Outdoor heavy-load AGV navigation control system and control method thereof

Abstract

The invention discloses an outdoor heavy-load AGV navigation control system and a control method thereof, and aims to solve the problems of low navigation precision and poor control stability of an existing outdoor heavy-load AGV in a complex environment. The system comprises a hardware system and a control method. The hardware system comprises a navigation box, a domain controller, a sensor assembly, an execution and interface assembly, a power supply assembly, a communication assembly, a man-machine interaction and monitoring assembly and an electrical control assembly; the control method comprises the following steps: 1) the environment sensing module based on multi-sensor fusion adopts a Kalman filtering algorithm to construct a high-precision map and detect obstacles; the path planning module is used for generating and optimizing a safe path in real time in combination with an improved A * algorithm; 3) the motion control module integrates a PID control algorithm and a fuzzy control algorithm to realize accurate tracking; 4) the system monitoring module monitors the state of the AGV in real time; and the navigation precision, the operation stability and the safety of the outdoor heavy-load AGV in a complex dynamic environment are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic guided vehicle (AGV), in particular to an outdoor heavy-load AGV navigation control system and a control method thereof, which is suitable for the navigation control of heavy-load AGV in complex outdoor environment, and especially for the high-precision and high-stability navigation and control requirements in large-tonnage transportation scenarios. BACKGROUND

[0002] AGV is widely used in industrial automation and logistics transportation; at present, it mainly concentrates in indoor environment and relies on preset path (such as magnetic stripe and two-dimensional code) navigation; the outdoor environment has significant challenges: such as complex and changeable terrain, weather influence, and a large number of dynamic obstacles (pedestrians and vehicles); for heavy-load AGV, it is particularly difficult to achieve high-precision navigation and stable control while carrying large-tonnage goods; the current outdoor AGV system generally has problems of insufficient navigation precision, poor environmental adaptability, and low control stability, which is difficult to meet the stringent requirements of outdoor heavy-load transportation. SUMMARY

[0003] The purpose of the present application is to overcome the deficiencies of the prior art, and to provide an outdoor heavy-load AGV navigation control system.

[0004] Another purpose of the present application is to provide a control method of the outdoor heavy-load AGV navigation control system.

[0005] The technical solution provided by the present application is: an outdoor heavy-load AGV navigation control system, which is characterized by comprising a navigation box, a core processing unit, a sensor assembly, an execution and interface unit, a power supply unit, a communication unit, a man-machine interaction and monitoring unit, an electrical control unit, and a laser control box hardware assembly. The navigation box comprises a high-power exhaust fan, a high-density filter screen, and a waterproof aviation socket. The core processing unit is a domain controller, which comprises a multi-core processor, a real-time operating system module, and a vehicle communication protocol stack interface. The sensor assembly comprises a 3D laser sensor, a millimeter wave radar, a GPS receiver, and a vehicle-mounted camera. The execution and interface unit comprises a small industrial computer IO box and an electric control box. The power supply unit comprises a DCDC module and a direct current circuit breaker. The communication unit comprises a wireless router, a 5G module, a wireless network bridge, and a POE switch. The man-machine interaction and monitoring unit comprises an industrial voice broadcaster, an RGB screen, a video monitoring NVR, an IOT device, and a mushroom head emergency stop button. The electrical control unit comprises a total electric switch, a system switch, a relay, and a contactor. The laser control box is connected with a 3D laser sensor; The domain controller is connected with a small industrial computer IO box, an electric control box, a GPS receiver, a video monitoring NVR, an RGB screen, an IOT device, a wireless router, a 5G module, a wireless network bridge, and a POE switch through a communication wire harness;The vehicle-mounted camera is connected with the video monitoring NVR through a wire harness;The DCDC module supplies power for the domain controller, the small industrial computer IO box, the electric control box, the 3D laser sensor, the vehicle-mounted camera, the GPS receiver, the millimeter wave radar, the IOT device, the video monitoring NVR, the relay, the contactor, the wireless router, the 5G module, the wireless network bridge, the POE switch, and the laser control box through a power supply wire harness.

