Bulk cargo wharf unmanned tallying control method and system based on path navigation

Abstract

The invention discloses a bulk cargo wharf unmanned tallying control method and system based on path navigation, and relates to the technical field of bulk cargo wharf production management, and the method comprises the steps: carrying out the grid modeling of a target warehouse; drawing an operation path based on a GIS map and wharf road network data; the vehicle-mounted APP receives operation path data issued by the background, and obtains vehicle real-time positioning broadcast information through an RTK positioning mode of connecting a differential base station; the background system constructs a vehicle operation state machine according to the operation path, and divides the operation process into a plurality of key point events which are executed in sequence; in the vehicle operation process, the background system performs anomaly detection based on vehicle real-time coordinates, speed and operation path data; and carrying out visual display on the unmanned tallying production process in the large monitoring screen based on a GIS (Geographic Information System) map. According to the invention, through operation path planning and vehicle-mounted navigation control, accurate guidance of a vehicle operation path is realized, and vehicle operation controllability and tallying accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of production management technology for bulk cargo terminals, and in particular to a method and system for unmanned cargo handling control at bulk cargo terminals based on path navigation. Background Technology

[0002] Bulk cargo terminals are an important part of the port logistics system, and their operations typically involve frequent vehicle transport between the quay, weighbridge, and storage yard. To ensure the accuracy and traceability of cargo loading, unloading, weighing, and storage processes, existing bulk cargo terminals generally rely on tally clerks to manually confirm and record vehicle operation processes on-site, including whether vehicles are traveling along predetermined routes, whether they have completed weighing, and whether they have arrived at the designated storage yard location.

[0003] However, traditional manual tallying requires long-term staffing on each work line, resulting in high labor costs. Furthermore, in the complex working environment of a port, tally workers frequently need to approach vehicles, loading and unloading machinery, and cargo stacking areas, posing significant safety risks. At the same time, manual tallying relies heavily on human observation and experience, making it susceptible to the influence of the working environment, staff fatigue, and subjective factors, hindering continuous, accurate, and automated control of vehicle operations.

[0004] As ports continue to expand and operational intensity increases, vehicle operation routes are becoming increasingly complex. The traditional manual methods of cargo handling and operational process monitoring are no longer sufficient to meet the demands of bulk cargo terminals in terms of operational efficiency, safety, and refined management. While existing technologies include vehicle monitoring solutions based on positioning technology or electronic fences, most only display vehicle locations or make simple area entry / exit judgments. These solutions fail to address the actual needs of bulk cargo terminal operations by systematically and automatically determining the complete vehicle operation path, key operational nodes, and abnormal behaviors. Therefore, they cannot completely replace manual cargo handling. Consequently, there is an urgent need for a technological solution that combines high-precision vehicle positioning, operation path navigation, and automated operational process control to achieve automated cargo handling and intelligent monitoring of vehicle operations at bulk cargo terminals, thereby reducing reliance on manual labor and improving operational safety and management efficiency. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by this invention is to address the high labor costs and significant safety risks associated with the existing bulk cargo terminal operations that rely on manual tallying for vehicle route confirmation, weighing and verification, and yard positioning. The invention aims to achieve automated control of the vehicle operation process and improve the safety, reliability, and management efficiency of bulk cargo terminal tallying operations.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide an unmanned cargo handling control method for a bulk cargo terminal based on path navigation, comprising: performing grid-based modeling of the target warehouse on a GIS map on a platform Web terminal; and having an operator select the warehouse area and rotate the coordinate axis to the target location before confirming and submitting the submission. The operation path is drawn on the platform's web interface based on GIS maps and wharf road network data; The vehicle-mounted APP receives the work path data sent by the background and obtains the vehicle's real-time location broadcast information through RTK positioning via a differential base station; After the driver logs into the vehicle APP and selects the work line, the system enters the work state. The background system constructs the vehicle work state machine according to the work path, divides the work process into multiple key point events to be executed sequentially, and stores each key point event in a distributed cache database in a linked list structure. During vehicle operation, the back-end system performs anomaly detection based on real-time vehicle coordinates, speed, and operation path data; The unmanned goods handling production process is visualized on a large monitoring screen based on GIS maps.

[0008] As a preferred embodiment of the unmanned cargo handling control method for bulk cargo terminals based on path navigation described in this invention, the method includes: gridding modeling of the target warehouse on the platform's web interface based on a GIS map; the operator selecting the warehouse area and rotating the coordinate axes to the target location before confirming and submitting the submission; and so on. The system divides the warehouse into multiple grid units corresponding to 3×3 meter areas in the physical world and saves the surface coordinate data of each grid unit. After the warehouse is gridded, the operator manually selects a rectangular area in the gridded warehouse as the work storage area confirmation area for the current work line. The system stores the work storage area confirmation area as an electronic fence in the warehouse for use in work path drawing and work process determination.

