A path lock control pre-computation method based on dynamic release distance and related components

Abstract

The application discloses a path lock control precalculation method based on dynamic release distance and related components, adopts the core technical route of offline precalculation, dynamic release lock, layered detection, one vehicle one strategy and precise precontrol, and completes the precalculation of the lock control relationship of all combinations in the whole road network. The lock control relationship and the dynamic release distance table are generated through offline precalculation, the collision detection is converted into a table lookup operation, the defects of the existing traffic management system are solved, and the response lag of real-time detection and the physical collision risk are completely eliminated. A convex polygon model is independently constructed for each vehicle combination and the exclusive dynamic release distance is calculated, the adaptation of one vehicle, one road and one working condition is realized, and the safety redundancy caused by the fixed release distance is avoided. The rigid mode of releasing only when reaching the node is replaced by the dynamic release, and the invalid locking time of the road section is greatly shortened. The real-time collision calculation of O(n2) order is converted into O(1) offline table lookup, so that the traffic management system does not need any complex geometric calculation during operation.

Description

Technical Field

[0001] This invention relates to the field of AGV traffic management technology, and in particular to a path locking pre-calculation method and related components based on dynamic release distance. Background Technology

[0002] The development of AGV path locking traffic management technology has mainly gone through three core stages. The first generation is a basic prototype with extremely limited application scenarios. The second and third generations are the current mainstream and advanced solutions in the industry. Although they have solved some of the pain points of the first generation, they still have the fundamental defects of not being able to balance safety, efficiency, multi-vehicle adaptation and system load.

[0003] The first generation is an intersection resource lock scheme. Its core logic is exclusive traffic management on the main road. The topology usually consists of a shared main road and multiple branch roads. Each AGV operates on its own road. When it needs to enter the main road, it must apply for global resources at the intersection. It avoids collisions by exclusively occupying critical path resources, thus achieving logical absolute isolation. This solution is simple in logic and easy to implement, and effective in small-scale fixed-path scenarios. However, its core defects are significant: It completely lacks safety control, only locking intersection nodes and having no conflict control capabilities for intersections, meeting points, or parallel curves in the middle of the path. There are no constraints on lateral or cross-collision risks, and it can only adapt to extremely simple road networks with single vehicle types, one-way traffic, and no intersections. Path utilization is extremely low; once an AGV enters the main road, it locks the entire main road, preventing other AGVs from entering and creating a system performance bottleneck. Sub-path concurrency is poor; only one AGV is allowed in each sub-path at a time, forcing multiple tasks to be queued and processed sequentially, failing to leverage the advantages of cluster scheduling. It lacks multi-vehicle adaptability, with no adaptation logic based on vehicle geometry and motion performance. Large and small vehicles share the same locking rules, leading to either insufficient safety margins or extreme resource waste.

[0004] The second generation is a path segmentation lock scheme. To solve the problem of low resource utilization in the first generation, it introduces the concept of segmentation, which divides the AGV's travel path into multiple small segments composed of nodes and edges for fine-grained management. The AGV requests the next segment of resources after each segment is traveled and releases them after use, thus realizing path reuse. This solution significantly improves path concurrency utilization, representing a major evolution of the first generation. However, its core shortcomings remain prominent: poor adaptability to different vehicle models; reserving a safety distance based on the largest vehicle model leads to small AGVs consuming excessive resources, while using the smallest model as the benchmark makes large AGVs prone to sweep-through collisions when turning or changing lanes, failing to dynamically adapt to the kinematic characteristics of different vehicle models; poor adaptability to topology maps, posing a risk of logical unlocking and physical collisions; when two nodes are physically close, even if AGVs are parked at different nodes, they may still make physical contact due to their long vehicle lengths; when paths at intersections or T-junctions intersect topologically but do not share a common point, it is impossible to predict merging conflicts; rigid resource release strategy, typically releasing resources from the previous node only when the AGV reaches the next node, causing long-distance sections to result in AGVs virtually occupying resources along the way for extended periods, leading to logical congestion; high deadlock risk; real-time detection is a reactive mode, only implementing locking and blocking when a conflict is about to occur, easily leading to problems such as multiple vehicles waiting in a loop at intersections and deadlocks when vehicles meet on two-way sections, resulting in excessively high deadlock frequencies in industrial scenarios requiring manual intervention.

[0005] The third generation is a real-time dynamic detection solution. To overcome the poor adaptability of the second generation to topology maps and vehicle models, it introduces a real-time feedback mechanism. It shifts from a passive mode of requesting lock control to an active detection mode of real-time monitoring and computational intervention, relying on the pose data reported by the AGV in real time for global collision detection. Theoretically, this solution can handle complex dynamic scenarios and has extremely high compatibility with maps and vehicle models. However, its core shortcomings are equally serious: control lag and safety hazards. Communication latency in industrial settings is typically 100 to 500 milliseconds, which, combined with computation time, means that by the time the system detects a conflict, the AGV may have already entered the conflict zone, forcing a passive emergency stop command. In heavy-load or high-speed scenarios, this can easily lead to material tipping, equipment wear, or even path deadlock. When the number of AGVs exceeds 50, the computational load surges, and latency and safety risks increase exponentially. The high computational load and poor system scalability are also issues; the time complexity of the real-time collision detection algorithm is typically O(n^2). 2 This requires traversing the paths of all online AGVs in pairs. When the cluster size exceeds 100 units, the CPU utilization of ordinary industrial controllers is easily saturated, leading to system lag, longer detection cycles, and difficulty in supporting large-scale cluster applications. The passive response mode leads to a high risk of deadlock. This mode is essentially a post-event remedy rather than a pre-event plan. It can only intervene when a conflict is about to occur, lacks a global perspective, and is very likely to form loops at intersections where multiple vehicles meet, waiting for deadlocks. In industrial scenarios, deadlocks can occur 5 to 10 times per month, which heavily relies on manual intervention and disrupts the continuity of production. Summary of the Invention

[0006] The purpose of this invention is to provide a path lock control pre-calculation method and related components based on dynamic release distance, aiming to solve the problems of poor multi-vehicle compatibility, difficulty in balancing safety and efficiency, high computational load, and prominent deadlock risk in AGV path lock control traffic management technology.

[0007] In a first aspect, embodiments of the present invention provide a method for pre-calculating path locking control of multi-model industrial AGVs, including: Collect geometric and operational parameters of AGVs of various models, construct a two-dimensional convex polygon model of the maximum outer edge of the planar projection of each model, and generate a basic dataset of the models; Load AGV operation road network data, extract node and road segment information, construct road network topology and node-road segment association index, and generate map basic dataset; Based on the vehicle model dataset and the map dataset, the locking relationship detection is performed between nodes, between road segments, and between road segments, and the dynamic release distance of the corresponding vehicle model combination is calculated to generate a locking relationship and release distance table.

[0008] Secondly, embodiments of the present invention provide a multi-model industrial AGV path locking pre-calculation device, comprising: The data acquisition module is used to collect the geometric and operational parameters of AGVs of various models, construct a two-dimensional convex polygon model of the maximum outer edge of the planar projection of each model, and generate a basic dataset of the model. The loading module is used to load AGV operation road network data, extract node and road segment information, construct the road network topology and node and road segment association index, and generate the basic map dataset. The execution module is used to perform node-to-node, road segment-to-node, and road segment-to-road segment locking relationship detection based on the vehicle model base dataset and the map base dataset, calculate the dynamic release distance of the corresponding vehicle model combination, and generate a locking relationship and release distance table.

[0009] Thirdly, embodiments of the present invention provide a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the multi-model industrial AGV path locking pre-calculation method described in the first aspect.

[0010] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-model industrial AGV path locking pre-calculation method described in the first aspect.

[0011] This invention discloses a path locking pre-calculation method and related components based on dynamic release distance. The method includes: collecting geometric and operational parameters of multi-model AGVs, constructing a two-dimensional convex polygon model of the maximum outer edge of the planar projection of each model, and generating a model basic dataset; loading AGV operation road network data, extracting node and road segment information, constructing a road network topology and node-road segment association index, and generating a map basic dataset; based on the model basic dataset and the map basic dataset, performing locking relationship detection between nodes, road segments, and road segments, and calculating the dynamic release distance of the corresponding model combinations, generating a locking relationship and release distance table. This invention, based on the two-dimensional convex polygon model of the model and the road network topology, pre-completes full collision detection between nodes, road segments, and road segments, ensuring that all potential conflicts are identified and locked before AGV passage, completely eliminating the response lag and physical collision risk of real-time detection. Regarding multi-vehicle adaptation, a convex polygon model is independently constructed for each vehicle combination, and a unique dynamic release distance is calculated. This achieves precise adaptation for each vehicle, road, and working condition, avoiding insufficient safety redundancy or resource waste caused by fixed release distances. In terms of resource utilization efficiency, the AGV position is monitored in real time through a dynamic release distance threshold. When the travel distance reaches the threshold, associated resources are unlocked promptly, replacing the rigid mode of releasing only upon reaching a node in traditional solutions, significantly shortening the invalid locking time of road segments. Regarding system load, real-time collision calculation is transformed into offline table lookup, eliminating the need for any complex geometric calculations during traffic management system operation. This significantly reduces CPU utilization and improves cluster scalability. Simultaneously, pre-judgment eliminates the risk of deadlock caused by multiple vehicles waiting in a loop. This invention also provides a multi-vehicle industrial AGV path locking pre-calculation device, a computer-readable storage medium, and a computer device, all possessing the aforementioned beneficial effects, which will not be elaborated further here. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating the pre-calculation method for path locking control in multi-model industrial AGVs; Figure 2 Another flowchart illustrating the path locking pre-calculation method for multi-model industrial AGVs; Figure 3 This is a planar geometric diagram of the Diff model. Figure 4 This is a planar geometric schematic diagram of a steering wheel vehicle. Figure 5 This is a schematic diagram illustrating the locking and control relationships between road segments and nodes; Figure 6 This is a schematic diagram showing the interlocking and control relationships between road segments; Figure 7 A schematic diagram showing the lock-related road segments and release distances for vehicle models T1 and T2; Figure 8 A schematic diagram showing the locking-related road segments and release distances for vehicle model T2; Figure 9 This is a schematic block diagram of a path locking pre-calculation device for multi-model industrial AGVs. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] It should be understood that, when used in this specification and the appended claims, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more of its features, integrals, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0017] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the relevant listed items and all possible combinations, and includes such combinations.

[0018] Please see Figure 1 and Figure 2 This embodiment provides a method for pre-calculating path locking control of multi-model industrial AGVs, including: S101: Collect the geometric and operational parameters of AGVs of multiple models, construct a two-dimensional convex polygon model with the maximum outer edge of the planar projection of each model, and generate a basic dataset of the models; In this embodiment, geometric and operational parameters of AGVs of multiple models are collected, and a two-dimensional convex polygon model with the maximum outer edge of the planar projection of each model is constructed to generate a basic dataset of the models, including: Read the vehicle configuration file of multi-vehicle AGV, extract the vehicle ID, geometric dimension polygon vertex coordinates, vehicle reference point coordinates, minimum turning radius data, and generate vehicle geometric parameter set; Based on the vehicle model geometric parameter set, the coordinates of the vertices of the geometric dimensions polygons of each vehicle model are transformed to a global coordinate system with the vehicle reference point coordinates as the origin, generating a standardized vehicle model vertex coordinate set; The convex hull algorithm is applied to the standardized vehicle vertex coordinate set to extract the outer vertex sequence and generate the maximum outer two-dimensional convex polygon model of each vehicle. Based on the maximum outer edge two-dimensional convex polygon model, the radius of the outer edge enclosing circle, the minimum outer edge size, and the safe collision radius parameters of the vehicle model are calculated to generate a set of vehicle model feature parameters. The vehicle model's geometric parameter set, vehicle model feature parameter set, and maximum outer edge two-dimensional convex polygon model are associated and bound to generate a vehicle model basic dataset.