[0006] The control method of the outdoor heavy-load AGV navigation control system comprises the following steps: a. Environment perception: collecting environment data through a 3D laser sensor, a millimeter wave radar, a vehicle-mounted camera, and a GPS receiver, performing multi-sensor data fusion by using a Kalman filtering algorithm, generating an environment map with centimeter-level precision, and detecting and marking the position and motion information of obstacles in real time; b. Path planning: generating an initial global path by using an improved A* algorithm according to a task target, an environment map, and obstacle information;The cost function of the improved A* algorithm is f(n) = g(n) + h(n) + a·L(n) + b·E(n), wherein g(n) is the actual cost from the starting point to node n, h(n) is the estimated cost of node n to the target, a is a load weight factor, L(n) is the load influence value at node n, b is an energy consumption coefficient, and E(n) is the path energy consumption estimate value at node n;Meanwhile, based on the dynamic obstacle information obtained in the environment perception step, a dynamic obstacle avoidance algorithm is used to adjust the local path in real time and calculate a safety corridor; c. Motion control: accurately adjusting the speed and direction of the AGV according to the path generated in the path planning step in combination with a PID control algorithm;In a complex environment, a fuzzy control algorithm is applied to dynamically adjust the control parameters, the input variables include the environment complexity and the path curvature, and the output variable is the PID parameter adjustment amount, so as to enhance the robustness and response speed of the system; d. System monitoring: monitoring the running state parameters of the AGV in real time, including the speed, position, and battery power;Through a preset fault diagnosis rule, the state is evaluated, and a three-level alarm mechanism is triggered: when the first-level alarm is triggered, a voice prompt and speed reduction are performed, when the second-level alarm is triggered, path re-planning is triggered and logs are uploaded, and when the third-level alarm is triggered, emergency shutdown and standby power are enabled;And the running data is remotely transmitted and monitored through a communication unit.

[0007] Further, the small industrial computer IO box participates in sensor data acquisition and preprocessing in the a step.

[0008] Further, the dynamic obstacle avoidance algorithm in the b step adopts gradient descent method to optimize the local path.

[0009] Further, the parameters of the PID control algorithm in the c step are set by Ziegler-Nichols method.

[0010] Further, the parameters of the PID control algorithm are set as follows: proportional coefficient Kp=2.5, integral time Ti=0.8, and differential time Td=0.2.

[0011] Further, the fuzzy control algorithm in the c step adopts a triangular membership function and contains a 25-rule IF-THEN rule base based on environmental complexity and path curvature.

[0012] Further, the d step realizes remote data transmission and monitoring through a wireless router, a 5G module or a wireless network bridge of the communication unit.

[0013] The present application has the following advantages: through the reliable support of the hardware system and the close cooperation of the four core function modules (environment perception, path planning, motion control, and system monitoring) and algorithm innovation, the key technical problems of high-precision navigation, stable control, dynamic obstacle avoidance, and reliable monitoring faced by outdoor heavy-load AGVs in complex dynamic environments are effectively solved, and efficient and safe outdoor heavy-load autonomous transportation is realized. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a hardware structure schematic diagram of the present application; Figure 2 is an environment perception architecture diagram of the present application; Figure 3 is a path planning logic diagram of the present application; Figure 4 is a motion control logic step of the present application; Figure 5 is a system monitoring logic diagram of the present application. DETAILED DESCRIPTION

[0015] In order to better understand and implement, the technical solutions of the present application are further described in detail below in combination with the drawings.

[0016] As shown in Figure 1 , an outdoor heavy-load AGV navigation control system includes the following hardware components: a navigation box, a core processing unit, a sensor component, an execution and interface unit, a power supply unit, a communication unit, a human-computer interaction and monitoring unit, an electrical control unit, and a laser control box. The navigation box includes a high-power exhaust fan, a high-density filter screen, and a waterproof aviation socket. The core processing unit is a domain controller, which includes a multi-core processor, a real-time operating system module, and a vehicle communication protocol stack interface. The sensor components include 3D laser sensors, millimeter-wave radar, GPS receivers, and vehicle-mounted cameras; The execution and interface unit includes a small industrial computer I / O box and an electrical control box; The power supply unit includes a DC-DC module and a DC circuit breaker; The communication unit includes a wireless router, a 5G module, a wireless bridge, and a PoE switch; The human-machine interaction and monitoring unit includes an industrial voice broadcaster, an RGB screen, a video surveillance NVR, IoT devices, and a mushroom-shaped emergency stop button; The electrical control unit includes the main power switch, system switches, relays, and contactors; The laser control box connects to the 3D laser sensor; Connect the domain controller to the industrial PC I / O box, electrical control box, GPS receiver, video surveillance NVR, RGB screen, IoT devices, wireless router, 5G module, wireless bridge, and PoE switch via communication wiring harnesses; connect the vehicle camera to the video surveillance NVR via wiring harnesses; the DC-DC module supplies power to the domain controller, industrial PC I / O box, electrical control box, 3D laser sensor, vehicle camera, GPS receiver, millimeter-wave radar, IoT devices, video surveillance NVR, relays, contactors, wireless router, 5G module, wireless bridge, PoE switch, and laser control box via power supply wiring harnesses.