[0009] As a preferred embodiment of the unmanned cargo handling control method for bulk cargo terminals based on path navigation described in this invention, the method includes: drawing the operation path on the platform's web interface based on a GIS map and terminal road network data, including: The operation path includes a return path and a non-return path, used to determine whether the vehicle passes the weighbridge during a round of operation; the operation path supports manual drawing and automatic drawing, wherein the automatic drawing path performs shortest path planning on road network data based on the set start point, end point, waypoint and avoidance point, and automatically avoids obstacle avoidance areas or restricted areas during the planning process, generating operation path data for vehicle terminal navigation.

[0010] As a preferred embodiment of the unmanned cargo handling control method for bulk cargo terminals based on path navigation described in this invention, the following steps are taken: The vehicle-mounted APP receives operation path data from the backend and obtains real-time vehicle positioning broadcast information via RTK positioning connected to a differential base station. This positioning broadcast information includes at least vehicle coordinates, driving speed, and driving direction. The vehicle-mounted APP performs path navigation for the vehicle based on the obtained real-time positioning information and judges the vehicle's driving speed according to the port area's preset speed limit. When speeding or deviation from the operation path is detected, the APP prompts the driver to correct the course or reduce speed via voice and uploads the corresponding abnormal information to the backend system.

[0011] As a preferred embodiment of the unmanned cargo handling control method for bulk cargo terminals based on path navigation described in this invention, the system enters the operation state after the driver logs into the vehicle-mounted APP and selects the work line. The background system constructs a vehicle operation state machine based on the operation path, divides the operation process into multiple key point events to be executed sequentially, and stores each key point event in a distributed cache database in a linked list structure, including: The system controls the vehicle's operation process through the work path and APP navigation. After the driver logs in to the APP, selects the work line and starts navigation, the vehicle enters the work state. The specific working mode of the vehicle's work state machine is as follows: drive to the weighbridge → return to the weighbridge → drive to the shore → load the goods → drive to the weighbridge → weigh the goods → drive to the warehouse entrance / exit → drive to the designated cargo pile → unload the goods → leave the cargo pile. The vehicle-mounted APP reports the vehicle's real-time coordinates to the backend at preset time intervals. Based on the vehicle coordinate data, the backend system uses a ray-mapping algorithm to determine whether the vehicle has entered or left the corresponding key point electronic fence. When it is determined that the vehicle has completed a key point event, the event is updated to the completed state, and the system continues to wait for the next key point event to be triggered until a round of operation is completed. The calculation formula for the ray tracing algorithm is as follows: ; in, Let y be the horizontal coordinate of the intersection point of the ray drawn horizontally from the vehicle's real-time coordinate point and the i-th side of the electronic fence polygon, and let y be the vertical coordinate of the vehicle in the planar coordinate system at the current moment. This refers to the lateral coordinates of the i-th vertex of the polygon forming the electronic fence in the planar coordinate system. Let be the vertical coordinate value of the i-th vertex of the polygon that constitutes the electronic fence in the planar coordinate system. Let x be the horizontal coordinate of the next vertex adjacent to the i-th vertex in the planar coordinate system. Let be the vertical coordinate value of the next vertex adjacent to the i-th vertex in the planar coordinate system.

[0012] As a preferred embodiment of the unmanned cargo handling control method for a bulk cargo terminal based on path navigation described in this invention, wherein: during vehicle operation, the backend system performs anomaly detection based on real-time vehicle coordinates, speed, and operation path data, including: the anomaly detection includes: Yaw detection: When the vehicle is not within the electronic fence, the system calculates the shortest distance between the vehicle's current position and the road network of the work path to determine whether the vehicle has deviated from the preset work path; Missed weighing detection: When the vehicle leaves the electronic fence of the weighbridge, the system calls the weighbridge system interface to obtain weighing records within a preset time range. If no corresponding weighing data is obtained, the system determines that the vehicle has missed weighing and sends a voice prompt to the vehicle's APP; Speeding detection: The system calculates the vehicle's speed based on the displacement distance within a fixed time period. When the speed exceeds a preset speed limit threshold, the system determines that the vehicle has speeding.

[0013] As a preferred embodiment of the unmanned cargo handling control method for bulk cargo terminals based on path navigation described in this invention, the unmanned cargo handling process is visualized on a large monitoring screen based on a GIS map, including: The exhibits include information on operating machinery, work shifts, and digital yard data.

