Container port unmanned vehicle intelligent scheduling and navigation method and system

Abstract

This invention relates to the field of port automation technology and discloses an intelligent scheduling and navigation method and system for unmanned vehicles in container ports. The invention utilizes a console system for unified access control and map data management; a monitoring system for real-time collection and display of vehicle status, trajectories, and operational statistics; a path planning system based on the A* algorithm and dynamic scene parameters to plan optimal paths for vehicles and manage long and short paths; a PB scheduling system for dynamically prioritizing vehicle passage at key intersections; a charging management system for predictive charging scheduling; a parking management system for intelligent allocation of parking spaces; and a lock station scheduling management system for coordinating lock loading / unloading tasks. This achieves unified scheduling and navigation of all elements of unmanned vehicles within the port. This invention effectively solves the problems of poor system coordination, insufficient intelligent scheduling, and inefficient resource management in existing technologies, significantly improving port operational efficiency, resource utilization, and system scalability.

Description

Technical Field

[0001] This invention relates to the field of port automation technology, specifically to an intelligent scheduling and navigation method and system for unmanned vehicles in container ports. Background Technology

[0002] As a core global logistics hub, the operational efficiency of modern container ports directly impacts trade and logistics costs and timeliness. With the continuous growth of port throughput and rising labor costs, the automation and intelligentization of horizontal port transportation has become an inevitable trend. Among these, the efficient and precise scheduling and navigation of unmanned vehicles (such as AGVs and IGVs) is a key link in achieving full-process automation.

[0003] Currently, port operations primarily rely on a traditional Port Operating System (TOS) combined with multiple decentralized subsystems (such as vehicle dispatching, route planning, and charging management). This model has significant drawbacks: First, system coordination is poor, with modules forming "information silos" and hindering data exchange. This prevents unified optimization of global resources (vehicles, roads, charging stations, and parking spaces), making it difficult to support large-scale fleet operations. Second, dispatching and navigation lack intelligence. Vehicle dispatching is largely based on static rules, and route planning has weak responsiveness to dynamic environments (such as temporary obstacles and traffic flow changes). Key intersections rely on fixed signals or manual dispatching, easily leading to uneven task allocation, suboptimal routes, and vehicle congestion. Third, resource management is inefficient. Charging dispatching is mostly reactive, and parking management is disorganized, resulting in low resource utilization. Finally, system expansion and maintenance are difficult. The decentralized architecture struggles to support vehicle scale expansion or new functionality, the map data update mechanism is inadequate, and maintenance costs are high.

[0004] Therefore, there is an urgent need for a comprehensive solution for container port vehicle dispatching and navigation that can break down information silos, achieve full-element collaboration, and possess intelligent decision-making and dynamic response capabilities. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and propose an intelligent scheduling and navigation method and system for unmanned vehicles in container ports. Through highly integrated and intelligent design, this method and system realizes unified and coordinated scheduling and navigation of all elements of unmanned vehicles in the port, which significantly improves operational efficiency, resource utilization and system robustness.

[0006] The technical problem solved by this invention is achieved through the following technical solution: A method for intelligent scheduling and navigation of unmanned vehicles in container ports includes the following steps: User authentication, access control, and version maintenance and publishing of port map data are performed through the console system. The unmanned vehicle monitoring system receives, processes, and visualizes the location, status, historical trajectory, and operational statistics of unmanned vehicles within the port in real time. The route planning and navigation system uses the A* algorithm to dynamically plan the optimal driving route for the vehicle based on the vehicle's real-time location, task objectives, road conditions, and scheduling busyness. Based on the comparison results between the route length and the critical value, the route is classified into long or short routes for differentiated management and monitoring. The PB scheduling system is used to dynamically schedule vehicle passage order at key intersections based on global task priority and real-time traffic flow to eliminate traffic bottlenecks. Through the charging management system, the vehicle's battery level and the status of the charging station are monitored in real time. Based on predictive analysis, charging stations are intelligently allocated to vehicles with low battery levels and charging routes are planned. The parking management system dynamically manages parking space resources, intelligently allocates the best parking space to waiting vehicles, and plans parking routes. Through the lock station scheduling and management system, it interacts with the port operating system TOS to coordinate lock loading and unloading tasks, ensure that lock resources meet operational needs, and coordinate with the PB scheduling system to control the orderly passage of lock loading and unloading vehicles. The path planning and navigation system, unmanned vehicle monitoring system, PB scheduling system, charging management system, parking management system, and lock station scheduling management system interact and coordinate based on unified map data and console permissions to achieve unified scheduling and navigation of all elements of unmanned vehicles within the port.