[0019] This embodiment reads configuration files from multiple vehicle models and extracts various core parameters. Combined with global coordinate system standardization, it constructs a unique geometric representation for each vehicle model, completely resolving the issue of poor compatibility across models and avoiding insufficient safety margins or resource waste caused by standardized approaches. A convex hull algorithm is used to extract peripheral vertices and generate a maximum-edge two-dimensional convex polygon model, eliminating redundant vertices and simplifying the vehicle's geometric representation. This significantly reduces the computational load for subsequent collision detection, avoiding the high complexity of traditional real-time detection. Multiple vehicle model feature parameters are calculated and all data is correlated to generate a basic vehicle dataset, providing accurate and unified foundational support for subsequent locking pre-calculation. This dynamically adapts to the spatial occupancy and movement characteristics of different vehicle models, balancing traffic safety and path utilization. Simultaneously, it provides data for pre-calculating locking relationships and dynamic release distances, reducing real-time decision lag and effectively mitigating deadlock risk.

[0020] Specifically, the system reads the vehicle configuration file for multi-vehicle AGVs. This configuration file is stored in JSON format, and each record contains a vehicle identifier, vehicle drive type, geometric dimensions (polygon vertex coordinate sequence), vehicle reference point coordinates, and minimum turning radius data. The system calls a JSON parsing library to read each vehicle record in the configuration file item by item, extracting the vehicle identifier and its corresponding geometric dimensions (polygon vertex coordinates, vehicle reference point coordinates, and minimum turning radius). This data is then organized by vehicle identifier and stored in the vehicle geometric parameter set, which provides the basic data source for all subsequent vehicle-related calculations.

[0021] The system then performs coordinate standardization based on the vehicle model's geometric parameter set. Since the vertex coordinates of the original geometric polygon are defined in the vehicle's own coordinate system, and the reference point positions differ between different vehicle models, and subsequent collision detection needs to be performed in a unified global coordinate system, the system iterates through each vehicle model in the geometric parameter set, extracting the vertex coordinate sequence of the geometric polygon for that model and the vehicle reference point coordinates. For each vertex coordinate, the system performs a coordinate transformation: subtracting the vehicle reference point coordinates from the vertex coordinates, shifting the origin to the vehicle reference point position, thus completing the standardized coordinate transformation. After the transformation, the system associates the standardized vertex coordinate sequence of each vehicle model with the vehicle model identifier, generating a standardized vehicle model vertex coordinate set. All vertices in this coordinate set have the vehicle reference point as the origin, providing a unified coordinate reference for subsequent convex hull calculations.

[0022] Next, the system performs convex hull algorithm processing on the standardized vertex coordinate set of vehicle models to extract the minimum outer contour of each model. The Andrew algorithm is used as the core algorithm for convex hull extraction. For each vehicle model's standardized vertex coordinate set, the algorithm first sorts the vertices in ascending order of their x-coordinates; if the x-coordinates are the same, they are sorted in ascending order of their y-coordinates, resulting in a sorted vertex sequence. Then, the upper and lower convex hulls are constructed: starting from the leftmost vertex, the sorted vertices are traversed sequentially. The cross product is used to determine if the vector formed by the current point and the top of the stack forms a right turn. If a right turn is formed, the vertex is popped from the stack; otherwise, the current point is pushed onto the stack. After completing the upper convex hull construction, the lower convex hull is constructed by traversing from the rightmost vertex to the left using the same logic. Finally, the upper and lower convex hulls are merged, and duplicate endpoints are removed to obtain the complete convex hull vertex sequence. Taking a steering wheel vehicle model as an example, its original polygon contains six vertices, which are simplified to a rectangular convex hull composed of four vertices after convex hull extraction, reducing the number of vertices by one-third. The system associates the extracted convex hull vertex sequence with the vehicle model identifier to generate a maximum outer edge two-dimensional convex polygon model for each vehicle model. This model fully preserves the minimum outer edge contour of the vehicle model on the horizontal plane, while removing redundant vertices located inside the original polygon, providing the simplest geometric expression for subsequent polygon collision detection.

[0023] Subsequently, based on the maximum outer edge 2D convex polygon model, the system calculates the feature parameters of each vehicle model. For the outer edge enclosing circle radius, the system traverses each vertex in the convex polygon vertex sequence, calculates the Euclidean distance from the vertex to the origin, and takes the maximum value among all vertex distances as the outer edge enclosing circle radius of the vehicle model. For the minimum outer edge dimension, the system traverses the convex polygon vertex sequence, finds the minimum and maximum values ​​of the x-coordinate and y-coordinate respectively, calculates the difference between the x-coordinate and y-coordinate, and obtains the width and length of the vehicle model. For the safe collision radius, the system performs a composite calculation based on the vehicle model's minimum turning radius, maximum driving speed, and braking response time, combined with a preset safety factor, to obtain the safe collision radius parameter of the vehicle model. The system associates the calculated outer edge enclosing circle radius, minimum outer edge dimension, and safe collision radius with the vehicle model identifier to generate a vehicle model feature parameter set.

[0024] The system then associates and binds the vehicle model's geometric parameter set, vehicle model feature parameter set, and the maximum outer edge two-dimensional convex polygon model. Using the vehicle model identifier as the primary key, a vehicle model feature structure is created. This structure includes fields such as vehicle model identifier, geometric dimension polygon vertex coordinates, vehicle reference point coordinates, minimum turning radius, outer edge convex polygon vertex sequence, outer edge enclosing circle radius, minimum outer edge size, and safe collision radius. The system stores the feature structure of each vehicle model in a hash table using the vehicle model identifier as the index, constructing a basic vehicle model dataset. This dataset uses an O(1) time complexity indexing mechanism, enabling subsequent node-node filtering, road segment-node filtering, and road segment-road segment filtering processes to quickly locate the convex polygon model and feature parameters of any vehicle model. During dataset construction, the system simultaneously performs data integrity verification, checking whether the convex polygon vertex sequence of each vehicle model is closed, whether the outer edge enclosing circle radius is positive, and whether the minimum outer edge size is reasonable, ensuring data quality. The establishment of the vehicle model basic dataset marks the completion of the vehicle model data preprocessing stage. This dataset extracts the complex information in the original geometric model into structured lightweight data. The radius of the outer edge enclosing circle serves as the core parameter for fast filtering and can be directly called in Euclidean distance comparison. The convex polygon vertex sequence serves as the geometric model for accurate detection and provides accurate boundary information in subsequent polygon intersection detection. The safe collision radius provides a key parameter for dynamic release distance calculation, laying a solid data foundation for lightweight optimization of the entire pre-calculation process.

[0025] In some embodiments, please refer to Figure 3When constructing the two-dimensional convex polygon model of the maximum outer edge of the planar projection of a multi-model AGV, the system establishes a planar polygon projection of the vehicle body based on the geometric dimensions of the AGV. Taking the geometric center of the vehicle body or the obstacle avoidance radar base point as the origin O, the vehicle width is represented along the X-axis and the vehicle length along the Y-axis. By extracting multiple boundary vertices of the maximum outer edge contour of the vehicle body, a closed two-dimensional convex polygon is formed. This polygon accurately represents the spatial occupancy area of ​​the AGV on the horizontal plane. Based on this, the system further generates an obstacle avoidance protection zone polygon. This zone, based on the vehicle body planar polygon, extends in the forward, backward, or turning directions according to the area allocation conditions of the safety scanner, forming a safety protection boundary surrounding the vehicle body. This boundary is used to identify potential risks in advance during collision detection. The obstacle avoidance radar base point is located inside the polygon, serving as a reference point for the sensor's sensing range, ensuring that the protection zone polygon matches the dynamic response characteristics of the vehicle body during actual operation. This model provides a standardized geometric representation for subsequent collision detection and release distance calculation.

[0026] In some embodiments, please refer to Figure 4 When constructing the two-dimensional convex polygon model of the maximum outer edge of the planar projection of multi-model AGVs, the system establishes a planar polygon projection of the vehicle body based on the geometric dimensions of the AGV. Taking the obstacle avoidance radar base point as the origin O, the vehicle width is represented along the X-axis, and the vehicle length along the Y-axis. By extracting multiple polygon vertices of the maximum outer edge contour of the vehicle body, a closed two-dimensional convex polygon is formed. This polygon accurately represents the spatial occupancy area of ​​the AGV on the horizontal plane. The system also extracts the wheelbase and track width parameters. The wheelbase is the distance between the front and rear axle centers, and the track width is the lateral distance between the left and right wheels. These two parameters, along with the arrangement of the steering wheels, jointly determine the steering characteristics and minimum turning radius of the AGV. As the drive and steering actuator of the AGV, the offset of the steering wheel's installation position relative to the origin directly affects the trajectory offset of the vehicle when driving in curves. The system incorporates the steering wheel parameters into the vehicle model's basic dataset for kinematic compensation during subsequent curvature factor calculations.

[0027] Building upon this foundation, the system further generates an obstacle avoidance protection zone polygon. This zone, based on the vehicle's planar polygon, expands in the forward, reverse, or turning directions according to the area allocation conditions of the safety scanner, forming a safety protection boundary surrounding the vehicle. The outward expansion distance of the obstacle avoidance protection zone polygon is related to the AGV's maximum speed, braking performance, and minimum turning radius. When the AGV turns with a specific curvature, the protection zone polygon dynamically adjusts with the vehicle's attitude, ensuring early identification of potential risks during collision detection. The minimum turning radius, as a core motion parameter of the vehicle model, is used to determine the differentiated expansion distance between the inner and outer sides of the curve when constructing the protection zone polygon, ensuring the protection zone conforms to the vehicle's actual driving trajectory.

[0028] The obstacle avoidance radar base point is located inside the vehicle's planar polygon, serving as a reference point for the sensor's sensing range. The relative coordinates of this base point with the steering wheel determine the coupling method between the radar scanning area and the vehicle's motion attitude. During the dynamic starting point release distance calculation, the system uses the obstacle avoidance radar base point as a reference, combining the boundary vertex coordinates of the vehicle's planar polygon and the obstacle avoidance protection area polygon to calculate the AGV's spatial occupancy boundary when traveling on the road segment. After projecting this boundary onto the road segment's travel plane, it performs intersection detection with other AGV models on the locking object. Vehicle length and width parameters are directly used for calculating the basic safety distance, while wheelbase and track width parameters indirectly affect the curvature factor's weighting coefficient by influencing the minimum turning radius, resulting in differentiated release distances for different vehicle models when traversing the same curved road segment.

[0029] S102: Load AGV operation road network data, extract node and road segment information, construct road network topology and node-road segment association index, and generate map basic dataset; In this embodiment, AGV operation road network data is loaded, node and road segment information is extracted, road network topology and node-road segment association index are constructed, and a map base dataset is generated, including: Load AGV operation road network data, read the waypoint set and lane set in the AGV operation road network data, extract the node identifier, node coordinates, waypoint type and orientation angle data of each node in the waypoint set, extract the road segment identifier, lane type, starting waypoint identifier, ending waypoint identifier, control point sequence, lane length and driving direction data of each road segment in the lane set, and generate the original road network information set; Based on the original road network information set, the node identifier, node coordinates, waypoint type, and orientation angle data of each node are parsed to generate a node attribute set; Based on the original road network information set, the road segment identification, lane type, starting road point identification, ending road point identification, control point sequence, lane length, and driving direction data of each road segment are analyzed to generate a road segment attribute set; Based on the node coordinates in the node attribute set and the start and end waypoint identifiers in the road segment attribute set, a directed connection relationship between nodes and road segments is established. Based on the directed connection relationship, the starting waypoint identifier and the ending waypoint identifier are associated with the corresponding node coordinates to generate the road network topology; Based on the road network topology, each node is associated with a list of all road segment identifiers that start or end at the node, and each road segment is associated with the coordinates of its starting node and the coordinates of its ending node, generating a node-road segment association index. Data is integrated and serialized based on node-segment association index, road network topology, node attribute set, and segment attribute set to generate a basic map dataset.

[0030] This embodiment loads road network data and extracts multi-dimensional detailed parameters of nodes and road segments, covering key information such as node orientation, road segment control point sequence, and driving direction. This provides accurate road network support for subsequent multi-vehicle adaptation, avoiding vehicle-to-road segment mismatch issues caused by missing road network information and overcoming the predicament of poor multi-vehicle compatibility. By splitting and generating node and road segment attribute sets, and then constructing directed connection relationships and road network topology, the spatial logic of the road network is clearly depicted, avoiding the logical unlocking and physical collision hazards caused by traditional topological abstraction, and balancing traffic safety and efficiency. Next, by generating node-road segment association indexes, rapid association retrieval of nodes and road segments is achieved, significantly reducing the retrieval and calculation time of subsequent lock control pre-calculation, reducing system load, and solving the problem of high computational load in traditional real-time detection. Finally, a standardized map base dataset is generated to achieve integrated management of road network data, providing efficient and accurate data support for subsequent pre-calculation of lock control relationships between nodes and road segments, avoiding real-time decision lag, effectively reducing deadlock risk, and adapting to large-scale complex road networks and multi-vehicle mixed scheduling scenarios.

