Grain depot intelligent navigation system

Abstract

The invention discloses an intelligent navigation system for a grain depot. The system comprises a vehicle navigation module, a user side and administrator side double-end management platform, a real-time position tracking module based on a Beidou satellite navigation system and a geographic information system module. Wherein the vehicle navigation module is used for acquiring longitude and latitude information of an operation vehicle and realizing real-time navigation of the operation vehicle by displaying a shortest path from a current position to a target operation area; the double-end management platform of the user side and the administrator side is based on a WeChat applet, and the administrator side limits the user permission through a background and issues a navigation permission to an operation vehicle entering a reservoir area; through multi-level innovative design including dynamic rapid adaptation, multi-vehicle cooperative scheduling, distributed signal interaction, dynamic path adjustment and an adaptive navigation mode, compared with the prior art, the system has remarkable advantages in the aspects of rapid adaptability, scheduling efficiency, traffic guarantee, data safety, driving experience and the like of vehicles.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent navigation, and in particular relates to an intelligent navigation system for a grain depot. Background Art

[0002] As the scale of grain storage areas continues to expand, the number of operating vehicles in grain storage areas has increased significantly, and the dispatching and management of vehicles in the storage area has become more complicated. The operating vehicles in the grain storage area undertake important tasks such as grain transportation, material transfer and equipment maintenance. Their efficient operation is of great significance to improving the overall management efficiency and operational safety of the grain storage. However, the existing technology still has many deficiencies in the navigation and dispatching of operating vehicles in the grain storage area, which are mainly manifested in the following aspects: Insufficient vehicle adaptability and low path planning efficiency: Traditional navigation systems in grain storage areas are mostly based on static path planning, which lacks effective support for the rapid adaptation of external vehicles after entering the storage area. This often results in the inability to quickly generate the optimal path for external vehicles when entering the storage area, increasing waiting time and affecting work efficiency. In addition, after the vehicle enters, due to the untimely response of path planning, it is easy to cause the vehicle's driving route to be too long, reducing work efficiency.

[0003] Lack of multi-vehicle coordinated dispatching capabilities can easily lead to traffic congestion: Existing navigation systems usually only focus on the navigation path of a single vehicle and lack the function of coordinated dispatching of multiple vehicles. When multiple operating vehicles are performing tasks at the same time, the traffic flow in the depot area increases, and the existing system cannot effectively manage the paths of multiple vehicles, resulting in traffic congestion, task conflicts and even safety hazards between vehicles. In addition, the system cannot effectively allocate vehicles to areas with lower traffic, which increases the difficulty of traffic management in the depot area.

[0004] Insufficient real-time avoidance mechanism, unable to quickly bypass obstacles: Traditional navigation systems are mostly one-way information transmission, and vehicles can only passively receive navigation instructions, lacking real-time interaction with other vehicles or obstacles. This static navigation method cannot bypass temporary obstacles or congestion in time, resulting in reduced vehicle driving efficiency and even increased driving safety risks.

[0005] Path information updates lag behind, lacking dynamic path adjustment capabilities: Most existing navigation systems in grain storage areas use preset routes for path planning, and lack the ability to perceive and adjust real-time road conditions in the storage area. When roads in the storage area are blocked or temporarily closed, the vehicle navigation path cannot automatically switch to a suboptimal path, causing the operating vehicle to have to travel along the original path, reducing the utilization efficiency of roads in the storage area and possibly causing unexpected traffic jams.

[0006] Lack of intelligent permission control and data management: The authority control of vehicles operating in the grain storage area is of great significance to safety. However, the existing system usually lacks refined management of vehicle authority, which easily leads to unauthorized vehicles entering the storage area. At the same time, there is a lack of efficient mechanism for authority control and real-time update of data, which poses a potential risk of data leakage and is not conducive to the safety management of the storage area.

[0007] The navigation mode is single and the driving experience lacks flexibility: Most existing navigation systems use fixed navigation modes and do not provide personalized navigation guidance based on the driver's proficiency or operating habits. For new vehicles entering the depot, the system cannot provide a simplified navigation mode, which causes the driver to easily deviate from the navigation path in an unfamiliar environment, increasing the difficulty of operation; for experienced drivers, the navigation information appears redundant, reducing the efficiency of the navigation system.

[0008] In summary, the existing navigation system for operating vehicles in grain storage areas has deficiencies in terms of vehicle adaptability, multi-vehicle coordination, real-time avoidance, dynamic path adjustment, authority control, and intelligent navigation mode, and cannot fully meet the needs of modern grain storage areas for efficient vehicle scheduling and precise navigation. Therefore, a smarter and more flexible navigation system is urgently needed to solve the above problems, thereby improving the operating efficiency and management level of operating vehicles in the grain storage area and ensuring the efficiency and safety of the operation process. Summary of the invention

[0009] In order to solve the problems raised in the above-mentioned background technology, the present invention provides an intelligent navigation system for grain depots. Through multi-level innovative design, including dynamic and rapid adaptation, multi-vehicle collaborative scheduling, distributed signal interaction, dynamic path adjustment and adaptive navigation mode, it has significant advantages over the existing technology in terms of rapid adaptability of vehicles, scheduling efficiency, traffic guarantee, data security and driving experience.

