layer realizes protocol conversion through the intelligent gateway module, and an external PLC or host controller can directly control the drive and control integrated machine through the standard industrial bus protocol; The device communication layer adopts the decentralized communication protocol, enabling a single integrated drive and control machine to autonomously complete inter-device linkage control, path planning, and conflict resolution. The sensor bus layer enables digital communication between the drive and control unit and the sensors.

3. The distributed intelligent drive and control integrated logistics equipment control system according to claim 1, characterized in that: The daisy-chain physical connection method is that adjacent drive and control integrated machines are connected in series through two ports.

4. The distributed intelligent drive and control integrated logistics equipment control system according to claim 1, characterized in that: It also includes an edge server, which is deployed in a local area network to receive non-real-time data and global optimization requests uploaded by the drive and control integrated machine, run a large-scale AI model to perform global scheduling optimization and energy consumption optimization, and distribute the optimization strategy to each of the drive and control integrated machines.

5. The distributed intelligent drive and control integrated logistics equipment control system according to claim 1, characterized in that, The intelligent gateway module supports, but is not limited to, the following protocols: PROFINET, EtherCAT, CC-Link IE, Modbus TCP, Modbus RTU, IO-Link, and AS-i, and can perform bidirectional data conversion between any two protocols.

6. The distributed intelligent drive and control integrated logistics equipment control system according to claim 1, characterized in that: The digital input / output channels of the I / O module can be switched via DIP switches, and the same physical channel can be configured as a digital input or a digital output.

7. The distributed intelligent drive and control integrated logistics equipment control system according to claim 1, characterized in that: The AI ​​acceleration module uses an NPU.

8. A distributed intelligent drive-control integrated logistics equipment control method, comprising the distributed intelligent drive-control integrated logistics equipment control system as described in any one of claims 1-7, characterized in that, Includes the following steps: Multiple drive and control units are installed on the racks of corresponding equipment in the logistics system. Each drive and control unit drives a motor and forms a communication network between the devices through a daisy-chain physical connection. In graphical configuration software, users can complete the topology configuration of the logistics system by dragging and dropping device icons and connecting lines. The software automatically parses the device connection relationship, generates the corresponding control logic and material scheduling strategy, and automatically generates the HMI interface. The control logic and scheduling strategy are compiled and downloaded to each drive and control unit, and each drive and control unit runs as an independent intelligent agent. During operation, each intelligent agent perceives the local state in real time through integrated I / O modules and sensor interfaces, performs real-time inference through AI acceleration modules, and autonomously decides the control behavior of the motor. Adjacent AI agents directly exchange control intentions and scheduling requests through a decentralized communication protocol, and autonomously complete inter-device linkage control, path planning and conflict resolution through a distributed negotiation algorithm.

9. The distributed intelligent drive and control integrated logistics equipment control method according to claim 8, characterized in that: The distributed negotiation algorithm in step S5 adopts a conflict resolution strategy based on priority, estimated arrival time, and energy consumption weighting. When there is a spatiotemporal conflict in the paths of multiple device agents, each agent independently calculates the path priority locally and compares them with each other. The path priority is calculated and scored by a combination of factors such as the current task priority of the goods being carried, directional consistency, segment occupancy time, estimated arrival time, waiting time limit, energy consumption, and fault status. The agent with the best score obtains priority passage.

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