[0031] Specifically, before performing pre-calculation of path locking for multi-model AGVs, the system first needs to complete the construction of a basic map dataset. This dataset provides the foundational support at the road network level for subsequent collision detection and release distance calculation. When loading AGV operation road network data, the system reads the pre-stored road network configuration file and extracts two core data groups: waypoint set and lane set. The waypoint set records the spatial information of all key nodes in the operating environment, including the unique identifier of each node, its three-dimensional coordinates in the global coordinate system, the waypoint type to distinguish whether the node is a normal path point, a parking point, or an intersection point, and the node orientation angle to characterize the allowed driving direction when the AGV passes through the node. The lane set records the structured information of all road segments, including the unique identifier of the road segment, the lane type to distinguish between straight lanes, curves, or ramps, the start and end waypoint identifiers to define the start and end boundaries of the road segment, the control point sequence to describe the Bézier curve or spline curve parameters of curved road segments, the lane length to characterize the spatial distance of the road segment, and the driving direction to identify whether the road segment is one-way or two-way. The system integrates the extracted data into a road network raw information set, which serves as the basis for subsequent analysis.

[0032] Based on the original road network information set, the system parses and generates node attribute sets and road segment attribute sets respectively. During the generation of the node attribute set, the system traverses each node in the waypoint set, extracting its node identifier, node coordinates, waypoint type, and orientation angle data. This information is encapsulated into a node attribute structure, and a fast query index is built using the node identifier as the key, enabling the location of any subsequent node's attributes within a constant time. During the generation of the road segment attribute set, the system traverses each road segment in the lane set, extracting the road segment identifier, lane type, starting waypoint identifier, ending waypoint identifier, control point sequence, lane length, and driving direction data. Similarly, an index table is built using the road segment identifier as the key, providing a foundation for accessing road segment geometric parameters and topological relationships.

[0033] After constructing the node attribute set and road segment attribute set, the system establishes directed connections between nodes and road segments. Specifically, the system parses the start and end waypoint identifiers of each road segment in the road segment attribute set, and uses these two identifiers to locate the corresponding start and end node coordinates in the node attribute set. Based on this, the system uses nodes as connection points to directionally process road segments according to the direction of travel: if the road segment is one-way, a one-way connection is established with the start node as the road segment entrance and the end node as the road segment exit; if the road segment is two-way, two bidirectional directed connections are established, one from the start node to the end node and the other from the end node to the start node. This directed connection records the adjacent nodes that can be reached from any node through a certain road segment, forming the foundation of the road network's connectivity.

[0034] Based on directed connections, the system constructs a complete road network topology. During topology construction, each node is treated as a vertex in a graph, and each directed road segment as a directed edge connecting two vertices. The system traverses all directed connections, recording directed paths between the starting and ending nodes as topological edges, and associating each topological edge with a corresponding road segment identifier, thus binding the geometric parameters of the road segment to the topological edge. This road network topology, in the form of a directed graph, fully represents all feasible travel paths of the AGV in the operating environment, providing graph theory support for subsequent path search and locking relationship traversal.

[0035] Based on the road network topology, the system further constructs a node-road segment association index. For each node, the system retrieves all directed edges originating from that node in the road network topology, extracts the road segment identifiers associated with these directed edges, and forms a list of outgoing road segments for that node; simultaneously, it retrieves all directed edges ending at that node, forming a list of incoming road segments for that node. For each road segment, the system directly locates the corresponding starting and ending node coordinates in the node attribute set based on its starting and ending waypoint identifiers, establishing the association between the road segment and its starting and ending nodes. This node-road segment association index enables the system to quickly obtain the starting node information of any road segment when performing road segment-node lock detection, and to quickly obtain the entrance and exit node information of any road segment when performing node-node lock detection.

[0036] Finally, the system integrates and serializes the node-segment association index, road network topology, node attribute sets, and segment attribute sets. During integration, the system encapsulates these four data components into a unified data structure for the map's basic dataset. This structure includes a node coordinate mapping table for rapid spatial distance calculation, a node-segment adjacency table for rapid topological traversal, and a segment geometric parameter table for curvature calculation and projection plane construction. The serialized map basic dataset is stored in a structured format in memory or a persistent cache, allowing the locking pre-calculation module to directly access it in subsequent collision detection filtering and dynamic release distance calculation stages, ensuring efficient and consistent road network data retrieval.

[0037] S103: Based on the vehicle model base dataset and the map base dataset, perform node-to-node, road segment-to-node, and road segment-to-road segment locking relationship detection, calculate the dynamic release distance of the corresponding vehicle model combination, and generate a locking relationship and release distance table; In this embodiment, based on the vehicle model base dataset and the map base dataset, the locking relationship detection is performed between nodes, between road segments, and between road segments, and the dynamic release distance of the corresponding vehicle model combination is calculated, generating a locking relationship and release distance table, including: Based on the vehicle model dataset and the map dataset, all node pairs are traversed. For the first and second nodes in each node pair, the first vehicle model list allowed to park at the first node and the second vehicle model list allowed to park at the second node are obtained respectively, and all combinations of first vehicle models are generated. For each first vehicle combination, based on the maximum outer edge two-dimensional convex polygon model, the convex polygon of the first vehicle is projected onto the stopping area plane of the first node, and the convex polygon of the second vehicle is projected onto the stopping area plane of the second node, and convex polygon intersection detection is performed. If there are intersecting areas, it is determined that the node pair has a locking relationship with the first vehicle model combination; For the combination of node pairs with locking relationship and the first vehicle model, based on the geometric feature parameters and motion state parameters of the first vehicle model in the vehicle model basic dataset, as well as the curvature features and path length of the connected path between the first node and the second node in the map basic dataset, the collision-related path segment between the first node and the second node is located, and the local curvature parameters of the collision-related path segment are extracted to obtain the first local curvature parameter. The geometric feature parameters, motion state parameters, and first local curvature parameters of the first vehicle model are input into the safety distance calculation function to generate the first dynamic starting point release distance; Store the correspondence between node pairs, the first vehicle type combination, and the first dynamic starting point release distance in the locking relationship and release distance table.

[0038] This embodiment uses standardized vehicle model datasets and map datasets as a unified execution foundation, providing a standardized and unified data source for the detection of global locking relationships between nodes, road segments, and road segments, ensuring consistency and traceability throughout the entire detection and calculation process. In the vehicle model combination generation stage, not all vehicle models are matched indiscriminately. Instead, corresponding vehicle model combinations are generated based on the list of vehicle models that are actually allowed to park at a node. This eliminates the possibility of matching vehicles without actual parking needs from the source, significantly reducing invalid collision detection and distance calculation operations and simplifying the overall computational workload. The collision detection stage relies on the 2D convex polygon model of the AGV's maximum outer edge, accurately projecting the vehicle model's convex polygon onto the parking area plane of the corresponding node to perform intersection detection. This aligns with the actual spatial occupancy characteristics of different AGV models, making the spatial judgment of collision detection more consistent with the actual operating scenarios of industrial AGVs. This results in more accurate and rigorous locking relationship determinations; a locking relationship is only determined when the projected areas intersect, ensuring the safety and rationality of locking relationship determination. The calculation of dynamic release distance is highly targeted and accurate, solving only for node pairs and vehicle combinations with established locking relationships. This avoids invalid calculations for combinations without locking relationships. Furthermore, instead of using the overall parameters of the connecting paths between nodes, the calculation first locates the collision-related path segments and extracts their local curvature parameters. These are then combined with vehicle-specific geometric and motion parameters for calculation, making the dynamic release distance calculation parameters more closely reflect actual collision conditions. This achieves personalized adaptation of the release distance to different node pairs and vehicle combinations. In addition, the correspondence between node pairs, the first vehicle combination, and the first dynamic starting point release distance is structured and stored in a locking relationship and release distance table. This achieves the associative and standardized storage of locking relationship and dynamic release distance data, making the data presentation clearer and facilitating rapid retrieval, indexing, and parsing of relevant data by the RCS traffic management module. It also provides a reusable standardized paradigm for subsequent locking relationship detection and data storage between road segments and nodes, and between road segments, forming a unified management system for locking relationship and release distance data across the entire domain.

[0039] Specifically, after constructing the vehicle model base dataset and the map base dataset, the system initiates the node-to-node locking relationship detection process. The system first iterates through all node pairs in the map base dataset, labeling each pair as the first node and the second node. The system extracts the list of allowed vehicle models for the first node from the node attribute set, denoted as the first vehicle model list, and simultaneously extracts the list of allowed vehicle models for the second node, denoted as the second vehicle model list. The system performs a Cartesian product operation on the first and second vehicle model lists to generate all possible combinations of the first vehicle model, each combination containing one vehicle model parked at the first node and one vehicle model parked at the second node.

[0040] For each generated first vehicle model combination, the system calls the maximum outer edge two-dimensional convex polygon model stored in the vehicle model basic dataset. Based on the node coordinates and parking area boundary information of the first node, the system projects the convex polygon model of the first vehicle model onto the parking area plane of the first node. During projection, the reference point of the first vehicle model coincides with the parking point of the first node, maintaining the spatial pose of the convex polygon consistent with the node orientation angle. Simultaneously, based on the node coordinates and parking area boundary information of the second node, the system projects the convex polygon model of the second vehicle model onto the parking area plane of the second node. After completing the bidirectional projection, the system performs convex polygon intersection detection. Using the separating axis theorem, the edge normal vectors of the two convex polygons are extracted as separating axes, and the two convex polygons are projected onto each separating axis sequentially, detecting whether the projection intervals overlap. If the projection intervals do not overlap on any separating axis, the two convex polygons are determined to have no intersecting region; if the projection intervals overlap on all separating axes, the two convex polygons are determined to have intersecting regions.

[0041] When an intersecting area is detected, the system determines that the node pair has a locking relationship with the current first vehicle type combination. This locking relationship indicates that when the AGV of the first vehicle type is parked at the first node and the AGV of the second vehicle type is parked at the second node, there is a risk of overlapping space occupation between the two, requiring a locking mechanism to manage this. For the node pair with the determined locking relationship and the first vehicle type combination, the system further calculates the dynamic starting point release distance.

[0042] The system extracts geometric feature parameters and motion state parameters of the first vehicle model from the vehicle model base dataset. Geometric feature parameters include the maximum outer dimensions of the first vehicle model, i.e., vehicle length and width, used to characterize the calibration benchmark for basic safety distances. Motion state parameters include the maximum driving speed and braking time of the first vehicle model, used to characterize the basis for calculating the speed factor. Simultaneously, the system extracts the connectivity path information between the first and second nodes from the map base dataset. Since multiple connectivity paths may exist between nodes, the system filters out the shortest or most frequently used connectivity path between the first and second nodes based on the road network topology, and extracts the curvature feature and path length of this connectivity path. The curvature feature is obtained through differential geometric calculation of the path control point sequence, and the path length is obtained through integral accumulation of the path control point sequence.

[0043] After acquiring the global curvature features of the connected path, the system locates the collision-related path segment between the first and second nodes. The collision-related path segment refers to the path interval corresponding to the area where the convex polygon projections of the two node resting regions overlap in space. Since the two nodes are spatially separated, the overlapping area of ​​the convex polygons is usually located near the boundaries of the two resting regions. The system uses inverse projection calculations to map the overlapping area back onto the connected path, determining the start and end position interval of the overlapping area on the path, and marking this interval as the collision-related path segment. The system extracts local curvature parameters from this collision-related path segment, denoted as the first local curvature parameter. This local curvature parameter, compared to the average curvature of the entire connected path, more accurately reflects the road deformation characteristics at the actual collision risk location.