[0010] To achieve the above object, the present invention provides the following technical solutions: An intelligent navigation system for vehicles operating in a grain storage area, the system comprises a vehicle navigation module, a user-end and an administrator-end dual-end management platform, a real-time location tracking module based on the Beidou satellite navigation system, and a geographic information system module; wherein: The vehicle navigation module is used to obtain the latitude and longitude information of the working vehicle, and realize the real-time navigation of the working vehicle by displaying the shortest path from the current position to the target working area; The dual-end management platform for users and administrators is based on WeChat applet. The administrator limits user permissions through the background and issues navigation permissions to vehicles entering the storage area. The permissions are automatically cleared after the vehicle leaves the storage area to ensure the safety of the system. The real-time location tracking module uses the Beidou satellite navigation system port data to track and dispatch the vehicles in the grain storage area in real time; The geographic information system module is combined with the reservoir area map to display the roads in the reservoir area, area planning and route planning. Through the map information update function, it can feedback the information of temporarily inaccessible areas to help drivers avoid obstacles.

[0011] Among them, the working method of the geographic information system module is: Step 1: Reservoir map display: Loading electronic maps of the reservoir area through the Geographic Information System, which contain roads, area boundaries and building details; Step 2: Area planning and path planning: Path planning calculates the shortest path for the vehicle using the Dijkstra algorithm or the A* algorithm; the path planning formula is: ; Cost(n): current node The total cost of is used as the evaluation function in the path planning algorithm; the smaller the Cost value, the better the path; : From the starting node to the current node The actual path cost, which indicates the distance or cost traveled on the path; h(n): the distance from the current node The heuristic estimated cost to the target node, i.e., the estimated remaining distance or cost, is used to guide the directionality of path planning; n: the node evaluated in the current path planning; Step 3: Map information update: The system regularly detects road conditions within the depot area, including obstacles and maintenance information; if there is an update, the system will mark the impassable area in red through the map information update function to remind the driver to take a detour.

[0012] Among them, the intelligent navigation system also includes a dynamic fast adaptation module, which includes: a license plate recognition and automatic registration unit, which is used to identify and register foreign vehicle information at the entrance of the storage area, and mark it as a "new vehicle" to activate the fast adaptation process; a path preloading unit, which completes the path loading of the target operation area through cloud computing before the vehicle officially enters the storage area; an intelligent vehicle speed adjustment and path optimization unit, which is used to measure the vehicle speed in real time and combine the dynamic traffic conditions in the storage area to automatically adjust the path to avoid congestion and guide new vehicles into non-congested areas.

[0013] Among them, the intelligent navigation system also includes a multi-vehicle collaborative scheduling module, which uses an artificial intelligence algorithm to allocate tasks according to job priority, congestion level in the target area and expected completion time, and includes a regional traffic flow prediction and dynamic load distribution unit, which is used to predict traffic flow in each area and optimize vehicle distribution; a collaborative path planning and avoidance strategy unit, which is used to generate a "virtual lane" for each vehicle to avoid congestion, and guide vehicles to pass safely through dynamic planning when multiple vehicles are heading to the same target area at different times or locations; and a temporary parking point and dynamic waiting area design unit to ensure that vehicles can stop and wait in an orderly manner during high-traffic periods.

[0014] Among them, the intelligent navigation system also includes a distributed intelligent signal interaction system, which sets up distributed signal interaction nodes in the storage area to realize short-range communication between vehicles. The system shares the vehicle navigation status to neighboring vehicles in real time through signal broadcasting and data links to form an automatic avoidance mechanism; and includes a real-time road condition sharing mechanism and a path intelligent switching unit, which is used to identify and notify neighboring vehicles to bypass congested areas, and automatically switch to a suboptimal path when an obstacle is detected to ensure smooth passage of vehicles.

[0015] Among them, the intelligent navigation system also includes an adaptive navigation mode module, which provides simplified "simple mode" navigation for foreign vehicles through an AI-based multi-mode navigation strategy, and guides foreign vehicles through voice and visual prompts; and includes a learning and adaptation mechanism, which can gradually increase the complexity of navigation according to the usage habits and driving characteristics of foreign vehicles, thereby improving the efficiency of subsequent work scheduling.

[0016] The method of applying the intelligent navigation system to the navigation of vehicles operating in the grain storage area is as follows: S1. Vehicles entering the warehouse area: S1.1: License Plate Recognition and Automatic Registration: When a vehicle approaches the entrance to the depot, the camera at the entrance automatically captures the license plate image and identifies the license plate number through optical character recognition technology; the system stores the identified license plate number in the database and marks the vehicle as a "new vehicle", activating the rapid adaptation process for subsequent path loading and navigation authority issuance;

