Carrying trolley dynamic path planning method based on real-time road condition

Abstract

The invention relates to the technical field of path planning, in particular to a carrying trolley dynamic path planning method based on real-time road conditions, which comprises the following steps of: based on node coordinate data in a full-amount map, executing clustering according to node space proximity and connectivity; according to the invention, based on clustering analysis of node space proximity and connectivity, connection nodes belonging to a plurality of different clusters at the same time are identified and extracted, accurate definition of region boundary nodes is realized, the node redundancy degree in a path planning process is reduced, and the clearness of a road network structure is improved; by calculating the shortcut passing cost between different boundary nodes, a clear shortcut passing cost list is established, and a topological graph is constructed based on the list, so that path selection is more accurate and efficient; when the state of the road network changes, the passing cost is dynamically recalculated and the weight of the topological graph is updated in time through real-time retrieval and rapid matching of the shortcuts corresponding to the changed road sections, so that the performance of path planning in real-time performance and adaptability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a dynamic path planning method for a transport vehicle based on real-time road conditions. Background Art

[0002] Path planning is one of the core technologies in the fields of transportation, robot navigation, driverless, etc. It mainly studies how to determine one or more optimal paths to achieve a safe, efficient, economical or comprehensively optimal route selection from an initial position to a target position.

[0003] In actual operation, the path planning schemes of the prior art mostly use fixed nodes or static preset paths as the planning basis, lacking an effective response to the dynamic changes of the real-time road network. As a result, when the traffic flow surges or some sections are suddenly congested, damaged, etc., the planned paths cannot be adjusted in time, and problems such as path delays or failures often occur during the actual transportation process. At the same time, the load conditions of the transport vehicles are not considered, and the differences in the passing costs under heavy load or different load states are not effectively distinguished, resulting in uncertainties in transportation efficiency and energy costs. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a dynamic path planning method for a transport vehicle based on real-time road conditions.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions. A dynamic path planning method for a transport vehicle based on real-time road conditions includes the following steps: Based on the node coordinate data in the full-scale map, perform clustering according to the node spatial proximity and connectivity, identify and extract the connection nodes that belong to two or more different clusters at the same time, and generate a hierarchical region boundary node set; Based on the hierarchical region boundary node set, for any two boundary nodes in the hierarchical region boundary node set, call the underlying road network data to calculate the shortcut passing cost, and establish a shortcut passing cost list; based on the shortcut passing cost list, use the boundary nodes as vertices, the shortcuts as edges, and assign the passing cost to the edge weights to construct a macro shortcut topology graph; Based on the macro shortcut topology graph and the shortcut passing cost list, retrieve and match all the shortcuts covering the changed sections in the graph to obtain the affected shortcut indexes; based on the affected shortcut indexes, recalculate the shortcut passing cost, and update the corresponding edge weights in the macro shortcut topology graph to generate a dynamic shortcut weight graph; Based on the dynamic shortcut weight map and the starting coordinates of the carrier vehicle, calculate the shortcut passing cost in the map in combination with the load state of the carrier vehicle, search for the macroscopic path from the starting area to the ending area, and obtain the macroscopic boundary node sequence; based on the macroscopic boundary node sequence, generate the collaborative navigation path of the carrier vehicle.

[0006] Preferably, the step of obtaining the hierarchical region boundary node set is as follows: Based on the node coordinate data in the full-scale map, calculate the Euclidean distance between any two nodes and mark whether there is a connection relationship in combination with the connectivity status identifier, cluster the node pairs that simultaneously satisfy the distance being less than the spatial proximity threshold and having a direct connection relationship into the same cluster, record the node sets and cluster sizes of all clusters, and generate the node spatial proximity clustering result; According to the node spatial proximity clustering result, count the number of clusters to which each node belongs, extract the belonging status of each node in each cluster, and calculate the connection value degree of each node; According to the connection value degree of each node, screen all nodes that satisfy the connection value degree being greater than or equal to 1 as candidate boundary nodes, calculate the mean and standard deviation of the connection value degrees of all nodes, and select the nodes ranked in the top 20% of the connection value degrees to form the hierarchical region boundary node set.

[0007] Preferably, the step of obtaining the shortcut passing cost list is as follows: Based on the hierarchical region boundary node set, traverse all boundary node pairs, call the road segment length, average passing speed of the road segment, and historical congestion ratio of the road segment recorded in the underlying road network data, construct all reachable paths between each pair of nodes, and screen the effective shortcut path groups that meet the maximum length and maximum segment number limit conditions to obtain the passable shortcut path groups; According to the passable shortcut path groups, count the number of road segments of each path and calculate the shortcut passing cost of each shortcut path; According to the shortcut passing costs of all shortcut paths, pair and label the shortcut passing cost of each shortcut path with the starting and ending boundary nodes, construct a mapping table of the corresponding boundary node pairs and time costs, and form the shortcut passing cost list.

[0008] Preferably, the step of obtaining the macroscopic shortcut topology map is as follows: Based on the shortcut passing cost list, extract the identifiers of the starting boundary node and the ending boundary node corresponding to each shortcut path in the shortcut passing cost list, remove duplicates from the boundary nodes one by one, and organize to obtain a complete boundary node identifier set that contains all boundary nodes and is non-repeating; Based on the complete set of boundary node identifiers, construct the node structure of the topological graph according to the node identifiers. Use the boundary node identifiers as the vertices of the graph. Extract the shortcut passing cost corresponding to each shortcut path from the shortcut generalized time cost list one by one. Establish the direct connection mapping relationship between the vertices according to the start node and end node mapping relationship of the paths between the nodes, and obtain the vertex connection relationship mapping set; Based on the vertex connection relationship mapping set, assign the shortcut passing cost corresponding to the shortcut path to the connection mapping relationship between the corresponding vertices one by one as the weight value of the edge between each vertex in the topological graph, and update the connection mapping relationship between the vertices to generate the macro shortcut topological graph.

[0009] Preferably, the step of obtaining the affected shortcut index is as follows: Based on the macro shortcut topological graph, extract the start and end boundary node identifiers corresponding to each shortcut path one by one. According to the start and end boundary node identifiers of each shortcut path, call the road segment set information corresponding to each shortcut path in the shortcut generalized time cost list to generate the mapping relationship between the shortcut path and the corresponding road segment set; According to the mapping relationship between the shortcut path and the corresponding road segment set, retrieve the road segment set information of each shortcut path one by one. Compare each road segment identifier included in the road segment set with the road segment identifier that has changed in real time one by one, and record the shortcut path identifier where the matching road segment is located to form an initial shortcut path identifier set covering the changed road segments; Based on the initial shortcut path identifier set covering the changed road segments, after deleting the duplicate shortcut path identifiers, sort and index the remaining shortcut path identifiers to generate the affected shortcut index.

[0010] Preferably, the step of obtaining the dynamic shortcut weight graph is as follows: Based on the affected shortcut index, call each affected shortcut path in the shortcut generalized time cost list one by one, extract all road segment identifiers of each affected shortcut path, and retrieve the real-time average passing speed and real-time congestion ratio of each road segment from the underlying road network data segment by segment to form a real-time road state data set; According to the real-time road state data set, calculate the real-time passing time of each road segment in the affected shortcut path one by one, obtain the shortcut passing cost of each affected shortcut path, and update the shortcut passing cost of the corresponding path in the shortcut generalized time cost list to form a dynamic shortcut passing cost list; Based on the dynamic shortcut passing cost list, call the connection edges of the boundary node pairs corresponding to the affected shortcut paths in the macro shortcut topological graph one by one, assign the shortcut passing cost to the weight attribute of the corresponding connection edge one by one, and update the weights of all affected connection edges to generate the dynamic shortcut weight graph.