[0044] The system inputs the geometric feature parameters, motion state parameters, and first local curvature parameters of the first vehicle model into the safety distance calculation function. This safety distance calculation function employs a multi-parameter coupled form, and its expression is as follows: release_distance1=base_distance1+speed_factor1+curvature_factor1+model_factor1 Among them, base_distance1 is the basic safety distance, calculated by multiplying the maximum outer edge dimension of the first vehicle model by a safety factor; speed_factor1 is the speed factor, calculated by multiplying the maximum speed of the first vehicle model by the sum of braking time and reaction time; curvature_factor1 is the curvature factor, calculated by multiplying the first local curvature parameter by a curvature weight coefficient; and model_factor1 is the vehicle model factor, calculated by multiplying the vehicle model-specific parameters of the first vehicle model by a vehicle model weight coefficient. After substituting each parameter into the function, the system calculates and generates the first dynamic starting point release distance release_distance1. This release distance represents the safety release space that the first vehicle model AGV needs to reserve when it departs from the first node to ensure that it does not collide with the second vehicle model AGV parked at the second node.

[0045] The system stores the currently processed node pair identifier, the first vehicle model combination, and the calculated first dynamic starting point release distance as a locking record in the locking relationship and release distance table. The node pair identifier uses a combination of the first node identifier and the second node identifier, and the first vehicle model combination uses a combination of the first vehicle model identifier and the second vehicle model identifier. The release distance is a floating-point number with two decimal places of precision. The system iterates through all node pairs and all first vehicle model combinations, repeating the above detection and calculation process until all node pairs and vehicle model combinations have been processed, forming a complete node-node locking relationship and release distance table.

[0046] In this embodiment, based on the vehicle model base dataset and the map base dataset, the locking relationship detection is performed between nodes, between road segments, and between road segments, and the dynamic release distance of the corresponding vehicle model combination is calculated. The generation of the locking relationship and release distance table also includes: Based on the map dataset, nodes adjacent to the road segment are selected according to the start or end point of the road segment, and a list of road segment node combinations is generated. Each road segment node combination in the list contains a road segment and a node. Based on the vehicle model dataset and the list of road segment node combinations, extract the list of third vehicle models that are allowed to pass through the road segment and the list of fourth vehicle models that are allowed to park at the node, and generate all second vehicle model combinations; Based on the maximum outer edge two-dimensional convex polygon model in the vehicle model basic dataset, for each second vehicle model combination, the convex polygon of the third vehicle model is projected onto the driving plane defined by the road point array of the road segment, and the convex polygon of the fourth vehicle model is projected onto the stopping area plane of the node, and convex polygon intersection detection is performed. If there is an intersecting area, it is determined that the road segment node combination and the second vehicle type combination have a locking relationship; For road segment node combinations with locking relationships and second vehicle combinations, based on the geometric feature parameters and motion state parameters of the third vehicle in the vehicle basic dataset, as well as the waypoint array and average curvature of the road segment in the map basic dataset, the collision-related waypoint intervals that collide with the nodes in the road segment are located, and the local curvature parameters of the collision-related waypoint intervals are extracted to obtain the second local curvature parameters. The geometric feature parameters, motion state parameters and second local curvature parameters of the third vehicle model are input into the safety distance calculation function to generate the second dynamic starting point release distance; Store the correspondence between road segment node combinations, second vehicle type combinations, and second dynamic starting point release distance in the locking relationship and release distance table.

[0047] This embodiment generates a combination list by filtering spatially adjacent nodes from the start and end points of road segments, eliminating invalid combinations without spatial relationships at the source, significantly reducing subsequent calculations. A second vehicle combination is generated based on the vehicle type traveling on the road segment and stopping at the node, closely reflecting actual working conditions and avoiding meaningless vehicle matching. During collision detection, convex polygons are projected onto the road segment's driving plane and the node's stopping plane respectively, matching the AGV's actual spatial occupancy scenario, making the locking relationship determination more accurate. Before calculating the release distance, the collision-related road point interval is located and local curvature is extracted. Combined with vehicle-specific parameters input into the calculation function, the generated dynamic distance has stronger adaptability and no fixed-distance safety redundancy. Simultaneously, relevant data is uniformly stored in the locking relationship and release distance table, forming a standardized storage system across the entire domain with other locking detection data, facilitating subsequent data retrieval, integration, and use.

[0048] Specifically, after completing the node-to-node interlocking relationship detection, the system further executes the road segment-to-node interlocking relationship detection process. Based on the map dataset, the system traverses all road segments. For each road segment, it uses its starting and ending nodes as a reference to filter nodes that are spatially adjacent to that road segment. The criterion for determining adjacent nodes is that the spatial distance between the node and the starting or ending point of the road segment is less than a preset neighborhood threshold. This neighborhood threshold is set according to actual needs to ensure that all nodes that may interact are included in the detection range. The system combines each road segment with its filtered adjacent nodes to generate a road segment-node combination list. Each combination in this list contains a road segment identifier and a node identifier.

[0049] The system then processes each road segment node combination sequentially based on the vehicle model dataset and the road segment node combination list. For the currently processed road segment node combination, the system extracts a list of vehicle models allowed to travel on the road segment (referred to as the third vehicle model list) and a list of vehicle models allowed to park at the node (referred to as the fourth vehicle model list). The third vehicle model list is derived from the allowed travel vehicle model field recorded in the road segment attribute set, and the fourth vehicle model list is derived from the allowed parking vehicle model field recorded in the node attribute set. The system performs a Cartesian product operation on the third and fourth vehicle model lists to generate all possible second vehicle model combinations. Each combination contains one vehicle model traveling on the road segment and one vehicle model parked at the node.

[0050] Then for each generated second vehicle combination, such as Figure 5 As shown, a trajectory envelope dataset for the corresponding vehicle model on road segments is constructed. For straight road segments, a rectangular envelope region is formed by offsetting half the AGV width to both sides of the path centerline and the sum of the safety distance, extending along the path direction, and incorporating a speed-related longitudinal safety distance. For turning road segments, the envelope is calculated using the center-of-circle method, the perpendicular line method, or the directional bounding box method: the trajectory radius of the outermost point of the AGV is calculated with the turning center as the center to form a fan-shaped envelope; or several feature points are taken on the turning path to form a path perpendicular line, and the intersection of the AGV's contour at each point with the perpendicular line is calculated to form an envelope line; or a directional bounding box with the same direction of travel as the AGV is generated for each feature point on the path, and the union of all directional bounding boxes constitutes the turning envelope. The system merges the envelopes of straight and turning segments, eliminates overlapping areas, forms a continuous overall envelope, and appropriately expands the envelope boundary considering the AGV's correction capability. For complex paths such as S-shaped and continuous turns, spline curves are used to fit the envelope, generating trajectory envelope datasets for each vehicle model on various road segments.

[0051] The trajectory envelope refers to the smallest continuous spatial area occupied by the AGV and its load during movement on a fixed path. It comprises four layers: geometric envelope, motion envelope, temporal envelope, and safety envelope. The geometric envelope determines the physical space occupied based on the sum of the spatial contours of the AGV's body size, load size, and safety margin. The motion envelope constrains the trajectory boundaries based on the AGV's speed, acceleration, turning radius, and other motion parameters to ensure that the movement conforms to mechanical and control capabilities. The temporal envelope uses a space-time-obstacle model to describe the spatiotemporal boundaries of the trajectory over time. The safety envelope includes the extended boundaries of the deceleration and braking zones to achieve graded obstacle avoidance.

[0052] During trajectory envelope projection, the system dynamically adjusts projection parameters according to road segment type: for straight segments, a rectangular envelope model is used, with the envelope width being the sum of the AGV width and the safety distance; for turning segments, a fan-shaped envelope or a directional bounding box union model is used, with the envelope boundary dynamically enlarged by 10% to 20% based on the turning radius; for continuous turning paths, a spline curve fitting envelope model is used. The deceleration and braking zones in the safety envelope are transformed into speed and braking distance factors in the dynamic release distance calculation, allowing the release distance to be dynamically adjusted with the real-time speed of the AGV.

[0053] Simultaneously, the system constructs a parking area plane for the node. This parking area is centered on the node's coordinates and bounded by the maximum outer dimension of the fourth vehicle model. The shape and orientation of the parking area are defined by combining the node's waypoint type and orientation angle. The system projects the convex polygon model of the fourth vehicle model onto this parking area plane, using the coincidence of the fourth vehicle model's reference point and the node's coordinates as a reference, maintaining the convex polygon's posture and the node's orientation angle consistent.

[0054] After completing the dual projection, the system performs convex polygon intersection detection. Using the separating axis theorem, the edge normal vectors of the two convex polygons are extracted as separating axes. The two convex polygons are then projected onto each separating axis sequentially, and the overlapping of the projection intervals is detected. If the projection intervals do not overlap on any separating axis, the two convex polygons are determined to have no intersecting region; if the projection intervals overlap on all separating axes, the two convex polygons are determined to have an intersecting region. When an intersecting region is detected, the system determines that the road segment node combination has a locking relationship with the current second vehicle type combination, indicating that when the third vehicle type AGV travels on this road segment and the fourth vehicle type AGV stops at this node, there is a risk of overlapping spatial occupancy between the two.

[0055] Next, for road segment node combinations with a lock-and-control relationship and the second vehicle type combination, the system further calculates the dynamic starting point release distance. The system extracts the geometric feature parameters and motion state parameters of the third vehicle type from the vehicle type base dataset. The geometric feature parameters include the maximum outer edge dimension of the third vehicle type, and the motion state parameters include the maximum driving speed and braking time of the third vehicle type. Simultaneously, the system extracts the waypoint array and average curvature of the road segment from the map base dataset. Based on the geometric features and motion state of the third vehicle type, combined with the waypoint array of the road segment, the system locates the collision-related waypoint intervals within the road segment that collide with the nodes. The localization process first determines the spatial projection range of the node's stopping area, then scans along the waypoint sequence of the road segment, calculating the shortest spatial distance between each waypoint and the node's stopping area. When this distance is less than the sum of the third vehicle type's maximum outer edge dimension and the safety distance, the waypoint is marked as a collision-related waypoint. The interval formed by all consecutively marked collision-related waypoints is determined as the collision-related waypoint interval. The system extracts local curvature parameters from the collision-related waypoint interval, denoted as the second local curvature parameter. This parameter is obtained by performing differential geometric calculations on the waypoint sequence within the interval, and can accurately reflect the road deformation characteristics at the collision location.

[0056] The system inputs the geometric feature parameters, motion state parameters, and second local curvature parameters of the third vehicle model into the safety distance calculation function. The expression of the safety distance calculation function is as follows: release_distance2=base_distance2+speed_factor2+curvature_factor2+model_factor2 Among them, base_distance2 is the basic safety distance, calculated by multiplying the maximum outer edge dimension of the third vehicle type by a safety factor; speed_factor2 is the speed factor, calculated by multiplying the maximum speed of the third vehicle type by the sum of braking time and reaction time; curvature_factor2 is the curvature factor, calculated by multiplying the second local curvature parameter by a curvature weight coefficient; and model_factor2 is the vehicle type factor, calculated by multiplying the vehicle type-specific parameters of the third vehicle type by a vehicle type weight coefficient. After substituting each parameter into the function, the system calculates and generates the second dynamic starting point release distance release_distance2. This release distance represents the safety release space that the third vehicle type AGV needs to reserve when departing from the starting point of this road segment to ensure that it does not collide with the fourth vehicle type AGV parked at this node.

[0057] Next, the system stores the currently processed road segment node combination, the second vehicle type combination, and the calculated second dynamic starting point release distance as a locking record in the locking relationship and release distance table. The road segment node combination uses a combination of road segment identifier and node identifier, and the second vehicle type combination uses a combination of third vehicle type identifier and fourth vehicle type identifier. The release distance is expressed as a floating-point number with two decimal places of precision. The system iterates through all road segment node combinations and all second vehicle type combinations, repeating the above detection and calculation process until all combinations have been processed, forming a complete road segment-node locking relationship and release distance table.