[0017] S1.2: Path preloading: The system generates the best path based on the vehicle's target operating area through cloud computing. Before the vehicle enters the depot, the system has loaded the pre-calculated path onto the navigation module, so that the vehicle can get real-time path guidance without waiting. S1.3: Intelligent speed regulation and route optimization: The system measures the current speed of the vehicle in real time and optimizes the route based on the real-time traffic flow in the depot area. If a congested area is detected, the system automatically adjusts the route to guide the vehicle to avoid the congested area and enter the target area. S2. Real-time vehicle navigation: S2.1: Vehicle positioning and path display: The vehicle navigation module obtains the vehicle's current position in real time through the port data of the Beidou satellite navigation system; the vehicle's navigation equipment displays the shortest path from the current position to the target operating area, ensuring that the driver can follow the navigation instructions in real time; S2.2: Path Planning: The system calculates the best path and displays the turn and distance information for each node on the path; During driving, the system continuously updates the path to ensure that the vehicle always drives on the optimal path; S2.3: Map information update: The system regularly checks the road conditions in the depot area; if any road closures, obstacles or maintenance information are found, the system marks these areas in red and updates them on the map. The driver can use the map update function to detour in advance to avoid delays in work tasks; S3. Multi-vehicle coordinated dispatching: S3.1: Task Allocation: The system uses artificial intelligence algorithms to dynamically assign tasks based on the vehicle's operational priority, the degree of congestion in the target area, and the urgency of the task; vehicles with higher task priorities will be given priority in routing; S3.2: Traffic flow prediction and load distribution: The system monitors the traffic flow in each area of ​​the depot in real time, predicts the congested areas and distributes the vehicles evenly to the areas with lower traffic; this load distribution ensures the smoothness of traffic in the depot and avoids unnecessary vehicle accumulation; S3.3: Collaborative Path Planning and Avoidance: The system assigns a "virtual lane" to each vehicle to avoid congestion on the same route; if multiple vehicles are heading to the same area, the system sets staggered or misaligned traffic through dynamic planning to guide vehicles to pass safely and avoid route conflicts; S3.4: Temporary parking spots and dynamic waiting areas: During peak hours, the system arranges some vehicles to stop temporarily in dynamic waiting areas or temporary parking spots to ensure smooth traffic in the depot area; when the road is restored, the system redirects these vehicles back on the road; S4. Distributed signal interaction: S4.1: Signal broadcast and data link: The signal interaction nodes installed in the depot area support short-range communication between vehicles; each vehicle broadcasts its current position and path information to surrounding vehicles to achieve automatic avoidance; S4.2: Real-time traffic sharing and route switching: When the vehicle receives information about congestion or obstacles, the system automatically switches to the suboptimal path and guides the vehicle around the obstacle area; real-time traffic sharing ensures that all vehicles can obtain the latest road information and adjust the path according to the situation; S5. Adaptive navigation mode: S5.1: Multi-mode navigation: The system provides "simple mode" navigation for external vehicles to help drivers adapt to the internal environment of the depot area; the system provides drivers with intuitive navigation guidance through voice and visual prompts; S5.2: Learning and Adaptation Mechanisms: The system records and analyzes the vehicle's driving path, navigation usage habits and driving speed information, and gradually optimizes the navigation mode; as the vehicle accumulates more experience in the depot area, the system will gradually increase the complexity of its navigation, allowing the vehicle to execute navigation instructions more efficiently in subsequent tasks; S6. Vehicle leaves the warehouse: S6.1: Clear permissions: When the system detects that the vehicle has left the coordinate area of ​​the warehouse, it automatically clears the navigation authority of the vehicle to ensure the information security of the warehouse area. After the vehicle leaves, the navigation authority is revoked and the internal map or path information of the warehouse area is no longer displayed. S6.2: Data storage and analysis: The system uploads the vehicle's driving trajectory, congestion information and stop point data to the database for subsequent data analysis and optimization improvements, providing more accurate path planning support for future vehicle navigation.

[0018] Compared with the prior art, the present invention has the following beneficial effects: Compared with the prior art, the intelligent navigation system of the present invention has the following significant beneficial effects in terms of navigation and dispatching of operating vehicles in the grain storage area: 1. Rapid vehicle adaptation and efficient path preloading The dynamic fast adaptation module in the present invention enables foreign vehicles to quickly adapt to the navigation system when entering the storage area through functions such as license plate recognition, automatic registration, and path preloading, and quickly obtain the best path to the target area. Compared with the existing technology, this dynamic adaptation function reduces the waiting time of the vehicle and greatly improves the operating efficiency of the vehicle after entering the storage area. It is particularly suitable for operating vehicles that frequently flow in large storage areas.

[0019] 2. Multi-vehicle coordinated dispatch and traffic flow optimization The multi-vehicle collaborative scheduling module of the present invention uses artificial intelligence algorithms to assign tasks to vehicles, taking into account job priority, congestion levels in the target area, and estimated completion time. This collaborative scheduling capability can optimize the distribution of vehicles in the depot area, reduce congestion, and improve overall operating efficiency. Compared with the traditional single-vehicle scheduling method, this multi-vehicle collaborative function is more suitable for efficient scheduling in complex environments, allowing the depot area to remain unobstructed under high traffic conditions.

[0020] 3. Real-time avoidance mechanism and traffic guarantee The distributed intelligent signal interaction system in the present invention sets up multiple signal interaction nodes in the storage area, and realizes real-time communication between vehicles through signal broadcasting and data links. This distributed signal system enables vehicles to automatically avoid neighboring vehicles and obstacles in the storage area, improving traffic efficiency. Compared with the static path planning method in the prior art, this real-time avoidance mechanism is more flexible and responsive, and can adapt to the dynamically changing road conditions in the storage area, ensuring the smoothness and safety of vehicle traffic.

[0021] 4. Intelligent path planning and dynamic switching The GIS module in the present invention combines dynamic path planning and real-time map update functions, which not only provides the optimal path for the vehicle, but also automatically adjusts the path according to the real-time road conditions and obstacle distribution in the reservoir area. When congestion or obstacles are detected, the system automatically switches to the suboptimal path to ensure smooth passage of vehicles in the reservoir area. This dynamic path adjustment capability has significant advantages over the static navigation system in the prior art and is particularly suitable for large and complex reservoir environments.

[0022] 5. Multi-level permission control and data security The dual-end management platform of the user end and the administrator end in the present invention strictly controls the navigation authority through the WeChat applet. The administrator can issue navigation authority to vehicles entering the storage area and automatically clear the authority after the vehicle leaves the storage area, thereby avoiding the risk of information leakage. Compared with the shortcomings of data authority management in the prior art, this dual-end management platform improves the security of the system and effectively prevents unauthorized vehicles from entering the storage area and the leakage of sensitive information.

[0023] 6. AI-based adaptive navigation improves driving experience The adaptive navigation mode module of the present invention provides a multi-mode navigation strategy through an AI algorithm, which can gradually increase the complexity of navigation according to the driver's usage habits, so that the driver can gradually adapt to the warehouse environment, thereby improving the driving experience. For foreign vehicles, the system initially provides a "simple mode" to help them familiarize themselves with the route. As the number of vehicle operations increases, the system gradually switches to an "advanced mode." Compared with the single navigation mode in the prior art, this adaptive function is more user-friendly and can adjust the navigation mode according to the driver's proficiency, reduce misoperation, and improve driving efficiency.