[0011] Preferably, the step of obtaining the macro boundary node sequence is as follows: Based on the dynamic shortcut weight map and the starting coordinates of the carrier vehicle, locate the boundary node to which the starting point belongs, and retrieve all complete path segments starting from this node and accessible to any boundary node in the end region from the dynamic shortcut weight map. Screen the paths whose path length and number of path edges both meet the maximum constraint conditions to generate a set of macro path candidates; According to the set of macro path candidates, combined with the basic passage cost and the load status of the carrier vehicle in each path, calculate the load equivalent time cost of each path; Based on the load equivalent time cost of each path, select the path with the minimum load equivalent time cost from the set of macro path candidates as the optimal path, and sequentially extract all boundary node identifiers included in this path to generate a macro boundary node sequence.

[0012] Preferably, the step of obtaining the collaborative navigation path of the carrier vehicle is as follows: Based on the macro boundary node sequence, extract the original coordinate information between the starting coordinates and the first boundary node, call the geometric shape, traffic direction and current road section status data of each section of the road in the underlying road network, and traverse all legal paths between the starting coordinates and the first boundary node to form a micro path segment from the starting point to the first boundary node; Based on the micro path segment from the starting point to the first boundary node, extract the node information between the last boundary node and the end coordinates in the macro boundary node sequence, construct a micro path segment between the last boundary node and the end coordinates, and generate a set of end micro path segments; Based on the micro path segment from the starting point to the first boundary node, the macro boundary node sequence and the set of end micro path segments, splice the contents of the three path segments in sequence according to the path logic order to generate the collaborative navigation path of the carrier vehicle.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through clustering analysis based on node spatial proximity and connectivity, connection nodes that belong to multiple different clusters simultaneously are identified and extracted, enabling the precise definition of regional boundary nodes, reducing the redundancy of nodes in the path planning process, and enhancing the clarity of the road network structure; by calculating the shortcut passing costs between different boundary nodes, a clear shortcut passing cost list is established, and a topological graph is constructed based on this, making path selection more precise and efficient; when the road network state changes, by retrieving and quickly matching the shortcuts corresponding to the changed sections in real time, the passing costs are dynamically recalculated and the weights of the topological graph are updated in a timely manner, improving the real-time performance and adaptability of path planning; on the premise of considering the actual load state of the carrier trolley, the load-sensitive path costs are calculated according to the dynamic weight information, the macro boundary node sequence between the starting point and the ending point is determined, and it is refined into a complete navigation path, improving the practicality and flexibility of the path planning results, enhancing the actual transportation efficiency, reducing costs, and enhancing the reliability of the transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] Please refer to Figure 1 , the present invention provides a technical solution, a dynamic path planning method for a carrier trolley based on real-time road conditions, including the following steps: Based on the node coordinate data in the full-scale map, clustering is performed according to node spatial proximity and connectivity, connection nodes that belong to two or more different clusters simultaneously are identified and extracted, and a hierarchical regional boundary node set is generated; Based on the hierarchical regional boundary node set, for any two boundary nodes in the hierarchical regional boundary node set, the underlying road network data is called to calculate the shortcut passing costs, and a shortcut passing cost list is established; based on the shortcut passing cost list, the boundary nodes are used as vertices, the shortcuts are used as edges, and the passing costs are assigned to the edge weights to construct a macro shortcut topological graph; Based on the macro shortcut topological graph and the shortcut passing cost list, all shortcuts covering the changed sections in the graph are retrieved and matched to obtain the affected shortcut indexes; based on the affected shortcut indexes, the shortcut passing costs are recalculated, and the corresponding edge weights in the macro shortcut topological graph are updated to generate a dynamic shortcut weight graph; Based on the dynamic shortcut weight map and the starting coordinates of the carrier vehicle, combined with the load status of the carrier vehicle, calculate the shortcut passing cost in the map, search for the macroscopic path from the starting area to the ending area, and obtain the macroscopic boundary node sequence; based on the macroscopic boundary node sequence, generate the cooperative navigation path of the carrier vehicle.

[0017] The steps for obtaining the hierarchical area boundary node set are as follows: Based on the node coordinate data in the full-scale map, calculate the Euclidean distance between any two nodes and mark whether there is a connection relationship in combination with the connectivity status identifier. Cluster the node pairs that simultaneously meet the conditions of the distance being less than the spatial proximity threshold and having a direct connection relationship into the same cluster, record the node sets and cluster sizes of all clusters, and generate the node spatial proximity clustering result; According to the node spatial proximity clustering result, count the number of clusters to which each node belongs and extract the membership status of each node in each cluster, and calculate the connection value degree of each node. The calculation formula is: ; Among them, is the connection value degree of the th node, is the total number of clusters to which the th node belongs, is the membership boolean flag (1 means belonging, 0 means not belonging) of the th node in the th cluster, is the total number of clusters, is the number of nodes contained in the th cluster, is the total number of all nodes in the map; According to the connection value degree of each node, screen all nodes that meet the condition that the connection value degree is greater than or equal to 1 as candidate boundary nodes, calculate the mean and standard deviation of the connection value degrees of all nodes, and select the nodes ranked in the top 20% of the connection value degrees to form the hierarchical area boundary node set.

[0018] Specifically, based on the node coordinate data in the full-scale map, first traverse all the nodes recorded in the map data, extract the two-dimensional coordinates of each node and the list of directly adjacent node identifiers. Subsequently, for any two node pairs , calculate their spatial straight-line distance , and at the same time check whether the node exists in the adjacent node list of the node to judge whether there is a direct connection relationship between the two. Next, set the spatial proximity threshold. The setting of this threshold refers to the map scale and road network density. The specific setting method is to count the lengths of all road segments in the map and calculate their average value , and set the spatial proximity threshold to , for example, if the average length of road segments in a certain urban area is 150 meters, then the spatial proximity threshold is set to meters. Then, taking an unclustered node as the starting point, create a new cluster. Recursively add all adjacent nodes that satisfy "Euclidean distance less than 180 meters" and "there is a direct connection relationship" with the current node to the same cluster through breadth-first search or depth-first search, and mark them as visited. Repeat this process until all nodes in the map are assigned to one or more clusters. After clustering is completed, the system will traverse all generated clusters, record all node identifiers included in each cluster, and count the total number of nodes in each cluster. Finally, integrate this information into a data structure containing the node list of each cluster and the corresponding size, and generate the node spatial proximity clustering result.

