Control method and system based on collaborative operation of stacking machine and unmanned container truck

Abstract

The invention relates to a control method based on collaborative operation of a stacking machine and an unmanned container truck. The method comprises the following steps: step 1, setting a virtual waiting area on a cloud platform; 2, analyzing the shortest reachable path, and planning a driving route of the vehicle to the waiting area; 3, the cloud platform receives the driving track point data uploaded by the unmanned container truck, preprocesses the data, sorts the data, and sends a sorting result to the stacking machine; 4, the stacking machine sends a car calling operation signal to the unmanned container truck to be operated currently, and the unmanned container truck goes to the operation position to operate after receiving the car calling operation signal of the stacking machine; 5, after the stacking machine completes operation, an operation completion signal is sent to the unmanned container truck, and after the unmanned container truck receives the operation completion signal, the unmanned container truck is driven away from the operation position; and step 6, after the stacking machine drives away the unmanned container trucks which finish the current operation, the step 4 is executed until all the unmanned container trucks in the sorting result finish the operation.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned container trucks, and in particular to a control method and system based on the collaborative operation of a forklift and an unmanned container truck. Background Art

[0002] In recent years, with the continued growth of global trade and the rapid increase in port throughput, traditional manual operation models have become unable to meet the demands of modern ports for efficient, safe, and cost-effective operations. Smart ports have become a mainstream trend in global port upgrades, with the development of automated container terminals being particularly crucial. According to the "Guidelines for Smart Port Construction" issued by the Ministry of Transport of China, by 2025, the proportion of unmanned operations at major Chinese ports must exceed 50%, placing higher demands on collaborative control technology for port equipment. In automated terminals, the collaborative operation of forklifts (RTGs / RMGs) and automated guided vehicles (AGVs / IGVs) is a core component, but existing technologies suffer from the following key issues: 1. Inadequate real-time interaction mechanisms between forklifts and vehicles. Current systems exhibit serious flaws in the interaction design between forklifts and vehicles, primarily in the control of operational sequence. Due to the lack of a reliable collaborative control mechanism, forklifts rely solely on simple distance detection to determine vehicle arrival, a unilateral approach that is prone to misjudgments. Two typical problems often arise in actual operations: First, the forklift starts prematurely before vehicles have precisely docked, increasing loading and unloading failures; second, vehicles wait excessively long after arriving, reducing operational efficiency. Second, dynamic scheduling is insufficient. The existing system exhibits significant intelligent deficiencies in multi-vehicle coordinated scheduling. When multiple vehicles simultaneously approach the same forklift, the system's static queuing algorithm fails to dynamically adjust to real-time operating conditions, leading to two typical problems: unnecessary waiting queues at the entrance to the work area, and a "deadlock" situation where multiple vehicles block each other. Especially during peak operating periods, these scheduling flaws can trigger a chain reaction: when forklifts are not dispatched according to the fleet's sequence, waiting vehicles occupy lane resources, hindering the passage of subsequent vehicles in the fleet, ultimately reducing overall operational efficiency. Summary of the Invention

[0003] In view of the above problems, the present invention provides a control system and method based on the collaborative operation of a forklift and an unmanned container truck to solve the technical problems such as the lack of a two-way confirmation process in the existing technology, resulting in a high misoperation rate; the inability to control other vehicles to wait for operation when multiple vehicles are working, causing the front and rear vehicles to be "locked", thereby reducing operating efficiency.

[0004] The present invention provides a control method based on the collaborative operation of a forklift and an unmanned container truck, the method comprising: step 1, a cloud platform sets up a virtual waiting area according to the number of unmanned container trucks, the length, width, safety spacing of each vehicle, available space in the yard, and the vehicle operation route; step 2, determining the current position of the vehicle according to the latitude and longitude and heading angle uploaded by the vehicle, analyzing the shortest reachable path by comparing the positional relationship between the unmanned container truck and the waiting area, and after evaluating the reachability and rationality of the path, planning the vehicle's driving route to the waiting area through a path planning algorithm for use by the vehicle; step 3, the cloud platform receives the driving trajectory point data uploaded by the unmanned container truck and pre-processes the data Processing, predict the arrival time of each vehicle in the waiting area according to the preprocessed data, and sort the vehicles in order according to the estimated arrival time at the waiting area, and then send the sorting results to the forklift; Step 4, the forklift sends a car-calling operation signal to the unmanned container truck currently waiting for operation according to the sorting result, and the unmanned container truck goes to the operation position to operate after receiving the car-calling operation signal from the forklift; Step 5, after the forklift completes the operation, it sends a work completion signal to the unmanned container truck, and the unmanned container truck leaves the work position after receiving the work completion signal; Step 6, after the unmanned container truck that has completed the current operation leaves, the forklift goes to Step 4 until all the unmanned container trucks in the sorting results have completed their operations.