[0024] 7. Improvement of the overall intelligence and automation level of the system The invented system integrates multiple modules to work together, realizing the automation of the entire process from vehicle entry, rapid adaptation, real-time navigation, multi-vehicle collaboration, dynamic avoidance to adaptive navigation, greatly improving the intelligence level of the system. Compared with the existing manual intervention and fixed navigation systems, the intelligent navigation system in the present invention has more automation and self-learning capabilities, which can significantly improve the overall efficiency and safety of operations in the storage area.

[0025] In summary, the intelligent navigation system of the present invention has significant advantages over the prior art in terms of rapid adaptability of vehicles, dispatching efficiency, traffic guarantee, data security and driving experience through multi-level innovative designs, including dynamic and rapid adaptation, multi-vehicle coordinated dispatching, distributed signal interaction, dynamic path adjustment and adaptive navigation mode. These beneficial effects can not only effectively improve the navigation and dispatching efficiency of operating vehicles in the grain storage area, but also significantly improve the traffic safety and management level in the storage area, providing a more advanced and reliable solution for the intelligent operation of large storage areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the system architecture and flow chart of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 , an intelligent navigation system for vehicles operating in a grain storage area, the system includes a vehicle navigation module, a user-end and administrator-end dual-end management platform, a real-time location tracking module based on the Beidou satellite navigation system, and a geographic information system module; wherein, The vehicle navigation module is used to obtain the latitude and longitude information of the working vehicle, and realize the real-time navigation of the working vehicle by displaying the shortest path from the current position to the target working area; The dual-end management platform for users and administrators is based on WeChat applet. The administrator limits user permissions through the background and issues navigation permissions to vehicles entering the storage area. The permissions are automatically cleared after the vehicle leaves the storage area to ensure the safety of the system. The real-time location tracking module uses the Beidou satellite navigation system port data to track and dispatch the vehicles in the grain storage area in real time; The geographic information system module is combined with the reservoir area map to display the roads in the reservoir area, area planning and route planning. Through the map information update function, it can feedback the information of temporarily inaccessible areas to help drivers avoid obstacles.

[0029] Furthermore, the working method of the geographic information system module is: Step 1: Reservoir map display: Loading electronic maps of the reservoir area through the Geographic Information System, which contain roads, area boundaries and building details; Step 2: Area planning and path planning: Path planning calculates the shortest path for the vehicle using the Dijkstra algorithm or the A* algorithm. The path planning formula is: ; Cost(n): current node The total cost of is used as the evaluation function in the path planning algorithm; the smaller the Cost value, the better the path; : From the starting node to the current node The actual path cost, which indicates the distance or cost traveled on the path; h(n): the distance from the current node The heuristic estimated cost to the target node, i.e., the estimated remaining distance or cost, is used to guide the directionality of path planning; n: the node evaluated in the current path planning; Step 3: Map information update: The system regularly detects road conditions within the depot area, including obstacles and maintenance information; if there is an update, the system will mark the impassable area in red through the map information update function to remind the driver to take a detour.

[0030] Furthermore, the intelligent navigation system also includes a dynamic fast adaptation module, which includes: a license plate recognition and automatic registration unit, which is used to identify and register foreign vehicle information at the entrance of the storage area, and mark it as a "new vehicle" to activate the fast adaptation process; a path preloading unit, which completes the path loading of the target operation area through cloud computing before the vehicle officially enters the storage area; an intelligent vehicle speed adjustment and path optimization unit, which is used to measure the vehicle speed in real time and combine the dynamic traffic conditions in the storage area to automatically adjust the path to avoid congestion and guide new vehicles into non-congested areas.

[0031] Specifically, the working process of the intelligent navigation system is: 1. License plate recognition and automatic registration unit Image acquisition and recognition: The entrance camera captures the vehicle image and uses OCR technology to recognize the license plate number. Data registration: The recognized license plate information Plate_ID is stored in the database and marked as "new vehicle", activating the fast adaptation process.

[0032] 2. Path preloading unit Target path calculation: The system receives the latitude and longitude of the vehicle's target area , calculate from the entrance position The best path to the target location.

[0033] Path loading: Generate the path in advance through cloud computing and load it to the navigation device before the vehicle enters the depot.

[0034] formula: ; Path_Cost: total cost of the path. The smaller the value, the better the path. N: total number of path nodes, usually 5 to 50, depending on the complexity of the storage area. :No. The cost of a node includes the following calculation formula ; n): The actual cost from the starting point to the current node (the cost of the path already taken). h(n): The estimated cost from the current node to the target node (the remaining distance). n: The node in the current path planning.

[0035] 3. Intelligent vehicle speed regulation and route optimization unit Real-time speed measurement: The system obtains the current speed of the vehicle .

[0036] Congestion detection and route adjustment: real-time monitoring of traffic flow in various areas of the depot Exceeding the threshold ,The system will automatically adjust the route to avoid congested areas.

[0037] formula: ; Adjusted Path: The optimal path after adjustment. M: The number of nodes in the adjusted path. Cost( ):node base cost. No. Traffic flow at a node. The congestion penalty coefficient is between 1.5 and 3.0.