[0014] The specific operating machinery includes: embedded berths, bollards, weighbridges, and warehouse layers, displaying the real-time location of all vehicles on duty; the system backend stores the coordinate data reported by the vehicle-mounted APP, and the web client establishes a WebSocket connection with the backend, transmitting vehicle location information from the backend to the web client, which then displays the animation effects of all vehicle movement; simultaneously, if a vehicle deviates from its course, misses a weighbridge, or speeds, the large screen will issue an alarm; clicking on the graphical vehicle icon displays the vehicle's current operating progress and historical data. The specific work shifts include: displaying all work order data for all work lines; the system backend stores real-time and historical vehicle coordinates, storing large amounts of data through database sharding and table partitioning; and simultaneously displaying historical trajectory playback of vehicle coordinate data on the web interface. The digital storage yard specifically includes: displaying the cargo storage status within the storage yard based on the work order data from the work line; and allowing the web client to request the backend interface to return the cargo storage information in the storage yard when the storage yard is clicked on on the map.

[0015] Secondly, embodiments of the present invention provide an unmanned cargo handling control system for a bulk cargo terminal based on path navigation, comprising: The modeling management module is used to perform gridded modeling of the target warehouse based on the GIS map on the platform's web interface. After the operator selects the warehouse area and rotates the coordinate axis to the target location, they confirm and submit. The route planning module is used to draw operation routes on the platform's web interface based on GIS maps and wharf road network data. The navigation and positioning module is used by the vehicle-mounted APP to receive the work path data sent by the background and obtain the vehicle's real-time positioning broadcast information through the RTK positioning method connected to the differential base station; The status control module is used to put the system into operation state after the driver logs in to the vehicle APP and selects the operation line. The background system constructs the vehicle operation state machine according to the operation path, divides the operation process into multiple key point events to be executed in sequence, and stores each key point event in a distributed cache database in a linked list structure. The anomaly detection module is used by the back-end system to detect anomalies based on the vehicle's real-time coordinates, speed, and work path data during vehicle operation. The visualization monitoring module is used to visualize the unmanned goods handling production process on a large monitoring screen based on a GIS map.

[0016] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a path navigation-based unmanned cargo handling control method for bulk cargo terminals as described in the first aspect of the present invention.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a path navigation-based unmanned cargo handling control method for a bulk cargo terminal as described in the first aspect of the present invention.

[0018] The beneficial effects of this invention are as follows: By performing grid-based modeling of bulk cargo terminal yards and combining it with GIS-based operation path planning and vehicle navigation control, this invention achieves precise guidance and automatic determination of vehicle operation routes and key operation nodes; by utilizing high-precision positioning and electronic fence determination mechanisms, it can identify and alarm in real time whether vehicles have completed weighing, whether they have arrived at the designated yard, and whether there are abnormal behaviors such as deviation, missed weighing, or speeding, thereby improving the controllability of vehicle operation processes and the accuracy of cargo handling. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1 Reference Figures 1-2 This is the first embodiment of the present invention, which provides a method for unmanned cargo handling control at a bulk cargo terminal based on path navigation, including: S1: On the platform's web interface, the target warehouse is modeled in a grid based on a GIS map. The operator selects the warehouse area, rotates the coordinate axes to the target location, and then confirms and submits the model.

[0025] Furthermore, the system divides the warehouse into multiple grid units corresponding to 3×3 meter areas in the physical world and saves the surface coordinate data of each grid unit. After the warehouse is gridded, the operator manually selects a rectangular area in the gridded warehouse as the work storage area confirmation area for the current work line. The system stores the work storage area confirmation area as an electronic fence within the warehouse for use in work path drawing and work process determination.

[0026] For example, taking warehouse A at a bulk cargo terminal as an example, the specific process of performing grid-based modeling and confirming the operational storage area for the warehouse is as follows: The operator logs into the platform's web interface and selects the geographical area corresponding to warehouse A in the GIS map interface. This area is marked as a polygon on the GIS map. Then, based on the warehouse's actual orientation, the operator uses the coordinate axis rotation function provided in the interface to rotate the warehouse's coordinate axes to an azimuth angle consistent with the direction of the warehouse's main passage, for example, rotating it to a direction with an angle of 15° to true north. The operator then confirms and submits the gridded modeling command for the warehouse. Upon receiving the command, the system backend uses the rotated coordinate axes as a reference to divide the warehouse area into equal-distance grid units, each corresponding to a 3×3 meter area in the physical world. For each grid unit, the system generates and stores its corresponding surface coordinate data, which includes the coordinate information of multiple vertices constituting that grid unit.

[0027] After completing the grid-based modeling of the warehouse, operators manually select a rectangular area within the warehouse as the designated work area in the web-based grid-based warehouse interface, based on the actual operational needs of the current work line. The operator selects a rectangular area composed of multiple consecutive grid cells, with a coverage area of ​​30 meters × 24 meters, used to store the bulk cargo corresponding to this work line. The system converts this work area into an electronic fence and saves its surface coordinate data in polygon format. This electronic fence serves as a key point area within the warehouse, used as a target point reference when drawing subsequent work paths, and is used by the backend system to determine whether a vehicle has reached the designated loading / unloading position during vehicle operations.

[0028] S2: Draw the operation path on the platform's web interface based on GIS maps and wharf road network data.