[0019] Formula: , the benefit of the formula is that this connection value formula can accurately quantify the importance of each node in the road network as a "transportation hub" or "boundary connection point". It not only considers the number of clusters connected by the node (breadth), but also assigns higher value weights to nodes connected to smaller and more unique regions (clusters) by introducing the logarithmic term of the inverse document frequency (IDF) idea. The first part ensures that only nodes connecting two or more clusters have a basic connection value, directly screening out nodes with the "bridge" attribute. The summation term in the second part further differentiates the importance of these "bridge" nodes. A node connecting multiple small and independent clusters obtains a higher value degree score than a node only connecting two large and dense clusters. This design makes the identified boundary nodes not only physical boundaries of regions, but also key connection points in terms of function, providing a high-quality vertex set for subsequent construction of a macroscopic shortcut map and improving the efficiency and macroscopic nature of path planning; The steps to obtain the parameter are as follows. Based on the node spatial proximity clustering result generated in the previous step, the system traverses all clusters recorded in this result. For the specified th node, count how many different cluster node sets its identifier appears in. This number of occurrences is 's value. For example, in the clustering result, if the node "ID-007" appears in the node lists of "cluster A", "cluster C", and "cluster F" at the same time, then the value of this node is 3; The steps to obtain the parameter are as follows. This parameter is a boolean flag, and its value is directly queried from the node spatial proximity clustering result. For the th node and the th cluster, the system checks the Whether the node set of a cluster contains the identifier of the th node. If it contains, then is assigned a value of 1, indicating that the node belongs to the cluster . If it does not contain, then is assigned a value of 0; The obtaining step of the parameter is that this parameter represents the total number of clusters generated after this clustering operation, and its value is directly obtained by counting the number of independent clusters in the node spatial proximity clustering result. For example, if the clustering process finally divides the entire road network into 150 independent clusters, then the value of The obtaining step of the parameter is that this parameter represents the total number of nodes contained in the th cluster. Its value has been calculated and stored together when generating the node spatial proximity clustering result and can be directly extracted from this result. For example, query the record about "cluster C" in the clustering result, and the value of its corresponding "cluster size" field is 258, then the value of The obtaining step of the parameter is that this parameter represents the total number of all nodes in the full map, which is a static value. In the initialization stage of the path planning method, by scanning the node database or file of the full map once, the total number of nodes is counted. For example, after loading the traffic road network data of a certain city, it is statistically obtained that there are 85,430 intersections and road section endpoints in total, then the value of

[0020] Calculation process: Taking the calculation of the connection value of the 58th node (i.e., ) in the road network as an example, first obtain the determined values of the parameters required by the formula from the relevant data.

[0021] The total number of nodes is statistically obtained from the full map data.

[0022] The total number of clusters is queried from the node spatial proximity clustering result.

[0023] It is queried from the node spatial proximity clustering result that the 58th node belongs to 3 different clusters, namely the 12th, 25th, and 77th clusters. Therefore, the total number of its affiliated clusters .

[0024] According to the membership relationship, the Boolean flag , , , and for all other clusters , .

[0025] Query the number of nodes contained in each of these three clusters from the node space proximity clustering results to obtain , , .

[0026] Substitute the above parameter values into the connection value degree calculation formula: ; Since is 1 only when is 12, 25, 77, and 0 for the rest, so the summation term can be simplified to: ; Substitute with specific numerical values for calculation: ; ; ; ; ; The result shows that the connection value degree of node No. 58 is 9.209. This value is a comprehensive importance score, and its value is much greater than 1, indicating that this node is a very critical connection point. It not only connects multiple regions, but also the regions it connects include highly localized regions with relatively small scales (such as cluster 25 and cluster 12), which makes it have high strategic value in macroscopic path planning.

[0027] According to the connection value degree of each node, first start the screening program, traverse all nodes and their corresponding connection value degree values, perform a comparison and judgment on each value once, compare the connection value degree with the preset hard threshold of 1. For any node that satisfies , its identifier is automatically extracted and stored in a temporary candidate boundary node list. This move aims to initially filter out all internal nodes that do not have the cross-region connection attribute. Subsequently, the system calculates the arithmetic mean and standard deviation of the connection value degree set of all nodes (regardless of whether they are selected into the candidate list). Next, enter the ranking and screening stage. The system sorts all the nodes in the candidate boundary node list according to their connection value degree from high to low, calculates 20% of the total scale of the candidate list as the final selection quantity, and supplements the selection of the node with the highest value degree for connecting each pair of adjacent clusters. Merge and deduplicate the nodes selected in the two parts. For example, if the candidate boundary node list contains 680 nodes, then the final selection quantity is For each node, the system will select the first 136 nodes in the sorted list, and these nodes form the set of boundary nodes of the final required hierarchical area.

[0028] The steps to obtain the shortcut travel cost list are as follows: Based on the set of boundary nodes of the hierarchical area, traverse all pairs of boundary nodes, call the road segment length, average travel speed of the road segment, and historical congestion ratio of the road segment recorded in the underlying road network data, construct all reachable paths between each pair of nodes, and filter out the effective shortcut path groups that meet the maximum length and maximum segment number limit conditions to obtain the passable shortcut path groups; According to the passable shortcut path groups, count the number of road segments of each path, calculate the shortcut travel cost of each shortcut path, and the calculation formula is: ; Where, is the shortcut travel cost of the th shortcut path, is the length of the th road segment in the th shortcut path, is the average travel speed of this road segment, is the historical congestion ratio of this road segment, is the total number of road segments included in the th shortcut path, is the congestion impact factor, is the path complexity penalty factor; According to the shortcut travel costs of all shortcut paths, pair and label the shortcut travel cost of each shortcut path with the start and end boundary nodes, construct a mapping table of the corresponding boundary node pairs and time costs, and form a shortcut travel cost list.

[0029] Specifically, based on the hierarchical region boundary node set obtained in the previous step, the system starts the shortcut generation program. First, it extracts all node identifiers from this node set and generates all unique combinations of boundary node pairs. For each boundary node pair (for example, node A and node B), it calls the path search algorithm, specifically the Yen's algorithm, to find K alternative paths connecting these two nodes on the underlying road network data, where the value of K is dynamically set according to the network scale. For example, it is set to 5 for the urban core area and 3 for the suburbs. During the search process, it will real-time call the attributes accurately recorded for each road segment in the underlying road network data, including the road segment length in meters, the average passing speed of the road segment in kilometers per hour obtained by statistical analysis of historical vehicle speed data, and the historical congestion ratio of the road segment ranging from 0 to 1 calculated through long-term traffic flow monitoring. After the search is completed, the system will perform validity screening on the generated K paths. The screening conditions include the maximum length and the maximum number of segments. The setting of the maximum length limit condition is based on the straight-line Euclidean distance between the two boundary nodes, specifically 2.5 times this distance. For example, if the straight-line distance between points A and B is 4 kilometers, any path with a total length exceeding 10 kilometers will be excluded. The maximum number of segments limit condition is set according to the average intersection spacing of the urban road network. For example, if there are 8 road segments per kilometer on average, for a path with a maximum length of 10 kilometers, its maximum number of segments limit can be set to 90. Any path that simultaneously meets these two limit conditions is classified as a valid shortcut and is summarized to obtain a group of passable shortcut paths.