[0038] Furthermore, the intelligent navigation system also includes a multi-vehicle collaborative scheduling module, which uses an artificial intelligence algorithm to allocate tasks according to job priority, congestion level in the target area and expected completion time, and includes a regional traffic flow prediction and dynamic load distribution unit for predicting traffic flow in each area and optimizing vehicle distribution; a collaborative path planning and avoidance strategy unit for generating a "virtual lane" for each vehicle to avoid congestion, and guiding vehicles to pass safely through staggered or dislocated dynamic planning when multiple vehicles are heading to the same target area; and a temporary parking point and dynamic waiting area design unit to ensure that vehicles can park and wait in an orderly manner during high-traffic periods.

[0039] Specifically, the working method of the multi-vehicle collaborative scheduling module is: 1. Task allocation unit The system prioritizes the work of each vehicle. , the congestion level in the target area and estimated completion time Perform task allocation. Task allocation is optimized through artificial intelligence algorithms, giving priority to high-priority vehicles and guiding them to areas with lower traffic.

[0040] formula: ; Priority_Score: Task priority score. The higher the score, the higher the task priority. Job priority, numeric type, ranging from 1 (lowest) to 10 (highest). : The congestion level (number of vehicles) of the target area, ranging from 0 to 50. : Estimated completion time in minutes, ranging from 1 to 60. and : Adjustment coefficient, used to balance priority, congestion level and estimated time, with a value between 0.5 and 2.

[0041] 2. Regional traffic flow prediction and dynamic load distribution unit The system uses historical data and real-time traffic Predict future traffic flow in each area , and dynamically allocate vehicles to areas with lower traffic based on load conditions.

[0042] formula: ; : Predicted regional traffic flow, in vehicles per hour. Real-time traffic flow, in vehicles per hour. Historical traffic flow data, in vehicles per hour. The weight coefficient ranges from 0.5 to 0.9 and is used to balance real-time and historical traffic data.

[0043] 3. Collaborative path planning and avoidance strategy unit The system generates a "virtual lane" for each vehicle to avoid overlapping vehicles on the same route. When multiple vehicles are heading to the same target area at the same time, the paths are adjusted through dynamic planning to form a staggered or dislocated traffic order. Formula: ; Adjusted_Path(n): The adjusted optimal path. The number of path nodes. Cost( ): No. The basic cost of a node. :No. Traffic flow at a node. Avoidance adjustment coefficient, ranging from 1 to 3.

[0044] 4. Temporary parking spot and dynamic waiting area design unit During high-traffic periods, the system guides some vehicles to temporary parking spots or dynamic waiting areas, and then redirects the vehicles back on the road once the route is clear again.

[0045] Furthermore, the intelligent navigation system also includes a distributed intelligent signal interaction system, which sets up distributed signal interaction nodes in the storage area to achieve short-range communication between vehicles. The system shares the vehicle navigation status to neighboring vehicles in real time through signal broadcasting and data links, forming an automatic avoidance mechanism; and includes a real-time road condition sharing mechanism and a path intelligent switching unit, which are used to identify and notify neighboring vehicles to bypass congested areas, and automatically switch to a suboptimal path when an obstacle is detected, to ensure smooth passage of vehicles.

[0046] Specifically, the working method of the distributed intelligent signal interaction system is: 1. Distributed signal interaction node settings Multiple signal interaction nodes (such as Bluetooth beacons or ultra-wideband signals) are distributed and installed in the depot area to support short-range communication between vehicles.

[0047] Each node broadcasts its current location and status to vehicles, ensuring that neighboring vehicles can receive and understand the traffic dynamics around them.

[0048] 2. Signal broadcast and data link The vehicle shares the navigation status (such as current position, speed, and target position) with neighboring vehicles in real time through signal broadcasting, realizing dynamic status updates. The system automatically establishes a data link between vehicles to ensure that each vehicle receives the status information of other vehicles so that it can automatically avoid them as needed.

[0049] formula: ; Shared_State: The shared state information of the vehicle, including position, speed and target position. pos: Current position coordinates , in meters. : Current speed, in km / h. dest: Target location coordinates , in meters.

[0050] 3. Real-time traffic sharing mechanism The system integrates the status information of each vehicle to form a real-time road map. If a congestion or obstacle is detected in a certain area, the system will send a detour notification to surrounding vehicles.

[0051] 4. Path intelligent switching unit When congestion or obstacles are detected, the system automatically switches to the suboptimal path for the affected vehicles to ensure smooth passage of vehicles.

[0052] formula: ; Adjusted_Path: The optimal path after switching. M: The number of path nodes. Cost(n_i): The number of nodes. Obstacle_Penalty: Obstacle penalty value, indicating the additional cost of bypassing the obstacle. Adjustment coefficient, used to control the impact of obstacles, with a value range of 1 to 3.

[0053] Furthermore, the intelligent navigation system also includes an adaptive navigation mode module, which provides simplified "simple mode" navigation for foreign vehicles through an AI-based multi-mode navigation strategy, and guides foreign vehicles through voice and visual prompts; and includes a learning and adaptation mechanism, which can gradually increase the complexity of navigation according to the usage habits and driving characteristics of foreign vehicles, thereby improving the efficiency of subsequent work scheduling.

[0054] Specifically, the working method of the adaptive navigation mode module is: 1. Multimodal navigation strategy The system provides "simple mode" navigation for foreign vehicles entering the depot area for the first time, simplifies the navigation path, and provides clear voice and visual prompts to help drivers quickly adapt to the depot environment.

[0055] "Simple Mode" navigation only displays major roads and key turning points, avoiding complex route guidance.

[0056] 2. Learning and Adaptation Mechanism The system records the navigation habits of external vehicles, including route selection, driving speed , stops, and average travel time As the number of vehicles operating in the depot increases As the number of navigation levels increases, the system gradually increases the complexity of navigation, switching from "simple mode" to "advanced mode".