[0005] Furthermore, the step 1 includes: step 11, calculating the minimum length of the waiting area L=L1+L2+...Ln+(n-1)*L0 according to the number of unmanned container trucks in operation, the length of each vehicle and the safety distance, wherein L1, L2...Ln represent the length of each unmanned container truck, n is the number of unmanned container trucks, and L0 is the safety distance; step 12, determining the width of the waiting area according to the width and safety distance of the unmanned container trucks to ensure that the vehicles can be arranged side by side or in a single row; step 13, determining the location of the waiting area according to the operation route to ensure smooth passage of the unmanned container trucks; step 14, adjusting the width of the waiting area so that the ratio of the waiting area area to the yard area is less than a preset threshold, while ensuring that the vehicles can be arranged in at least a single row.

[0006] Furthermore, the step 2 includes: step 21, determining the real-time position and driving direction of the vehicle based on the latitude, longitude and heading angle information of the vehicle, combined with a pre-built high-precision electronic map; step 22, preliminarily analyzing the shortest reachable path by comparing the current position of the vehicle with the position relationship of the waiting area; step 23, the cloud platform determines the driving status of the vehicle based on the speed information and driving trajectory points uploaded by the vehicle; step 24, the cloud platform predicts the driving path of the vehicle by analyzing the driving trajectory points; step 25, evaluating the accessibility and rationality of going to the waiting area based on the predicted driving path and driving status; step 26, after evaluating the accessibility and rationality of the driving path, the optimal driving path is planned for the vehicle to use through the path planning algorithm, as well as the current position of the vehicle, the target waiting area location, and the surrounding road network information.

[0007] Furthermore, the driving status of the vehicle includes: normal driving, deceleration, and parking.

[0008] Furthermore, step 3 includes: step 31, after receiving the driving trajectory point data sent by the unmanned container truck, the cloud platform excludes abnormal points that exceed the threshold as noise data; step 32, based on the position and time of adjacent points, the missing values ​​are estimated and filled by linear interpolation; step 33, after removing duplicate trajectory points, the timestamps of different formats are unified into a standard format, and the longitude and latitude of different coordinate systems are uniformly converted to the target coordinate system to ensure data consistency; step 34, through the processed trajectory points, the arrival time of each vehicle in the waiting area is predicted, and the vehicles are sorted in order according to the time they are expected to arrive at the waiting area, and the sorting results are sent to the forklift.

[0009] Furthermore, the threshold is a preset distance threshold or speed threshold.

[0010] Furthermore, the step 3 also includes: step 35, the cloud platform monitors the vehicle status in real time, dynamically adjusts the sorting result, and sends the adjusted sorting result to the forklift.

[0011] Furthermore, step 4 further includes: after the unmanned container truck arrives at the working position, it sends a signal to the forklift indicating that it has reached the destination, and the forklift starts working after receiving the signal.

[0012] The present invention also provides a control system based on the collaborative operation of a forklift and an unmanned container truck, the system comprising: an unmanned container truck, for uploading the latitude and longitude, heading angle and driving trajectory points of the vehicle to a cloud platform, and after driving to a waiting area according to the driving path planned by the cloud platform, after receiving a taxi operation signal from the forklift, going to the operation position to perform the operation, and after receiving a work completion signal from the forklift, leaving the operation position; a forklift, respectively connected to the unmanned container truck and a motion scheduling platform, for sending a taxi operation signal to the unmanned container truck in the current sequence according to the sorting result sent by the cloud platform, and after completing the operation, sending a work completion signal to the unmanned container truck, and continuing to send a taxi operation signal to the unmanned container truck in the current sequence after the unmanned container truck in the current operation leaves. The cloud platform is connected to the unmanned container trucks and forklifts respectively, and is used to set up a virtual waiting area according to the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation route. The current position of the vehicle is determined according to the latitude and longitude and heading angle uploaded by the vehicle. By comparing the positional relationship between the unmanned container trucks and the waiting area, the shortest reachable path is analyzed. After evaluating the accessibility and rationality of the path, the driving route of the vehicle to the waiting area is planned. According to the driving trajectory point data of the unmanned container trucks, the arrival time of each vehicle in the waiting area is predicted, and the vehicles are sorted in order of the estimated arrival time at the waiting area, and the sorting results are sent to the forklift.