[0058] In this embodiment, based on the vehicle model base dataset and the map base dataset, the locking relationship detection is performed between nodes, between road segments, and between road segments, and the dynamic release distance of the corresponding vehicle model combination is calculated. The generation of the locking relationship and release distance table also includes: Based on the map's basic dataset, a list of all road segment pairs is generated, with each road segment pair in the list containing a first road segment and a second road segment; Based on the vehicle model dataset and the road segment pair list, extract the fifth vehicle model list that is allowed to pass through the first road segment and the sixth vehicle model list that is allowed to pass through the second road segment, and generate all third vehicle model combinations; Based on the maximum outer edge two-dimensional convex polygon model in the vehicle model basic dataset, for each third vehicle model combination, the convex polygon of the fifth vehicle model is projected onto the driving plane defined by the waypoint array of the first road segment, and the convex polygon of the sixth vehicle model is projected onto the driving plane defined by the waypoint array of the second road segment, and convex polygon intersection detection is performed. If there are intersecting areas, it is determined that the road segment has a locking relationship with the third vehicle type combination; For road segment pairs with locking relationships and the third vehicle type combination, based on the geometric feature parameters and motion state parameters of the fifth vehicle type in the vehicle type basic dataset, and the waypoint array and average curvature of the first road segment in the map basic dataset, the collision-related waypoint intervals that collide with the second road segment in the first road segment are located, and the local curvature parameters of the collision-related waypoint intervals are extracted to obtain the third local curvature parameters. The geometric feature parameters, motion state parameters, and third local curvature parameters of the fifth vehicle model are input into the safety distance calculation function to generate the third dynamic starting point release distance; Store the correspondence between road segment pairs, third vehicle type combinations, and third dynamic starting point release distance in the locking relationship and release distance table.

[0059] This embodiment first generates a complete list of road segments, achieving full coverage of lock control detection between road segments and ensuring no blind spots. A third vehicle combination is generated based on the actual permitted vehicle types for each road segment, aligning with the operating conditions of industrial AGVs and eliminating meaningless vehicle matching from the source, reducing invalid calculations. During collision detection, convex polygons are projected onto the corresponding road segment's driving plane, matching the actual space occupancy scenario of the AGV, making the lock control relationship determination more realistic and the results more accurate. Before calculating the release distance, the collision-related road point interval is located and local curvature is extracted to replace the average curvature of the entire road segment. Combined with vehicle-specific parameters, the generated dynamic release distance has stronger adaptability and avoids safety redundancy at fixed distances. Simultaneously, relevant data is uniformly stored in the lock control relationship and release distance table, forming a standardized storage system across the entire domain with data between nodes and road segment nodes, facilitating subsequent data retrieval, integration, and index construction.

[0060] Specifically, after completing the detection of locking relationships between nodes and road segments, the system further executes the locking relationship detection process between road segments. Based on the map's basic dataset, the system traverses all road segments, combining each road segment with all other road segments to generate a list of road segment pairs. Each road segment pair in this list contains a first road segment and a second road segment, and the first and second road segments can be the same or different. For self-locking detection of the same road segment, the system determines whether there is a risk of AGVs following or meeting other vehicles traveling in the same direction on the same road segment based on the length of the road segment and the type of vehicle traveling. The system adopts a directed graph traversal strategy to ensure that each pair of road segments generates two independent records according to the direction of travel: one from the first road segment to the second road segment and the other from the second road segment to the first road segment, to adapt to the differentiated locking requirements in two-way traffic scenarios.

[0061] The system processes each road segment pair sequentially based on a vehicle model dataset and a list of road segment pairs. For the currently processed road segment pair, the system extracts the list of vehicle models allowed to travel on the first road segment, designated as the fifth vehicle model list. This list is derived from the allowed vehicle model field recorded in the road segment attribute set, representing all AGV vehicle models capable of traveling on that road segment. Simultaneously, the system extracts the list of vehicle models allowed to travel on the second road segment, designated as the sixth vehicle model list, also derived from the road segment attribute set. The system performs a Cartesian product operation on the fifth and sixth vehicle model lists to generate all possible third vehicle model combinations. Each combination contains one vehicle model that travels on the first road segment and one vehicle model that travels on the second road segment.

[0062] For each generated third vehicle combination, such as Figure 6 As shown, the system constructs a trajectory envelope dataset for the corresponding vehicle model on the first road segment A and the second road segment B to complete the trajectory envelope projection.

[0063] After completing the dual projection, the system performs convex polygon intersection detection. The detection uses the separating axis theorem, extracting all edge normal vectors of the convex polygons projected by the fifth and sixth vehicle models as candidate separating axes. The system sequentially projects the two convex polygons onto each candidate separating axis, calculates the minimum and maximum values ​​of their respective projection intervals, and determines whether the two intervals overlap. If the projection intervals on any separating axis do not overlap, the two convex polygons are determined to have no intersecting region, and the current road segment pair does not have a locking relationship with the third vehicle model combination; the system skips subsequent calculations. If the projection intervals on all separating axes overlap, the two convex polygons are determined to have intersecting regions, indicating that when the AGV of the fifth vehicle model travels in the first road segment and the AGV of the sixth vehicle model travels in the second road segment, there is a risk of overlapping spatial occupancy between the two. The system determines that the road segment pair has a locking relationship with the third vehicle model combination.

[0064] For road segments with a lock-and-control relationship and the third vehicle type, the system further calculates the dynamic start-point release distance. The system extracts the geometric feature parameters and motion state parameters of the fifth vehicle type from the vehicle type dataset. The geometric feature parameters include the maximum outer dimensions of the fifth vehicle type, i.e., its length and width, while the motion state parameters include its maximum speed, braking time, and reaction time. Simultaneously, the system extracts the waypoint array and average curvature of the first road segment from the map dataset. Based on the geometric features and motion state of the fifth vehicle type, combined with the waypoint array of the first road segment, the system locates the collision-related waypoint interval between the first and second road segments. The localization process first obtains the intersection region of the projected convex polygons of the fifth and sixth vehicle types, maps this intersection region back to the centerline of the first road segment, and determines the start and end positions of the path corresponding to the intersection region. The system scans along the waypoint sequence of the first road segment, marking all consecutive waypoints within this start-and-end position interval as collision-related waypoints; the interval formed by these waypoints is the collision-related waypoint interval. The system extracts local curvature parameters from the collision-associated waypoint interval, denoted as the third local curvature parameter. The extraction process is achieved by performing differential geometric calculations on the waypoint sequence within the interval. Specifically, this involves calculating the rate of change of the orientation angle between adjacent waypoints, and then averaging the curvature values ​​at all waypoints within the interval to obtain a local curvature value representing the overall bending degree of the collision segment.

[0065] The system inputs the geometric feature parameters, motion state parameters, and third local curvature parameters of the fifth vehicle model into the safety distance calculation function. This safety distance calculation function employs a multi-parameter coupled form, and its expression is as follows: release_distance3=base_distance3+speed_factor3+curvature_factor3+model_factor3 Among them, base_distance3 is the basic safety distance, calculated by multiplying the maximum outer edge dimension of the fifth vehicle model by a safety factor. The safety factor is dynamically adjusted according to the spatial topology of the road segment pairs, taking a larger value for intersecting road segments and a smaller value for parallel adjacent road segments; speed_factor3 is the speed factor, calculated by multiplying the maximum speed of the fifth vehicle model by the sum of braking time and reaction time, reflecting the additional safety space required by the vehicle in emergency braking situations; curvature_factor3 is the curvature factor, calculated by multiplying the third local curvature parameter by the curvature weight coefficient. The curvature weight coefficient is related to the minimum turning radius of the fifth vehicle model, and the curvature factor increases significantly when the collision-related road point interval is located on a curve; model_factor3 is the vehicle model factor, calculated by multiplying the vehicle model-specific parameters of the fifth vehicle model by the vehicle model weight coefficient. The vehicle model-specific parameters comprehensively consider factors such as the vehicle's drive mode, acceleration performance, and scanner layout. After substituting the parameters into the function, the system calculates and generates the third dynamic starting point release distance, release_distance3. This release distance represents the safety release space that the fifth type of AGV needs to reserve when it departs from the starting point of the first road segment, so as to ensure that it does not collide with the sixth type of AGV traveling on the second road segment.

[0066] The system stores the currently processed road segment pair, the third vehicle type combination, and the calculated third dynamic starting point release distance as a locking record in the locking relationship and release distance table. Road segment pairs use a combination of the first and second road segment identifiers, and the third vehicle type combination uses a combination of the fifth and sixth vehicle type identifiers. Release distances are expressed as floating-point numbers with two decimal places of precision. The system iterates through all road segment pairs and all third vehicle type combinations, repeating the above detection and calculation process until all combinations have been processed, forming a complete road segment-road segment locking relationship and release distance table.

[0067] In this embodiment, the JSON routine for the locking relationship and release distance table is described as follows: It is compatible with 2 vehicle types (T1: heavy-duty AGV, T2: light-duty AGV), 5 lanes (L1-L5), and 6 nodes (N1-N6). The map information, locking relationship (lane-node, lane-lane) and dynamic release distance are all in line with the actual AGV traffic management scenario. The values ​​are reasonable values ​​in industrial scenarios and can be directly used for examples or system testing.

[0068] JSON Format Description: The overall structure is a key-value pair structure, with three core modules: basic map information, lane-node locking relationships, and lane-to-lane locking relationships. The meaning and hierarchical structure of each field are as follows, strictly matching the core design of "map information + locking relationships + dynamic release distance": mapCode: String type, a unique map identifier used to associate the target road network map in the AGV traffic management system, ensuring that the lock control relationship table matches the actual operating location. Figure 1 One-to-one correspondence to avoid mismatches across maps.

[0069] version: numeric type, the version number of the lock control relationship table, used for version iteration management. When the road network structure, vehicle parameters, or release distance rules are adjusted, different versions of the lock control table can be distinguished by the version number, which is convenient for traceability and updates.

[0070] mutexLaneNode: An array type that stores the lane-node locking relationship. The hierarchical structure strictly follows "Lane L1 array → Vehicle type T1 array → Node N1 array → Vehicle type T2 array". Specific details are as follows: laneL1: String type, main lane identifier (e.g., L1-L5), representing the core lane that needs to be locked.

[0071] carTypeT1: An array that stores all vehicle types (T1 and T2 in this example) that may be traveling on the main lane (laneL1). Each element contains the vehicle type (type) and a list of nodes (nodeN1) that need to be locked for that vehicle type.

[0072] nodeN1: An array that stores the nodes (e.g., N1-N6) that require locking when the corresponding vehicle type (carTypeT1) is driving in the main lane (laneL1). Each element contains a node identifier (node) and a list of vehicle types (carTypeT2) that have locking conflicts with that node.

[0073] carTypeT2: An array that stores all vehicle types that have lock conflicts with the main lane (laneL1), the corresponding vehicle type (carTypeT1), and the corresponding node (nodeN1). Each element contains the vehicle type (type) and the corresponding dynamic release distance (releaseDistance, unit: m).

[0074] `mutexLaneLane`: An array type that stores lane-lane locking relationships. The hierarchical structure strictly follows the pattern "Lane L1 array → Vehicle type T1 array → Lane L2 array → Vehicle type T2 array". Specific details are as follows: laneL1: String type, main lane identifier (e.g., L1-L5), representing the core lane that needs to be locked.

[0075] carTypeT1: An array that stores all vehicle types that may travel on the main lane (laneL1) (T1 and T2 in this example). Each element contains the vehicle type and a list of lanes (laneL2) where there is a lock conflict when that vehicle type is traveling.

[0076] laneL2: An array that stores all lanes that have lock conflicts with the main lane (laneL1) and the corresponding vehicle type (carTypeT1). Each element contains the lane identifier (lane) and a list of vehicle types (carTypeT2) that have lock conflicts in that lane.

[0077] carTypeT2: An array that stores all vehicle types that have locking conflicts with the main lane (laneL1), the corresponding vehicle type (carTypeT1), and the corresponding conflicting lane (laneL2). Each element contains the vehicle type (type) and the corresponding dynamic release distance (releaseDistance, unit: m).

[0078] It should be noted that the first, second, third, fourth, fifth, and sixth vehicle models in the above embodiments can be the same vehicle model or different vehicle models, and the specific vehicle model is set according to the actual situation.

[0079] In some embodiments, the method further includes: extracting the main road segment identifier, main vehicle type identifier, and corresponding static and dynamic starting point release distance of the target road segment based on the locking relationship and release distance table; and extracting the current number of AGVs and the location information of each AGV on the target road segment and its associated locking road segments based on the AGV distribution data obtained in real time by the RCS traffic management module, and generating real-time traffic density parameters of the target road segment. Based on real-time traffic density parameters, the design capacity and length of the target road segment are extracted, the occupancy rate of the target road segment in the current time window is calculated, and the real-time occupancy status of all associated road segments and nodes that have a locking relationship with the target road segment is extracted to generate the congestion coefficient of the target road segment. Based on the congestion coefficient and the static and dynamic starting point release distance, the congestion coefficient is used as a multiplicative factor and multiplied with the static and dynamic starting point release distance to generate the adjusted dynamic starting point release distance. Replace the static dynamic start-up release distances of the corresponding target road segment and main vehicle type in the locking relationship and release distance table with the adjusted dynamic start-up release distances, and generate an updated locking relationship and release distance table.