[0057] Switching condition: When the vehicle's operation times When the threshold is exceeded, the system switches to "Advanced Mode".

[0058] 3. Gradually increase navigation complexity The system adjusts the navigation complexity after each navigation based on the vehicle's performance, such as the number of deviations from the route and the completion time. As the vehicle becomes more familiar with the route, the complexity increases and the navigation information will include more detailed routes, secondary roads and real-time traffic information.

[0059] Furthermore, the intelligent navigation system is applied to the navigation method of the operating vehicles in the grain storage area as follows: S1. Vehicles entering the warehouse area: S1.1: License Plate Recognition and Automatic Registration: When a vehicle approaches the entrance to the storage area, the camera at the entrance automatically captures the license plate image and identifies the license plate number through optical character recognition technology; the system stores the identified license plate number in the database, marks the vehicle as a "new vehicle", and activates the quick adaptation process for subsequent path loading and navigation authority issuance.

[0060] S1.2: Path preloading: The system generates the best path based on the vehicle's target operating area through cloud computing. Before the vehicle enters the depot, the system has loaded the pre-calculated path onto the navigation module, so that the vehicle can get real-time path guidance without waiting. S1.3: Intelligent speed regulation and route optimization: The system measures the current speed of the vehicle in real time and optimizes the route based on the real-time traffic flow in the depot area. If a congested area is detected, the system automatically adjusts the route to guide the vehicle to avoid the congested area and enter the target area. S2. Real-time vehicle navigation: S2.1: Vehicle positioning and path display: The vehicle navigation module obtains the vehicle's current position in real time through the port data of the Beidou satellite navigation system; the vehicle's navigation equipment displays the shortest path from the current position to the target operating area, ensuring that the driver can follow the navigation instructions in real time; S2.2: Path Planning: The system calculates the best path and displays the turn and distance information for each node on the path; During driving, the system continuously updates the path to ensure that the vehicle always drives on the optimal path; S2.3: Map information update: The system regularly checks the road conditions in the depot area; if any road closures, obstacles or maintenance information are found, the system marks these areas in red and updates them on the map. The driver can use the map update function to detour in advance to avoid delays in work tasks; S3. Multi-vehicle coordinated dispatching: S3.1: Task Allocation: The system uses artificial intelligence algorithms to dynamically assign tasks based on the vehicle's operational priority, the degree of congestion in the target area, and the urgency of the task; vehicles with higher task priorities will be given priority in routing; S3.2: Traffic flow prediction and load distribution: The system monitors the traffic flow in each area of ​​the depot in real time, predicts the congested areas and distributes the vehicles evenly to the areas with lower traffic; this load distribution ensures the smoothness of traffic in the depot and avoids unnecessary vehicle accumulation; S3.3: Collaborative Path Planning and Avoidance: The system assigns a "virtual lane" to each vehicle to avoid congestion on the same route; if multiple vehicles are heading to the same area, the system sets staggered or misaligned traffic through dynamic planning to guide vehicles to pass safely and avoid route conflicts; S3.4: Temporary parking spots and dynamic waiting areas: During peak hours, the system arranges some vehicles to stop temporarily in dynamic waiting areas or temporary parking spots to ensure smooth traffic in the depot area; when the road is restored, the system redirects these vehicles back on the road; S4. Distributed signal interaction: S4.1: Signal broadcast and data link: The signal interaction nodes installed in the depot area support short-range communication between vehicles; each vehicle broadcasts its current position and path information to surrounding vehicles to achieve automatic avoidance; S4.2: Real-time traffic sharing and route switching: When the vehicle receives information about congestion or obstacles, the system automatically switches to the suboptimal path and guides the vehicle around the obstacle area; real-time traffic sharing ensures that all vehicles can obtain the latest road information and adjust the path according to the situation; S5. Adaptive navigation mode: S5.1: Multi-mode navigation: The system provides "simple mode" navigation for external vehicles to help drivers adapt to the internal environment of the depot. The system provides intuitive navigation guidance to drivers through voice and visual prompts; S5.2: Learning and Adaptation Mechanisms: The system records and analyzes the vehicle's driving path, navigation usage habits and driving speed information, and gradually optimizes the navigation mode; as the vehicle accumulates more experience in the depot area, the system will gradually increase the complexity of its navigation, allowing the vehicle to execute navigation instructions more efficiently in subsequent tasks; S6. Vehicle leaves the warehouse: S6.1: Clear permissions: When the system detects that the vehicle has left the coordinate area of ​​the warehouse, it automatically clears the navigation authority of the vehicle to ensure the information security of the warehouse area. After the vehicle leaves, the navigation authority is revoked and the internal map or path information of the warehouse area is no longer displayed. S6.2: Data storage and analysis: The system uploads the vehicle's driving trajectory, congestion information and stop point data to the database for subsequent data analysis and optimization improvements, providing more accurate path planning support for future vehicle navigation.

[0061] Specifically, when a vehicle enters the grain depot: 1. Vehicle enters the warehouse area - dynamic fast adaptation module When an external vehicle enters the depot, the dynamic quick adaptation module first identifies the vehicle's license plate, automatically registers it, and activates the quick adaptation process. The path preloading unit generates the best path for the vehicle to the target area in advance, and the intelligent vehicle speed adjustment unit monitors the vehicle speed in real time and optimizes the path.

[0062] After completing vehicle identification and path loading, the generated initial path information is passed to the vehicle navigation module, and the path is adjusted according to traffic conditions during the entire navigation process, ready to transmit location information to the distributed intelligent signal interaction system at any time to share the status.

[0063] 2. Real-time navigation and dispatch-Geographic Information System Module The GIS module combines the reservoir area map and the path planning algorithm to generate the optimal path for the vehicle and update it in real time. When obstacles appear in the reservoir area, the map information update unit will generate a new detour path to guide the vehicle to avoid the impassable area.