[0029] Furthermore, the operation path includes a return path and a non-return path, used to determine whether the vehicle passes through the weighbridge during a round of operation; the operation path supports manual drawing and automatic drawing, wherein the automatically drawn path performs shortest path planning on the road network data based on the set start point, end point, waypoint and avoidance point, and automatically avoids obstacle avoidance areas or restricted areas during the planning process, generating operation path data for vehicle terminal navigation.

[0030] For example, taking the A-line operation at a bulk cargo terminal as an example, the process of drawing the vehicle operation path is as follows: The operator logs into the platform's web interface and loads the warehouse and terminal road network data involved in the A-line operation into the GIS map interface. The operator can choose to draw the operation path manually or automatically. Manual route drawing: Operators click on the vehicle's starting point, ending point, and key nodes along the route on the GIS map, such as loading points on the shore, weighbridge entrances, warehouse entrances and exits, and the center point of the cargo stack. The system generates route lines based on the clicked nodes and automatically connects the shortest feasible road network paths between the nodes to form a complete operation route.

[0031] Automatic route drawing: Operators input the starting point (e.g., loading area beside a ship), the destination (e.g., designated warehouse yard), and waypoints (e.g., weighbridge location) on the platform, and mark avoidance points or restricted areas (e.g., construction zones or obstacle areas) on the map interface. The system backend performs shortest path planning based on GIS road network data, using a road network topology analysis algorithm to calculate the optimal path from the starting point to the destination, while automatically avoiding avoidance points and restricted areas during the planning process. The system generates two types of paths based on the planning results: a return route (used when vehicles return to the starting point after passing the weighbridge) and a non-return route (driving directly to the warehouse without passing the weighbridge), and stores the path data as a work path file for subsequent vehicle terminal navigation.

[0032] Example of a path data structure: Each path consists of multiple path points. Each path point contains coordinates (x, y), a road segment identifier, a permitted vehicle speed, and key node markers (such as parking, weighing, and unloading).

[0033] Example of automatic path planning: Path starting point coordinates P1(x1,y1) → Weighbridge entrance coordinates P5(x5,y5) → Warehouse entrance coordinates P 10 (x 10 ,y 10 → Center point P of the work pile 12 (x 12 ,y 12 ).

[0034] S3: The vehicle-mounted APP receives the work path data sent by the background and obtains the vehicle's real-time location broadcast information through the RTK positioning method connected to the differential base station.

[0035] Furthermore, the location broadcast information includes at least vehicle coordinates, driving speed, and driving direction; the vehicle APP uses the acquired real-time location information to navigate the vehicle and judges the vehicle speed according to the port area's preset speed limit. When speeding or deviation from the working path is detected, the APP prompts the driver to correct the course or reduce speed via voice and uploads the corresponding abnormal information to the backend system.

[0036] For example, taking the operation of vehicles on the A work line of a bulk cargo terminal as an example: the vehicle driver starts the operation APP on the vehicle tablet and connects to the back-end system via wireless network. The back-end system sends the pre-planned operation route data (including route point coordinates, road segment signs, key nodes and speed limit information) to the vehicle APP. The vehicle APP stores the received operation route data in local memory for real-time navigation.

[0037] The in-vehicle APP connects to the RTK positioning module of the differential base station via the in-vehicle tablet to obtain real-time vehicle positioning broadcast information. The positioning broadcast information includes at least: the vehicle's current coordinates (x, y); and the vehicle's current speed v. The vehicle's direction of travel is θ. Positioning data is updated at a frequency of 1 Hz (once per second), achieving meter-level accuracy, sufficient for navigation along the work path. The onboard app calculates the vehicle's lateral deviation and forward distance from the path in real time based on the vehicle's current location and work path data, determining if the vehicle has deviated from the preset route. If the deviation exceeds a set threshold (e.g., 1 meter), the app triggers a path correction prompt. The app monitors the vehicle's speed according to the port area's preset speed limit (e.g., 15 km / h): when the vehicle speed v > 15 km / h, the app issues a voice prompt to the driver to slow down; speeding information, along with the vehicle's current coordinates, speed, and direction, are simultaneously uploaded to the backend system for recording operational anomalies and visual monitoring. Deviation warning: "Please adjust your direction and stay within the work route." Speeding warning: "Current speed exceeds the limit; please reduce speed to below the specified limit."

[0038] S4: After the driver logs into the vehicle APP and selects the work line, the system enters the work state. The background system constructs the vehicle work state machine according to the work path, divides the work process into multiple key point events to be executed sequentially, and stores each key point event in a distributed cache database in a linked list structure.