[0030] Formula: , The benefit of the formula is that it constructs a comprehensive passing cost model that goes beyond simple time or distance considerations. The first summation term, by introducing the historical congestion ratio and the congestion impact factor , makes a realistic correction to the ideal passing time. The denominator simulates the attenuation effect of congestion on the actual passing speed, making the cost calculation closer to the real road conditions. The second part introduces a penalty for path complexity, non-linearly penalizing the total number of road segments in the path through the logarithmic function . This can effectively suppress paths that are too tortuous and have frequent turns, and can also avoid imposing too heavy a penalty on slightly longer but necessary paths. This dual optimization of time and complexity makes the finally planned path not only fast but also provides a better driving experience, reducing the operation complexity and potential risks of the carrier cart; The steps to obtain the parameter are that this parameter represents the th shortcut path and the The length of a road segment is directly obtained by querying from the underlying road network database, which pre-stores the geometric information of surveyed road segments in meters. For example, when querying "the 3rd segment of XX Road" included in path c, the length attribute value of 450 meters is directly read from the database; The parameter acquisition steps are as follows. This parameter represents the historical average traffic speed of a specific road segment and is obtained through big data analysis of speed information collected from geomagnetic coils, video detectors, and online car-hailing floating car data deployed in the road network over the past six months. The speeds at different time periods (such as morning rush hour, evening rush hour, flat peak, and night) will be weighted and averaged to obtain a comprehensive average speed value in kilometers per hour. For example, after data analysis, the comprehensive average speed of "the 3rd segment of XX Road" on weekdays is calculated and recorded as 35 km / h; The parameter acquisition steps are as follows. This parameter is the historical congestion ratio of a road segment, and its calculation is based on historical travel time data. The calculation formula is: , where is the historical average travel time, is the free flow time calculated based on the legal speed limit. Both of these time values are obtained from historical traffic data analysis. This ratio ranges from 0 to 1. For example, the legal speed limit of "the 3rd segment of XX Road" is 50 km / h, and its free flow time is , while the historical average travel time is , then its congestion ratio is ; The parameter acquisition steps are as follows. This parameter is the total number of road segments included in the th shortcut path. After the path search algorithm (such as Yen's algorithm) generates a path, it is obtained by directly counting the number of road segment identifiers included in the path data structure. This is a procedural statistic and no additional data collection is required. For example, if path c consists of "road segment 1 - road segment 2 - road segment 3", then ; The parameter acquisition steps are as follows. The congestion impact factor is a calibration parameter used to adjust the influence degree of the historical congestion ratio on the travel time. Its value is determined through regression analysis of a set of sample path sets containing actual travel times, with the goal of minimizing the root mean square error between the model predicted time and the actual time. The specific process is as follows: Select 100 paths covering different congestion levels and test for all values within the interval [0.5, 1.0] with a step of 0.05, and calculate each The root mean square error between the predicted time and the actual time at a certain value is calculated, and the value that minimizes the error is selected. For example, through calculation, it is found that when the root mean square error is the smallest, so is set; The step to obtain the parameter is that the path complexity penalty factor is used to quantify the impact of the tortuosity of the path on the cost. Its setting refers to the driver preference survey and operation efficiency analysis. By showing multiple pairs of paths (with similar time but different complexities) to 20 experienced drivers and recording their choices, a preference model is established. The goal is to set a value such that the lowest-cost path calculated by the formula is consistent with the preferred choices of more than 75% of the drivers. For example, through analysis, it is found that when the model's choice has the highest matching degree with the drivers' preferences, reaching 78%. Therefore, finally is set.

[0031] Calculation process: Taking the calculation of the passing cost of a specific shortcut path as an example, this path consists of 3 road segments (i.e., ), and the parameter values of each road segment are as follows: For road segment 1 (g = 1): length km, average speed km / h, historical congestion ratio . .

[0032] For road segment 2 (g = 2): length km, average speed km / h, historical congestion ratio .

[0033] For road segment 3 (g = 3): length km, average speed km / h, historical congestion ratio .

[0034] Congestion impact factor , path complexity penalty factor .

[0035] First, calculate the passing time (unit: hour) of each road segment after congestion correction within the brackets: Time of road segment 1: hours.

[0036] Time of road segment 2: hours.

[0037] Section 3 Time: hours.

[0038] Then, add up the times of each section to obtain the total basic travel time: ; Next, calculate the path complexity penalty factor: ; Finally, multiply the two parts to obtain the final shortcut travel cost: ; Convert the result to minutes: minutes.

[0039] This result indicates that after comprehensively considering historical congestion and path complexity, the shortcut travel cost of path is equivalent to 6.20 minutes.

[0040] According to the shortcut travel costs of all shortcut paths, enter the list construction stage. The data processed in this stage is the group of passable shortcut paths generated for each pair of boundary nodes in the previous steps and their corresponding shortcut travel cost values. First, initialize an empty data structure to store the final shortcut travel cost list. Then, traverse each pair of boundary nodes, implement for example (boundary node A, boundary node B), and retrieve all passable shortcuts and their costs connecting this pair of nodes. Compare all the shortcut costs of this pair of nodes once, select the one with the smallest value among them, and the only shortcut path corresponding to this lowest cost. This path is regarded as the optimal shortcut connecting A and B. Subsequently, the system creates a new mapping record, which contains three core fields: the identifier of the starting boundary node (for example, 'A'), the identifier of the ending boundary node (for example, 'B'), and the selected lowest shortcut travel cost (for example, 6.20). This record is added to the final list. This process will be repeated for all pairs of boundary nodes with passable shortcuts until all pairs of nodes are traversed, and finally a complete and non-redundant shortcut travel cost list is formed.

[0041] The steps to obtain the macro shortcut topology map are as follows: Based on the shortcut travel cost list, extract the identifiers of the starting boundary node and the ending boundary node corresponding to each shortcut path in the shortcut travel cost list, remove duplicates for each boundary node one by one, and organize to obtain a complete set of boundary node identifiers that contains all boundary nodes and has no duplicates; Based on the complete set of boundary node identifiers, construct the node structure of the topology graph according to the node identifiers, use the boundary node identifiers as the vertices of the graph, extract the shortcut passing costs corresponding to each shortcut path from the shortcut generalized time cost list one by one, and establish the direct connection mapping relationship between the vertices according to the start node and end node mapping relationship of the paths between the nodes, so as to obtain the vertex connection relationship mapping set; Based on the vertex connection relationship mapping set, assign the shortcut passing costs corresponding to the shortcut paths to the connection mappings between the corresponding vertices one by one as the weight values of the edges between the vertices in the topology graph, and update the connection mappings between the vertices to generate the macro shortcut topology graph.

[0042] Specifically, based on the shortcut passing cost list, initialize a hash set (HashSet) data structure to store all unique boundary node identifiers. Subsequently, the program reads the records in the shortcut passing cost list one by one. For each record, for example, a shortcut representing a path from boundary node "BN-034" to "BN-152" is implemented. The start boundary node identifier "BN-034" and the end boundary node identifier "BN-152" are respectively extracted and successively added to the hash set. Using the natural duplicate removal feature of the hash set, even if "BN-034" or "BN-152" appears again in the subsequent records, only one copy will be retained in the set. This process continues until all records in the shortcut passing cost list have been processed. Finally, the program sorts out all the non-repeating boundary node identifiers stored in the hash set and converts them into a list or array structure to obtain a complete set of boundary node identifiers that contains all boundary nodes and is non-repeating.

[0043] Based on the complete set of boundary node identifiers generated in the previous step, start constructing the basic skeleton of the graph. First, the system initializes a graph data structure in the form of an adjacency list and traverses the complete set of boundary node identifiers. For each unique boundary node identifier in the set (for example, in the implementation, "BN-034"), a corresponding vertex is created in the adjacency list, and the adjacency list of this vertex is initialized to be empty. After all vertices are created, the system traverses the shortcut passing cost list again. For each record in the list, for example, a record that records the shortcut connecting "BN-034" and "BN-152", the system searches for these two vertices in the adjacency list and establishes a two-way connection relationship between them. The specific operation is to add the identifier of "BN-152" to the adjacency list of "BN-034", and at the same time add the identifier of "BN-034" to the adjacency list of "BN-152". This process does not assign weights such as access cost or distance to the connection, but only records the connectivity between nodes. By performing the same operation on all records in the shortcut passing cost list, the system finally constructs an unweighted graph that describes the shortcut connectivity between all boundary nodes and obtains the vertex connection relationship mapping set.