[0017] A control method for an outdoor heavy-duty AGV navigation control system includes the following steps: The first step, environmental perception steps: such as Figure 2 As shown, the environmental perception module integrates a 3D laser sensor (for high-precision 3D environment modeling and static obstacle detection), millimeter-wave radar (for close-range dynamic obstacle detection in adverse weather conditions), an onboard camera (providing visual information), and a GPS receiver (providing global position). It collects environmental data through the 3D laser sensor, millimeter-wave radar, onboard camera, and GPS receiver, and uses a Kalman filter algorithm to fuse multi-source heterogeneous sensor data in real time, constructing an environmental map with centimeter-level accuracy. It accurately identifies, locates, and predicts the position and movement information of obstacles (including dynamic obstacles), providing a reliable environmental cognitive basis for navigation decisions. Small industrial control computer I / O boxes participate in sensor data acquisition and preprocessing; The second step, path planning steps: (e.g.) Figure 3As shown, the path planning module receives the output of the environment perception module according to the task target, the environment map and the obstacle information, and generates an initial global path using an improved A* algorithm; the cost function of the improved A* algorithm is f(n) = g(n) + h(n) + a L(n) + b E(n), wherein g(n) is the actual cost from the starting point to node n, h(n) is the estimated cost of node n to the target, a is a load weight factor, L(n) is a load influence value at node n, b is an energy consumption coefficient, and E(n) is a path energy consumption estimate value at node n; the cost function of the algorithm innovatively introduces a load influence factor (a L(n)) and a path energy consumption factor (b E(n)), optimizes the path to reduce mechanical loss and energy consumption caused by heavy load under the premise of ensuring the safety and feasibility of the path, and improves the transportation efficiency; at the same time, a dynamic obstacle avoidance algorithm is integrated, the dynamic obstacle information (position, speed, predicted trajectory) provided by the environment perception module in real time is used to adjust the local path online, the gradient descent method is used to calculate the safety corridor, and it is ensured that the AGV can actively and smoothly avoid the moving obstacles, and the operation safety is ensured; The third step is a motion control step: as shown in Figure 4 The motion control module is responsible for accurately tracking the path generated by the path planning module; according to the path generated by the path planning step, the speed and direction of the AGV are accurately adjusted by combining the PID control algorithm; and in a complex environment, a fuzzy control algorithm is applied to dynamically adjust the control parameters, the input variables include the environment complexity and the path curvature, and the output variable is the PID parameter adjustment amount, so as to enhance the robustness and response speed of the system; The PID control algorithm provides basic speed and direction closed-loop control to ensure tracking accuracy under normal working conditions; the parameters (such as Kp, Ti, and Td) can be optimized by Ziegler-Nichols engineering method; the parameter settings are: the proportional coefficient Kp = 2.5, the integral time Ti = 0.8, and the differential time Td = 0.2; The fuzzy control algorithm uses a triangular membership function and contains a 25-rule IF-THEN rule base based on environment complexity and path curvature; in view of the uncertainty and nonlinearity of outdoor complex environment (such as uneven road surface, crosswind, and load change), a fuzzy rule base (for example, based on the "environment complexity" and "path curvature" input, the "PID parameter adjustment amount" is output) is designed; through fuzzy reasoning, the PID parameters are dynamically adjusted or a compensation control amount is directly generated, which significantly enhances the robustness, adaptability and response speed of the system under disturbance, and ensures smooth driving of the heavy load AGV; The fourth step is a system monitoring step: as shown in Figure 5 The system monitoring module monitors the overall state of the AGV and manages the safety; Real-time monitoring: continuously collect and display AGV's core state parameters, including real-time speed, accurate position (fusion of GPS and odometer, etc.), battery remaining capacity, key component temperature, etc. Fault diagnosis and alarm: preset intelligent diagnosis rules for analyzing the running state; three-level alarm mechanism: first-level alarm (minor fault, such as local sensor anomaly): trigger industrial voice alarm to prompt and instruct AGV to run at a reduced speed; second-level alarm (moderate fault, such as severe path obstruction or key communication interruption): automatically trigger path re-planning module, and upload fault log to remote monitoring center; third-level alarm (serious fault, such as emergency stop button trigger or serious mechanical and electrical fault): immediately execute emergency stop instruction, and start backup power system to maintain power supply for core controller and communication module, ensuring minimal state reporting and remote communication capabilities; Remote monitoring: through wireless router, 5G module or wireless network bridge, AGV running data and video monitoring screen are transmitted in real time to remote monitoring center (NVR and upper computer platform), supporting remote operators to monitor the state, query historical data and necessary remote control intervention; IOT devices can be extended to access a wider range of device state monitoring networks.