[0039] Furthermore, the system controls the vehicle's operation process through the work path and APP navigation. After the driver logs into the APP, selects the work line and starts navigation, he enters the work state. The specific working mode of the vehicle's work state machine is as follows: drive to the weighbridge → return to the weighbridge for weighing → drive to the shore → load cargo → drive to the weighbridge → weigh cargo → drive to the warehouse entrance / exit → drive to the designated cargo pile → unload cargo → leave the cargo pile. The vehicle-mounted APP reports the vehicle's real-time coordinates to the backend at preset time intervals. Based on the vehicle coordinate data, the backend system uses a ray-mapping algorithm to determine whether the vehicle has entered or left the corresponding key point electronic fence. When it is determined that the vehicle has completed a key point event, the event is updated to the completed state, and the system continues to wait for the next key point event to be triggered until a round of operation is completed. The calculation formula for the ray tracing algorithm is as follows: ; in, Let y be the horizontal coordinate of the intersection point of the ray drawn horizontally from the vehicle's real-time coordinate point and the i-th side of the electronic fence polygon, and let y be the vertical coordinate of the vehicle in the planar coordinate system at the current moment. This refers to the lateral coordinates of the i-th vertex of the polygon forming the electronic fence in the planar coordinate system. Let be the vertical coordinate value of the i-th vertex of the polygon that constitutes the electronic fence in the planar coordinate system. Let x be the horizontal coordinate of the next vertex adjacent to the i-th vertex in the planar coordinate system. Let be the vertical coordinate value of the next vertex adjacent to the i-th vertex in the planar coordinate system.

[0040] For example, taking a vehicle's operation process on Line A of a bulk cargo terminal as an example, the specific process is as follows: The driver logs into their account on the vehicle's APP, selects Line A, and clicks the "Start Operation" button. The system backend constructs a vehicle operation state machine based on the selected operation path and divides the operation process into multiple key event points, such as: heading towards the weighbridge; returning to the weighbridge; heading towards the shore; loading; heading towards the weighbridge; weighing the cargo; heading towards the warehouse entrance / exit; heading towards the designated cargo pile; unloading; leaving the cargo pile. Each key event point is stored in a distributed cache database in a linked list structure for real-time updates of the operation status. The vehicle's APP reports the vehicle's real-time coordinates (x, y), speed, and direction to the backend once per second. Based on the real-time coordinate data, the system backend uses a ray-mapping algorithm to determine whether the vehicle has entered or left the key point electronic fence.

[0041] When a vehicle is identified as having entered a key point's electronic fence, the backend system updates the status of that key point event to "completed." The system then waits for the vehicle to trigger the next key point event, updating the linked list structure sequentially until one round of the operation is completed. The vehicle travels from the starting point to the weighbridge, completes the tare weighing event within the weighbridge's electronic fence, and then travels to the shore for loading, entering the shore's electronic fence; the system records the loading event as complete. The vehicle drives to the warehouse entrance and exit, then enters the designated cargo stack's electronic fence to complete the unloading event; after one round of operations is completed, the vehicle returns to the starting point and re-enters the next round of operations.

[0042] S5: During vehicle operation, the back-end system performs anomaly detection based on real-time vehicle coordinates, speed, and operation path data.

[0043] Furthermore, the anomaly detection includes: Yaw detection: When the vehicle is not within the electronic fence, the system calculates the shortest distance between the vehicle's current position and the road network of the work path to determine whether the vehicle has deviated from the preset work path; Missed weighing detection: When the vehicle leaves the electronic fence of the weighbridge, the system calls the weighbridge system interface to obtain weighing records within a preset time range. If no corresponding weighing data is obtained, the system determines that the vehicle has missed weighing and sends a voice prompt to the vehicle's APP; Speeding detection: The system calculates the vehicle's speed based on the displacement distance within a fixed time period. When the speed exceeds a preset speed limit threshold, the system determines that the vehicle has speeding.

[0044] For example, taking a vehicle on Operation Line A of a bulk cargo terminal as an example, the anomaly detection process during vehicle operation is as follows: When the vehicle is not within any key point electronic fence, the backend system obtains the vehicle's real-time coordinates (x, y) and direction θ, as well as the road network data of the preset operation path. The system calculates the shortest distance from the vehicle's current position to each segment of the operation path, using Heron's formula or Euclidean distance calculation method to determine the closest path point for the vehicle. If the vehicle deviates from the operation path by more than a set threshold (e.g., 1 meter), the system determines that the vehicle has a yaw anomaly and prompts the driver to adjust the direction via voice prompts through the vehicle's APP, while simultaneously uploading the yaw anomaly information to the backend system for recording. When the vehicle leaves the electronic fence of the weighbridge, the backend system calls the weighbridge system REST interface to query the vehicle's weighing records within a preset time range (e.g., the past 5 minutes). The query parameters include the operation line ID, weighbridge code, and vehicle license plate number. If the interface returns no corresponding weighing record, the system determines that the vehicle has a missed weighing anomaly, and simultaneously issues a voice prompt through the vehicle's APP: "Missing weighing, please handle carefully," and records the anomaly information in the backend system log. The system acquires the vehicle's position coordinates at fixed time intervals (e.g., every 1 second) and calculates the vehicle's displacement distance d during that time period, thereby calculating the vehicle's speed v = d / Δt. If the calculated speed v exceeds the port area's preset speed limit threshold (e.g., 15 km / h), the system determines that the vehicle is speeding. The APP prompts the driver to slow down via voice: "Current speed exceeds the limit, please slow down to below the specified speed," and uploads the speeding information to the backend system for operational anomaly statistics and visual alarms.