[0044] Based on the established vertex connection relationship mapping set and the original shortcut passing cost list, the system starts to assign weights to the edges of the topological graph. The specific operation is to traverse the shortcut passing cost list for the third time. For each record in the list, for example, a record that contains the start node "BN-034", the end node "BN-152", and the shortcut passing cost "6.20 minutes", the system locates the connection between "BN-034" and "BN-152" in the vertex connection relationship mapping set (i.e., the adjacency list) and assigns the value "6.20" as the weight of this connection edge. In the adjacency list data structure, this means updating the simple identifier in the adjacency list to a composite object that contains the neighbor identifier and the corresponding weight. For example, update "BN-152" in the adjacency list of "BN-034" to {neighbor: "BN-152", weight: 6.20}, and perform the same operation on the adjacency list of "BN-152". The system processes all entries in the shortcut passing cost list in sequence, assigns the passing cost of each shortcut to the corresponding edge in the graph, and finally completes the weight assignment for all connection edges, generating a macro shortcut topological graph with a complete structure and clear weights.

[0045] The steps to obtain the affected shortcut index are as follows: Based on the macro shortcut topological graph, extract the start and end boundary node identifiers corresponding to each shortcut path one by one. According to the start and end boundary node identifiers of each shortcut path, call the section set information corresponding to each shortcut path in the shortcut generalized time cost list to generate the mapping relationship between the shortcut path and the corresponding section set; According to the mapping relationship between the shortcut paths and the corresponding section sets, retrieve the section set information of each shortcut path one by one, compare each section identifier included in the section set with the section identifiers that have changed in real time one by one, and record the shortcut path identifiers where the successfully matched sections are located to form an initial shortcut path identifier set covering the changed sections; Based on the initial shortcut path identifier set covering the changed sections, after deleting the duplicate shortcut path identifiers, sort and index the remaining shortcut path identifiers to generate an affected shortcut index.

[0046] Specifically, based on the macroscopic shortcut topology graph, the system starts the mapping relationship construction program. First, initialize a key-value pair storage structure, such as a hash table (HashMap) in the implementation, for storing the final mapping relationship. The key is the unique identifier of the shortcut path (defined by the start and end boundary node pairs), and the value is the list of section identifiers included in this shortcut path. The system traverses each edge in the macroscopic shortcut topology graph. Each edge represents an optimal shortcut. For each edge, the system extracts the two vertices it connects, that is, the start and end boundary node identifiers, such as "BN-034" and "BN-152" in the implementation. Then, the system uses this pair of node identifiers as the query index to accurately find and call out the corresponding section set information in the detailed record version of the shortcut passage cost list generated in the previous step (this version needs to additionally store the specific section sequence included in each optimal shortcut). This information is an ordered list containing a series of section unique identifiers, such as ["RD-101", "RD-105", "RD-233"] in the implementation. Finally, the system stores the shortcut path identifier formed by this pair of start and end boundary node identifiers as the key and the queried section set as the value into the hash table. This process is repeated until all shortcut edges in the graph are processed, generating the mapping relationship between the shortcut paths and the corresponding section sets.

[0047] Based on the mapping relationship between the shortcut paths generated in the previous step and the corresponding road segment sets, the system starts to perform the matching retrieval of the changed road segments. First, the system obtains a list of currently changed road segments from the real-time traffic monitoring center. This list contains the unique identifiers of all road segments whose traffic capacity has significantly decreased due to traffic accidents, temporary construction, or severe congestion. Examples of implementation are ["RD-105", "RD-567"]. Then, the system traverses each record in the mapping relationship between the shortcut paths and the corresponding road segment sets one by one. For each shortcut path, the system obtains its corresponding road segment set and compares each road segment identifier in this set with all the identifiers in the real-time changed road segment list one by one. For example, when retrieving the shortcut from "BN-034" to "BN-152", the system will check its road segment set ["RD-101", "RD-105", "RD-233"] and find that "RD-105" in it matches successfully with "RD-105" in the real-time changed road segment list. Once a match is found, the system immediately records the identifier of this shortcut path (i.e., "BN-034" to "BN-152") in a temporary list. This process will continue until all shortcut paths are retrieved, forming an initial set of shortcut paths that cover at least one changed road segment, that is, the initial shortcut path identifier set covering the changed road segments.

[0048] Based on the initial shortcut path identifier set covering the changed road segments, the system starts the generation process of the final index. First, since in the previous step, the same shortcut path may be repeatedly recorded because it contains multiple changed road segments, the system needs to remove duplicates from this initial set. Specifically, it can be done by converting the list to a hash set and then converting it back to a list to remove all duplicate shortcut path identifiers, ensuring that each affected shortcut is only recorded once. After the duplicate removal operation is completed, the system will sort the remaining, unique shortcut path identifiers. The sorting basis can be the lexicographical order of the shortcut path identifiers, or according to some attribute of the shortcut (such as the original travel cost). For example, the implementation is to sort in ascending order according to the alphabetical and numerical order of the starting boundary node identifiers. After sorting, the system assigns a consecutive integer index starting from 0 to each sorted shortcut path identifier, and finally stores these indexes together with the corresponding shortcut path identifiers to generate a structured and ordered affected shortcut index.

[0049] The steps to obtain the dynamic shortcut weight graph are as follows: Based on the affected shortcut index, each affected shortcut path in the shortcut generalized time cost list is called one by one, and all road segment identifiers of each affected shortcut path are extracted. The real-time average travel speed and real-time congestion ratio of each road segment are retrieved from the underlying road network data segment by segment to form a real-time road state data set; According to the real-time road status data set, calculate the real-time travel time of each road segment in the affected shortcut paths one by one, obtain the shortcut travel cost of each affected shortcut path, update the shortcut travel cost of the corresponding path in the shortcut generalized time cost list, and form a dynamic shortcut travel cost list; Based on the dynamic shortcut travel cost list, call the connection edges of the boundary node pairs corresponding to the affected shortcut paths in the macro shortcut topology graph one by one, assign the shortcut travel cost to the weight attribute of the corresponding connection edge one by one, and update the weights of all affected connection edges to generate a dynamic shortcut weight graph.

[0050] Specifically, based on the affected shortcut index, start the data update process. First, traverse each shortcut path identifier in the index. For each identifier, take the implemented shortcut, for example, representing a shortcut from "BN-034" to "BN-152". According to the mapping relationship between the shortcut path and the corresponding road segment set generated in the previous step, query and extract the list of all road segment identifiers that make up this shortcut. For example, ["RD-101", "RD-105", "RD-233"]. Then, the system iteratively processes each road segment identifier in this list, uses each identifier as a query key, sends a request to the real-time traffic data center, and retrieves the current latest traffic status parameters of this road segment from the underlying road network data. Specifically, it includes the real-time average travel speed (unit: km / h) calculated based on the data of vehicles passing through the detector in the past 5 minutes, and the real-time congestion ratio (range 0-1) calculated by comparing the real-time traffic flow with the road design capacity. The system will obtain these real-time data for all road segments under each affected shortcut path and organize them into a data set with the shortcut path as the unit to form a real-time road status data set.