[0064] The real-time navigation path information is transmitted to the distributed intelligent signal interaction system, which will share the path status with surrounding vehicles. At the same time, according to the multi-vehicle traffic conditions, it interacts with the multi-vehicle collaborative scheduling module to optimize the path planning through traffic flow prediction and load distribution.

[0065] 3. Multi-vehicle collaborative scheduling - Multi-vehicle collaborative scheduling module The multi-vehicle collaborative scheduling module achieves efficient scheduling of multiple vehicles working simultaneously through functions such as task allocation units, virtual lane generation, and dynamic waiting areas. Tasks are prioritized based on job priority, congestion conditions, and estimated completion time, and virtual lanes are generated for each vehicle to reduce congestion. During high-traffic periods, some vehicles are dynamically arranged to enter temporary waiting areas.

[0066] Through the scheduling module, the system can adjust the path planning parameters based on real-time traffic conditions and mission requirements before generating the navigation path. The optimized path instructions and traffic flow information are fed back to the geographic information system module, and its location and status information is transmitted to the distributed intelligent signal interaction system for sharing.

[0067] 4. Road condition perception and automatic avoidance - Distributed intelligent signal interaction system The distributed intelligent signal interaction system realizes short-range communication between vehicles through signal nodes, broadcasts the navigation status, location, speed and other information of the vehicle to neighboring vehicles, and realizes automatic avoidance and path switching. When congestion or obstacles are detected, the system automatically issues detour instructions to the affected vehicles.

[0068] The distributed intelligent signal interaction system shares data with the dynamic rapid adaptation module, geographic information system module and multi-vehicle collaborative scheduling module to ensure that all vehicles can obtain the latest navigation information and avoidance paths when road conditions change suddenly.

[0069] 5. Adaptive Navigation Mode - Adaptive Navigation Mode Module The adaptive navigation mode module provides initial "simple mode" navigation for foreign vehicles, guiding the vehicle to familiarize itself with the depot area through voice and visual prompts. As the number of operations increases, the system records the driver's driving habits and gradually increases the complexity of navigation, eventually switching to "advanced mode" to adapt to the needs of skilled drivers.

[0070] The adaptive navigation mode module is combined with the dynamic fast adaptation module and the geographic information system module to provide a simplified path in the initial adaptation stage, and gradually switch to the advanced mode to optimize navigation guidance and improve the driver's operating efficiency in the depot area.

[0071] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Grain depot intelligent navigation system, characterized by: The system includes a vehicle navigation module, a dual-end management platform for user and administrator ends, a real-time location tracking module based on the Beidou satellite navigation system, and a geographic information system module; wherein, The vehicle navigation module is used to obtain the latitude and longitude information of the working vehicle, and realize the real-time navigation of the working vehicle by displaying the shortest path from the current position to the target working area; The dual-end management platform for users and administrators is based on WeChat applet. The administrator limits user permissions through the background and issues navigation permissions to vehicles entering the storage area. The permissions are automatically cleared after the vehicle leaves the storage area to ensure the safety of the system. The real-time location tracking module uses the Beidou satellite navigation system port data to track and dispatch the vehicles in the grain storage area in real time; The geographic information system module is combined with the reservoir area map to display the roads in the reservoir area, area planning and route planning. Through the map information update function, it can feedback the information of temporarily inaccessible areas to help drivers avoid obstacles.

2. The grain depot intelligent navigation system according to claim 1 is characterized in that: The working methods of the GIS module are: S1: Reservoir map display: Loading electronic maps of the reservoir area through the Geographic Information System, which contain roads, area boundaries and building details; S2: Area planning and path planning: Path planning calculates the shortest path for the vehicle using the Dijkstra algorithm or the A* algorithm; the path planning formula is: ； Cost(n): current node The total cost of is used as the evaluation function in the path planning algorithm; the smaller the Cost value, the better the path; : From the starting node to the current node The actual path cost, which indicates the distance or cost traveled on the path; h(n): the distance from the current node The heuristic estimated cost to the target node, i.e., the estimated remaining distance or cost, is used to guide the directionality of path planning; n: the node evaluated in the current path planning; S3: Map information update The system regularly detects road conditions within the depot area, including obstacles and maintenance information; if there is an update, the system will mark the impassable area in red through the map information update function to remind the driver to take a detour.

3. The grain depot intelligent navigation system according to claim 1 is characterized in that: The intelligent navigation system also includes a dynamic fast adaptation module, which includes: a license plate recognition and automatic registration unit, which is used to recognize and register the information of foreign vehicles at the entrance of the storage area, and mark it as a "new vehicle" to activate the fast adaptation process; a path preloading unit, which completes the path loading of the target operation area through cloud computing before the vehicle officially enters the storage area; an intelligent vehicle speed adjustment and path optimization unit, which is used to measure the vehicle speed in real time and combine the dynamic traffic conditions in the storage area to automatically adjust the path to avoid congestion and guide new vehicles into non-congested areas.

4. The grain depot intelligent navigation system according to claim 3 is characterized in that: The intelligent navigation system also includes a multi-vehicle collaborative scheduling module, which uses an artificial intelligence algorithm to allocate tasks according to job priority, congestion level of the target area and expected completion time, and includes a regional traffic flow prediction and dynamic load distribution unit for predicting traffic flow in each area and optimizing vehicle distribution; a collaborative path planning and avoidance strategy unit for generating a "virtual lane" for each vehicle to avoid congestion, and guiding vehicles to pass safely by dynamically planning staggered or staggered times when multiple vehicles are heading to the same target area; As well as temporary parking spots and dynamic waiting area design units to ensure orderly parking and waiting of vehicles during high traffic periods.