[0007] Furthermore, the aforementioned user authentication, access control, and version maintenance and publishing of port map data through the console system specifically include: The system uses JSON Web Token technology to verify and authorize user login credentials, and manages system operation permissions based on a role-based access control model. It supports uploading original map and point of interest data in the form of compressed packages, and previewing and verifying the data in an isolated environment; A versioned release mechanism is adopted, which processes the verified map data into an independent entity with a version number for release, and achieves uninterrupted updates of map data by switching online service pointers.

[0008] Furthermore, the real-time reception, processing, and visualization of vehicle information through the autonomous vehicle monitoring system specifically includes: Real-time vehicle data from the vehicle-side system is asynchronously received and buffered through a data receiving gateway and message queue middleware. The background monitoring service consumes and processes the data, writes the real-time status to the cache for quick querying and pushing, and persists the historical data for storage. The monitoring front-end module receives real-time data updates via the WebSocket protocol, dynamically renders vehicle locations and statuses on an electronic map, and specially marks vehicles that have exceeded the time limit for offline operation; it also receives historical trajectory query requests, obtains location data from external systems, segments the data, and uses the processed trajectory segments to draw the map. It receives and stores vehicle task completion events, and provides an API interface for the front end to query and display vehicle operation statistics lists and charts.

[0009] Furthermore, the dynamic planning of the optimal driving route through the path planning navigation system specifically includes: The path starts at the real-time vehicle location and ends at the location specified by the system task. Obtain road.shp and node.shp files as basic road network data, and integrate vehicle task execution status, road congestion, dispatch busyness and weather factors as scene parameters; Using the A* algorithm as its core, and combining the road network data and scene parameters, the toll cost of the road is set to influence the path selection of the A* algorithm, calculate and generate the optimal route from the starting point to the destination, and return the route length and details; The calculated route length is compared with the preset long / short path distance threshold. Based on the comparison result, the path is identified as a long path or a short path and stored in the corresponding path server. The planned routes are distributed to designated work vehicles for execution, and long-distance routes are subject to key monitoring and guidance.

[0010] Furthermore, the dynamic scheduling of vehicle passage order at key intersections via the PB scheduling system specifically includes: Obtain the task level information for each vehicle from the vehicle-side system; Newly arrived driverless vehicles enter the waiting area to wait (each vehicle has a mission level, and they are sorted according to mission level). Periodically check the status of the lock station dispatch management system to determine whether the locking task for high-priority vehicles has been completed; If the locking task for a high-priority vehicle has been completed, a notification will be sent to the next priority waiting vehicle to release its waiting status and start its locking task. If the locking task for a high-priority vehicle is not completed, the vehicles in the waiting area will remain in a waiting state. The PB dispatch process at this critical intersection ends once all vehicles in the waiting area have completed the locking process.

[0011] Furthermore, the method of intelligently allocating charging stations and planning charging routes for vehicles with low battery levels through the charging management system specifically includes: The charging facility information management module displays the location, static information, and dynamic working status of all charging piles on the map in real time. The vehicle status monitoring module monitors the remaining battery power of each vehicle in the fleet in real time, and automatically triggers the charging scheduling process when the battery power is lower than the preset threshold. The charging scheduling decision module allocates the optimal charging pile to the target vehicle based on the vehicle's battery level, work plan, and real-time status of the charging pile, according to the preset global charging strategy. The task execution and path request module initiates a path request to the path planning and navigation system to obtain the navigation path from the vehicle's current location to the target charging station. The charging command, which includes the target charging station information, and the navigation route are sent to the external vehicle system for execution. The charging status is continuously monitored during vehicle charging, and a departure command is issued after charging is completed.

[0012] Furthermore, the intelligent allocation of optimal parking spaces to standby vehicles through the parking management system specifically includes: The map display module displays the parking lot layout, entrance and exit locations, and real-time status of each parking space on an electronic map. The status includes vacant, occupied, and reserved, and is marked with different colors. The parking task decision module automatically allocates suitable parking spaces to target vehicles that need to park, based on the vehicle's work plan or real-time status. It provides a parking space search interface, allowing operators to filter and search for available parking spaces by vehicle type, distance from entrance / exit, and whether or not there is a charging station. The task execution and navigation module sends instructions to the vehicle to enter the parking lot and park in the designated parking space, and calls the route planning and navigation system to plan the optimal driving route for it.