[0051] Based on the real-time road status data set, the system starts the cost recalculation process. First, the system makes a copy of the original shortcut passing cost list as the basis for update. Then, the system traverses each record in the real-time road status data set. Each record corresponds to an affected shortcut path and the real-time status data of all its sections. For each shortcut path, the system calls the same shortcut passing cost calculation formula as in the initial cost calculation. However, this time the calculation uses the just-obtained real-time average passing speed and real-time congestion ratio instead of historical data. For example, for the shortcut from "BN-034" to "BN-152", the system calculates the real-time passing time section by section, then sums them up, and multiplies by the path complexity penalty factor to obtain a new shortcut passing cost that reflects the current road conditions. After the calculation, the system uses the unique identifier of the shortcut (i.e., from "BN-034" to "BN-152") to find the corresponding entry in the copied shortcut passing cost list and updates the old cost value to the newly calculated real-time cost value. This process is repeated for all affected shortcut paths, and finally a partially updated dynamic shortcut passing cost list that reflects the latest road conditions is generated.

[0052] Based on the dynamic shortcut passing cost list, start updating the weights of the macro shortcut topology graph. The specific process is as follows: The system traverses all the updated entries in the dynamic shortcut passing cost list. For each updated record, for example, for the shortcut connecting "BN-034" and "BN-152", whose passing cost has been updated to 9.85 minutes, the system searches for the connecting edge defined by these two boundary node identifiers in the macro shortcut topology graph (usually stored in the form of an adjacency list). Once this edge is located, the system updates its weight attribute value from the original historical cost to the new dynamic cost "9.85". This operation is completed simultaneously in the adjacency list of "BN-034" and the adjacency list of "BN-152" to maintain the symmetry of the graph. The system processes all the shortcuts that have changed in the dynamic shortcut passing cost list in sequence, updating the weights of the corresponding edges in the graph one by one until the weights of all affected connecting edges are consistent with the latest real-time road condition costs, generating a dynamic shortcut weight graph.

[0053] The steps to obtain the macro boundary node sequence are as follows: Based on the dynamic shortcut weight graph and the starting coordinates of the carrier cart, locate the boundary node to which the starting point belongs, and retrieve all complete path segments starting from this node and leading to any boundary node in the end area from the dynamic shortcut weight graph. Filter the paths that meet the maximum constraint conditions in terms of path length and the number of path edges to generate a macro path candidate set; According to the macro path candidate set, combined with the basic passing cost and the load status of the carrier cart in each path, calculate the load equivalent time cost of each path. The calculation formula is: ; Among them, is the load equivalent time cost of the th macro path, is the number of shortcut edges included in the th macro path, is the shortcut passing cost of the th shortcut edge in the dynamic shortcut weight graph, is the total load mass of the carrier vehicle on the th path, is the unit load adaptation coefficient of the th shortcut edge, is the load sensitivity coefficient, which is used to adjust the magnification of the impact of the load on the passing time; Based on the load equivalent time cost of each path, select the path with the minimum load equivalent time cost from the macro path candidate set as the optimal path, and sequentially extract all the boundary node identifiers included in this path to generate a macro boundary node sequence.

[0054] Specifically, based on the dynamic shortcut weight graph and the starting coordinates of the carrier vehicle, the system first performs starting point positioning. By calculating the Euclidean distance between the current GPS coordinates of the carrier vehicle and all the boundary node coordinates in the hierarchical region boundary node set, filter out the boundary node with the closest distance as the macro starting point of this path planning. At the same time, according to the task end coordinates, use the same method to locate all the boundary nodes in the area where the end point belongs to form an end point boundary node set. Subsequently, the system takes the macro starting point as the source point and executes the K shortest path search algorithm on the dynamic shortcut weight graph, such as Yen's algorithm, where the K value is set to 5, to retrieve 5 different macro paths leading to any node in the end point boundary node set. After the search is completed, perform constraint screening on these 5 paths. The maximum constraint condition for the path length is set to 1.8 times the shortest path cost between the starting point and the center point of the end point region. For example, if the shortest path cost is 30 minutes, the maximum length constraint is 54 minutes. The maximum constraint condition for the number of path edges is set to 2 times the number of shortest path edges plus 1. For example, if the shortest path contains 4 shortcut edges, the maximum number of edges constraint is 9. The system will check these 5 candidate paths one by one, eliminate any path with a total cost exceeding 54 minutes or containing more than 9 shortcut edges, and finally summarize all the paths that meet the conditions to generate a macro path candidate set.

[0055] Formula: , The benefit of the formula is that by introducing the load state of the carrier vehicle itself ( ) and the adaptability of the path to the load ( ), the passing cost is extended from a single time dimension to a comprehensive dimension of time and energy consumption (or driving difficulty). The term The additional penalty on travel time caused by increased load is simulated, and this penalty varies dynamically depending on the load adaptability of the path. For example, for a shortcut with a large slope and poor road surface ( low value), the cost penalty for heavy-load travel will increase significantly. This design enables the path planning result to intelligently avoid sections that seem to be shortcuts but are actually difficult to pass and consume high energy for heavy-load vehicles, thus optimizing the economy and safety of transportation while ensuring timeliness; The steps for obtaining the parameter are as follows. This parameter represents the number of shortcut edges included in the th macro path, and its value can be directly obtained from the data structure of the corresponding path in the macro path candidate set. When the path search algorithm generates a macro path, the path is represented as a series of ordered shortcut edges, and only by counting this sequence can the value be obtained. For example, if the qth path is connected by 3 shortcut edges, then ; The steps for obtaining the parameter are as follows. This parameter is the shortcut passing cost of the th shortcut edge in the dynamic shortcut weight graph. Its value is the edge weight calculated according to the real-time road conditions and updated to the macro shortcut topology graph in the previous step. Therefore, when calculating the load equivalent time cost, directly read the weight value of each shortcut edge included in a specific macro path from the dynamic shortcut weight graph; The steps for obtaining the parameter are as follows. This parameter is the total load mass of the transport vehicle when performing the th path transportation task, in kilograms. Its value is provided by the central dispatching system according to the current task order information, or measured and uploaded in real time by the weighing sensor installed on the transport vehicle. For example, in a transportation task, the transport vehicle needs to transport a batch of goods, and the total load mass measured by the on-vehicle weighing system is 800 kilograms, then ; The steps for obtaining the parameter are as follows. The unit load adaptation coefficient is a comprehensive score that quantifies the friendliness of the th shortcut edge to load transportation. Its value is calculated from the attributes of the underlying road sections that make up this shortcut, and the calculation formula is: where is the reference load coefficient, set to 1000, is the number of road sections included in this shortcut, is the road bump index (0-1, obtained by analyzing the vehicle acceleration sensor data). For example, a shortcut consists of two sections. Section 1 has a slope of 2% and a bump index of 0.1, and section 2 has a slope of -1% and a bump index of 0.2. ; The parameters are obtained as follows: the load sensitivity coefficient is a calibration parameter used to adjust the magnification of the load's impact on travel time. Its value is determined by regression analysis of actual driving data under different load levels. The specific operation is to organize the transport vehicle to drive on 10 standard test paths with different load adaptability in three states: empty, half-loaded (500kg), and fully loaded (1000kg). The actual travel time is recorded and the actual time increment is fitted with the model prediction increment by the least squares method ( ) to obtain the optimal For example, through regression analysis, we can find out the value of When , the model prediction accuracy is the highest.