5. The grain depot intelligent navigation system according to claim 4 is characterized in that: The intelligent navigation system also includes a distributed intelligent signal interaction system, which sets distributed signal interaction nodes in the storage area to achieve short-range communication between vehicles. The system shares the vehicle navigation status to neighboring vehicles in real time through signal broadcasting and data links to form an automatic avoidance mechanism; and includes a real-time road condition sharing mechanism and a path intelligent switching unit, which are used to identify and notify neighboring vehicles to bypass congested areas, and automatically switch to a suboptimal path when an obstacle is detected to ensure smooth passage of vehicles.

6. The grain depot intelligent navigation system according to claim 5 is characterized in that: The intelligent navigation system also includes an adaptive navigation mode module, which provides simplified "simple mode" navigation for foreign vehicles through an AI-based multi-mode navigation strategy, and guides foreign vehicles through voice and visual prompts; and includes a learning and adaptation mechanism that can gradually increase the complexity of navigation according to the usage habits and driving characteristics of foreign vehicles, thereby improving the efficiency of subsequent operation scheduling.

7. The grain depot intelligent navigation system according to claim 6 is characterized in that: The intelligent navigation system is applied to the navigation method of vehicles operating in the grain storage area as follows: S1. Vehicles entering the warehouse area: S1.1: License Plate Recognition and Automatic Registration: When a vehicle approaches the entrance to the storage area, the camera at the entrance automatically captures the license plate image and identifies the license plate number through optical character recognition technology; the system stores the identified license plate number in the database and marks the vehicle as a "new vehicle", activating the rapid adaptation process for subsequent path loading and navigation authority issuance; S1.2: Path preloading: The system generates the best path based on the vehicle's target operating area through cloud computing. Before the vehicle enters the depot, the system has loaded the pre-calculated path onto the navigation module, so that the vehicle can get real-time path guidance without waiting. S1.3: Intelligent speed regulation and route optimization: The system measures the current speed of the vehicle in real time and optimizes the route based on the real-time traffic flow in the depot area. If a congested area is detected, the system automatically adjusts the route to guide the vehicle to avoid the congested area and enter the target area. S2. Real-time vehicle navigation: S2.1: Vehicle positioning and path display: The vehicle navigation module obtains the vehicle's current position in real time through the port data of the Beidou satellite navigation system; the vehicle's navigation equipment displays the shortest path from the current position to the target operating area, ensuring that the driver can follow the navigation instructions in real time; S2.2: Path Planning: The system calculates the best path and displays the turn and distance information for each node on the path; During driving, the system continuously updates the path to ensure that the vehicle always drives on the optimal path; S2.3: Map information update: The system regularly checks the road conditions in the depot area; if any road closures, obstacles or maintenance information are found, the system marks these areas in red and updates them on the map. The driver can use the map update function to detour in advance to avoid delays in work tasks; S3. Multi-vehicle coordinated dispatching: S3.1: Task Allocation: The system uses artificial intelligence algorithms to dynamically assign tasks based on the vehicle's operational priority, the degree of congestion in the target area, and the urgency of the task; vehicles with higher task priorities will be given priority in routing; S3.2: Traffic flow prediction and load distribution: The system monitors the traffic flow in each area of ​​the depot in real time, predicts the congested areas and distributes the vehicles evenly to the areas with lower traffic; this load distribution ensures the smoothness of traffic in the depot and avoids unnecessary vehicle accumulation; S3.3: Collaborative Path Planning and Avoidance: The system assigns a "virtual lane" to each vehicle to avoid congestion on the same route; if multiple vehicles are heading to the same area, the system sets staggered or misaligned traffic through dynamic planning to guide vehicles to pass safely and avoid route conflicts; S3.4: Temporary parking spots and dynamic waiting areas: During peak hours, the system arranges some vehicles to stop temporarily in dynamic waiting areas or temporary parking spots to ensure smooth traffic in the depot area; when the road is restored, the system redirects these vehicles back on the road; S4. Distributed signal interaction: S4.1: Signal broadcast and data link: The signal interaction nodes installed in the depot area support short-range communication between vehicles; each vehicle broadcasts its current position and path information to surrounding vehicles to achieve automatic avoidance; S4.2: Real-time traffic sharing and route switching: When the vehicle receives information about congestion or obstacles, the system automatically switches to the suboptimal path and guides the vehicle around the obstacle area; real-time traffic sharing ensures that all vehicles can obtain the latest road information and adjust the path according to the situation; S5. Adaptive navigation mode: S5.1: Multi-mode navigation: The system provides "simple mode" navigation for external vehicles to help drivers adapt to the internal environment of the depot area; the system provides drivers with intuitive navigation guidance through voice and visual prompts; S5.2: Learning and Adaptation Mechanisms: The system records and analyzes the vehicle's driving path, navigation usage habits and driving speed information, and gradually optimizes the navigation mode; as the vehicle accumulates more experience in the depot, the system will gradually increase the complexity of its navigation, allowing the vehicle to execute navigation instructions more efficiently in subsequent tasks; S6. Vehicle leaves the warehouse: S6.1: Clear permissions: When the system detects that the vehicle has left the coordinate area of ​​the warehouse, it automatically clears the navigation authority of the vehicle to ensure the information security of the warehouse area. After the vehicle leaves, the navigation authority is revoked and the internal map or path information of the warehouse area is no longer displayed. S6.2: Data storage and analysis: The system uploads the vehicle's driving trajectory, congestion information and stop point data to the database for subsequent data analysis and optimization improvements, providing more accurate path planning support for future vehicle navigation.

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