[0013] Furthermore, the cloud platform is also used to monitor vehicle status in real time, dynamically adjust the sorting results, and send the adjusted sorting results to the forklift.

[0014] The present invention provides a control method and system based on the collaborative operation of a forklift and an unmanned container truck. This technical solution establishes a two-way interactive link between the forklift and the vehicle. The forklift can only enter the next stage after verification by both parties. This mechanism ensures operational safety. This technical solution effectively prevents multi-vehicle deadlock through a centralized scheduling strategy. This solution also constructs a vehicle-cloud-forklift three-party collaborative architecture, and ensures the stable operation of the system under complex working conditions through real-time information sharing and a closed-loop command verification mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a control method based on the collaborative operation of a forklift and an unmanned container truck provided by the present invention; Figure 2 A flow chart of the method for setting a virtual waiting area provided by the present invention; Figure 3 A flow chart of the method for planning a vehicle route provided by the present invention; Figure 4 This is a flow chart of the method for preprocessing trajectory point data and sending sorting results provided by the present invention. DETAILED DESCRIPTION

[0016] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0017] Example 1: The present invention provides a control method and system based on the collaborative operation of a stacker and an unmanned container truck. The system comprises an unmanned container truck, a stacker and a cloud platform. Figure 1 As shown, the method includes the following steps.

[0018] Step 1: Set up a virtual waiting area based on the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation routes; Unmanned container trucks are used to upload the vehicle's latitude and longitude, heading angle, and driving trajectory points to the cloud platform. According to the driving route planned by the cloud platform, after driving to the waiting area, they receive the forklift's call operation signal and proceed to the operation location to perform the operation. After receiving the forklift's operation completion signal, they leave the operation location.

[0019] Step 2: Determine the vehicle's current location based on the latitude, longitude, and heading angle uploaded by the vehicle. By comparing the positional relationship between the unmanned truck and the waiting area, analyze the shortest accessible path. After evaluating the accessibility and rationality of the path, use the path planning algorithm to plan a route for the vehicle to the waiting area for use. Step 3: The cloud platform receives the driving trajectory data uploaded by the unmanned container trucks and pre-processes the data. It predicts the arrival time of each vehicle at the waiting area based on the pre-processed data, sorts the vehicles according to their expected arrival time at the waiting area, and then sends the sorting results to the forklift. The cloud platform is connected to the unmanned container trucks and forklifts respectively. It is used to set up a virtual waiting area based on the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation route. The current position of the vehicle is determined according to the latitude, longitude and heading angle uploaded by the vehicle. By comparing the positional relationship between the unmanned container trucks and the waiting area, the shortest reachable path is analyzed. After evaluating the accessibility and rationality of the path, the vehicle's route to the waiting area is planned. Based on the driving trajectory point data of the unmanned container trucks, the arrival time of each vehicle in the waiting area is predicted, and the vehicles are sorted in order according to the expected arrival time of the waiting area, and the sorting results are then sent to the forklift.

[0020] Step 4: The forklift sends a call signal to the unmanned container truck currently waiting for operation based on the sorting result. After receiving the call signal from the forklift, the unmanned container truck goes to the operation location to perform the operation. The forklift is connected to the unmanned container truck and the motion scheduling platform respectively. It is used to send a taxi operation signal to the unmanned container truck in the current sequence according to the sorting results sent by the cloud platform. After completing the operation, it sends a job completion signal to the unmanned container truck. After the unmanned container truck in the current operation leaves, it continues to send a taxi operation signal to the unmanned container truck in the current sequence until all the operations of the unmanned container trucks in the sorting results are completed.