[0056] Calculation process: To calculate a path in the macro path candidate set Load equivalent time cost For example, the path consists of two shortcut edges (i.e. ), the load mass of the carrying trolley kg, load sensitivity factor .

[0057] path The shortcut edge attributes included are as follows: Shortcut edge 1 (h=1): reads its shortcut cost from the dynamic shortcut weight graph Minutes, the unit load adaptation coefficient is calculated as .

[0058] Shortcut edge 2 (h=2): reads its shortcut cost from the dynamic shortcut weight graph Minutes, the unit load adaptation coefficient is calculated as .

[0059] Substitute the above parameters into the load equivalent time cost calculation formula: ; Calculate the load equivalent time cost of the two shortcut edges separately: Cost of shortcut edge 1: ; Cost of shortcut edge 2: ; Add the costs of the two shortcut edges to obtain the total load equivalent time cost of the entire macro path: ; This result shows that for a carrier cart with a load of 800 kg, the comprehensive cost of the passing path is equivalent to 23.46 minutes. This value is higher than its basic passing cost (10 + 12 = 22 minutes). The extra 1.46 minutes is due to considering the impact of the load on the passing efficiency. Especially for the second shortcut edge with poor load adaptability, the cost increases by 8%. This final cost value will be used as the final basis for path selection to compare among all candidate paths.

[0060] Based on the load equivalent time cost of each path, start the screening of the optimal path. First, the system traverses each path in the macro path candidate set and retrieves the load equivalent time cost calculated in the previous step. The system initializes two variables. One is used to record the currently encountered minimum cost value, with an initial value set to a very large value, and the other is used to store the corresponding optimal path, with an initial value of empty. During the traversal process, the system compares the load equivalent time cost of each candidate path with the currently recorded minimum cost value. For example, if the cost of the first path is 23.46 minutes, then the minimum cost is updated to 23.46, and the optimal path is also updated to this path. When processing the second path, if its cost is 22.80 minutes, which is less than 23.46, then the minimum cost is updated to 22.80, and the optimal path is also replaced accordingly. This process continues until all candidate paths have been compared. At this time, the path stored in the optimal path variable is the finally selected path. Subsequently, the system analyzes the structure of this optimal path. This path consists of a series of ordered shortcut edges. The system sequentially extracts the boundary node identifiers connected by these shortcut edges. For example, if the optimal path is (BN - A, BN - B)->(BN - B, BN - C), then the system will extract the node sequence [BN - A, BN - B, BN - C] to generate the macro boundary node sequence.

[0061] The steps to obtain the collaborative navigation path of the carrier cart are as follows: Based on the macro boundary node sequence, extract the original coordinate information between the starting point coordinate and the first boundary node, call the geometric shape, passing direction, and current road section status data of each section of the road in the underlying road network, and traverse all legal paths between the starting point coordinate and the first boundary node to form the microscopic path segment from the starting point to the first boundary node; Based on the microscopic path segment from the starting point to the first boundary node, extract the node information between the last boundary node in the macro boundary node sequence and the end point coordinate, construct the microscopic path segment between the last boundary node and the end point coordinate, and generate the set of end microscopic path segments; Based on the microscopic path segment from the starting point to the first boundary node, the macroscopic boundary node sequence, and the set of end microscopic path segments, the contents of the three path segments are sequentially spliced in the logical order of the path to generate the collaborative navigation path of the carrier vehicle.

[0062] Specifically, based on the macroscopic boundary node sequence, the system first extracts the identifier and coordinates of the first boundary node from this sequence, and uses the current starting point coordinates of the carrier vehicle as the starting point for microscopic path planning. Subsequently, in the underlying road network data, the system matches the starting point coordinates to the closest physical node in the road network through the nearest neighbor search algorithm, and uses this as the precise starting point for path search. Next, with this precise starting point as the source point and the first boundary node as the target point, the system executes the A* (A-Star) shortest path search algorithm on the underlying road network graph. In each step of the search, when expanding from one node to its adjacent nodes, the system will call the geometric shape of the road segment recorded in the underlying road network data to obtain its precise length, and check its traffic direction attribute to ensure that traffic rules such as one-way streets are not violated. At the same time, the system calls the current road segment status data to obtain the latest real-time traffic speed, and divides the road segment length by the real-time traffic speed as the traffic cost of this road segment. The A* algorithm explores the path based on this dynamic cost, and finally traverses and filters out a legal path with the lowest cost. This path composed of a series of detailed road segments forms the microscopic path segment from the starting point to the first boundary node.

[0063] Based on the microscopic path segment from the starting point to the first boundary node, the system continues to process the end part of the path. First, it extracts the identifier and coordinates of the last boundary node from the macroscopic boundary node sequence, and obtains the final end point coordinates of the task. Similar to the starting point processing method, the system matches the end point coordinates to the closest physical node in the underlying road network as the precise end point for microscopic path planning. Then, with the last boundary node as the source point and the precise end point as the target point, the system starts the A* shortest path search on the underlying road network again. This process also strictly relies on the geometric shape, traffic direction, and current road segment status data of each road segment in the underlying road network, and uses the exact same real-time cost function as when calculating the starting point microscopic path segment to evaluate each possible path segment. By traversing all legal paths between the last boundary node and the end point coordinates, the algorithm finally determines a path with the lowest overall traffic cost and stores this optimal path as a singleton set to generate the set of end microscopic path segments.

[0064] Based on the micro path segment from the starting point to the first boundary node, the macro boundary node sequence, and the set of end micro path segments, the system performs the splicing and integration of the final path. First, the system creates an empty ordered list to store the final complete navigation path. Then, the system adds the micro path segment from the starting point to the first boundary node (a detailed sequence of road segments) completely to this list. Next, the system traverses the macro boundary node sequence. Starting from the first node, it processes each pair of adjacent boundary nodes in turn (for example, in the implementation, the th and the th boundary nodes). For each pair of nodes, the system uses the identifiers of this pair of nodes as an index to query and retrieve the detailed shortcut path connecting these two boundary nodes in the mapping relationship between the shortcut paths generated in the previous step and the corresponding road segment sets. This shortcut path itself is also an ordered sequence of road segments. The system appends this sequence of road segments to the end of the final navigation path list. This process is repeated until all the shortcuts in the macro boundary node sequence are spliced. Finally, the system takes out the only micro path segment from the set of end micro path segments and appends it to the final position of the list. Thus, the three parts are seamlessly spliced into a continuous and detailed complete path from the starting point to the end point, generating the collaborative navigation path for the carrier vehicle.

[0065] The above are only the preferred embodiments of the present invention and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic path planning method for a carrier vehicle based on real-time road conditions, characterized in that It includes the following steps: Based on the node coordinate data in the full-scale map, perform clustering according to the node spatial proximity and connectivity, identify and extract the connection nodes that belong to more than two different clusters at the same time, and generate a hierarchical regional boundary node set; Based on the hierarchical regional boundary node set, for any two boundary nodes in the hierarchical regional boundary node set, call the underlying road network data to calculate the shortcut passing cost, and establish a shortcut passing cost list; Based on the shortcut passing cost list, use the boundary nodes as vertices, the shortcuts as edges, and assign the passing cost to the edge weights to construct a macro shortcut topology graph; Based on the macro shortcut topology graph and the shortcut passing cost list, retrieve and match all the shortcuts covering the changed road sections in the graph, and obtain the affected shortcut indexes; Based on the affected shortcut indexes, recalculate the shortcut passing cost and update the corresponding edge weights in the macro shortcut topology graph to generate a dynamic shortcut weight graph; Based on the dynamic shortcut weight graph and the starting coordinates of the transport trolley, calculate the shortcut passing cost in the graph in combination with the load state of the transport trolley, search for the macro path from the starting area to the ending area, and obtain the macro boundary node sequence; Based on the macro boundary node sequence, generate a collaborative navigation path for the transport trolley.