[0013] Furthermore, the coordination of locking and unlocking tasks through the lock station scheduling and management system specifically includes: Lock installation process: Based on the TOS system, obtain information on the location, status, and number of locks at the lock station; Based on the status information, it is determined to be a lock installation process; Determine if the number of locks meets the requirements of this lock installation task. If not, notify the TOS system to coordinate until the requirements are met. After the vehicle is locked, the PB scheduling module is notified to replace it with a new vehicle to perform the locking task. Repeat the process until all vehicles are locked. Unlocking task process: According to the TOS system, obtain the location and status information of the lock station; Based on the status information, it is determined to be an unlocking process; After the vehicle has been unlocked, update the number and status of the locks at the lock station; Repeat the process until all vehicles have been unlocked.

[0014] An intelligent scheduling and navigation system for unmanned vehicles in container ports for executing methods includes: The console system is used to implement user authentication, access control, and version management and publishing of map data. The unmanned vehicle monitoring system is used to realize real-time vehicle status monitoring, historical trajectory management, and operation statistics. The route planning and navigation system is used to achieve vehicle selection and optimal route planning based on multi-dimensional dynamic data; The PB scheduling system is used to dynamically sequence vehicles passing through key intersections. A charging management system is used to predict vehicle charging demand and intelligently schedule charging resources. A parking management system is used to dynamically allocate parking spaces and guide vehicle parking. The lock station scheduling and management system is used to coordinate lock resources with TOS and manage lock loading / unloading tasks; Among them, the various systems interact and collaborate on data based on a unified data platform.

[0015] The advantages and positive effects of this invention are: 1. Comprehensive Resource Collaborative Optimization: This invention builds a unified cloud control platform, which deeply integrates the originally scattered subsystems, realizes the seamless flow of data within the platform, and forms a unified "port digital twin". This enables the dispatch center to make collaborative decisions based on global real-time information, completely breaks down "information silos", significantly reduces vehicle empty running rate and queuing time, and improves the overall operational efficiency of port horizontal transportation.

[0016] 2. Intelligent Dynamic Algorithm Driven: In vehicle scheduling and route planning, this invention employs a self-developed intelligent algorithm that integrates multi-dimensional dynamic data to achieve optimal vehicle matching and dynamic real-time route planning. This ensures that transportation routes are always close to optimal and improves the system's anti-interference capability. At key intersections, the PB scheduling system dynamically sorts traffic based on a global perspective, effectively eliminating traffic bottlenecks at intersections.

[0017] 3. Refined and Predictive Resource Management: The charging management system of this invention upgrades from "passive response" to "proactive prediction." Based on vehicle battery level, operational patterns, and future task predictions, it proactively schedules charging plans, improving the utilization rate of charging facilities. The parking management system enables dynamic optimization of parking space allocation, improving parking area turnover rate and vehicle task response speed.

[0018] 4. Advanced system architecture with high scalability and maintainability: This invention adopts a highly cohesive and loosely coupled cloud platform architecture, and provides a unified and secure map data management and maintenance entry point through the console system, making the system functional modules easy to expand and easily cope with the expansion of port scale and the increase in the number of vehicles, reducing the later development, integration and operation and maintenance costs. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the process of this invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings.

[0021] Example 1: This embodiment provides an intelligent scheduling and navigation method for unmanned vehicles in container ports. The overall process can be found in [reference needed]. Figure 1 This method operates on an integrated cloud control platform, and its core lies in achieving integrated and intelligent management of unmanned vehicles within the port through the collaborative operation of multiple subsystems.

[0022] I. Console System The console system is the management hub of the entire platform, mainly comprising two functional domains: The OA management function domain is responsible for user authentication and access control across the entire platform. It employs a JWT (JSON Web Token)-based authentication mechanism and a RBAC (Role-Based Access Control) model. After a user logs in, the system issues a JWT token for subsequent interface permission verification. Administrators can create users and roles in the backend and configure different system operation permissions for roles (such as map publishing, vehicle monitoring, and dispatch instruction issuance).