[0021] Step 5: After the forklift completes the operation, it sends a signal to the unmanned container truck. After receiving the signal, the unmanned container truck leaves the operation location. Step 6: After the unmanned container truck that has completed its current operation leaves, the forklift goes to step 4 until all the unmanned container trucks in the sorting results have completed their operations.

[0022] The present invention provides a control method and system based on the collaborative operation of a forklift and an unmanned container truck. This technical solution establishes a two-way interactive link between the forklift and the vehicle. The forklift can only enter the next stage after verification by both parties. This mechanism ensures operational safety. This technical solution effectively prevents multi-vehicle deadlock through a centralized scheduling strategy. This solution also constructs a vehicle-cloud-forklift three-party collaborative architecture, and ensures the stable operation of the system under complex working conditions through real-time information sharing and a closed-loop command verification mechanism.

[0023] Example 2: The present invention provides a control method and system based on the collaborative operation of a stacker and an unmanned container truck. The system comprises an unmanned container truck, a stacker and a cloud platform. Figure 1 As shown, the method includes the following steps.

[0024] Step 1: Set up a virtual waiting area based on the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation routes; Compared with the existing technology, the virtual waiting area set by the present invention can make the waiting work wait in the waiting area before the forklift is ready to work. After the forklift is ready, it will notify the vehicle of the previous work position. After the work is completed, it will notify the current vehicle to leave and notify other vehicles in the waiting area to enter the work area, ensuring that the vehicle is in place accurately and there is no other vehicle interfering with the work safety. Figure 2 As shown, the step 1 includes: Step 11: Calculate the minimum length of the waiting area based on the number of unmanned container trucks in operation, the length of each truck, and the safety distance. L = L1 + L2 + ... Ln + (n-1) * L0, where L1, L2, ... Ln represent the length of each unmanned container truck, n is the number of unmanned container trucks, and L0 is the safety distance. Step 12: Determine the width of the waiting area based on the width and safety spacing of the unmanned container trucks to ensure that the vehicles can be arranged side by side or in a single row; Step 13: Determine the location of the waiting area based on the operation route to ensure smooth passage of unmanned trucks; Step 14: Adjust the width of the waiting area so that the ratio of the waiting area area to the yard area is less than a preset threshold, while ensuring that vehicles can at least be arranged in a single row.

[0025] Step 2: Determine the vehicle's current location based on the latitude, longitude, and heading angle uploaded by the unmanned container truck. Compare the location of the unmanned container truck with the waiting area to analyze the shortest accessible path. After evaluating the accessibility and rationality of the path, a path planning algorithm is used to plan a route for the vehicle to the waiting area. In the present invention, the unmanned container truck uploads the vehicle position (latitude and longitude), speed, heading angle and other data sensed by the combined navigation, IMU, wheel speed meter and other sensors on the unmanned container truck to the cloud platform in real time through the 5G network or dedicated wireless communication module. Figure 3 As shown, the step 2 includes: Step 21, based on the latitude, longitude and heading angle information of the vehicle, combined with a pre-built high-precision electronic map, determine the real-time position and driving direction of the vehicle; Step 22, by comparing the position relationship between the current position of the vehicle and the waiting area, preliminarily analyzing the shortest achievable path; Step 23: The cloud platform determines the driving status of the vehicle based on the speed information and driving trajectory points uploaded by the vehicle; The driving status of the vehicle includes: normal driving, deceleration, and parking.

[0026] Step 24: The cloud platform predicts the vehicle's driving path by analyzing the driving trajectory points; Step 25: Evaluate the accessibility and rationality of going to the waiting area based on the predicted driving path and driving status; Step 26, after evaluating the accessibility and rationality of the driving route, the optimal driving route is planned for the vehicle through the path planning algorithm, as well as the current position of the vehicle, the location of the target waiting area, and the surrounding road network information.

[0027] Step 3: The cloud platform receives the driving trajectory data uploaded by the unmanned container trucks and pre-processes the data. It predicts the arrival time of each vehicle at the waiting area based on the pre-processed data, sorts the vehicles according to their expected arrival time at the waiting area, and then sends the sorting results to the forklift. Step 4: The forklift sends a call signal to the unmanned container truck currently waiting for operation based on the sorting result. After receiving the call signal from the forklift, the unmanned container truck goes to the operation location to perform the operation. Step 5: After the forklift completes the operation, it sends a signal to the unmanned container truck. After receiving the signal, the unmanned container truck leaves the operation location. Step 6: After the unmanned container truck that has completed its current operation leaves, the forklift goes to step 4 until all the unmanned container trucks in the sorting results have completed their operations.