[0080] Specifically, after completing the pre-calculation of the locking relationship and release distance table, the system enters the real-time dynamic adjustment phase. The RCS traffic management module first extracts the main road segment identifier, main vehicle type identifier, and corresponding static dynamic starting release distance of the target road segment from the locking relationship and release distance table. This static release distance is the basic safety threshold calculated based on vehicle type parameters and road segment geometric features during the pre-calculation phase. Simultaneously, the RCS traffic management module obtains the distribution data of all AGVs currently on the network through the real-time positioning system, including the vehicle type identifier, current location coordinates, current road segment, and driving direction of each AGV. Using the main road segment identifier of the target road segment as a benchmark, the system extracts the number of all AGVs located on that target road segment and the specific location of each AGV. Then, based on all locking relationships recorded in the locking relationship and release distance table for that target road segment, it extracts all associated road segments and associated nodes that have locking relationships with that target road segment, and obtains the current number of AGVs and the location information of each AGV on these associated road segments and associated nodes. The system aggregates the above data, uses the target road segment as the statistical unit, calculates the number of AGVs per unit length of road segment, and generates real-time traffic density parameters for the target road segment.

[0081] The system further calculates the congestion coefficient based on real-time traffic density parameters. First, it extracts the design capacity and length of the target road segment from the map dataset. The design capacity represents the maximum number of AGVs that can safely pass through the road segment per unit time, determined by the road segment length, lane width, vehicle size, and safe following distance. The system calculates the road segment occupancy rate based on the ratio of the total number of AGVs on the target road segment to the design capacity within the current time window. The formula is: occupancy rate equals the current number of AGVs divided by the design capacity. Simultaneously, the system traverses all associated road segments and nodes with locking relationships to the target road segment, as recorded in the locking relationship and release distance table. It queries the real-time occupancy status of each associated object, including the current number of AGVs on the associated road segment, whether there are any AGVs parked at the associated nodes, and their parking duration. The system weightedly integrates the road segment occupancy rate with the real-time occupancy status of each associated object to generate the congestion coefficient of the target road segment. The congestion coefficient ranges from 1 to 1. When the target road segment and its associated objects are in a low-occupancy state, the congestion coefficient approaches 1; when the target road segment or its associated objects are in a high-occupancy or congested state, the congestion coefficient increases accordingly.

[0082] The system multiplies the calculated congestion coefficient with the static / dynamic release distance extracted from the locking relationship and release distance table to generate an adjusted dynamic release distance. This multiplication is performed using the formula: adjusted_release_distance = congestion_coefficient × static_release_distance, where static_release_distance is the base release distance generated during the pre-calculation phase, and adjusted_release_distance is the dynamic release distance corrected for real-time traffic conditions. When the congestion coefficient (congestion_coefficient) is greater than 1, the adjusted release distance is larger than the static value, meaning a larger safety margin needs to be reserved during peak traffic periods to cope with the chain reaction caused by congestion. When the congestion coefficient is equal to 1, the adjusted release distance remains consistent with the static value, indicating that the current traffic condition is under the ideal conditions assumed in the pre-calculation.

[0083] The system replaces the static dynamic start-up release distance of the corresponding target road segment and main vehicle type identifier in the lock control relationship and release distance table with the adjusted dynamic start-up release distance. The replacement operation uses the main road segment identifier and main vehicle type identifier as a joint key to locate the target record in the lock control relationship and release distance table, updating the release distance field of that record to the adjusted value. For multiple lock control records corresponding to the same target road segment and main vehicle type combination (e.g., multiple records for different lock control objects), the system calculates the congestion coefficient for each lock control object and updates it separately. Because the real-time occupancy status on different lock control objects may differ, the required adjustment range for each lock control relationship will vary. After updating all relevant records, the system generates an updated lock control relationship and release distance table. This table retains the entire lock control relationship structure from the pre-calculation stage, replacing the release distance field with a dynamic value that incorporates the real-time traffic status. When the RCS traffic management module subsequently receives an AGV passage request, it directly queries and makes decisions based on the updated lock control relationship and release distance table, realizing a collaborative mechanism between static pre-calculation and real-time dynamic adjustment.

[0084] In some embodiments, based on the vehicle model base dataset and the map base dataset, the locking relationship detection is performed between nodes, between road segments, and between road segments, and the dynamic release distance for the corresponding vehicle model combination is calculated. After generating the locking relationship and release distance table, the process includes: Based on the lock control relationship and release distance table, extract the main road segment identifier, lock control object identifier, main vehicle type identifier, and lock control vehicle type identifier from each lock control record, and combine the main road segment identifier, lock control object identifier, main vehicle type identifier, and lock control vehicle type identifier into a unique key; Traverse all locking records in the locking relationship and release distance table. When two or more locking records have the same unique key, keep only one locking record and delete the other duplicate records to generate a deduplicated locking relationship and release distance table. Based on the deduplicated locking relationship and release distance table, the main road segment identifier is extracted from each locking record. Using the main road segment identifier as the first-level index key, locking records with the same main road segment identifier are grouped into the same index group to generate a main road segment dimension index table. Based on the main road segment dimension index table, the lock control object type in each lock control record is extracted. Using the lock control object type as the second-level index key, lock control records with the same lock control object type are further classified in each index group of the main road segment dimension index table to generate a lock control object type dimension index table. Based on the lock control object type dimension index table, the main vehicle model identifier is extracted from each lock control record. Using the main vehicle model identifier as the third-level index key, lock control records with the same main vehicle model identifier are further classified within each index group of the lock control object type dimension index table to generate a multi-dimensional index table.

[0085] This embodiment uses a multi-field combined unique key to accurately identify duplicate locking records, eliminate redundant data, and ensure the uniqueness and accuracy of the locking relationship and release distance tables, reducing invalid calculations in subsequent data retrieval. A three-level hierarchical index is employed, classifying data sequentially by main road segment identifier, locking object type, and main vehicle type identifier. The index hierarchy aligns with the actual query logic of AGV traffic management, achieving precise grouping of locking records and significantly improving data retrieval and retrieval efficiency. Simultaneously, the hierarchical index significantly enhances the structuring of the entire locking data, facilitating subsequent data maintenance, updates, and management. It unifies the locking data storage system between nodes, between road segment nodes, and between road segments, adapting to large-scale applications with multiple vehicle types and paths, and providing efficient data support for rapid decision-making by the RCS traffic management module.

[0086] Specifically, after detecting the three types of locking relationships—node-to-node, segment-to-node, and segment-to-segment—and calculating the dynamic starting point release distance, the system generates a complete table of locking relationships and release distances. This table records all combinations with locking relationships and their corresponding release distances. However, since duplicate records may occur during the traversal process due to parallel computation or multiple iterations, the system initiates a data optimization process to deduplicate the locking relationship and release distance table and construct a multi-dimensional index.

[0087] The system then first iterates through each locking record in the locking relationship and release distance table, extracting four fields from each record: main road segment identifier, locking object identifier, main vehicle type identifier, and locking vehicle type identifier. The main road segment identifier is the controlled road segment number in the current locking relationship; the locking object identifier is the target object number that has a locking relationship with this road segment (this object may be a road segment identifier or a node identifier); the main vehicle type identifier is the vehicle type number that travels on the main road segment; and the locking vehicle type identifier is the vehicle type number that travels on the locking object or is stationed at the locking node. The system concatenates these four fields into a string in a predetermined order, forming the unique key for that locking record. The construction method of the unique key ensures that two records will only generate the same unique key if the main road segment, locking object, main vehicle type, and locking vehicle type are completely identical.

[0088] The system then uses the unique key as the criterion to traverse all records in the lock control relationship and release distance table, establishing a mapping structure with the unique key as the key and the lock control record as the value. When multiple records with the same unique key are encountered, the system retains only one record and deletes the rest. The selection of retained records follows preset rules, prioritizing the record with the highest release distance calculation accuracy or the record with the earliest initial generation time, ensuring the accuracy and traceability of the deduplicated data. After the traversal is complete, the system generates a deduplicated lock control relationship and release distance table. Each record in this table has a unique four-dimensional combination identifier, completely eliminating data redundancy and potential conflicts caused by duplicate records.

[0089] After deduplication, the system constructs a multi-dimensional index for the deduplicated lock control relationship and release distance table. First, the system extracts the main road segment identifier for each lock control record in the table, using this identifier as the first-level index key. The system iterates through all records, grouping lock control records with the same main road segment identifier into the same index group, forming a main road segment dimension index table. This index table uses a hash mapping structure, with the main road segment identifier as the key and a list or set of pointers to all lock control records belonging to that main road segment as the value. Through this index table, the system can locate all lock control relationships involved in any road segment in constant time complexity, without needing to traverse the entire table for filtering.

[0090] Next, based on the main road segment dimension index table, the system further constructs a second-level index. For each index group in the main road segment dimension index table, the system traverses all lock control records within the group and extracts the lock control object type for each record. The lock control object type is determined by the encoding rules of the lock control object identifier. If the lock control object identifier belongs to the encoding range of the road segment identifier, it is determined to be a road segment lock control type; if it belongs to the encoding range of the node identifier, it is determined to be a node lock control type. Using the lock control object type as the second-level index key, within each main road segment group, lock control records with the same lock control object type are further grouped into the same subgroup, generating a lock control object type dimension index table. This index table forms a two-level nested mapping structure from main road segment identifiers to lock control object types, enabling the system to quickly locate the corresponding subgroup when querying a specific type of lock control relationship under a certain main road segment.

[0091] Next, the system constructs a third-level index based on the lock control object type dimension index table. For each second-level subgroup in the lock control object type dimension index table, the system traverses all lock control records within the subgroup and extracts the primary vehicle model identifier for each record. Using the primary vehicle model identifier as the third-level index key, the system further groups lock control records with the same primary vehicle model identifier into the same subgroup within each lock control object type subgroup, generating a multi-dimensional index table. This multi-dimensional index table forms a three-level nested mapping structure from primary road segment identifier to lock control object type to primary vehicle model identifier, with each level using hash mapping to achieve constant-time positioning.

[0092] The resulting multi-dimensional index table is stored in association with the deduplicated locking relationship and release distance table. Each node in the index table stores a pointer or reference to the original locking record, rather than copying the data, ensuring data consistency and avoiding storage redundancy. This multi-dimensional index table provides an efficient query interface for the RCS traffic management module. When the traffic management module receives a dispatch instruction, it can quickly locate the corresponding locking relationship record based on three dimensions: current road segment identifier, locking object type, and current vehicle type identifier, using the three-level index to directly read the dynamic start-up release distance without needing to perform a linear traversal search in the original table.

[0093] When the traffic management system receives an AGV passage request, it queries the locking relationship and release distance table based on the request to obtain the locking object and dynamic release distance threshold of the target road segment; then it controls the AGV to pass through the target road segment and monitors the distance between the AGV's current position and the locking object in real time; when the distance between the AGV's current position and the locking object reaches the dynamic release distance threshold, it releases the occupancy lock on the locking object.

[0094] Specifically, when the traffic management system receives a passage request from an AGV, it first parses the target road segment identifier and the AGV's vehicle type identifier from the request. Using these two identifiers as a joint key, the system queries the loaded lock-control relationship and release distance table to obtain a list of all lock-control objects for that vehicle type on the target road segment. Each lock-control object includes the object type, object identifier, and corresponding dynamic start-point release distance threshold. The traffic management system issues a passage instruction to the requesting AGV to enter the target road segment, and the AGV begins its journey from the segment's starting point. Simultaneously, the system starts a real-time monitoring thread, continuously receiving the current position coordinates reported by the AGV's onboard positioning device and calculating the spatial Euclidean distance between the AGV and each lock-control object. For lock-control objects that are road segments, the shortest distance from the AGV's current position to the segment's centerline is calculated; for lock-control objects that are nodes, the direct distance to the node's coordinates is calculated. The system compares the real-time calculated distances with the corresponding dynamic release distance thresholds frame by frame. When the real-time distance to a locked object first exceeds or equals its release distance threshold, the AGV is determined to have safely passed the collision risk zone of that locked object. The system immediately releases the locked object from the current AGV's occupancy and marks the locked object as idle. Once all locked objects have been released, the system reports a complete release signal for the target road segment to the traffic management system, allowing subsequent AGVs in the queue to request road segment resources.