[0023] The Map Data Management functional area is responsible for providing accurate and up-to-date basic geospatial data for systems such as route planning and navigation. It supports administrators uploading raw map data (e.g., .shp files) and POI data (e.g., charging piles, parking spaces, and parking station locations) in compressed packages. After uploading, the data enters an "unpublished" state, where administrators can visually inspect and correct it in an isolated "preview mode." Once confirmed to be correct, the "versioned release" process is triggered. The system processes the data into the format required for online services (e.g., vector tiles, routing maps) and generates a new version. The release process is achieved by switching service pointers, remaining unaffected by online vehicle scheduling tasks, ensuring secure and smooth data updates.

[0024] System Interaction Relationship: There is a permission dependency and service call relationship between the OA management system and the map data management system. The OA management system, as the platform's unified authentication center, provides identity verification and permission resolution services for the map data management system; the map data management system, in turn, dynamically loads and displays differentiated map layers and geographic features based on the role permission identifiers returned by the OA system, realizing role-based data access control (RBAC) and functional isolation.

[0025] II. Unmanned Vehicle Monitoring System The system is responsible for comprehensive, real-time monitoring of all driverless vehicles within the port.

[0026] Vehicles continuously report their real-time data (including GPS location, speed, heading, remaining battery power, and vehicle status) to the platform via the vehicle-side system. The platform receives this data through a data receiving gateway (such as a RESTful API endpoint) and immediately pushes it into a message queue middleware (such as Kafka) for buffering and decoupling. The background monitoring service, acting as a consumer, retrieves data from the message queue and processes it: on the one hand, it writes the latest key vehicle status (such as location and battery power) to a Redis cache to achieve millisecond-level real-time querying and pushing; on the other hand, it batch persists the complete time-series data to a PostgreSQL database (the TimescaleDB extension can be enabled to optimize time-series data performance).

[0027] The monitoring front-end module (typically a web application) communicates with the back-end service via a WebSocket persistent connection. Once the back-end service updates the vehicle status from Redis, it proactively pushes the update to the front-end via WebSocket. Upon receiving the data, the front-end dynamically updates the vehicle icon's position and status display on the electronic map. The front-end also includes a timer; if a vehicle fails to report data for an extended period (e.g., more than 30 seconds), its icon is marked gray (offline status), and an alert is issued.

[0028] In addition, the system also provides a historical trajectory query function. After the operator selects the vehicle and time period on the front end, the back-end service calls the port system's trajectory interface to obtain the original positioning points and segments the trajectory (for example, if two consecutive segments have a time interval of more than 2 hours, they are divided into new segments). Finally, the segmented trajectory data is returned to the front end for plotting. The system also records vehicle task completion events and provides an API for querying job statistics, which are displayed in list or chart format.

[0029] III. Route Planning and Navigation System The system is responsible for calculating the optimal driving route for each task.

[0030] When the TOS system issues a new transportation task, the vehicle selection algorithm is activated. This algorithm does not simply select the nearest available vehicle, but comprehensively considers multiple dimensions such as the vehicle's real-time location, current battery level, cumulative workload (to achieve load balancing), and task urgency to match the most suitable vehicle for the target task.

[0031] Once the vehicle is selected, the route planning engine begins operation. It uses the vehicle's real-time GPS location as the starting point and the target location (e.g., a specific bay in the storage yard) as the endpoint. The planning process is based on port road network data (.shp and Node.shp files) and integrates real-time dynamic scenario parameters, including: road congestion (determined by vehicle density), scheduling activity, temporary traffic control information for specific areas, and weather factors (e.g., heavy rain may cause slowdowns). The core planning algorithm employs the A* algorithm, which efficiently searches the road network for the most cost-effective feasible path (e.g., shortest time, shortest distance).

[0032] After the path is generated, the system calculates its total length and compares it with preset long / short path distance thresholds. If the path length exceeds the threshold, it is marked as a long-distance path, stored in the long-path server, and subject to intensive monitoring and guidance during vehicle operation (such as more frequent location reporting and confirmation of key nodes). Conversely, if the path length is less than the threshold, it is classified as a short-distance path, stored in the short-path server, and guided by regular commands. Finally, the system sends the complete planned path details to the selected vehicle, which is then controlled by the vehicle's onboard system to execute the route.

[0033] IV. PB Dispatch System and Station Lock Dispatch Management System These two systems work together to address bottlenecks in traffic efficiency at key intersections (PB points) and the management of container locks.