[0028] The present invention provides a control method and system based on the collaborative operation of a forklift and an unmanned container truck. This technical solution establishes a two-way interactive link between the forklift and the vehicle. The forklift can only enter the next stage after verification by both parties. This mechanism ensures operational safety. This technical solution effectively prevents multi-vehicle deadlock through a centralized scheduling strategy. This solution also constructs a vehicle-cloud-forklift three-party collaborative architecture, and ensures the stable operation of the system under complex working conditions through real-time information sharing and a closed-loop command verification mechanism.

[0029] Example 3: The present invention provides a control method and system based on the collaborative operation of a stacker and an unmanned container truck. The system comprises an unmanned container truck, a stacker and a cloud platform. Figure 1 As shown, the method includes the following steps.

[0030] Step 1: Set up a virtual waiting area based on the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation routes; Step 2: Determine the vehicle's current location based on the latitude, longitude, and heading angle uploaded by the vehicle. By comparing the positional relationship between the unmanned truck and the waiting area, analyze the shortest accessible path. After evaluating the accessibility and rationality of the path, use the path planning algorithm to plan a route for the vehicle to the waiting area for use. Step 3: The cloud platform receives the driving trajectory data uploaded by the unmanned container trucks and pre-processes the data. It predicts the arrival time of each vehicle at the waiting area based on the pre-processed data, sorts the vehicles according to their expected arrival time at the waiting area, and then sends the sorting results to the forklift. By pre-processing the vehicle trajectory data, the arrival time of each vehicle in the waiting area is predicted, and the vehicles are sorted according to their expected arrival time. For example, if vehicle A is expected to arrive in 10 minutes and vehicle B is expected to arrive in 15 minutes, then vehicle A will be queued before vehicle B. Figure 4 As shown, step 3 includes: Step 31: After receiving the driving trajectory data sent by the unmanned container truck, the cloud platform excludes abnormal points that exceed the threshold as noise data; The threshold is a preset distance threshold or speed threshold.

[0031] Step 32, based on the positions and times of adjacent points, estimate and fill the missing values ​​by linear interpolation; Step 33: After removing duplicate trajectory points, the timestamps in different formats are unified into a standard format, and the longitudes and latitudes in different coordinate systems are uniformly converted to the target coordinate system to ensure data consistency; Step 34 , using the processed trajectory points, predict the time for each vehicle to arrive at the waiting area, sort the vehicles in order of their expected arrival time at the waiting area, and then send the sorting results to the forklift.

[0032] Step 35: The cloud platform monitors the vehicle status in real time, dynamically adjusts the sorting results, and sends the adjusted sorting results to the forklift.

[0033] In this system, unmanned container trucks upload real-time status signals to a cloud-based dispatch center. The cloud calculates the optimal operation sequence based on a dynamic algorithm and transmits the signal to the forklifts. The forklifts then operate strictly in sequence, effectively preventing multi-vehicle deadlocks through this centralized dispatching strategy.

[0034] Step 4: The forklift sends a call signal to the unmanned container truck currently waiting for operation based on the sorting result. After receiving the call signal from the forklift, the unmanned container truck goes to the operation location to perform the operation. The step 4 also includes: after the unmanned container truck arrives at the working position, it sends an arrival signal to the forklift, and the forklift starts working after receiving the signal. The forklift performs the taxi-hailing operation according to the working vehicle sequence received from the cloud platform, and the vehicle receives the forklift signal and feedback in real time to ensure that the operation is carried out efficiently. The present invention establishes a two-way interactive protocol between the forklift and the vehicle, and ensures the safety of the operation through a multi-level status confirmation mechanism. The system design has four key interactive links: preparation, confirmation, execution, and completion. Each link must be verified by both parties before entering the next stage, which completely solves the operational risks caused by unilateral false triggering. In addition, after the unmanned container truck arrives at the working position, when sending the arrival signal, it will monitor the position information in real time through the vehicle's own high-precision positioning system, and pass the precise coordinates to the forklift. The forklift combines the received vehicle position data with its own sensor information for double verification to ensure that the vehicle is completely and accurately in place when the operation starts.