[0095] In one specific embodiment, please refer to Figure 7 and Figure 8 When performing the interlocking relationship detection between road segments, taking the interlocking scenario of road segment A and road segment B as an example, after generating a list of road segment pairs, for the combination of road segment A and road segment B, the lists of vehicle types T1 and T2 allowed to pass through road segment A and road segment B are extracted, generating different vehicle type combinations such as T1-T1, T1-T2, and T2-T2. For the T1-T1 combination, the convex polygon of vehicle type T1 is projected onto the driving plane of road segment A, and simultaneously projected onto the driving plane of road segment B. Convex polygon intersection detection is performed. Because the two vehicles are of the same type and there is a risk of spatial overlap in their driving paths, it is determined that this road segment pair has an interlocking relationship with the T1-T1 combination. For the T1-T2 combination, the convex polygon of vehicle type T1 is projected onto the driving plane of road segment A, and the convex polygon of vehicle type T2 is projected onto the driving plane of road segment B. Similarly, spatial overlap risk is detected, and an interlocking relationship is determined to exist.

[0096] For road segment pairs and vehicle type combinations with interlocking relationships, the parameters of the corresponding vehicle type are extracted to calculate the dynamic release distance. For the combination of T1 in road segment A and T1 in road segment B, a shorter starting release distance is calculated by combining the geometric and motion parameters of T1 with the local curvature of road segment A, adapting to the space occupation and braking requirements of the same vehicle type, ensuring that the two vehicles maintain a preset safe distance during travel. For the combination of T1 in road segment A and T2 in road segment B, because T2 has larger geometric parameters such as vehicle length and width, its braking distance is longer, and the calculated starting release distance is longer, adapting to the space occupation and braking requirements of T2, ensuring that the safe distance between the two vehicles is not less than a preset threshold. Subsequently, the road segment pairs, vehicle type combinations, and corresponding starting release distances are stored in the interlocking relationship and release distance table, clearly marking the differences in release distances corresponding to different vehicle type combinations, forming independent interlocking rules for road segment vehicle type combinations.

[0097] When a request for vehicle type T2 to enter road segment B is received, the system queries the locking relationship and release distance table to find that the interlocked road segment for road segment B is road segment A, the corresponding vehicle type combinations are road segment A-T1 and road segment B-T2, and the corresponding starting release distance. The system monitors the travel distance of vehicle type T1 on road segment A in real time. When the travel distance of T1 on road segment A (distance from the starting point) is greater than the release distance of the corresponding vehicle type combination on road segment A, it determines that the safety distance has been met, releases the starting lock of road segment B, and allows T2 to enter road segment B. This release logic only applies to the combinations of road segment A-T1 and road segment B-T2 and does not affect the interlocking relationship of other vehicle type combinations. For example, the release rule for the T1-T1 combination still needs to wait for T1 on road segment A to travel beyond its corresponding shorter release distance before releasing the lock on road segment B for T1, ensuring that the release rules for different vehicle types take effect independently and avoiding misunderstandings of locking across combinations.

[0098] The following comparison is based on mainstream traffic management technologies using path locking control of three generations of AGVs. Focusing on the core innovation of this embodiment, a horizontal benchmarking is conducted from six core dimensions: collision detection mode, release strategy, computational complexity, multi-vehicle compatibility, deadlock risk, and safety performance. This comprehensively highlights the technological progress and innovative value of this solution, as shown in Table 1. Table 1

[0099] As can be seen from the above, this embodiment completely eliminates the risk of response lag by changing from post-event response to pre-event prediction. The system's computational load is reduced from O(n²) real-time calculation to O(1) table lookup, and the core load is reduced by more than 90%. From logical congestion to precise release, the passage efficiency is improved by more than 20%. From passive handling to active pre-control, the risk of deadlock is eliminated from the root.

[0100] Please see Figure 9 This embodiment provides a multi-model industrial AGV path locking pre-calculation device, including: The data acquisition module is used to collect the geometric and operational parameters of AGVs of various models, construct a two-dimensional convex polygon model of the maximum outer edge of the planar projection of each model, and generate a basic dataset of the model. The loading module is used to load AGV operation road network data, extract node and road segment information, construct the road network topology and node and road segment association index, and generate the basic map dataset. The execution module is used to perform node-to-node, road segment-to-node, and road segment-to-road segment locking relationship detection based on the vehicle model base dataset and the map base dataset, calculate the dynamic release distance of the corresponding vehicle model combination, and generate a locking relationship and release distance table.

[0101] Furthermore, the multi-model industrial AGV path locking pre-calculation device also includes an output module, which outputs the locking relationship and release distance table to the traffic management system when the traffic management system needs to read the locking relationship and release distance table, so as to query the vehicle locking relationship and release distance table on the relevant road segment nodes.

[0102] Furthermore, the acquisition module includes: The reading unit is used to read the vehicle configuration file of multi-vehicle AGVs, extract the vehicle ID, geometric dimension polygon vertex coordinates, vehicle reference point coordinates, minimum turning radius data, and generate a set of vehicle geometric parameters; The conversion unit is used to convert the coordinates of the polygon vertices of each vehicle model to a global coordinate system with the coordinates of the vehicle reference point as the origin, based on the vehicle model geometric parameter set, and generate a standardized vehicle model vertex coordinate set. The sequence extraction unit is used to perform convex hull algorithm processing on the standardized vehicle vertex coordinate set, extract the outer vertex sequence, and generate the maximum outer edge two-dimensional convex polygon model of each vehicle. The size calculation unit is used to calculate the radius of the outer edge of the vehicle body's enclosing circle, the minimum outer edge size, and the safe collision radius parameters based on the maximum outer edge two-dimensional convex polygon model, and to generate a set of vehicle body feature parameters. The association and binding unit is used to associate and bind the vehicle geometric parameter set, the vehicle feature parameter set, and the maximum outer edge two-dimensional convex polygon model to generate a vehicle basic dataset.

[0103] Furthermore, the loading module includes: The data extraction unit is used to load AGV operation road network data, read the waypoint set and lane set in the AGV operation road network data, extract the node identifier, node coordinates, waypoint type and orientation angle data of each node in the waypoint set, and extract the road segment identifier, lane type, starting waypoint identifier, ending waypoint identifier, control point sequence, lane length and driving direction data of each road segment in the lane set, and generate the original road network information set. The node data parsing unit is used to parse the node identifier, node coordinates, waypoint type, and orientation angle data of each node based on the original road network information set, and generate a node attribute set. The road segment data parsing unit is used to parse the road segment identifier, lane type, starting road point identifier, ending road point identifier, control point sequence, lane length, and driving direction data of each road segment based on the original road network information set, and generate a road segment attribute set. The relationship establishment unit is used to establish a directed connection relationship between nodes and road segments based on the node coordinates in the node attribute set and the starting waypoint identifier and ending waypoint identifier in the road segment attribute set. The identification association unit is used to associate the starting waypoint identifier and the ending waypoint identifier with the corresponding node coordinates based on the directed connection relationship, thereby generating a road network topology. The index generation unit is used to associate a list of all road segment identifiers with the node as the starting point or ending point for each node based on the road network topology, and associate the coordinates of the starting node and the ending node for each road segment to generate a node-road segment association index. The data integration unit is used to integrate and serialize data based on the node-road segment association index, the road network topology, the node attribute set, and the road segment attribute set to generate a basic map dataset.

[0104] Furthermore, the execution module includes: The node pair traversal unit is used to traverse all node pairs based on the vehicle model base dataset and the map base dataset. For the first node and the second node in each node pair, it obtains the first vehicle model list allowed to park at the first node and the second vehicle model list allowed to park at the second node, respectively, and generates all combinations of first vehicle models. The first projection unit is used to project the convex polygon of the first vehicle model onto the parking area plane of the first node based on the maximum outer edge two-dimensional convex polygon model for each first vehicle model combination, and to project the convex polygon of the second vehicle model onto the parking area plane of the second node, and to perform convex polygon intersection detection. The first determination unit is used to determine that if there is an intersecting region, the node pair has a locking relationship with the first vehicle model combination. The positioning unit is used to locate the collision-related path segment between the first node and the second node for node pairs with a locking relationship and the first vehicle model combination, based on the geometric feature parameters and motion state parameters of the first vehicle model in the vehicle model basic dataset, and the curvature features and path length of the connected path between the first node and the second node in the map basic dataset, and extract the local curvature parameters of the collision-related path segment to obtain the first local curvature parameter. The first input unit is used to input the geometric feature parameters, motion state parameters and the first local curvature parameters of the first vehicle model into the safety distance calculation function to generate the first dynamic starting point release distance; The storage unit is used to store the correspondence between the node pairs, the first vehicle model combination, and the first dynamic starting point release distance into the locking relationship and release distance table.

[0105] Furthermore, the execution module also includes: The filtering unit is used to filter nodes that are spatially adjacent to the road segment based on the map base dataset and the starting point or ending point of the road segment, and generate a list of road segment node combinations. Each road segment node combination in the list of road segment node combinations contains a road segment and a node. The list extraction unit is used to extract the list of third vehicle types that are allowed to pass through the road segment and the list of fourth vehicle types that are allowed to stop at the node, based on the vehicle type basic dataset and the road segment node combination list, and generate all second vehicle type combinations; The second projection unit is used to project the convex polygon of the third vehicle model onto the driving plane defined by the waypoint array of the road segment for each second vehicle model combination based on the maximum outer edge two-dimensional convex polygon model in the vehicle model basic dataset, and to project the convex polygon of the fourth vehicle model onto the stopping area plane of the node, and to perform convex polygon intersection detection. The second determination unit is used to determine that if there is an intersecting area, the road segment node combination and the second vehicle type combination have a locking relationship. The parameter extraction unit is used to locate the collision-related waypoint intervals in the road segment that collide with the node for the combination of road segment nodes with a locking relationship and the combination of the second vehicle type, based on the geometric feature parameters and motion state parameters of the third vehicle type in the vehicle type basic dataset, and the waypoint array and average curvature of the road segment in the map basic dataset, and extract the local curvature parameters of the collision-related waypoint intervals to obtain the second local curvature parameters. The second input unit is used to input the geometric feature parameters, motion state parameters and the second local curvature parameters of the third vehicle model into the safety distance calculation function to generate the second dynamic starting point release distance; The relationship storage unit is used to store the corresponding relationship between the road segment node combination, the second vehicle type combination, and the second dynamic starting point release distance into the locking relationship and release distance table.

[0106] Furthermore, the execution module also includes: The list generation unit is used to generate a list of all road segment pairs based on the map base dataset, wherein each road segment pair in the list contains a first road segment and a second road segment; The vehicle model list extraction unit is used to extract the fifth vehicle model list that is allowed to pass through the first road segment and the sixth vehicle model list that is allowed to pass through the second road segment based on the vehicle model basic dataset and the road segment pair list, and generate all third vehicle model combinations; The third projection unit is used to project the convex polygon of the fifth vehicle model onto the driving plane defined by the waypoint array of the first road segment, and project the convex polygon of the sixth vehicle model onto the driving plane defined by the waypoint array of the second road segment, based on the maximum outer edge two-dimensional convex polygon model in the vehicle model basic dataset, for each third vehicle model combination, and perform convex polygon intersection detection. The third determination unit is used to determine that if there is an intersecting area, the road segment has a locking relationship with the third vehicle type combination. The interval positioning unit is used to locate the collision-related waypoint interval in the first road segment that collides with the second road segment for road segment pairs with locking relationships and third vehicle type combinations, based on the geometric feature parameters and motion state parameters of the fifth vehicle type in the vehicle type basic dataset, and the waypoint array and average curvature of the first road segment in the map basic dataset, and extract the local curvature parameters of the collision-related waypoint interval to obtain the third local curvature parameter; The third input unit is used to input the geometric feature parameters, motion state parameters and the third local curvature parameters of the fifth vehicle model into the safety distance calculation function to generate the third dynamic starting point release distance; The storage unit is used to store the correspondence between the road segment pairs, the third vehicle type combination, and the third dynamic starting point release distance into the locking relationship and release distance table.