[0018] The above four functional modules work together, and the modules are implemented as follows: I. Environment perception module implementation: 1. 3D laser sensor (such as 128-line laser radar) scans the surrounding environment at a frequency of 10Hz, generating three-dimensional point cloud data; 2. Millimeter wave radar (such as 77GHz forward radar) detects the distance and speed of obstacles within a short distance (0-150 meters) at a frequency of 20Hz; 3. Vehicle-mounted high-definition camera (1080P, 30fps) collects front road image information; 4. GPS receiver (supports RTK) provides global positioning information; 5. Domain controller runs data fusion software, using Kalman filter algorithm to convert laser point cloud data, millimeter wave radar target list, visual recognition results, GPS / IMU positioning information to AGV body coordinate system and global coordinate system, and real-time constructs and updates centimeter-level precision (<5cm) grid map or feature map, and accurately marks the position of static obstacles and the position and speed vector of dynamic obstacles.

[0019] II. Path planning module implementation: 1. Task scheduling system sends target point coordinates to path planning module; 2. Path planning module calls improved A* algorithm: 2.1 Initialize Open List and Closed List; 2.2 Add the start point to Open List; 2.3 Loop: Choose the node with the smallest f(n) from Open List as the current node. If the current node is the goal, backtracking to generate the path; 2.4 Otherwise, move the current node to Closed List and examine its adjacent nodes; 2.5 For each adjacent node, calculate g(n) (actual cost from the start point to the node, considering distance and turning), h(n) (heuristic cost, such as Euclidean distance to the goal), L(n) (estimated load impact coefficient of the node area, such as slope and road type impact on heavy load), and E(n) (estimated energy consumption through the node). Substitute the cost function f(n) = g(n) + h(n) + a·L(n) + β·E(n) to calculate the total cost (a, β are set according to AGV load and endurance requirements); 2.6 Update the parent node and path of the adjacent node according to the cost; 3. During the operation of AGV, the environmental perception module continuously provides dynamic obstacle information (such as pedestrian position and speed prediction); 4. Dynamic obstacle avoidance algorithm is activated: 4.1 According to the current position and predicted trajectory of the obstacle, calculate the safety area (safety corridor) that AGV should maintain; 4.2 If the current path conflicts with the predicted trajectory of the obstacle, then within the safety corridor, use gradient descent method to quickly optimize and generate a new local obstacle avoidance path, which is smoothly connected to the original global path.