[0035] Step 5: After the forklift completes the operation, it sends a signal to the unmanned container truck. After receiving the signal, the unmanned container truck leaves the operation location. Step 6: After the unmanned container truck that has completed its current operation leaves, the forklift goes to step 4 until all the unmanned container trucks in the sorting results have completed their operations.

[0036] The present invention provides a control method and system based on the collaborative operation of a forklift and an unmanned container truck. This technical solution establishes a two-way interactive link between the forklift and the vehicle. The forklift can only enter the next stage after verification by both parties. This mechanism ensures operational safety. This technical solution effectively prevents multi-vehicle deadlock through a centralized scheduling strategy. This solution also constructs a vehicle-cloud-forklift three-party collaborative architecture, and ensures the stable operation of the system under complex working conditions through real-time information sharing and a closed-loop command verification mechanism.

[0037] In summary, the technical solution of this invention significantly improves the safety, efficiency, and reliability of port automation operations through its innovative system architecture and intelligent algorithms. The system establishes a bidirectional collaborative control mechanism between forklifts and vehicles, overcoming the limitations of traditional one-way communication. This system establishes a multi-level status confirmation process, ensuring that each operation step must undergo dual verification before execution, fundamentally eliminating the risk of false triggering. By integrating high-precision positioning with laser scanning, it achieves centimeter-level vehicle arrival detection, bringing loading and unloading accuracy to industry-leading levels. Regarding dynamic scheduling, the system utilizes an intelligent priority algorithm to automatically generate the optimal operation sequence. This innovation significantly improves the efficiency of multi-vehicle coordination and effectively resolves the deadlock and congestion issues common in traditional systems. The combination of cloud-based centralized scheduling and digital twin rehearsal ensures both scientific decision-making and the executability of commands. A specially designed "vehicle-cloud-machine" collaborative architecture, leveraging 5G dual-channel transmission and edge-cloud collaborative computing, creates a highly reliable communication network, ensuring the continuity of operational processes. This solution significantly improves overall port operational efficiency and significantly reduces manpower requirements and energy costs. The system's modular design supports rapid expansion and can flexibly adapt to the needs of automated terminals of varying sizes, providing a standardized technical platform for smart port development. This technical solution has been proven in practical applications to achieve industry breakthroughs in operational safety, intelligent scheduling, and system stability, setting a new technological benchmark for automated port operations.

[0038] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A control method based on the collaborative operation of a forklift and an unmanned container truck, characterized in that: The method comprises: Step 1: The cloud platform sets up a virtual waiting area based on the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation route; Step 2: Determine the vehicle's current location based on the latitude, longitude, and heading angle uploaded by the vehicle. By comparing the positional relationship between the unmanned truck and the waiting area, analyze the shortest accessible path. After evaluating the accessibility and rationality of the path, use the path planning algorithm to plan a route for the vehicle to the waiting area for use. Step 3: The cloud platform receives the driving trajectory data uploaded by the unmanned container trucks and pre-processes the data. It predicts the arrival time of each vehicle at the waiting area based on the pre-processed data, sorts the vehicles according to their expected arrival time at the waiting area, and then sends the sorting results to the forklift. Step 4: The forklift sends a call signal to the unmanned container truck currently waiting for operation based on the sorting result. After receiving the call signal from the forklift, the unmanned container truck goes to the operation location to perform the operation. Step 5: After the forklift completes the operation, it sends a signal to the unmanned container truck. After receiving the signal, the unmanned container truck leaves the operation location. Step 6: After the unmanned container truck that has completed its current operation leaves, the forklift goes to step 4 until all the unmanned container trucks in the sorting results have completed their operations.

2. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 1, characterized in that: The step 1 comprises: Step 11: Calculate the minimum length of the waiting area based on the number of unmanned container trucks in operation, the length of each truck, and the safety distance. L = L1 + L2 + ... Ln + (n-1) * L0, where L1, L2, ... Ln represent the length of each unmanned container truck, n is the number of unmanned container trucks, and L0 is the safety distance. Step 12: Determine the width of the waiting area based on the width and safety spacing of the unmanned container trucks to ensure that the vehicles can be arranged side by side or in a single row; Step 13: Determine the location of the waiting area based on the operation route to ensure smooth passage of unmanned trucks; Step 14: Adjust the width of the waiting area so that the ratio of the waiting area area to the yard area is less than a preset threshold, while ensuring that vehicles can at least be arranged in a single row.

3. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 1, characterized in that: The step 2 includes: Step 21, based on the latitude, longitude and heading angle information of the vehicle, combined with a pre-built high-precision electronic map, determine the real-time position and driving direction of the vehicle; Step 22, by comparing the current position of the vehicle with the position relationship of the waiting area, preliminarily analyze the shortest reachable path; Step 23: The cloud platform determines the driving status of the vehicle based on the speed information and driving trajectory points uploaded by the vehicle; Step 24: The cloud platform predicts the vehicle's driving path by analyzing the driving trajectory points; Step 25: Evaluate the accessibility and rationality of going to the waiting area based on the predicted driving path and driving status; Step 26, after evaluating the accessibility and rationality of the driving route, the optimal driving route is planned for the vehicle through the path planning algorithm, as well as the current position of the vehicle, the location of the target waiting area, and the surrounding road network information.

4. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 3, characterized in that: The driving status of the vehicle includes: normal driving, deceleration, and parking.

5. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 1, characterized in that: The step 3 includes: Step 31: After receiving the driving trajectory data sent by the unmanned container truck, the cloud platform excludes abnormal points that exceed the threshold as noise data; Step 32, based on the positions and times of adjacent points, estimate and fill the missing values ​​by linear interpolation; Step 33: After removing duplicate trajectory points, the timestamps in different formats are unified into a standard format, and the longitudes and latitudes in different coordinate systems are uniformly converted to the target coordinate system to ensure data consistency; Step 34 , using the processed trajectory points, predict the time for each vehicle to arrive at the waiting area, sort the vehicles in order of their expected arrival time at the waiting area, and then send the sorting results to the forklift.

6. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 5, characterized in that: The threshold is a preset distance threshold or speed threshold.

7. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 5, characterized in that: The step 3 also includes: step 35, the cloud platform monitors the vehicle status in real time, dynamically adjusts the sorting result, and sends the adjusted sorting result to the forklift.

8. The control method based on the collaborative operation of a forklift and an unmanned container truck according to claim 1, characterized in that: The step 4 further includes: after the unmanned container truck arrives at the working position, it sends a signal to the forklift that it has reached the destination, and the forklift starts working after receiving the signal.

9. A system for implementing the control method based on the collaborative operation of a forklift and an unmanned container truck according to claims 1-7, characterized in that: The system comprises: Unmanned container trucks are used to upload the vehicle's latitude and longitude, heading angle, and driving trajectory points to the cloud platform. Following the driving route planned by the cloud platform, the trucks drive to the waiting area and proceed to the operating location after receiving the forklift's call signal. They then leave the operating location after receiving the forklift's completion signal. The forklift is connected to the unmanned container truck and the motion scheduling platform respectively. It is used to send a ride-hailing operation signal to the unmanned container truck in the current sequence according to the sorting results sent by the cloud platform. After completing the operation, it sends a job completion signal to the unmanned container truck. After the unmanned container truck in the current operation leaves, it continues to send ride-hailing operation signals to the unmanned container truck in the current sequence until all the unmanned container trucks in the sorting results have completed their operations. The cloud platform is connected to the unmanned container trucks and forklifts respectively. It is used to set up a virtual waiting area based on the number of unmanned container trucks, the length and width of each vehicle, the safety distance, the available space in the yard, and the vehicle operation route. The current position of the vehicle is determined according to the latitude, longitude and heading angle uploaded by the vehicle. By comparing the positional relationship between the unmanned container trucks and the waiting area, the shortest reachable path is analyzed. After evaluating the accessibility and rationality of the path, the vehicle's route to the waiting area is planned. Based on the driving trajectory point data of the unmanned container trucks, the arrival time of each vehicle in the waiting area is predicted, and the vehicles are sorted in order according to the expected arrival time of the waiting area, and the sorting results are then sent to the forklift.

10. According to the control system based on the collaborative operation of a forklift and an unmanned container truck as described in claim 8, the cloud platform is also used to monitor the vehicle status in real time, dynamically adjust the sorting results, and send the adjusted sorting results to the forklift.

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