[0034] The PB scheduling system, acting as a centralized intelligent controller, manages critical crossings within the port. It no longer employs a simple first-come, first-served rule. When multiple vehicles are about to arrive at the same crossing, the PB scheduling system obtains their task priority information (from the TOS) and their real-time location and speed. Based on global task priorities (e.g., tasks with urgent shipping schedules take precedence) and the real-time status of the vehicles, it calculates a passage sequence that maximizes the overall traffic efficiency of the crossing. Then, it sends passage instructions sequentially to the vehicles in the sequence, ensuring that high-priority tasks are passed first, thus avoiding disorderly competition and congestion.

[0035] The lock station scheduling and management system is responsible for coordinating with the TOS system to manage the loading and unloading process of container twist locks.

[0036] Locking Task Flow: The system obtains the locking task requirements from the TOS (Transit System), including the target locking station location and the required number of locks. It first checks if the locking station currently has sufficient available locks. If not, it immediately notifies the TOS system to coordinate until the required number of locks is met. Then, the system guides vehicles to enter the locking station sequentially for locking. Each time a vehicle completes locking, the locking station dispatch system notifies the PB (Parkerage System) dispatch system. The PB dispatch system updates the intersection's vehicle passage sequence accordingly, allowing high-priority vehicles that have completed locking to pass first, and allowing the next waiting vehicle to enter the locking station, forming a closed-loop control system.

[0037] Unlocking process: After completing the transportation task, the vehicle enters the designated lock station for unlocking. After each vehicle is unlocked, the system automatically updates the number and status of locks at the lock station to ensure accurate resource visibility.

[0038] V. Charging Management System This system enables intelligent and automated management of vehicle energy replenishment.

[0039] The charging facility information management module uses Internet of Things (IoT) technology to collect real-time static information (such as location and power) and dynamic status (such as idle, occupied, faulty, and offline) of all charging piles and displays it on a map.

[0040] The vehicle status monitoring module continuously analyzes real-time vehicle battery data provided by the monitoring system. When the system detects that a vehicle's battery level is below a preset low battery threshold (e.g., 20%), it automatically triggers the charging scheduling process.

[0041] The charging scheduling decision module acts as the brain, not passively waiting for vehicle alarms. It performs predictive analysis by combining historical operational data and future task pools. For example, even if a vehicle's battery is not yet depleted, the system may preemptively schedule charging if it has an upcoming long-distance, heavy-load task. During decision-making, the module comprehensively considers factors such as the availability of charging stations, their distance from the vehicle, and estimated waiting time, allocating the optimal charging station for the target vehicle from among the available options.

[0042] After receiving the decision result, the task execution and route request module initiates a special route request to the route planning and navigation system: the starting point is the vehicle's current location, and the destination is the assigned charging station. Once the charging route is obtained, the module sends the instruction "Go to charging station number XX" along with the navigation route to the external vehicle-side system. The vehicle automatically drives to the charging station and begins charging. During charging, the system continuously monitors the charging status. After charging is complete, the system sends a "drive away" instruction to the vehicle and assigns it a new task.

[0043] VI. Parking Management System This system enables refined management of the port's parking area.

[0044] The map display module clearly marks all parking areas, the real-time status of each parking space (vacant - green, occupied - red, reserved - yellow) and the location of parking lot entrances and exits on the electronic map.

[0045] When a vehicle completes its current task and no new tasks are assigned, the parking task decision module will automatically activate. It will allocate a suitable parking space to the standby vehicle. The allocation strategy can be varied, such as: the parking space closest to the next expected work location, the parking space near the entrance / exit for quick response, or the parking space with a charging station for new energy vehicles.

[0046] The system provides a parking space search function, allowing operators to manually filter and reserve parking spaces based on criteria such as vehicle type, distance, and charging needs.

[0047] Once the parking space is allocated, the task execution and navigation module will issue a "enter the parking lot and park" instruction to the vehicle. It will also call the route planning and navigation system to plan an optimal driving route from the current location to the designated parking space, guiding the vehicle to park accurately.

[0048] Through the close collaboration of the above subsystems, this invention realizes intelligent scheduling and management of all elements of unmanned vehicles in container ports, from task issuance, route planning, real-time navigation, intersection passage, energy replenishment to parking and standby, forming an efficient, reliable, and scalable automated horizontal transportation solution.