[0045] S6: Based on GIS maps, the unmanned goods handling production process is visualized on a large monitoring screen.

[0046] Furthermore, the exhibits include information on operating machinery, work shifts, and digital yard data; The specific operating machinery includes: embedded berths, bollards, weighbridges, and warehouse layers, displaying the real-time location of all vehicles on duty; the system backend stores the coordinate data reported by the vehicle-mounted APP, and the web client establishes a WebSocket connection with the backend, transmitting vehicle location information from the backend to the web client, which then displays the animation effects of all vehicle movement; simultaneously, if a vehicle deviates from its course, misses a weighbridge, or speeds, the large screen will issue an alarm; clicking on the graphical vehicle icon displays the vehicle's current operating progress and historical data. The specific work shifts include: displaying all work order data for all work lines; the system backend stores real-time and historical vehicle coordinates, storing large amounts of data through database sharding and table partitioning; and simultaneously displaying historical trajectory playback of vehicle coordinate data on the web interface. The digital storage yard specifically includes: displaying the cargo storage status within the storage yard based on the work order data from the work line; and allowing the web client to request the backend interface to return the cargo storage information in the storage yard when the storage yard is clicked on on the map.

[0047] For example, taking the unmanned tallying operation of Line A at a bulk cargo terminal as an example, the operation process is visualized on a large monitoring screen based on a GIS map. Specifically, the system embeds berth, bollard, weighbridge, and storage yard layers onto the GIS map, displaying the main operating areas of the terminal. The real-time location of all vehicles on duty is reported to the backend system via an onboard APP. The backend establishes a WebSocket connection with the web client to transmit real-time coordinate information to the large screen. The page uses coordinate data to display vehicle movement animations, achieving a dynamic driving effect. When a vehicle deviates from its course, misses a weighbridge, or exceeds the speed limit, the large screen displays an alarm prompt, facilitating timely intervention by the dispatcher. Clicking on a vehicle icon will pop up a window displaying the vehicle's current operation progress, key event completion status, and historical operation ticket information. The system displays operation ticket data for all lines, including the operating vehicle, start and end times, operation route, and completion status. Historical vehicle coordinate data is stored in the backend database using sharding technology, supporting large-scale data storage and efficient querying. The web client can retrieve historical coordinate data to replay the operation trajectory, facilitating analysis of the operation process and abnormal events. The system displays the cargo storage status in the warehouse based on the work order data from the production line, including the cargo stack number, location, and quantity. When an operator clicks on a warehouse on the GIS map, the web client requests the backend interface to obtain detailed cargo storage information for that warehouse, which is then displayed graphically on the large screen for easy scheduling and management.

[0048] This embodiment also provides an unmanned cargo handling control system for a bulk cargo terminal based on path navigation, including: The modeling management module is used to perform gridded modeling of the target warehouse based on the GIS map on the platform's web interface. After the operator selects the warehouse area and rotates the coordinate axis to the target location, they confirm and submit. The route planning module is used to draw operation routes on the platform's web interface based on GIS maps and wharf road network data. The navigation and positioning module is used by the vehicle-mounted APP to receive the work path data sent by the background and obtain the vehicle's real-time positioning broadcast information through the RTK positioning method connected to the differential base station; The status control module is used to put the system into operation state after the driver logs in to the vehicle APP and selects the operation line. The background system constructs the vehicle operation state machine according to the operation path, divides the operation process into multiple key point events to be executed in sequence, and stores each key point event in a distributed cache database in a linked list structure. The anomaly detection module is used by the back-end system to detect anomalies based on the vehicle's real-time coordinates, speed, and work path data during vehicle operation. The visualization monitoring module is used to visualize the unmanned goods handling production process on a large monitoring screen based on a GIS map.

[0049] Example 2 This embodiment is the second embodiment of the present invention. This embodiment provides a method for unmanned cargo handling control at a bulk cargo terminal based on path navigation. In order to verify the beneficial effects of the present invention, a comparative experiment is conducted for scientific demonstration.

[0050] Experimental location: Warehouse A of a bulk cargo terminal; Warehouse size: 180m × 90m; Operating vehicles: 4 unmanned cargo handling vehicles equipped with onboard APP and RTK differential base station positioning system; Operating materials: 120 batches of standard bulk cargo, approximately 2 tons per batch; Operating routes: including return routes and non-return routes, involving the shore, weighbridge, and cargo piles in the warehouse; Comparison method: traditional manual cargo handling (2 cargo handlers per operating line) and the unmanned cargo handling control method of this invention.