[0020] III. Motion control module implementation: 1. The path planning module outputs the desired path point sequence (including position, desired speed, and desired heading angle); 2. PID controller works: 2.1 Set value: desired speed V_ref and desired heading angle θ_ref of the current path point; 2.2 Feedback value: actual speed V_actual and actual heading angle θ_actual obtained through encoder / IMU / GPS fusion; 2.3 Calculate speed error e_v = V_ref - V_actual and heading angle error e_θ = θ_ref - θ_actual; 2.4 Use PID formula (proportional term Kpe, integral term Ki∫edt, differential term Kd*de / dt) to calculate the drive motor control (adjust speed) and steering engine control (adjust direction) respectively; Parameters are set by Ziegler-Nichols method, for example: Kp=2.5, Ki=Kp / Ti=2.5 / 0.8≈3.125, Kd=Kp*Td=2.5*0.2=0.5; 3. Fuzzy controller works in parallel (especially in rough road, strong crosswind or sharp turn): 3.1 Input variable 1: Environmental complexity (EC) - evaluated according to sensor data (such as bump detection, anemometer, dynamic obstacle density), fuzzy set: {low, medium, high}; 3.2 Input variable 2: Path curvature (PC) - the degree of bending of the current tracking path segment, fuzzy set: {straight, gentle bend, sharp bend}; 3.3 Output variable: PID parameter adjustment amount (ΔKp, ΔKi, ΔKd) or directly output compensation control amount. Fuzzy set: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}; 3.4 Membership function: use triangular distribution; 3.5 Rule base: contains 25 IF-THEN rules; for example: 3.5.1 IF (EC is high) AND (PC is sharp bend) THEN (ΔKp is positive large, ΔKd is positive large) / / increase response and damping; 3.5.2 IF (EC is low) AND (PC is straight) THEN (ΔKp is zero, ΔKi is negative small) / / fine-tune, possibly reduce integral anti-saturation; 4. After defuzzification of fuzzy reasoning results, real-time adjustment of PID controller parameters or superposition of a compensation control amount to the final output, sent to the electric control box to drive the motor and steering mechanism.

[0021] Four, system monitoring module implementation: 1. Domain controller collects in real time through CAN bus, digital IO (connects small industrial computer IO box): 1.1 speed (motor driver feedback); 1.2 position (GPS / IMU / odometer fusion results); 1.3 battery voltage, current, temperature, remaining power (SOC); 1.4 key controller temperature; 1.5 mushroom head emergency stop button status; 1.6 Communication status of each main sensor / actuator; 2. Monitor software preset diagnostic rules: 2.1 Rule 1: If SOC < 20%, trigger level 1 alarm (voice broadcast "low power, prepare to return to charge", AGV automatically slows down and plans path to charging station); 2.2 Rule 2: If communication with 3D laser sensor is lost for more than 3 seconds, trigger level 2 alarm (voice broadcast "laser radar failure", try to reconnect, trigger path re-planning using other sensors, record fault log and upload); 2.3 Rule 3: If mushroom head emergency stop button is pressed or motor driver reports serious overcurrent / overheating, trigger level 3 alarm (voice broadcast "emergency stop!", immediately cut off main drive power, start backup power to maintain domain controller, GPS, 5G module power supply, send emergency status and location information to monitoring center through 5G network); 3. Video monitoring NVR continuously records vehicle-mounted camera pictures, and can transmit real-time pictures and key state data to remote monitoring center through POE switch, wireless router / 5G module / wireless bridge. Operators can view all AGV states, alarm information, real-time location and historical trajectory on the monitoring center software, and can send remote control instructions if necessary (such as confirming alarm to release stop, modifying task target point).

[0022] The outdoor heavy-load AGV navigation control system and its control method effectively overcome the challenges of complex outdoor environments to heavy-load AGVs through hardware architecture, multi-sensor fusion, improved path planning, hybrid motion control, and intelligent monitoring algorithms, significantly improving navigation accuracy, environmental adaptability, running stability, and safety, providing a reliable technical solution for large-tonnage material automated transportation in port, mine, and large factory scenarios.

[0023] It should be understood that technical features not described in detail in the specification are all prior art. Although the embodiments of the present application are described, the present application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative and not limiting, and those of ordinary skill in the art can make more forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which are all within the protection scope of the present application.