2. The dynamic path planning method for a carrier vehicle based on real-time road conditions according to claim 1, wherein The obtaining steps of the hierarchical regional boundary node set are as follows: Based on the node coordinate data in the full-scale map, calculate the Euclidean distance between any two nodes and mark whether there is a connection relationship in combination with the connectivity status identifier, cluster the node pairs that simultaneously meet the conditions of the distance being less than the spatial proximity threshold and having a direct connection relationship into the same cluster, record the node sets and cluster sizes of all clusters, and generate a node spatial proximity clustering result; According to the node spatial proximity clustering result, count the number of clusters to which each node belongs, extract the belonging status of each node in each cluster, and calculate the connection value degree of each node; According to the connection value degree of each node, screen all the nodes that meet the condition that the connection value degree is greater than or equal to 1 as candidate boundary nodes, calculate the mean and standard deviation of the connection value degrees of all nodes, and select the nodes ranked in the top 20% of the connection value degrees to form a hierarchical regional boundary node set.

3. The method for dynamic path planning of a transport vehicle based on real-time road conditions according to claim 1, characterized in that: The obtaining steps of the shortcut passing cost list are as follows: Based on the hierarchical regional boundary node set, traverse all boundary node pairs, call the road section length, average passing speed of the road section, and historical congestion ratio of the road section recorded in the underlying road network data, construct all reachable paths between each pair of nodes, and screen the effective shortcut path groups that meet the maximum length and maximum number of sections limit conditions to obtain the passable shortcut path groups; According to the passable shortcut path groups, count the number of road sections of each path and calculate the shortcut passing cost of each shortcut path; According to the shortcut passing costs of all shortcut paths, pair and label the shortcut passing cost of each shortcut path with the starting and ending boundary nodes, construct a mapping table of the corresponding boundary node pairs and time costs, and form a shortcut passing cost list.

4. The dynamic path planning method for a carrier trolley based on real-time road conditions according to claim 1, wherein The obtaining steps of the macro shortcut topology graph are as follows: Based on the list of shortcut passing costs, extract the identifiers of the starting boundary nodes and ending boundary nodes corresponding to each shortcut path in the list of shortcut passing costs, remove duplicates from the boundary nodes one by one, and organize to obtain a complete set of boundary node identifiers that contains all boundary nodes and has no duplicates; Based on the complete set of boundary node identifiers, construct the node structure of the topological graph according to the node identifiers. Use the boundary node identifiers as the vertices of the graph. Extract the shortcut passing costs corresponding to each shortcut path from the list of shortcut generalized time costs one by one. According to the mapping relationship between the starting node and the ending node of the path between nodes, establish a direct connection mapping relationship between the vertices to obtain a set of vertex connection relationship mappings; Based on the set of vertex connection relationship mappings, assign the shortcut passing costs corresponding to the shortcut paths to the connection mappings between the corresponding vertices one by one as the weight values of the edges between the vertices in the topological graph, and update the connection mappings between the vertices to generate a macroscopic shortcut topological graph.

5. The dynamic path planning method for a carrier vehicle based on real-time road conditions according to claim 1, characterized in that, The steps for obtaining the affected shortcut index are as follows: Based on the macroscopic shortcut topological graph, extract the starting and ending boundary node identifiers corresponding to each shortcut path one by one. According to the starting and ending boundary node identifiers of each shortcut path, call the information of the road segment set corresponding to each shortcut path in the list of shortcut generalized time costs to generate a mapping relationship between the shortcut path and the corresponding road segment set; According to the mapping relationship between the shortcut path and the corresponding road segment set, retrieve the information of the road segment set of each shortcut path one by one. Compare each road segment identifier included in the road segment set with the road segment identifier that has changed in real time one by one, and record the shortcut path identifier where the road segment with a successful match is located to form an initial set of shortcut path identifiers covering the changed road segments; Based on the initial set of shortcut path identifiers covering the changed road segments, after deleting the duplicate shortcut path identifiers, sort and index the remaining shortcut path identifiers to generate an affected shortcut index.

6. The dynamic path planning method for a carrier vehicle based on real-time road conditions according to claim 1, wherein The steps for obtaining the dynamic shortcut weight graph are as follows: Based on the affected shortcut index, call each affected shortcut path in the list of shortcut generalized time costs one by one, extract all the road segment identifiers of each affected shortcut path, and retrieve the real-time average passing speed and real-time congestion ratio of each road segment from the underlying road network data segment by segment to form a set of real-time road state data; According to the set of real-time road state data, calculate the real-time passing time of each road segment in the affected shortcut path one by one, obtain the shortcut passing cost of each affected shortcut path, and update the shortcut passing cost of the corresponding path in the list of shortcut generalized time costs to form a list of dynamic shortcut passing costs; Based on the list of dynamic shortcut passing costs, call the connection edges of the boundary node pairs corresponding to the affected shortcut paths in the macroscopic shortcut topological graph one by one, assign the shortcut passing cost to the weight attribute of the corresponding connection edge one by one, and update the weights of all affected connection edges to generate a dynamic shortcut weight graph.

7. The dynamic path planning method for a carrier cart based on real-time road conditions according to claim 1, wherein, The steps for obtaining the macroscopic boundary node sequence are as follows: Based on the dynamic shortcut weight map and the starting coordinates of the carrier vehicle, locate the boundary node to which the starting point belongs, and retrieve all complete path segments starting from this node and accessible to any boundary node in the end region from the dynamic shortcut weight map. Screen the paths that meet the maximum constraint conditions in terms of both path length and number of path edges to generate a set of candidate macro paths; According to the set of candidate macro paths, combine the basic passage cost in each path and the load state of the carrier vehicle to calculate the load equivalent time cost of each path; Based on the load equivalent time cost of each path, select the path with the minimum load equivalent time cost from the set of candidate macro paths as the optimal path, and sequentially extract all boundary node identifiers included in this path to generate a macro boundary node sequence.

8. The dynamic path planning method for a carrier vehicle based on real-time road conditions according to claim 1, wherein The steps for obtaining the collaborative navigation path of the carrier vehicle are as follows: Based on the macro boundary node sequence, extract the original coordinate information between the starting point coordinates and the first boundary node, call the geometric shape, traffic direction, and current road section status data of each section of the road in the underlying road network, and traverse all legal paths between the starting point coordinates and the first boundary node to form a micro path segment from the starting point to the first boundary node; Based on the micro path segment from the starting point to the first boundary node, extract the node information between the last boundary node in the macro boundary node sequence and the end point coordinates, construct a micro path segment between the last boundary node and the end point coordinates, and generate a set of end micro path segments; Based on the micro path segment from the starting point to the first boundary node, the macro boundary node sequence, and the set of end micro path segments, sequentially splice the content of the three path segments in the logical order of the path to generate the collaborative navigation path of the carrier vehicle.

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