[0051] Experimental steps: Control group (manual cargo handling): Cargo handlers guide vehicles to complete loading, unloading, and weighing according to the work plan at the warehouse site; cargo handlers manually record vehicle arrival at key nodes, completion of loading and unloading, and weighing information; after the work is completed, cargo handlers organize the work records, and summarize the work time and abnormal situations; Experimental group (unmanned cargo handling in this invention).

[0052] The experimental results are shown in Table 1:

[0053] Table 1. Comparison of the Invention with Traditional Manual Inventory Handling Table 1 illustrates that, through experimental comparison, the unmanned cargo handling control method based on path navigation of the present invention can significantly improve operational efficiency, reduce missed weighing and deviation rates, and achieve real-time anomaly monitoring, thereby reducing manual intervention and safety risks. This effectively improves the safety, accuracy, and management efficiency of bulk cargo terminal operations, verifying the beneficial effects of the present invention.

[0054] This embodiment also provides a computer device applicable to a path navigation-based unmanned cargo handling control method for a bulk cargo terminal, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the path navigation-based unmanned cargo handling control method for a bulk cargo terminal as proposed in the above embodiment.

[0055] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0056] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a path navigation-based unmanned cargo handling control method for a bulk cargo terminal as proposed in the above embodiments.

[0057] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for unmanned cargo handling control at a bulk cargo terminal based on path navigation, characterized in that, include: On the platform's web interface, the target warehouse is modeled in a grid based on a GIS map. The operator selects the warehouse area, rotates the coordinate axes to the target location, and then confirms and submits the data. The operation path is drawn on the platform's web interface based on GIS maps and wharf road network data; The vehicle-mounted APP receives the work path data sent by the background and obtains the vehicle's real-time location broadcast information through RTK positioning via a differential base station; After the driver logs into the vehicle APP and selects the work line, the system enters the work state. The background system constructs the vehicle work state machine according to the work path, divides the work process into multiple key point events to be executed sequentially, and stores each key point event in a distributed cache database in a linked list structure. During vehicle operation, the back-end system performs anomaly detection based on real-time vehicle coordinates, speed, and operation path data; The unmanned goods handling production process is visualized on a large monitoring screen based on GIS maps.

2. The method for unmanned cargo handling control at a bulk cargo terminal based on path navigation as described in claim 1, characterized in that, The process of creating a grid-based model of the target warehouse on the platform's web interface based on a GIS map, followed by the operator selecting the warehouse area, rotating the coordinate axes to the target location, and then confirming and submitting, includes: The system divides the warehouse into multiple grid units corresponding to 3×3 meter areas in the physical world and saves the surface coordinate data of each grid unit. After the warehouse is gridded, the operator manually selects a rectangular area in the gridded warehouse as the work storage area confirmation area for the current work line. The system stores the work storage area confirmation area as an electronic fence in the warehouse for use in work path drawing and work process determination.

3. The method for unmanned cargo handling control at a bulk cargo terminal based on path navigation as described in claim 1, characterized in that, The process of drawing the operation path based on GIS maps and wharf road network data on the platform's web interface includes: The operation path includes a return path and a non-return path, used to determine whether the vehicle passes the weighbridge during a round of operation; the operation path supports manual drawing and automatic drawing, wherein the automatic drawing path performs shortest path planning on road network data based on the set start point, end point, waypoint and avoidance point, and automatically avoids obstacle avoidance areas or restricted areas during the planning process, generating operation path data for vehicle terminal navigation.

4. The method for unmanned cargo handling control at a bulk cargo terminal based on path navigation as described in claim 1, characterized in that, The vehicle-mounted APP receives the work path data sent by the backend and obtains the vehicle's real-time positioning broadcast information through RTK positioning connected to the differential base station. The positioning broadcast information includes at least the vehicle coordinates, driving speed, and driving direction. The vehicle-mounted APP performs path navigation for the vehicle based on the obtained real-time positioning information and judges the vehicle's driving speed according to the port area's preset speed limit standard. When speeding or deviation from the work path is detected, the APP prompts the driver to correct the deviation or reduce the speed through voice prompts and uploads the corresponding abnormal information to the backend system.