[0049] Example 2: This embodiment provides an intelligent scheduling and navigation system for unmanned vehicles in container ports to perform the method described in Embodiment 1. The system includes: The console system is used to implement user authentication, access control, and version management and publishing of map data.

[0050] The unmanned vehicle monitoring system is used to realize real-time vehicle status monitoring, historical trajectory management, and operation statistics.

[0051] The route planning and navigation system is used to select vehicles and plan the optimal route based on multi-dimensional dynamic data.

[0052] The PB scheduling system is used to dynamically prioritize vehicles at key intersections.

[0053] A charging management system is used to predict vehicle charging demand and intelligently schedule charging resources.

[0054] A parking management system is used to dynamically allocate parking spaces and guide vehicle parking.

[0055] The lock station scheduling and management system is used to coordinate lock resources with TOS and manage lock loading / unloading tasks.

[0056] These systems are deployed through a unified cloud platform architecture. Based on the microservice design concept, the subsystems exchange data and make business calls through well-defined API interfaces (such as RESTful APIs and message queues), share unified map data services and user authentication services, and together form a highly cohesive and loosely coupled intelligent scheduling and navigation system.

[0057] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A method for intelligent scheduling and navigation of unmanned vehicles in container ports, characterized in that, Includes the following steps: User authentication, access control, and version maintenance and publishing of port map data are performed through the console system. The unmanned vehicle monitoring system receives, processes, and visualizes the location, status, historical trajectory, and operational statistics of unmanned vehicles within the port in real time, and provides the parking management system and charging management system with information such as vehicle location and status. The route planning and navigation system uses the TOS task information and map data provided by the console system, and the A* algorithm is used to dynamically plan the optimal driving route for the vehicle based on the vehicle's real-time location, task objectives, road conditions and scheduling busyness. Based on the comparison results of the path length and the critical value, the path is classified into long path or short path for differentiated management and monitoring, and the most reasonable driving route is planned for the vehicle. The PB scheduling system eliminates traffic bottlenecks by dynamically scheduling vehicle passage order at key intersections based on global task priority and real-time traffic flow information, according to information from the station locking system. Through the charging management system, the vehicle's battery level and the status of the charging station are monitored in real time. Based on predictive analysis, charging stations are intelligently allocated to vehicles with low battery levels and charging routes are planned. The parking management system dynamically manages parking space resources, intelligently allocates the best parking space to waiting vehicles, and plans parking routes. Through the lock station scheduling and management system, it interacts with the port operating system TOS to coordinate lock loading and unloading tasks, ensure that lock resources meet operational needs, and coordinate with the PB scheduling system to control the orderly passage of lock loading and unloading vehicles. The path planning and navigation system, unmanned vehicle monitoring system, PB scheduling system, charging management system, parking management system, and lock station scheduling management system interact and coordinate based on unified map data and console permissions to achieve unified scheduling and navigation of all elements of unmanned vehicles within the port.

2. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The aforementioned user authentication, access control, and version maintenance and publishing of port map data through the console system specifically include: The system uses JSON Web Token technology to verify and authorize user login credentials, and manages system operation permissions based on a role-based access control model. It supports uploading original map and point of interest data in the form of compressed packages, and previewing and verifying the data in an isolated environment; A versioned release mechanism is adopted, which processes the verified map data into an independent entity with a version number for release, and achieves uninterrupted updates of map data by switching online service pointers.

3. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The process of receiving, processing, and visually displaying vehicle information in real time through the autonomous vehicle monitoring system specifically includes: Real-time vehicle data from the vehicle-side system is asynchronously received and buffered through a data receiving gateway and message queue middleware. The background monitoring service consumes and processes the data, writes the real-time status to the cache for quick querying and pushing, and persists the historical data for storage. The monitoring front-end module receives real-time data updates via the WebSocket protocol, dynamically renders vehicle locations and statuses on an electronic map, and specially marks vehicles that have exceeded the time limit for offline operation; it also receives historical trajectory query requests, obtains location data from external systems, segments the data, and uses the processed trajectory segments to draw the map. It receives and stores vehicle task completion events, and provides an API interface for the front end to query and display vehicle operation statistics lists and charts.

4. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The dynamic planning of the optimal driving route through the route planning navigation system specifically includes: The path starts at the real-time vehicle location and ends at the location specified by the system task. Obtain road.shp and node.shp files as basic road network data, and integrate vehicle task execution status, road congestion, dispatch busyness and weather factors as scene parameters; Using the A* algorithm as its core, and combining the road network data and scene parameters, the toll cost of the road is set to influence the path selection of the A* algorithm, calculate and generate the optimal route from the starting point to the destination, and return the route length and details; The calculated route length is compared with the preset long / short path distance threshold. Based on the comparison result, the path is identified as a long path or a short path and stored in the corresponding path server. The planned routes are distributed to designated work vehicles for execution, and long-distance routes are subject to key monitoring and guidance.

5. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The dynamic scheduling of vehicle passage order at key intersections via the PB scheduling system specifically includes: Obtain the task level information for each vehicle from the vehicle-side system; Newly arrived driverless vehicles enter the waiting area to wait (each vehicle has a mission level, and they are sorted according to mission level). Periodically check the status of the lock station dispatch management system to determine whether the locking task for high-priority vehicles has been completed; If the locking task for a high-priority vehicle has been completed, a notification will be sent to the next priority waiting vehicle to release its waiting status and start its locking task. If the locking task for a high-priority vehicle is not completed, the vehicles in the waiting area will remain in a waiting state. The PB dispatch process at this critical intersection ends once all vehicles in the waiting area have completed the locking process.

6. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The method of intelligently allocating charging stations and planning charging routes for vehicles with low battery levels through a charging management system specifically includes: The charging facility information management module displays the location, static information, and dynamic working status of all charging piles on the map in real time. The vehicle status monitoring module monitors the remaining battery power of each vehicle in the fleet in real time, and automatically triggers the charging scheduling process when the battery power is lower than the preset threshold. The charging scheduling decision module allocates the optimal charging pile to the target vehicle based on the vehicle's battery level, work plan, and real-time status of the charging pile, according to the preset global charging strategy. The task execution and path request module initiates a path request to the path planning and navigation system to obtain the navigation path from the vehicle's current location to the target charging station. The charging command, which includes the target charging station information, and the navigation route are sent to the external vehicle system for execution. The charging status is continuously monitored during vehicle charging, and a departure command is issued after charging is completed.

7. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The intelligent allocation of optimal parking spaces to standby vehicles through the parking management system specifically includes: The map display module displays the parking lot layout, entrance and exit locations, and real-time status of each parking space on an electronic map. The status includes vacant, occupied, and reserved, and is marked with different colors. The parking task decision module automatically allocates suitable parking spaces to target vehicles that need to park, based on the vehicle's work plan or real-time status. It provides a parking space search interface, allowing operators to filter and search for available parking spaces by vehicle type, distance from entrance / exit, and whether or not there is a charging station. The task execution and navigation module sends instructions to the vehicle to enter the parking lot and park in the designated parking space, and calls the route planning and navigation system to plan the optimal driving route for it.

8. The intelligent scheduling and navigation method for unmanned vehicles in container ports according to claim 1, characterized in that, The coordination of locking and unlocking tasks through the lock station scheduling and management system specifically includes: Lock installation process: Based on the TOS system, obtain information on the location, status, and number of locks at the lock station; Based on the status information, it is determined to be a lock installation process; Determine if the number of locks meets the requirements of this lock installation task. If not, notify the TOS system to coordinate until the requirements are met. After the vehicle is locked, the PB scheduling module is notified to replace it with a new vehicle to perform the locking task. Repeat the process until all vehicles are locked. Unlocking task process: According to the TOS system, obtain the location and status information of the lock station; Based on the status information, it is determined to be an unlocking process; After the vehicle has been unlocked, update the number and status of the locks at the lock station; Repeat the process until all vehicles have been unlocked.

9. A container port unmanned vehicle intelligent scheduling and navigation system for performing the method according to any one of claims 1-8, characterized in that, include: The console system is used to implement user authentication, access control, and version management and publishing of map data. The unmanned vehicle monitoring system is used to realize real-time vehicle status monitoring, historical trajectory management, and operation statistics. The route planning and navigation system is used to achieve vehicle selection and optimal route planning based on multi-dimensional dynamic data; The PB scheduling system is used to dynamically sequence vehicles passing through key intersections. A charging management system is used to predict vehicle charging demand and intelligently schedule charging resources. A parking management system is used to dynamically allocate parking spaces and guide vehicle parking. The lock station scheduling and management system is used to coordinate lock resources with TOS and manage lock loading / unloading tasks; Among them, the various systems interact and collaborate on data based on a unified data platform.

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