[0107] Furthermore, the execution module also includes: The identifier extraction unit is used to extract the main road segment identifier, the lock control object identifier, the main vehicle type identifier, and the lock control vehicle type identifier from each lock control record based on the lock control relationship and release distance table, and combine the main road segment identifier, the lock control object identifier, the main vehicle type identifier, and the lock control vehicle type identifier into a unique key; The duplicate deletion unit is used to traverse all lock records in the lock control relationship and release distance table. When the unique key of two or more lock control records is the same, only one lock control record is retained, and the remaining duplicate records are deleted to generate a deduplicated lock control relationship and release distance table. The first classification unit is used to extract the main road segment identifier from each lock control record based on the deduplicated lock control relationship and release distance table, and use the main road segment identifier as the first-level index key to classify lock control records with the same main road segment identifier into the same index group to generate a main road segment dimension index table. The second classification unit is used to extract the lock control object type in each lock control record based on the main road segment dimension index table, and use the lock control object type as the second-level index key to further classify the lock control records with the same lock control object type in each index group of the main road segment dimension index table to generate a lock control object type dimension index table. The third classification unit is used to extract the main vehicle model identifier from each lock control record based on the lock control object type dimension index table, and use the main vehicle model identifier as the third-level index key to further classify lock control records with the same main vehicle model identifier in each index group of the lock control object type dimension index table to generate a multi-dimensional index table.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0109] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, can implement the methods provided in the above embodiments. The storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0110] The present invention also provides a computer device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the methods provided in the above embodiments. Of course, the computer device may also include various network interfaces, power supplies, and other components.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this invention.

[0112] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusivity.

[0113] The term "comprises" implies that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for pre-calculating path locking control in multi-model industrial AGVs, characterized in that, include: Collect geometric and operational parameters of AGVs of various models, construct a two-dimensional convex polygon model of the maximum outer edge of the planar projection of each model, and generate a basic dataset of the models; Load AGV operation road network data, extract node and road segment information, construct road network topology and node and road segment association index, and generate map basic dataset; Based on the vehicle model dataset and the map dataset, the locking relationship detection is performed between nodes, between road segments, and between road segments, and the dynamic release distance of the corresponding vehicle model combination is calculated to generate a locking relationship and release distance table.

2. The multi-model industrial AGV path locking pre-calculation method according to claim 1, characterized in that, The process involves collecting geometric and operational parameters of AGVs of various models, constructing a two-dimensional convex polygon model with the maximum outer edge of the planar projection for each model, and generating a basic dataset for each model, including: Read the vehicle configuration file of multi-vehicle AGV, extract the vehicle ID, geometric dimension polygon vertex coordinates, vehicle reference point coordinates, minimum turning radius data, and generate vehicle geometric parameter set; Based on the vehicle model geometric parameter set, the vertex coordinates of the polygons representing the geometric dimensions of each vehicle model are transformed to a global coordinate system with the vehicle reference point coordinates as the origin, thereby generating a standardized vehicle model vertex coordinate set. The convex hull algorithm is applied to the standardized vehicle vertex coordinate set to extract the outer vertex sequence and generate the maximum outer edge two-dimensional convex polygon model for each vehicle model. Based on the maximum outer edge two-dimensional convex polygon model, calculate the vehicle's outer edge enclosing circle radius, minimum outer edge size, and safe collision radius parameters to generate a vehicle feature parameter set; The vehicle model's geometric parameter set, the vehicle model's feature parameter set, and the maximum outer edge two-dimensional convex polygon model are associated and bound to generate a vehicle model basic dataset.

3. The multi-model industrial AGV path locking pre-calculation method according to claim 1, characterized in that, The process of loading AGV operation network data, extracting node and road segment information, constructing a road network topology and node-road segment association index, and generating a basic map dataset includes: Load AGV operation road network data, read the waypoint set and lane set in the AGV operation road network data, extract the node identifier, node coordinates, waypoint type and orientation angle data of each node in the waypoint set, and extract the road segment identifier, lane type, starting waypoint identifier, ending waypoint identifier, control point sequence, lane length and driving direction data of each road segment in the lane set to generate the original road network information set; Based on the original road network information set, the node identifier, node coordinates, waypoint type, and orientation angle data of each node are parsed to generate a node attribute set; Based on the original road network information set, the road segment identifier, lane type, starting road point identifier, ending road point identifier, control point sequence, lane length, and driving direction data of each road segment are parsed to generate a road segment attribute set; Based on the node coordinates in the node attribute set and the starting waypoint and ending waypoint identifiers in the road segment attribute set, a directed connection relationship between nodes and road segments is established. Based on the directed connection relationship, the starting waypoint identifier and the ending waypoint identifier are associated with the corresponding node coordinates to generate the road network topology. Based on the road network topology, a list of all road segment identifiers that start or end at the node is associated with each node, and the coordinates of the starting node and the ending node are associated with each road segment to generate a node-road segment association index. Based on the node-road segment association index, the road network topology, the node attribute set, and the road segment attribute set, data is integrated and serialized to generate a basic map dataset.

4. The multi-model industrial AGV path locking pre-calculation method according to claim 1, characterized in that, The process of detecting locking relationships between nodes, road segments, and road segments based on the vehicle model dataset and the map dataset, and calculating the dynamic release distance for corresponding vehicle model combinations to generate a locking relationship and release distance table includes: Based on the vehicle model dataset and the map dataset, all node pairs are traversed. For the first node and the second node in each node pair, the first vehicle model list allowed to park at the first node and the second vehicle model list allowed to park at the second node are obtained respectively, and all combinations of first vehicle models are generated. For each of the first vehicle model combinations, based on the maximum outer edge two-dimensional convex polygon model, the convex polygon of the first vehicle model is projected onto the stopping area plane of the first node, and the convex polygon of the second vehicle model is projected onto the stopping area plane of the second node, and convex polygon intersection detection is performed. If there is an intersecting region, it is determined that the node pair has a locking relationship with the first vehicle model combination; For node pairs with locking relationships and the first vehicle model combination, based on the geometric feature parameters and motion state parameters of the first vehicle model in the vehicle model basic dataset, and the curvature features and path length of the connected path between the first node and the second node in the map basic dataset, the collision-related path segment between the first node and the second node is located, and the local curvature parameters of the collision-related path segment are extracted to obtain the first local curvature parameter. The geometric feature parameters, motion state parameters, and first local curvature parameters of the first vehicle model are input into the safety distance calculation function to generate the first dynamic starting point release distance; The correspondence between the node pairs, the first vehicle model combination, and the first dynamic starting point release distance is stored in the locking relationship and release distance table.

5. The multi-model industrial AGV path locking pre-calculation method according to claim 1, characterized in that, The process of detecting locking relationships between nodes, road segments, and road segments based on the vehicle model dataset and the map dataset, calculating the dynamic release distance for corresponding vehicle model combinations, and generating a locking relationship and release distance table also includes: Based on the map dataset, and taking the start or end point of the road segment as a reference, nodes that are spatially adjacent to the road segment are filtered to generate a list of road segment node combinations. Each road segment node combination in the list contains a road segment and a node. Based on the vehicle model dataset and the road segment node combination list, extract the third vehicle model list that is allowed to pass through the road segment and the fourth vehicle model list that is allowed to stop at the node, and generate all second vehicle model combinations; Based on the maximum outer edge two-dimensional convex polygon model in the vehicle model basic dataset, for each second vehicle model combination, the convex polygon of the third vehicle model is projected onto the driving plane defined by the waypoint array of the road segment, and the convex polygon of the fourth vehicle model is projected onto the stopping area plane of the node, and convex polygon intersection detection is performed. If there is an intersecting area, it is determined that the road segment node combination and the second vehicle type combination have a locking relationship; For road segment node combinations with locking relationships and second vehicle combinations, based on the geometric feature parameters and motion state parameters of the third vehicle in the vehicle basic dataset, and the waypoint array and average curvature of the road segment in the map basic dataset, the collision-related waypoint intervals that collide with the nodes in the road segment are located, and the local curvature parameters of the collision-related waypoint intervals are extracted to obtain the second local curvature parameters. The geometric feature parameters, motion state parameters, and second local curvature parameters of the third vehicle model are input into the safety distance calculation function to generate the second dynamic starting point release distance; The correspondence between the road segment node combination, the second vehicle type combination, and the second dynamic starting point release distance is stored in the locking relationship and release distance table.

6. The multi-model industrial AGV path locking pre-calculation method according to claim 1, characterized in that, The process of detecting locking relationships between nodes, road segments, and road segments based on the vehicle model dataset and the map dataset, calculating the dynamic release distance for corresponding vehicle model combinations, and generating a locking relationship and release distance table also includes: Based on the aforementioned map dataset, a list of all road segment pairs is generated, wherein each road segment pair in the list contains a first road segment and a second road segment; Based on the vehicle model dataset and the road segment pair list, extract the fifth vehicle model list that is allowed to pass through the first road segment and the sixth vehicle model list that is allowed to pass through the second road segment, and generate all third vehicle model combinations; Based on the maximum outer edge two-dimensional convex polygon model in the vehicle model basic dataset, for each of the third vehicle model combinations, the convex polygon of the fifth vehicle model is projected onto the driving plane defined by the waypoint array of the first road segment, and the convex polygon of the sixth vehicle model is projected onto the driving plane defined by the waypoint array of the second road segment, and convex polygon intersection detection is performed. If there is an intersecting area, it is determined that the road segment has a locking relationship with the third vehicle type combination; For road segment pairs with locking relationships and combinations with the third vehicle type, based on the geometric feature parameters and motion state parameters of the fifth vehicle type in the vehicle type basic dataset, and the waypoint array and average curvature of the first road segment in the map basic dataset, the collision-related waypoint intervals that collide with the second road segment in the first road segment are located, and the local curvature parameters of the collision-related waypoint intervals are extracted to obtain the third local curvature parameters. The geometric feature parameters, motion state parameters, and third local curvature parameters of the fifth vehicle model are input into the safety distance calculation function to generate the third dynamic starting point release distance; The correspondence between the road segment pairs, the third vehicle type combination, and the third dynamic starting point release distance is stored in the locking relationship and release distance table.

7. The multi-model industrial AGV path locking pre-calculation method according to claim 1, characterized in that, The process of detecting locking relationships between nodes, road segments, and road segments based on the vehicle model dataset and the map dataset, calculating the dynamic release distance for corresponding vehicle model combinations, and generating a locking relationship and release distance table includes: Based on the lock control relationship and release distance table, extract the main road segment identifier, lock control object identifier, main vehicle type identifier, and lock control vehicle type identifier from each lock control record, and combine the main road segment identifier, lock control object identifier, main vehicle type identifier, and lock control vehicle type identifier into a unique key; Traverse all locking records in the locking relationship and release distance table. When two or more locking records have the same unique key, keep only one locking record and delete the remaining duplicate records to generate a deduplicated locking relationship and release distance table. Based on the deduplicated locking relationship and release distance table, the main road segment identifier is extracted from each locking record. Using the main road segment identifier as the first-level index key, locking records with the same main road segment identifier are grouped into the same index group to generate a main road segment dimension index table. Based on the main road segment dimension index table, the lock control object type in each lock control record is extracted. Using the lock control object type as the second-level index key, lock control records with the same lock control object type are further classified in each index group of the main road segment dimension index table to generate a lock control object type dimension index table. Based on the lock control object type dimension index table, the main vehicle model identifier is extracted from each lock control record. Using the main vehicle model identifier as the third-level index key, lock control records with the same main vehicle model identifier are further classified in each index group of the lock control object type dimension index table to generate a multi-dimensional index table.

8. A path locking pre-calculation device for multi-vehicle industrial AGVs, characterized in that, include: The data acquisition module is used to collect the geometric and operational parameters of AGVs of various models, construct a two-dimensional convex polygon model of the maximum outer edge of the planar projection of each model, and generate a basic dataset of the model. The loading module is used to load AGV operation road network data, extract node and road segment information, construct the road network topology and node and road segment association index, and generate the basic map dataset. The execution module is used to perform node-to-node, road segment-to-node, and road segment-to-road segment locking relationship detection based on the vehicle model base dataset and the map base dataset, calculate the dynamic release distance of the corresponding vehicle model combination, and generate a locking relationship and release distance table.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-model industrial AGV path locking pre-calculation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the multi-model industrial AGV path locking pre-calculation method as described in any one of claims 1 to 7.

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