5. The unmanned cargo handling control method for a bulk cargo terminal based on path navigation as described in claim 1, characterized in that, After the driver logs into the vehicle-mounted app and selects a work line, the system enters the work state. The backend system constructs a vehicle work state machine based on the work path, divides the work process into multiple key event points to be executed sequentially, and stores each key event point in a distributed cache database in a linked list structure, including: The system controls the vehicle's operation process through the work path and APP navigation. After the driver logs in to the APP, selects the work line and starts navigation, the vehicle enters the work state. The specific working mode of the vehicle's work state machine is as follows: drive to the weighbridge → return to the weighbridge → drive to the shore → load the goods → drive to the weighbridge → weigh the goods → drive to the warehouse entrance / exit → drive to the designated cargo pile → unload the goods → leave the cargo pile. The vehicle-mounted APP reports the vehicle's real-time coordinates to the backend at preset time intervals. Based on the vehicle coordinate data, the backend system uses a ray-mapping algorithm to determine whether the vehicle has entered or left the corresponding key point electronic fence. When it is determined that the vehicle has completed a key point event, the event is updated to the completed state, and the system continues to wait for the next key point event to be triggered until a round of operation is completed. The calculation formula for the ray tracing algorithm is as follows: ； in, Let y be the horizontal coordinate of the intersection point of the ray drawn horizontally from the vehicle's real-time coordinate point and the i-th side of the electronic fence polygon, and let y be the vertical coordinate of the vehicle in the planar coordinate system at the current moment. This refers to the lateral coordinates of the i-th vertex of the polygon forming the electronic fence in the planar coordinate system. Let be the vertical coordinate value of the i-th vertex of the polygon that constitutes the electronic fence in the planar coordinate system. Let x be the horizontal coordinate of the next vertex adjacent to the i-th vertex in the planar coordinate system. Let be the vertical coordinate value of the next vertex adjacent to the i-th vertex in the planar coordinate system.

6. The method for unmanned cargo handling control at a bulk cargo terminal based on path navigation as described in claim 1, characterized in that, During vehicle operation, the backend system performs anomaly detection based on real-time vehicle coordinates, speed, and operation path data, including: the anomaly detection includes: Yaw detection: When the vehicle is not within the electronic fence, the system calculates the shortest distance between the vehicle's current position and the road network of the work path to determine whether the vehicle has deviated from the preset work path; Missed weighing detection: When the vehicle leaves the electronic fence of the weighbridge, the system calls the weighbridge system interface to obtain weighing records within a preset time range. If no corresponding weighing data is obtained, the system determines that the vehicle has missed weighing and sends a voice prompt to the vehicle's APP; Speeding detection: The system calculates the vehicle's speed based on the displacement distance within a fixed time period. When the speed exceeds a preset speed limit threshold, the system determines that the vehicle has speeding.

7. The method for unmanned cargo handling control at a bulk cargo terminal based on path navigation as described in claim 1, characterized in that, The visualization of the unmanned goods handling production process on a large monitoring screen based on a GIS map includes: The exhibits include information on operating machinery, work shifts, and digital yard data. The specific operating machinery includes: embedded berths, bollards, weighbridges, and warehouse layers, displaying the real-time location of all vehicles on duty; the system backend stores the coordinate data reported by the vehicle-mounted APP, and the web client establishes a WebSocket connection with the backend, transmitting vehicle location information from the backend to the web client, which then displays the animation effects of all vehicle movement; simultaneously, if a vehicle deviates from its course, misses a weighbridge, or speeds, the large screen will issue an alarm; clicking on the graphical vehicle icon displays the vehicle's current operating progress and historical data. The specific work shifts include: displaying all work order data for all work lines; the system backend stores real-time and historical vehicle coordinates, storing large amounts of data through database sharding and table partitioning; and simultaneously displaying historical trajectory playback of vehicle coordinate data on the web interface. The digital storage yard specifically includes: displaying the cargo storage status within the storage yard based on the work order data from the work line; and allowing the web client to request the backend interface to return the cargo storage information in the storage yard when the storage yard is clicked on on the map.

8. A path navigation-based unmanned cargo handling control system for a bulk cargo terminal, characterized in that, include: The modeling management module is used to perform gridded modeling of the target warehouse based on the GIS map on the platform's web interface. After the operator selects the warehouse area and rotates the coordinate axis to the target location, they confirm and submit. The route planning module is used to draw operation routes on the platform's web interface based on GIS maps and wharf road network data. The navigation and positioning module is used by the vehicle-mounted APP to receive the work path data sent by the background and obtain the vehicle's real-time positioning broadcast information through the RTK positioning method connected to the differential base station; The status control module is used to put the system into operation state after the driver logs in to the vehicle APP and selects the operation line. The background system constructs the vehicle operation state machine according to the operation path, divides the operation process into multiple key point events to be executed in sequence, and stores each key point event in a distributed cache database in a linked list structure. The anomaly detection module is used by the back-end system to detect anomalies based on the vehicle's real-time coordinates, speed, and work path data during vehicle operation. The visualization monitoring module is used to visualize the unmanned goods handling production process on a large monitoring screen based on a GIS map.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the unmanned cargo handling control method for a bulk cargo terminal based on path navigation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the unmanned cargo handling control method for a bulk cargo terminal based on path navigation as described in any one of claims 1 to 7.

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