Claims

1. An outdoor heavy-duty AGV navigation and control system, characterized in that, It includes a navigation box, core processing unit, sensor components, execution and interface unit, power supply unit, communication unit, human-machine interaction and monitoring unit, electrical control unit, and laser control box hardware components; The navigation box includes a high-power exhaust fan, a high-density filter, and a waterproof aviation socket; The core processing unit is a domain controller, which includes a multi-core processor, a real-time operating system module, and a vehicle communication protocol stack interface. The sensor assembly includes a 3D laser sensor, a millimeter-wave radar, a GPS receiver, and a vehicle-mounted camera; The execution and interface unit includes a small industrial control computer I / O box and an electrical control box; The power supply unit includes a DC-DC module and a DC circuit breaker; The communication unit includes a wireless router, a 5G module, a wireless bridge, and a PoE switch; The human-machine interaction and monitoring unit includes an industrial voice broadcaster, an RGB screen, a video surveillance NVR, an IoT device, and a mushroom-shaped emergency stop button; The electrical control unit includes a main power switch, a system switch, relays, and contactors; The laser control box is connected to a 3D laser sensor; The domain controller is connected to the industrial PC I / O box, electrical control box, GPS receiver, video surveillance NVR, RGB screen, IoT device, wireless router, 5G module, wireless bridge, and PoE switch via communication cabling; the vehicle-mounted camera is connected to the video surveillance NVR via cabling; the DC-DC module supplies power to the domain controller, industrial PC I / O box, electrical control box, 3D laser sensor, vehicle-mounted camera, GPS receiver, millimeter-wave radar, IoT device, video surveillance NVR, relay, contactor, wireless router, 5G module, wireless bridge, PoE switch, and laser control box via power supply cabling.

2. The control method of the outdoor heavy-duty AGV navigation control system according to claim 1, characterized in that, Includes the following steps: a. Environmental perception: Collect environmental data through 3D laser sensors, millimeter-wave radar, vehicle-mounted cameras, and GPS receivers, use Kalman filtering algorithm to fuse multi-sensor data, generate an environmental map with centimeter-level accuracy, and detect and mark the position and movement information of obstacles in real time; b. Path Planning: Based on the task objective, environmental map, and obstacle information, an improved A* algorithm is used to generate an initial global path. The cost function of the improved A* algorithm is: f(n) = g(n) + h(n) + α·L(n) + β·E(n), where g(n) is the actual cost from the starting point to node n, h(n) is the estimated cost from node n to the objective, α is the load weighting factor, L(n) is the load impact value at node n, β is the energy consumption coefficient, and E(n) is the estimated path energy consumption at node n. Simultaneously, based on the dynamic obstacle information obtained in the environmental perception step, a dynamic obstacle avoidance algorithm is used to adjust the local path in real time and calculate the safe corridor. c. Motion control: Based on the path generated by the path planning steps, the speed and direction of the AGV are precisely adjusted by combining the PID control algorithm; and in complex environments, the control parameters are dynamically adjusted by applying the fuzzy control algorithm. The input variables include environmental complexity and path curvature, and the output variable is the PID parameter adjustment amount, so as to enhance the robustness and response speed of the system. d System monitoring: Real-time monitoring of AGV operating status parameters, including speed, position, and battery level; The system performs status assessments based on preset fault diagnosis rules, triggering a three-level alarm mechanism: a level 1 alarm triggers a voice prompt and speed reduction; a level 2 alarm triggers path replanning and log uploads; and a level 3 alarm triggers an emergency shutdown and activates the backup power supply. The system also enables remote transmission and monitoring of operational data through a communication unit.

3. The control method for an outdoor heavy-duty AGV navigation control system according to claim 2, characterized in that, In step a, the small industrial control computer I / O box participates in sensor data acquisition and preprocessing.

4. The control method for an outdoor heavy-duty AGV navigation control system according to claim 2, characterized in that, The dynamic obstacle avoidance algorithm in step b uses gradient descent to optimize the local path.

5. The control method for an outdoor heavy-duty AGV navigation control system according to claim 2, characterized in that, The parameters of the PID control algorithm in step c are tuned using the Ziegler-Nichols method.

6. The control method for an outdoor heavy-duty AGV navigation control system according to claim 5, characterized in that, The parameters of the PID control algorithm are set as follows: proportional coefficient Kp = 2.5, integral time Ti = 0.8, and derivative time Td = 0.

2.

7. The control method for an outdoor heavy-duty AGV navigation control system according to claim 2, characterized in that, The fuzzy control algorithm in step c uses a triangular membership function and includes a base of 25 IF-THEN rules based on environmental complexity and path curvature.

8. The control method for an outdoor heavy-duty AGV navigation control system according to claim 2, characterized in that, Step d achieves remote data transmission and monitoring through the wireless router, 5G module, or wireless bridge of the communication unit.

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